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  • The Happiest Man in Atlantis (A Short Story)

    AI transcript
    0:00:04 All right. Today, I want to actually tell you a story. So there’s a story called The Happiest
    0:00:10 Man in Atlantis. Have you heard it? Probably not, because I wrote it this morning. But I want to
    0:00:12 read this to you. I think it’s a good one. So let’s try it out.
    0:00:25 All right. So once upon a time, there was a prince, and this prince was the richest man in
    0:00:30 Atlantis. He drank the finest wines. He wore the finest clothes. He slept with the finest women.
    0:00:35 He had servants attending to his every need. And yet the prince was unhappy. Every day,
    0:00:40 he would wake up in the palace. The servants would bathe him and robe him, and he would look out to
    0:00:44 the land, and he would just feel empty and unhappy. And so one night, the prince decided,
    0:00:50 enough of this. I’m leaving here. I’m running away. And he dresses up like a common man. He sneaks out
    0:00:55 of the castle. And along the way, as he’s leaving the castle, trying to leave the royal palace,
    0:01:01 he stops at a bar, and he’s drinking his sorrows away. And he hears two sailors talking about
    0:01:07 somebody who they call the happiest man in Atlantis. They said that the man is very old,
    0:01:12 and that he’s supposed to die in three days. So the prince interrupts them right away. He says,
    0:01:18 I need to meet this man before he dies. They said, well, you could try, but he lives far away. He lives
    0:01:23 in the cave near the cliffs. He used to be a rich king, and now he’s given it all up. He lives in this cave.
    0:01:28 So the prince decides, what do I have to lose? Just another day of elegant misery? No.
    0:01:32 Packs a bag, and he grabs his backpack, and he just decides, okay, I’m going to go.
    0:01:38 So he gets to the cave, and he says, he meets the man, and he says,
    0:01:44 they call you the happiest man in Atlantis. And the man says, well, names are curious things.
    0:01:48 I’ve never claimed such a title, but it seems to have found me. What do you seek?
    0:01:54 And the prince says, happiness. I have everything that a man wants, and yet I feel nothing but
    0:02:01 emptiness. The man says, I see. You seek what cannot be bought, but is freely available to all.
    0:02:06 And I could teach you. It’s actually very simple. He draws a triangle on the cave wall. He says,
    0:02:12 everything you need is in this triangle. And he says, okay, tell me. He says, come back tomorrow
    0:02:17 before sunrise, and I’ll tell you then. And the prince says, tomorrow? Can you just tell me now?
    0:02:21 And the man says, what kind of guru would I be? This is a story. I can’t just tell you now.
    0:02:25 Come back tomorrow before sunrise. So the prince agrees, and he decides to come back.
    0:02:33 Hey, quick message from our sponsor, HubSpot. You know, marketing in 2025 is wild. Customers can spot
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    0:02:45 what’s changing. It shows you exactly how to deal with it. Everything is backed by research, and it’s
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    0:03:01 So he comes back, and he says, okay, I’m ready. Tell me what it is. What’s the answer?
    0:03:06 I want to be happy. I want the answer. And he says, I could tell you, but you really wouldn’t
    0:03:12 understand. So I will help you learn. And he says, okay. And then the man gives him a coin,
    0:03:18 single gold coin. And he says, I want you to go down to the market. Do you know that shop at the top
    0:03:22 of the market? And he says, yeah, it’s the one with all the finest antiques. He says, yes. Go in there.
    0:03:30 I want you to find their finest golden cup and buy it with this coin. And he says, one coin. I mean,
    0:03:38 that would cost thousands. He says, go buy it with one coin. So the prince agrees, and he goes down to
    0:03:43 the market, and he goes to the shop, and he takes the coin, and he picks up the cup, and he says,
    0:03:49 I’d like to buy this. They said, okay, sure. And he hands them one coin. They say, is this a joke?
    0:03:55 This costs thousands of coins. He says, I’ll take it for the coin. Can I buy it for this one coin?
    0:04:02 And they say, get out of here. And they shoo him away. He comes back to the cave, and he’s pissed.
    0:04:08 He said, that was stupid. Were you just trying to embarrass me? That was impossible. Why did you do
    0:04:13 that? He says, why was it impossible? He says, because that cup is so valuable. Why would he
    0:04:18 give it to me for one coin? He won’t just give away something valuable. That’s essentially free.
    0:04:25 And so the man asked him, let me ask you, how valuable is your happiness? He says, what do you
    0:04:31 mean? He says, how valuable is your happiness? It’s the most valuable. It’s priceless. He says,
    0:04:36 then why do you give it away for so cheap? A servant spills your wine, and you trade it for a piece of
    0:04:42 your anger. A rival succeeds, and you exchange it for envy. A storm delays your ship, and you barter your
    0:04:48 joy for worry. These small inconveniences, the forgetful servant, the rainy day, you gladly hand over
    0:04:55 your happiness to the lowest bidder. The prince was upset, but he knew it was true. And the man goes up to
    0:05:01 the triangle, and he adds one word. He writes the word focus. He said, today you learned to focus your
    0:05:06 attention on what matters. Because at any given time, the world is throwing a thousand things at you
    0:05:12 that you could choose to focus on. But choosing to focus on what’s valuable, on what actually matters,
    0:05:18 and not the small, trivial inconveniences of your life, is the first thing. It is robbing you of your
    0:05:24 happiness. The prince turns over the coin, and he reads what’s on the back of the coin. It turns out this
    0:05:28 wasn’t just a normal gold coin, and it says, never trade what is precious for what is trivial.
    0:05:34 So the prince says, okay, I got it. What about the other two? He says, come back tomorrow for number
    0:05:42 two. So he returns the second day, and he says, I’m ready. Tell me. And the man lays out two bowls,
    0:05:47 two clay bowls, and he fills them both with water. And so they’re filled with water, and now he takes a
    0:05:52 pile of rocks that he has. And in the first bowl, he just splashes the rocks in, just dumps them in from a
    0:05:57 height. And it’s splashing. The water is turbulent. It’s spilling over the edges. And in the second,
    0:06:03 he just takes it, takes each rock, puts it in the water gently, and the water remains roughly calm.
    0:06:08 Small ripple, but roughly calm. And the prince is just watching this, and he says, did you notice?
    0:06:16 He says, yeah, of course. He says, great. Today, I want you to go to the market, and for the first hour,
    0:06:21 I want you to react like the first bowl. Really exaggerate it, okay? So whenever anything happens
    0:06:25 to you, I want you to react fully and violently, just similar to that bowl. But when the lunch bell
    0:06:31 rings, I want you to mentally change your state and react like the second bowl, okay?
    0:06:37 So he goes down to the market, and sure enough, he’s looking for a fight. Somebody bumps into him,
    0:06:41 and he shoves him. He curses at them, and he’s reacting to every little thing that every person does.
    0:06:45 Somebody shortchanges him. One of the merchants shortchanges him, and he gets angry at him
    0:06:49 and grabs his coins from him. But then the lunch bell rings, and he remembers the second bowl,
    0:06:58 and he changes his state of mind, and he waits for it. Sure enough, a boy comes, and he steps on his
    0:07:04 foot, basically smudges his shoe, spills his tea. But instead of reacting and shouting at the boy,
    0:07:08 he simply helps him to his feet, buys him a new tea. The boy smiles, thanks him, gives him a hug,
    0:07:14 and carries on. It begins to rain, but instead of reacting to the rain, he simply sits under an awning
    0:07:19 and enjoys the pattern of the puddles that are being made. And then he goes back up to the cave.
    0:07:25 And the man asks him, he says, well, what did you learn? And the prince says, I saw that the world
    0:07:30 did not change, but I did. The first bowl, everything was a disturbance. And the second bowl, the events
    0:07:36 were simply events. They were neither good or bad until I made them so. The man says, yes, this is the
    0:07:41 second key. You learned to calm your state of mind. You became the second bowl. Because you cannot control
    0:07:46 the pebbles, but you can’t control the nature of your water. The calm bowl still feels the pebbles,
    0:07:51 but it does not allow a small disturbance to overtake its entire nature. And this is the difference between
    0:08:00 reacting and responding. And so he wrote on the wall, the second word, state. He said, when you learn to
    0:08:05 manage your state, when you enter a situation, choose how you will feel. Because if you enter a situation
    0:08:11 optimistic and charming and playful and determined, you will make totally different choices than if
    0:08:17 you enter a room feeling tired and stressed or frustrated, you’ll make completely different
    0:08:22 decisions. And those decisions become your destiny. So control your state of mind and you will control
    0:08:26 your life. Come back tomorrow for challenge three. It’s going to be the hardest one yet.
    0:08:32 So he comes back the next day and he’s ready for challenge number three. But he says, before you
    0:08:37 come, make sure you bring all the gold in your vault. All the gold in your vault? I can’t do that.
    0:08:43 This is too much. He said, bring a thousand coins of gold. So the man goes back and he drags a thousand
    0:08:49 coins of gold to the cave. He’s like, oh, you’re one of those kind of gurus, huh? And the guru laughs and
    0:08:56 he says, okay, I will take the coins in exchange for the stone. And he hands him this small stone that
    0:09:03 looks, you know, average. And the prince says, is this a joke? I’m not buying this stone for a thousand
    0:09:10 gold coins. He says, really? Because this stone was given to me by your father. Your father who died
    0:09:15 when you were young, he came to visit me at the cave as well before he died. And he told me, he said,
    0:09:21 one day, I hope this never happens, but if my son ever comes to give him this stone. He says, is that
    0:09:29 true? He said, it’s true. And he said, so this stone might look worthless to others, but to you, I’m
    0:09:35 assuming it means something. He says, it does. And he agrees to trade him the gold for the stone. He says,
    0:09:39 no, no, no, I don’t need your gold. I’m dying tomorrow. But you can see you’ve learned the third
    0:09:48 lesson, which is the power of a story. And he goes to the triangle and he writes the word story. He said,
    0:09:56 you see, a story can make a stone precious or worthless. A story can change a punishment into
    0:10:03 a blessing. It could take a disaster and turn it into a triumph. And the world is simply full of events
    0:10:08 that are really meaningless until you choose to give them a label, you choose to give them a meaning. It is
    0:10:13 the story you apply to something that gives it its weight and worth in your life. It gives it the
    0:10:18 meaning in your life. And he says, these three together that I’ve showed you, your focus will
    0:10:25 determine what you see. Your state will determine how you feel. And the story determines what it means.
    0:10:31 You master these three and you’ve mastered the art of living. And the prince understood. The prince
    0:10:37 gets up, he thanks him. And the man says, you know, you came to find the happiest man in Atlantis. Did you
    0:10:42 find him? And the prince laughs. He says, I did not find him, but I became him.
    0:10:49 All right, let’s take a quick break because as you know, we are on the HubSpot Podcast Network, but we’re
    0:10:52 not the only ones. There’s other podcasts on this network too. And maybe you liked them. Maybe you should
    0:10:56 check them out. One of them that I want to draw your attention to is called Nudge by Phil Agnew.
    0:11:00 And whether you’re a marketer or a salesperson and you’re looking for the small changes you could
    0:11:04 make, the new habits you could do, the small decisions you could make that will make a big
    0:11:09 difference. That’s what that podcast is all about. Check it out. It’s called Nudge and you can get it
    0:11:15 wherever you get your podcasts. All right. I hope you enjoyed my story. I had fun writing it. I hope you
    0:11:18 had fun listening to it. Maybe you learned a little thing or two. Maybe you like the prince can learn to
    0:11:25 either up your level of focus, focusing on what is truly valuable, not the trivial state. So choosing how you
    0:11:29 step into a situation, which will change the way you respond and those decisions become your destiny.
    0:11:34 And lastly, the story. So the labels you put on things, you broke up with your boyfriend. Is that
    0:11:39 the end or is it the beginning of something, right? You get to decide. That meaning is up to you and that
    0:11:45 meaning will totally change how you approach the next situation in your life. So hope you enjoyed this
    0:11:50 story. I’m Sean. Thanks for listening. Oh, and by the way, if you’re here right now, I don’t know when
    0:11:53 you’re listening to this, but April 16th, I’m doing something fun. You should check it out.
    0:11:59 I’m doing a CEO bootcamp for anybody. It’s free, totally free. You can come. And I’m basically
    0:12:05 going to share my method of running companies that is very similar to what in YC they’re calling
    0:12:08 founder mode. And I want to explain what I think founder mode is and how you actually do it.
    0:12:13 It’s going to be pretty awesome. April 16th, you can find the link in the description below.
    0:12:14 Thank you for listening.
    0:12:18 I feel like I can rule the world. I know I could be what I want to.
    0:12:24 I put my all in it like no days off on the road. Let’s travel. Never looking back.
    0:12:31 New York City founders. If you’ve listened to my first million before, you know, I’ve got this
    0:12:36 company called Hampton and Hampton is a community for founders and CEOs. A lot of the stories and ideas
    0:12:41 that I get for this podcast, I actually got it from people who I met in Hampton. We have this big
    0:12:45 community of a thousand plus people and it’s amazing. But the main part is this eight person
    0:12:50 core group that becomes your board of advisors for your life and for your business. And it’s life
    0:12:56 changing. Now, to the folks in New York City, I’m building a in real life core group in New York
    0:13:01 City. And so if you meet one of the following criteria, your business either does 3 million in
    0:13:05 revenue, or you’ve raised 3 million in funding, or you’ve started and sold a company for at least
    0:13:12 $10 million, then you are eligible to apply. So go to joinhampton.com and apply. I’m going to be
    0:13:17 reviewing all of the applications myself. So put that you heard about this on MFM. So I know to
    0:13:19 give you a little extra love. Now back to the show.

    💰 Steal Shaan’s $20M Pitch Deck: https://clickhubspot.com/nov

    Join Nick Huber and Shaan Puri for a free CEO Bootcamp on April 16th at 2pm EST: https://lu.ma/5kbwnk7v

    Episode 695: Shaan Puri ( https://x.com/ShaanVP ) tells the story of finding what is truly valuable. 

    —

    Check Out Shaan’s Stuff:

    Need to hire? You should use the same service Shaan uses to hire developers, designers, & Virtual Assistants → it’s called Shepherd (tell ‘em Shaan sent you): https://bit.ly/SupportShepherd

    —

    Check Out Sam’s Stuff:

    • Hampton – https://www.joinhampton.com/

    • Ideation Bootcamp – https://www.ideationbootcamp.co/

    • Copy That – https://copythat.com

    • Hampton Wealth Survey – https://joinhampton.com/wealth

    • Sam’s List – http://samslist.co/

    My First Million is a HubSpot Original Podcast // Brought to you by HubSpot Media // Production by Arie Desormeaux // Editing by Ezra Bakker Trupiano

  • Policymaking Is Not a Science — Yet (Update)

    Policymaking Is Not a Science — Yet (Update)
    Policymaking Is Not a Science — Yet (Update)
    AI transcript
    0:00:05 Hey there, it’s Stephen Dubner.
    0:00:11 We just published a two-part series on what some people call sludge, meaning all the frictions
    0:00:17 that make it hard to fill out tax forms or find a health care provider or even cancel
    0:00:17 a subscription.
    0:00:23 One part of our series involved government sludge and how it interferes with getting
    0:00:24 policy done.
    0:00:29 The series reminded me of another episode we once made that I thought was worth hearing
    0:00:32 again, so we’re playing it for you here as a bonus episode.
    0:00:36 It is called Policymaking is Not a Science Yet.
    0:00:39 We have updated facts and figures as necessary.
    0:00:42 As always, thanks for listening.
    0:00:56 Usually when children are born deaf, they call it nerve deafness, but it’s really not the actual
    0:00:56 nerve.
    0:01:00 It’s little tiny hair cells in the cochlea.
    0:01:05 Dana Susskind is a physician scientist at the University of Chicago and, more dramatically,
    0:01:10 she is a pediatric surgeon who specializes in cochlear implants.
    0:01:17 My job is to implant this incredible piece of technology which bypasses these defective
    0:01:24 hair cells and takes the sound from the environment, the acoustic sound, and transforms it into electrical
    0:01:27 energy, which then stimulates the nerve.
    0:01:36 And somebody who is severe to completely profoundly deaf after implantation can have normal levels
    0:01:36 of hearing.
    0:01:38 And it is pretty phenomenal.
    0:01:40 It is pretty phenomenal.
    0:01:47 If you ever need a good cry, a happy cry, just type in cochlear implant activation on YouTube.
    0:01:53 You’ll see little kids hearing sound for the first time and their parents flipping out with joy.
    0:01:59 Good job!
    0:01:59 Good job!
    0:02:02 She’s smiley.
    0:02:07 Oh, that’s great!
    0:02:11 She’s so smiley.
    0:02:13 Yeah, that’s your ears.
    0:02:14 Yeah.
    0:02:22 The cochlear implant is a remarkable piece of technology, but really it’s just one of
    0:02:29 many remarkable advances in medicine and elsewhere, created by devoted researchers and technologists
    0:02:31 and sundry smart people.
    0:02:33 You know what’s even more remarkable?
    0:02:37 How often we fail to take advantage of these advances.
    0:02:43 One of the most compelling examples is the issue of hypertension.
    0:02:47 About a third of all Americans have high blood pressure.
    0:02:50 First of all, the awareness rate is about only 80%.
    0:02:53 Of the total amount, only 50% actually are controlled.
    0:02:55 We have great drugs, right?
    0:03:02 But you can see the cascade of issues when you have to disseminate, you have to adhere, etc.,
    0:03:05 and the public health ramifications of that.
    0:03:10 Those blood pressure numbers are even worse today than they were when we first published
    0:03:11 this episode in 2020.
    0:03:16 Clearly, we still have not figured out how to get the science to the people who need it.
    0:03:20 Prescription adherence is a very difficult nut to crack.
    0:03:21 That’s John List.
    0:03:24 He’s an economist at the University of Chicago.
    0:03:30 They actually have to go and get the medicines, which a lot of people have a very hard time doing.
    0:03:35 Even though it’s sitting next to your bed every night, people don’t take it.
    0:03:38 And they don’t take it because they forget.
    0:03:45 They don’t take it because the side effect is a lot worse than the benefit they think they’re getting.
    0:03:52 All of these types of problems, as humans, including myself, we do a really bad job in trying to solve.
    0:03:55 All of us, our lives get busy.
    0:03:56 We forget.
    0:04:01 You wouldn’t think you’d have an adherence issue with something like the cochlear implant.
    0:04:03 It has such an obvious upside.
    0:04:05 And yet…
    0:04:09 When I put the internal device in, it stays there.
    0:04:14 But it actually requires an external portion as well, sort of like a hearing aid.
    0:04:20 And that is the part where you see issues related to adherence.
    0:04:27 Just because I put the internal part doesn’t mean that an individual or a child will be wearing the external part.
    0:04:32 In one study, only half of the participants wore their device full-time.
    0:04:40 I mean, we have figured through randomized control trials to understand causation, real impact in the small scale.
    0:04:46 But the next step is understanding the science of how to use this science.
    0:04:54 Because, you know, how you do it on the small scale in perfect conditions is very different than the messy real world.
    0:04:56 And that is a very real issue.
    0:05:01 Today on Freakonomics Radio, what to do about that very real issue.
    0:05:06 Because you see the same thing not just in medicine, but in education and economic policy and elsewhere.
    0:05:11 Solutions that look foolproof in the research stage are failing to scale up.
    0:05:14 People said, let’s just put it out there.
    0:05:17 And then we quickly realized that it’s far more complicated.
    0:05:23 There might be something that you think would be great, but it’s never going to be able to be implemented in the real world.
    0:05:27 We need to know, what is the magic sauce?
    0:05:30 We’ll go in search of that magic sauce right after this.
    0:05:54 This is Freakonomics Radio, the podcast that explores the hidden side of everything, with your host, Stephen Dubner.
    0:06:10 John List is a pioneer in the relatively recent movement to give economic research more credibility in the real world.
    0:06:17 If you turn back the clock to the 1990s, there was a credibility revolution in economics,
    0:06:25 focusing on what data and modeling assumptions are necessary to go from correlation to causality.
    0:06:29 List responded by running dozens and dozens of field experiments.
    0:06:35 Now, my contribution in the credibility revolution was instead of working with secondary data,
    0:06:44 I actually went to the world and used the world as my lab and generated new data to test theories and estimate program effects.
    0:06:50 Okay, so you and others moved experiments out of the lab and into the real world.
    0:06:57 But have you been able to successfully translate those experimental findings into, let’s say, good policy?
    0:07:07 I think moving our work into policymaking circles and having a very strong impact has just not been there.
    0:07:10 And I think one of the most important questions is,
    0:07:15 how are we going to make that natural progression of field experiments within the social sciences
    0:07:23 to more keenly talk to policymakers, the broader public, and actually the scientific community as a whole?
    0:07:30 The way List sees it, academics like him work hard to come up with evidence for some intervention
    0:07:33 that’s supposed to help alleviate poverty or improve education,
    0:07:37 to help people quit smoking or take their blood pressure medicine.
    0:07:43 The academic then writes up their paper for an incredibly impressive-looking academic journal,
    0:07:45 impressive at least to fellow academics.
    0:07:48 The rest of us, it’s jargony and indecipherable.
    0:07:54 But then, with paper in hand, the academic goes out proselytizing to policymakers.
    0:07:55 He might say,
    0:08:00 you politicians always talk about making evidence-based policy.
    0:08:04 Well, here’s some new evidence for an effective and cost-effective way
    0:08:08 of addressing that problem you say you care so much about.
    0:08:10 And then the policymaker may say,
    0:08:13 well, the last time we listened to an academic like you,
    0:08:16 we did just what they told us, but it didn’t work.
    0:08:19 And it cost three times what they said it would.
    0:08:21 And we got hammered in the press.
    0:08:23 And here’s the thing.
    0:08:27 The politician and the academic may both be right.
    0:08:31 John List has seen this from both sides now.
    0:08:35 In a past life, I worked in the White House advising the president
    0:08:39 on environmental and resource issues within economics.
    0:08:42 This was in the early 2000s under George W. Bush.
    0:08:47 A harsh lesson that I learned was you have to evaluate the effects of public policy
    0:08:49 as opposed to its intentions.
    0:08:52 Because the intentions are obviously good.
    0:08:56 For instance, improving literacy for grade schoolers
    0:08:59 or helping low-income high schoolers get to college.
    0:09:04 When you step back and look at the amount of policies
    0:09:08 that we put in place that don’t work,
    0:09:10 it’s just a travesty.
    0:09:14 List has firsthand experience with the failure to scale.
    0:09:17 So down in Chicago Heights,
    0:09:20 I ran a series of interventions.
    0:09:23 And one of the more powerful interventions
    0:09:25 was called the Parent Academy.
    0:09:30 That was a program that brought in parents every few weeks.
    0:09:34 And we taught them what are the best mechanisms and approaches
    0:09:37 that they can use with their 3-, 4-, and 5-year-old children
    0:09:41 to push both their cognitive skills
    0:09:43 and their executive function skills.
    0:09:45 Things like self-control.
    0:09:49 What we found was within three to six months,
    0:09:52 we can move a child in very short order
    0:09:55 to have very strong cognitive test scores
    0:09:58 and very strong executive function skills.
    0:10:00 So, of course, we’re very optimistic
    0:10:02 after getting this type of result,
    0:10:03 and we want the whole world
    0:10:06 to now do parent academies.
    0:10:09 The UK approaches us and said,
    0:10:11 we want to roll it out across London
    0:10:13 and the boroughs around London.
    0:10:16 What we found is that it failed miserably.
    0:10:19 It wasn’t that the program was bad.
    0:10:21 It failed miserably
    0:10:25 because no parents actually signed up.
    0:10:28 So if you want your program to work
    0:10:30 at higher levels,
    0:10:32 you have to figure out
    0:10:34 how to get the right people
    0:10:37 and all the people, of course,
    0:10:38 into the program.
    0:10:40 If you had asked me to guess
    0:10:42 all the ways that a program like that could fail,
    0:10:44 it would have taken me a while
    0:10:45 to guess that you simply
    0:10:47 didn’t get parental uptake.
    0:10:48 The main problem is
    0:10:50 we just don’t understand
    0:10:52 the science of scaling.
    0:10:54 If you were to attach a noun
    0:10:56 to what this is,
    0:10:58 the scalability blank,
    0:11:01 is it a problem?
    0:11:02 Is it a dilemma?
    0:11:03 Is it a crisis?
    0:11:05 I do think it’s a crisis
    0:11:06 in that
    0:11:08 if we don’t take care of it
    0:11:09 as scientists,
    0:11:11 I think everything we do
    0:11:13 can be undermined
    0:11:15 in the eyes of the policymaker
    0:11:15 and the broader public.
    0:11:17 We don’t understand
    0:11:20 how to use our own science
    0:11:22 to make better policies.
    0:11:25 So John List and Dana Susskind
    0:11:27 and some other researchers
    0:11:29 are on a quest to address
    0:11:30 this scalability crisis.
    0:11:31 They’ve been writing
    0:11:32 a series of papers,
    0:11:33 for instance,
    0:11:35 The Science of Using Science
    0:11:36 Towards an Understanding
    0:11:39 of the Threats to Scaling Experiments.
    0:11:40 A lot of their focus
    0:11:42 is on early education,
    0:11:43 since that is a particular
    0:11:44 passion of Susskind’s.
    0:11:46 I guess you could say
    0:11:48 I’m a surgeon by day
    0:11:50 and social scientist by night.
    0:11:51 My clinical work
    0:11:53 is about taking care
    0:11:54 of one child at a time.
    0:11:56 My research
    0:11:57 really comes out
    0:11:57 of the fact
    0:11:59 that not all children
    0:12:00 do as well as others
    0:12:01 after surgery
    0:12:03 and trying to figure out
    0:12:04 the best ways
    0:12:04 to allow
    0:12:05 all my patients
    0:12:06 and really
    0:12:07 children born
    0:12:09 into low-income backgrounds
    0:12:10 to reach
    0:12:12 their educational potentials.
    0:12:13 It is kind of like
    0:12:14 a superhero in reverse.
    0:12:15 During the day,
    0:12:16 you’re doing
    0:12:17 the big dramatic stuff
    0:12:18 and at night,
    0:12:19 you’re going home
    0:12:20 to analyze the data
    0:12:20 and figure out
    0:12:21 what’s happening.
    0:12:22 I think that really
    0:12:23 the hard part
    0:12:25 is the night part.
    0:12:27 I love doing surgery.
    0:12:29 I adore my patients,
    0:12:30 but it’s actually
    0:12:32 not as hard
    0:12:33 as many of the complex issues
    0:12:34 in this world.
    0:12:36 And was that a recognition
    0:12:38 that some kids
    0:12:39 after the surgery
    0:12:41 sort of zoomed up
    0:12:42 the education ladder
    0:12:43 and others didn’t?
    0:12:43 Yeah.
    0:12:44 It’s not simply
    0:12:46 about hearing loss.
    0:12:47 It’s because language
    0:12:47 is the food
    0:12:48 for the developing brain.
    0:12:49 Before surgery,
    0:12:50 they all looked like
    0:12:52 they’d have the same potential
    0:12:53 to, as you say,
    0:12:54 zoom up the educational ladder.
    0:12:56 After surgery,
    0:12:56 there were very
    0:12:57 different outcomes.
    0:12:59 And too often
    0:13:00 that difference
    0:13:00 fell along
    0:13:01 socioeconomic lines.
    0:13:03 That made me start
    0:13:04 searching outside
    0:13:05 the operating room
    0:13:05 for understanding
    0:13:06 why and what
    0:13:07 I could do about it.
    0:13:08 And it has taken me
    0:13:09 on a journey.
    0:13:11 So Dana and I met
    0:13:12 back in 2012
    0:13:15 and we were introduced
    0:13:16 by a mutual friend
    0:13:17 and we did the usual
    0:13:19 ignore each other
    0:13:19 for a few years
    0:13:21 because we’re too busy.
    0:13:24 And push came to shove.
    0:13:25 Dana and I started
    0:13:26 to work on
    0:13:27 early childhood research.
    0:13:29 And after that,
    0:13:31 research turned to love.
    0:13:34 I always joke
    0:13:36 that I was wooed
    0:13:37 with spreadsheets
    0:13:38 and hypotheses.
    0:13:40 Is that true?
    0:13:41 Yes.
    0:13:42 Yes.
    0:13:43 In fact,
    0:13:44 the reason I decided
    0:13:45 to marry him
    0:13:46 was because I wanted
    0:13:47 this area of scaling
    0:13:49 to be a robust area
    0:13:50 of research for him
    0:13:51 because it really
    0:13:52 is a major issue.
    0:13:58 Suskind started
    0:13:58 what was then called
    0:14:00 the 30 million words
    0:14:00 initiative.
    0:14:02 30 million being
    0:14:03 an estimate
    0:14:04 of how many fewer
    0:14:05 words a child
    0:14:06 from a low-income home
    0:14:07 will have heard
    0:14:08 than an affluent child
    0:14:09 by the time
    0:14:09 they turn four.
    0:14:11 But these days,
    0:14:12 the project is called
    0:14:13 the TMW Center
    0:14:14 for Early Learning
    0:14:15 and Public Health.
    0:14:17 we’ve actually moved
    0:14:18 away from the term
    0:14:19 30 million words
    0:14:20 because it’s such
    0:14:21 a hot-button issue.
    0:14:22 Hot-button because
    0:14:23 it’s so hard to believe
    0:14:24 that the number
    0:14:24 is legit?
    0:14:26 Well, no.
    0:14:27 I mean,
    0:14:28 some people say,
    0:14:28 look,
    0:14:29 it’s a deficit mentality.
    0:14:30 You’re talking about
    0:14:31 what’s not there.
    0:14:33 And then the replication,
    0:14:35 somebody did another study
    0:14:35 that said,
    0:14:37 oh, it’s only 4 million.
    0:14:38 And it really isn’t
    0:14:40 actually even the point
    0:14:40 because it’s not
    0:14:41 even about words.
    0:14:42 It’s about the interaction.
    0:14:44 So I just made
    0:14:44 the decision.
    0:14:45 I’d rather be focusing
    0:14:47 on developing the research
    0:14:48 than fighting
    0:14:49 a naming battle.
    0:14:50 So you didn’t make
    0:14:51 TMW stand
    0:14:53 for something else.
    0:14:53 Well,
    0:14:54 that’s what
    0:14:55 everybody gives me
    0:14:56 trouble for.
    0:14:57 It stands for
    0:14:57 30 million words,
    0:14:59 but only I know that.
    0:14:59 Okay,
    0:15:01 now you all know it too.
    0:15:03 Anyway,
    0:15:04 they started the center
    0:15:06 with this idea.
    0:15:07 With this idea
    0:15:08 that, you know,
    0:15:09 we need to
    0:15:10 take a public health
    0:15:11 or a population-level
    0:15:12 approach
    0:15:13 during the early years
    0:15:14 to optimize
    0:15:15 early foundational
    0:15:16 brain development
    0:15:17 because the research
    0:15:18 is pretty clear
    0:15:20 that parent talk
    0:15:20 and interaction
    0:15:22 in the first
    0:15:23 three years of life
    0:15:24 are the catalyst
    0:15:25 for brain development.
    0:15:26 And so
    0:15:27 that’s basically
    0:15:28 our work.
    0:15:29 Okay,
    0:15:30 so far so good.
    0:15:31 The research is clear
    0:15:32 that heavy exposure
    0:15:33 to language
    0:15:34 is good for
    0:15:35 the developing brain.
    0:15:36 But how do you
    0:15:37 turn that research
    0:15:38 finding into action?
    0:15:39 And how do you
    0:15:40 scale it up?
    0:15:41 Initially,
    0:15:42 we started with
    0:15:43 an intensive
    0:15:44 home visiting
    0:15:44 program,
    0:15:45 but understanding
    0:15:46 that to reach
    0:15:47 population-level
    0:15:48 impact,
    0:15:49 you need to
    0:15:50 develop programs
    0:15:51 both with an
    0:15:53 eye for scaling
    0:15:54 as well as an eye
    0:15:55 for understanding
    0:15:56 where parents
    0:15:57 go regularly.
    0:15:58 Because healthcare,
    0:15:59 unlike the education
    0:16:00 system,
    0:16:01 the first three years
    0:16:02 of life really
    0:16:03 don’t have any
    0:16:04 infrastructure
    0:16:05 in which to
    0:16:06 disseminate programs.
    0:16:08 So we actually
    0:16:09 expanded our
    0:16:09 model.
    0:16:10 We have this
    0:16:12 multifaceted program
    0:16:13 that reached parents
    0:16:14 where they were,
    0:16:16 from maternity wards
    0:16:17 into pediatrics
    0:16:17 offices,
    0:16:19 into the homes,
    0:16:20 as well as group
    0:16:20 sessions.
    0:16:21 Those programs
    0:16:22 that are most
    0:16:23 vulnerable to the
    0:16:24 issues of scale
    0:16:25 are the complex
    0:16:26 sort of service
    0:16:27 delivery interventions.
    0:16:28 You know,
    0:16:29 anything that takes
    0:16:31 a human service
    0:16:31 delivery.
    0:16:33 Scaling isn’t
    0:16:34 an end.
    0:16:34 It’s really
    0:16:36 just a continuation.
    0:16:41 You know,
    0:16:42 it’s a hard one.
    0:16:43 That is
    0:16:44 Patti Chamberlain,
    0:16:45 senior research
    0:16:46 scientist at
    0:16:47 Oregon Social
    0:16:47 Learning Center.
    0:16:49 And I do
    0:16:51 research and
    0:16:52 implementation
    0:16:53 of evidence-based
    0:16:55 practices in
    0:16:56 child welfare,
    0:16:57 juvenile justice,
    0:16:58 mental health,
    0:16:59 and education
    0:17:00 systems.
    0:17:01 Chamberlain also
    0:17:02 looks at scaling
    0:17:03 as a process.
    0:17:05 So it’s almost
    0:17:05 like there’s
    0:17:06 stages that you
    0:17:06 have to go
    0:17:07 through.
    0:17:08 And if the
    0:17:09 first stage
    0:17:09 is research
    0:17:10 that involves
    0:17:11 an RCT,
    0:17:11 a randomized
    0:17:12 controlled trial,
    0:17:14 there’s already
    0:17:14 an important
    0:17:15 choice to make.
    0:17:16 You’re far
    0:17:17 better off
    0:17:18 to situate
    0:17:19 your RCT
    0:17:20 in a real
    0:17:21 world setting
    0:17:22 than a
    0:17:22 university clinic
    0:17:24 so that you’re
    0:17:24 learning from
    0:17:25 the beginning
    0:17:26 what’s feasible
    0:17:26 and what’s
    0:17:27 not feasible.
    0:17:29 There might be
    0:17:29 something that you
    0:17:30 think would be
    0:17:30 great,
    0:17:31 but it’s never
    0:17:31 going to be able
    0:17:32 to be implemented
    0:17:33 in the real
    0:17:33 world.
    0:17:34 I’ve been
    0:17:35 at this
    0:17:35 now for,
    0:17:36 oh,
    0:17:36 probably
    0:17:38 25 years,
    0:17:40 and I learned
    0:17:41 sort of through
    0:17:41 failing.
    0:17:43 One program
    0:17:43 Chamberlain founded
    0:17:44 is called
    0:17:45 Treatment Foster
    0:17:46 Care Oregon.
    0:17:48 Kids tend to
    0:17:48 commit crimes
    0:17:49 together.
    0:17:50 It’s a team
    0:17:50 sport.
    0:17:51 But then,
    0:17:52 oddly,
    0:17:54 the way that
    0:17:55 we’re set up
    0:17:56 to deal with
    0:17:57 kids who,
    0:17:57 you know,
    0:17:58 reach the level
    0:17:59 where they’re
    0:17:59 really being
    0:18:01 unsafe to
    0:18:01 themselves
    0:18:01 and to
    0:18:02 the community
    0:18:03 is we put
    0:18:03 them in
    0:18:04 group homes
    0:18:04 together.
    0:18:05 We’re putting
    0:18:06 kids in a
    0:18:07 situation where
    0:18:08 they’re more
    0:18:09 likely to
    0:18:11 commit crimes.
    0:18:13 So we decided
    0:18:13 what if we
    0:18:14 placed a child
    0:18:16 singly in a
    0:18:17 family that
    0:18:18 was completely
    0:18:19 devoted to
    0:18:21 using evidence-based
    0:18:23 parenting skills
    0:18:24 to help that
    0:18:25 child do well
    0:18:27 with peers in
    0:18:28 school and in
    0:18:28 the family
    0:18:29 setting?
    0:18:30 what if we
    0:18:31 gave the
    0:18:31 parents,
    0:18:32 the biological
    0:18:33 parents of
    0:18:34 that kid,
    0:18:35 the same kind
    0:18:36 of skills that
    0:18:37 the treatment
    0:18:38 foster care
    0:18:39 family had?
    0:18:41 What if we
    0:18:41 gave the kid
    0:18:42 individual therapy?
    0:18:43 The biological
    0:18:44 family was
    0:18:44 getting family
    0:18:45 therapy.
    0:18:45 We were giving
    0:18:46 the kids
    0:18:46 support at
    0:18:47 school.
    0:18:48 So we were
    0:18:49 basically wrapping
    0:18:50 all these services
    0:18:51 around an
    0:18:52 individual child
    0:18:53 in a family
    0:18:53 home.
    0:18:55 What we found
    0:18:56 was, yeah,
    0:18:57 the kids do a
    0:18:57 lot better.
    0:18:58 They have a lot
    0:18:59 fewer arrests.
    0:19:00 they spend
    0:19:01 less days in
    0:19:02 institutions.
    0:19:02 They use
    0:19:03 fewer drugs.
    0:19:05 And guess what?
    0:19:06 It costs a lot
    0:19:07 less as well.
    0:19:08 Because you do
    0:19:09 not have a
    0:19:09 facility.
    0:19:11 You do not
    0:19:12 have 24-7 staff
    0:19:13 that you’re paying
    0:19:14 in shifts.
    0:19:15 You do not
    0:19:16 have, you know,
    0:19:17 all of the
    0:19:18 stuff that it
    0:19:19 takes to run
    0:19:20 an institution.
    0:19:21 You have a
    0:19:21 family.
    0:19:23 The success of
    0:19:23 Chamberlain’s
    0:19:24 program caught
    0:19:24 the eye of
    0:19:25 researchers who
    0:19:26 were working on
    0:19:26 a program for a
    0:19:27 federal agency
    0:19:28 called the
    0:19:29 Office of
    0:19:29 Juvenile Justice
    0:19:30 and Delinquency
    0:19:31 Prevention.
    0:19:32 And so we
    0:19:33 got this call
    0:19:34 saying, you
    0:19:35 know, we
    0:19:36 want you to
    0:19:37 implement your
    0:19:37 program in
    0:19:39 15 sites.
    0:19:40 If the
    0:19:40 program was
    0:19:41 successful at
    0:19:42 one site, how
    0:19:43 hard could it be
    0:19:44 to make it work
    0:19:44 at 15?
    0:19:46 I went in
    0:19:47 thinking that it
    0:19:48 wouldn’t be that
    0:19:50 hard because we
    0:19:50 had good outcomes.
    0:19:51 We showed that we
    0:19:52 could save money.
    0:19:55 And yet, we
    0:19:55 were absolutely
    0:19:56 not ready.
    0:19:58 It wasn’t because
    0:19:58 we didn’t have
    0:19:59 enough data.
    0:20:01 We had, at that
    0:20:02 point, plenty of
    0:20:02 data.
    0:20:04 But we didn’t
    0:20:05 have the know-how
    0:20:06 of how to put
    0:20:07 this thing down
    0:20:08 in the real
    0:20:08 world.
    0:20:10 And it blew up.
    0:20:11 One reason?
    0:20:12 Systemic
    0:20:13 complication.
    0:20:15 The three
    0:20:16 systems, child
    0:20:17 welfare, juvenile
    0:20:18 justice, and
    0:20:19 mental health, all
    0:20:20 put some money in
    0:20:21 the pot to fund
    0:20:22 this implementation.
    0:20:24 I was completely
    0:20:25 delighted.
    0:20:25 I thought, oh,
    0:20:26 this is going to
    0:20:28 be great because
    0:20:29 we have all the
    0:20:30 relevant systems
    0:20:31 buying into
    0:20:31 this.
    0:20:32 Well, what
    0:20:34 happened was
    0:20:35 when we tried
    0:20:35 to implement,
    0:20:37 we ran into
    0:20:39 tremendous
    0:20:40 barriers because
    0:20:42 if we satisfied
    0:20:43 the policies
    0:20:44 and procedures
    0:20:44 of one
    0:20:46 system, we
    0:20:46 were at
    0:20:47 odds with
    0:20:47 the policies
    0:20:48 and procedures
    0:20:48 in the
    0:20:49 other system.
    0:20:51 Patty
    0:20:52 Chamberlain had
    0:20:52 run up against
    0:20:53 something that
    0:20:54 Dana Susskind
    0:20:54 had come to
    0:20:55 see as an
    0:20:56 inherent disconnect
    0:20:57 when you try
    0:20:58 to scale up
    0:20:58 a research
    0:20:59 finding.
    0:20:59 There’s
    0:21:00 obviously the
    0:21:00 implementation,
    0:21:02 everybody focusing
    0:21:02 on adherence,
    0:21:03 but there’s
    0:21:04 also sort of
    0:21:05 the infrastructure
    0:21:06 delivery mechanism,
    0:21:07 which I think
    0:21:08 is an issue,
    0:21:09 whether it’s
    0:21:09 government or
    0:21:10 health care,
    0:21:11 that they’re
    0:21:12 just not
    0:21:12 set up for
    0:21:13 interventions,
    0:21:14 which are
    0:21:14 sort of like
    0:21:15 innovations.
    0:21:16 So you’ve got
    0:21:16 these researchers
    0:21:17 who think of
    0:21:18 themselves as
    0:21:20 scientific entrepreneurs
    0:21:21 developing the
    0:21:22 next best thing,
    0:21:24 thinking you build
    0:21:25 it and they
    0:21:25 will come,
    0:21:26 and then you’ve
    0:21:27 got organizations
    0:21:28 that are sort of
    0:21:29 built for
    0:21:30 efficiency rather
    0:21:30 than effectiveness
    0:21:31 that can’t
    0:21:32 uptake it.
    0:21:33 If only there
    0:21:34 were another
    0:21:34 science,
    0:21:35 a science to
    0:21:36 help these
    0:21:37 scientific
    0:21:38 entrepreneurs
    0:21:39 and institutions
    0:21:40 come together
    0:21:40 to implement
    0:21:41 this new
    0:21:41 research.
    0:21:43 Maybe something
    0:21:43 that could
    0:21:44 be called
    0:21:45 Implementation
    0:21:45 science.
    0:21:46 Implementation
    0:21:46 science.
    0:21:47 Implementation
    0:21:48 science.
    0:21:48 Implementation
    0:21:49 science.
    0:21:50 Okay, let’s
    0:21:51 define
    0:21:51 implementation
    0:21:52 science.
    0:21:53 It’s the
    0:21:54 study of how
    0:21:55 programs get
    0:21:56 implemented into
    0:21:57 practice and
    0:21:58 how the quality
    0:21:59 of that
    0:22:00 implementation may
    0:22:01 affect how well
    0:22:01 that program
    0:22:02 works or
    0:22:03 doesn’t work.
    0:22:04 That is
    0:22:05 Lauren Suplee.
    0:22:06 When we spoke
    0:22:06 with her,
    0:22:07 Suplee was the
    0:22:07 deputy chief
    0:22:08 operating officer
    0:22:09 of a nonprofit
    0:22:10 called Child
    0:22:11 Trends, which
    0:22:12 promotes evidence
    0:22:13 based policy to
    0:22:14 improve children’s
    0:22:14 lives.
    0:22:16 This whole science
    0:22:17 is maybe 15
    0:22:18 years old.
    0:22:19 It’s really
    0:22:21 coming out of
    0:22:22 this movement of
    0:22:23 evidence based
    0:22:24 policy and
    0:22:24 programs where
    0:22:25 people said,
    0:22:26 well, we have
    0:22:27 this program.
    0:22:28 It appears to
    0:22:28 change important
    0:22:29 outcomes.
    0:22:30 Let’s just put
    0:22:31 it out there
    0:22:31 and then we
    0:22:32 quickly realized
    0:22:33 that there are
    0:22:34 a lot of
    0:22:35 issues and
    0:22:36 actually that
    0:22:36 put it out
    0:22:37 there is far
    0:22:38 more complicated.
    0:22:39 A lot of the
    0:22:39 evidence based
    0:22:40 programs we have
    0:22:41 were designed
    0:22:42 by academic
    0:22:44 researchers who
    0:22:45 were testing it
    0:22:46 in the maybe
    0:22:47 more ideal
    0:22:48 circumstances that
    0:22:49 they had available
    0:22:50 to them that
    0:22:50 might have
    0:22:51 included graduate
    0:22:52 students.
    0:22:53 It might have
    0:22:53 been a school
    0:22:54 district that
    0:22:55 was very amenable
    0:22:56 to research.
    0:22:57 And then you
    0:22:57 take the results
    0:22:58 of that and
    0:22:59 trying to put
    0:22:59 that into
    0:23:01 another location
    0:23:01 is where the
    0:23:03 challenge happened.
    0:23:06 So coming up
    0:23:07 after the break,
    0:23:09 can implementation
    0:23:10 science really
    0:23:10 help?
    0:23:11 You know, I want
    0:23:12 policy science not
    0:23:14 to be an oxymoron.
    0:23:15 You’re listening to
    0:23:16 Freakonomics Radio.
    0:23:17 I’m Stephen Dubner.
    0:23:17 We will be right
    0:23:17 back.
    0:23:33 What randomized
    0:23:34 controlled trials
    0:23:35 tell us about
    0:23:35 an intervention
    0:23:38 is what that
    0:23:39 actual intervention
    0:23:41 does in a
    0:23:42 particular population
    0:23:44 in a particular
    0:23:44 context.
    0:23:46 It doesn’t mean
    0:23:46 that it’s
    0:23:47 generalizable.
    0:23:48 That, again,
    0:23:49 is Dana Susskind
    0:23:50 from the University
    0:23:51 of Chicago.
    0:23:52 But you have to
    0:23:53 continue the science
    0:23:54 so you can understand
    0:23:55 how it’s going to
    0:23:55 work in a different
    0:23:56 place, in a different
    0:23:57 context, in a different
    0:23:59 population and have
    0:23:59 the same effect.
    0:24:00 And that’s part of
    0:24:02 the scaling science.
    0:24:03 The scaling science.
    0:24:05 That is what Susskind
    0:24:06 and her economist
    0:24:07 collaborator John List,
    0:24:08 who’s also her
    0:24:09 husband, and other
    0:24:10 researchers have been
    0:24:11 working on.
    0:24:12 They’ve been
    0:24:13 systematically examining
    0:24:14 why interventions
    0:24:15 that work well in
    0:24:16 experimental or
    0:24:17 research settings
    0:24:18 often fail to
    0:24:19 scale up.
    0:24:20 You can see why
    0:24:21 this is an
    0:24:22 important puzzle
    0:24:22 to solve.
    0:24:24 Scaling up a new
    0:24:25 intervention, like
    0:24:26 a medical procedure
    0:24:27 or a teaching
    0:24:28 method, has the
    0:24:29 potential to help
    0:24:31 thousands, millions,
    0:24:32 maybe billions of
    0:24:32 people.
    0:24:34 But what if it
    0:24:36 simply fails at
    0:24:36 scale?
    0:24:37 What if it ends up
    0:24:39 costing way more
    0:24:40 than anticipated or
    0:24:41 creates serious
    0:24:43 unintended consequences?
    0:24:44 That’ll make it that
    0:24:45 much harder for the
    0:24:46 next set of
    0:24:46 researchers to
    0:24:47 persuade the next
    0:24:48 set of policymakers
    0:24:49 to listen to them.
    0:24:50 So List and
    0:24:51 Susskind have been
    0:24:52 looking at scaling
    0:24:53 failures from the
    0:24:54 past and trying to
    0:24:55 categorize what went
    0:24:56 wrong.
    0:25:00 You can kind of
    0:25:01 put what we’ve
    0:25:03 learned into three
    0:25:04 general buckets that
    0:25:05 seem to encompass the
    0:25:06 failures.
    0:25:08 Bucket number one is
    0:25:09 that the evidence was
    0:25:10 just not there to
    0:25:12 justify scaling the
    0:25:12 program in the first
    0:25:13 place.
    0:25:15 The Department of
    0:25:16 Education did this
    0:25:18 broad survey on
    0:25:20 prevention programs
    0:25:20 attempting to
    0:25:22 attenuate youth
    0:25:24 substance and crime
    0:25:25 and aspects like
    0:25:25 that.
    0:25:26 And what they
    0:25:28 found is that only
    0:25:29 8% of those
    0:25:31 programs were
    0:25:32 actually backed by
    0:25:33 research evidence.
    0:25:35 Many programs that
    0:25:37 we put in place
    0:25:39 really don’t have
    0:25:41 the research findings
    0:25:42 to support them.
    0:25:43 And this is what a
    0:25:44 scientist would call a
    0:25:44 false positive.
    0:25:46 So are we talking
    0:25:47 about bad research?
    0:25:48 Are we talking
    0:25:49 about cherry picking?
    0:25:49 Are we talking
    0:25:50 about publication
    0:25:51 bias?
    0:25:52 So here we’re
    0:25:52 talking about none
    0:25:53 of those.
    0:25:54 We’re talking about
    0:25:55 a small-scale
    0:25:57 research finding
    0:25:58 that was the
    0:25:59 truth in that
    0:26:00 finding.
    0:26:02 But because of the
    0:26:03 mechanics of
    0:26:04 statistical inference,
    0:26:06 and it just won’t
    0:26:06 be right,
    0:26:08 what you were
    0:26:09 getting into is
    0:26:11 what I would call
    0:26:12 the second bucket
    0:26:13 of why things
    0:26:14 fail, and that’s
    0:26:15 what I call the
    0:26:16 wrong people were
    0:26:17 studied.
    0:26:18 You know, these
    0:26:19 are studies that
    0:26:21 have a particular
    0:26:22 sample of people
    0:26:25 that shows really
    0:26:26 large program
    0:26:27 effect sizes,
    0:26:28 but when you
    0:26:30 program is gone
    0:26:31 to general
    0:26:32 populations,
    0:26:33 that effect
    0:26:34 disappears.
    0:26:34 So essentially,
    0:26:35 we were looking
    0:26:36 at the wrong
    0:26:37 people and scaling
    0:26:38 to the wrong
    0:26:38 people.
    0:26:39 And when you
    0:26:39 say the wrong
    0:26:40 people, the
    0:26:41 people that are
    0:26:41 being studied
    0:26:42 then are to
    0:26:42 what?
    0:26:45 They are the
    0:26:46 people who
    0:26:47 are the
    0:26:48 fraction or
    0:26:49 the group of
    0:26:50 people who
    0:26:51 receive the
    0:26:52 largest program
    0:26:53 benefits.
    0:26:54 So I think
    0:26:55 of some of the
    0:26:55 experiments that
    0:26:56 are done on
    0:26:57 college campuses,
    0:26:57 right, where
    0:26:58 there’s a
    0:26:59 professor who’s
    0:27:00 looking to find
    0:27:00 out something
    0:27:01 about, let’s
    0:27:02 say, altruism,
    0:27:04 and the
    0:27:04 experimental
    0:27:05 setting is a
    0:27:06 classroom where
    0:27:07 20 college
    0:27:07 students will
    0:27:08 come in, and
    0:27:08 they’re a pretty
    0:27:10 homogeneous population,
    0:27:11 they’re pretty
    0:27:12 motivated, maybe
    0:27:12 they’re very
    0:27:13 disciplined, and
    0:27:14 that may not
    0:27:15 represent what
    0:27:16 the world
    0:27:16 actually is.
    0:27:17 Is that what
    0:27:18 you’re talking
    0:27:18 about?
    0:27:19 That’s one
    0:27:20 piece of it.
    0:27:22 Another piece
    0:27:23 is who will
    0:27:24 sign their
    0:27:25 kids up for
    0:27:26 Head Start or
    0:27:27 for a program
    0:27:29 in a neighborhood
    0:27:30 that advances
    0:27:31 the reading
    0:27:32 skills of the
    0:27:32 child?
    0:27:33 Who’s going
    0:27:33 to be first
    0:27:34 in line?
    0:27:35 The people who
    0:27:36 really care about
    0:27:37 education and
    0:27:38 the people who
    0:27:39 think their
    0:27:40 child will
    0:27:40 receive the
    0:27:41 most benefits
    0:27:41 from the
    0:27:42 program.
    0:27:43 Now, another
    0:27:44 way to get
    0:27:44 it is sort
    0:27:45 of along the
    0:27:45 lines that
    0:27:46 you talked
    0:27:46 about.
    0:27:46 It could
    0:27:47 be the
    0:27:49 researcher knows
    0:27:50 something about
    0:27:51 the population
    0:27:52 that other
    0:27:53 people don’t
    0:27:53 know.
    0:27:55 Like, I want
    0:27:55 to give my
    0:27:56 program its
    0:27:57 best shot of
    0:27:58 working.
    0:27:59 Okay, and
    0:28:00 what’s in your
    0:28:00 third bucket
    0:28:02 of scaling
    0:28:02 failures?
    0:28:03 The third
    0:28:04 bucket is
    0:28:05 something that
    0:28:06 we call
    0:28:08 the wrong
    0:28:09 situation was
    0:28:09 used.
    0:28:11 And what I
    0:28:11 mean by that
    0:28:12 is that certain
    0:28:13 aspects of the
    0:28:15 situation change
    0:28:16 when you go
    0:28:17 from the
    0:28:17 original research
    0:28:18 to the scaled
    0:28:19 research program.
    0:28:21 We don’t
    0:28:23 understand what
    0:28:24 properties of
    0:28:25 the situation
    0:28:26 or features of
    0:28:27 the environment
    0:28:28 will matter.
    0:28:30 there are a
    0:28:31 really large
    0:28:32 group of
    0:28:33 implementation
    0:28:35 scientists who
    0:28:35 have explored
    0:28:36 this question
    0:28:37 for years.
    0:28:39 Now, what
    0:28:40 they emphasize
    0:28:41 and focus on
    0:28:42 is something
    0:28:43 called voltage
    0:28:44 drop.
    0:28:46 And voltage
    0:28:47 drop essentially
    0:28:48 means I
    0:28:49 found a really
    0:28:51 good result in
    0:28:52 my original
    0:28:52 research study,
    0:28:53 but then when
    0:28:54 they do it at
    0:28:55 scale, that
    0:28:57 voltage drop
    0:28:58 ends up being,
    0:28:58 for example,
    0:29:00 a tenth of
    0:29:00 the original
    0:29:01 result or a
    0:29:02 quarter of the
    0:29:03 original result.
    0:29:05 An example of
    0:29:06 this is when
    0:29:07 you look at
    0:29:07 Head Start’s
    0:29:08 home visiting
    0:29:10 services, what
    0:29:10 they do there
    0:29:11 is this is an
    0:29:11 early childhood
    0:29:13 intervention that
    0:29:14 found huge
    0:29:16 improvements in
    0:29:17 both child and
    0:29:17 parent outcomes
    0:29:18 in the original
    0:29:20 study, except
    0:29:20 when they tried
    0:29:21 to scale that
    0:29:22 up and do
    0:29:24 home visits at
    0:29:24 a much larger
    0:29:26 scale, what
    0:29:27 they found is
    0:29:28 that, for
    0:29:29 example, home
    0:29:30 visits for
    0:29:31 at-risk families
    0:29:32 involved a lot
    0:29:33 more distractions
    0:29:34 in the house
    0:29:35 and there was
    0:29:36 less time on
    0:29:37 child-focused
    0:29:38 activities.
    0:29:38 So this is
    0:29:40 sort of the
    0:29:41 wrong dosage or
    0:29:41 the wrong
    0:29:42 program is given
    0:29:43 at scale.
    0:29:46 There are many
    0:29:46 factors that
    0:29:47 contribute to
    0:29:48 this voltage
    0:29:49 drop, including
    0:29:50 the admirably
    0:29:51 high standards
    0:29:52 set by the
    0:29:53 original researchers.
    0:29:54 when the
    0:29:55 researcher starts
    0:29:56 his or her
    0:29:57 experiment, the
    0:29:59 inclination is
    0:29:59 I’m going to
    0:30:00 get the best
    0:30:01 tutors in the
    0:30:02 world, so I’m
    0:30:02 going to be able
    0:30:03 to show how
    0:30:03 effective my
    0:30:04 intervention is.
    0:30:05 Dana Susskind
    0:30:06 again.
    0:30:07 you only needed
    0:30:08 10 math tutors
    0:30:09 and you happen
    0:30:10 to get the
    0:30:10 PhD students
    0:30:11 from the
    0:30:11 University of
    0:30:12 Chicago, and
    0:30:13 then what
    0:30:14 happens is you
    0:30:14 show this
    0:30:15 tremendous effect
    0:30:16 size, and in
    0:30:17 the scaling, all
    0:30:18 of a sudden, you
    0:30:19 need a hundred or
    0:30:21 a thousand, and you
    0:30:22 no longer have that
    0:30:23 access to those
    0:30:24 individuals, and you
    0:30:26 go either down the
    0:30:27 supply chain with
    0:30:28 individuals who are
    0:30:29 not quite as well
    0:30:31 trained, or you end
    0:30:32 up having to pay a
    0:30:32 whole lot more
    0:30:33 money to
    0:30:34 maintain the
    0:30:35 trained tutor
    0:30:36 program, and one
    0:30:37 way or the other,
    0:30:39 either the impacts
    0:30:40 of the intervention
    0:30:42 go down, or your
    0:30:43 costs go up
    0:30:44 significantly.
    0:30:46 Another problem in
    0:30:46 this third bucket,
    0:30:48 it’s a big bucket,
    0:30:49 is when the person
    0:30:50 who designed the
    0:30:51 intervention and
    0:30:52 masterminded the
    0:30:53 initial trial can
    0:30:54 no longer be so
    0:30:55 involved once the
    0:30:57 program scales up to
    0:30:57 multiple locations.
    0:30:59 Imagine if instead
    0:31:00 of talking about an
    0:31:01 educational or
    0:31:02 medical program, we
    0:31:03 were talking about
    0:31:03 a successful
    0:31:04 restaurant and the
    0:31:05 original chef.
    0:31:07 When you think about
    0:31:08 the chef, if a
    0:31:09 restaurant succeeds
    0:31:11 because of the
    0:31:13 magical work of
    0:31:15 the chef, and you
    0:31:16 think about scaling
    0:31:18 that, if you can’t
    0:31:20 scale the magic in
    0:31:22 the chef, that’s not
    0:31:22 scalable.
    0:31:25 Now, if the magic is
    0:31:26 because of the mix
    0:31:28 of ingredients, and
    0:31:29 the secret sauce, like
    0:31:30 Domino’s, for
    0:31:31 example, the secret
    0:31:33 sauce or Papa John’s
    0:31:35 is the actual
    0:31:36 ingredients, then
    0:31:37 that will be
    0:31:38 scalable.
    0:31:43 Now, if you are
    0:31:43 the kind of pizza
    0:31:44 eater who doesn’t
    0:31:45 think Domino’s or
    0:31:47 Papa John’s is good
    0:31:49 pizza, well, welcome
    0:31:50 to the scaling
    0:31:50 dilemma.
    0:31:52 Going big means you
    0:31:53 have to be many
    0:31:54 things to many
    0:31:54 people.
    0:31:56 Going big means you
    0:31:57 will face a lot of
    0:31:57 trade-offs.
    0:31:59 Going big means you’ll
    0:31:59 have a lot of people
    0:32:01 asking you, do you
    0:32:01 want this done
    0:32:03 fast, or do you
    0:32:03 want it done right?
    0:32:05 Once you peer
    0:32:07 inside these failure
    0:32:08 buckets that List and
    0:32:09 Susskind describe, it’s
    0:32:11 not so surprising that
    0:32:12 so many good ideas
    0:32:13 fail to scale up.
    0:32:15 So, what do they
    0:32:16 propose that could
    0:32:16 help?
    0:32:18 Now, our proposal
    0:32:20 is that we do not
    0:32:22 believe that we
    0:32:24 should scale a
    0:32:27 program until you’re
    0:32:29 95% certain the
    0:32:30 result is true.
    0:32:32 So, essentially, what
    0:32:34 that means is we
    0:32:35 need the original
    0:32:37 research and then
    0:32:40 three or four well-powered
    0:32:42 independent replications
    0:32:43 of the original
    0:32:44 findings.
    0:32:45 And how often is that
    0:32:47 already happening in the
    0:32:48 real world of, let’s
    0:32:50 say, education reform
    0:32:50 research?
    0:32:52 I can’t name one.
    0:32:52 Wow.
    0:32:53 How about in the
    0:32:55 realm of medical
    0:32:56 compliance research?
    0:32:58 My intuition is that
    0:33:00 they’re probably not far
    0:33:01 away from three or four
    0:33:03 well-powered independent
    0:33:03 replications.
    0:33:07 In the hard sciences, in
    0:33:08 many cases, you not only
    0:33:10 have the original
    0:33:13 research, but you have a
    0:33:16 first replication also
    0:33:17 published in science.
    0:33:19 you know, the current
    0:33:21 credibility crisis in
    0:33:22 science is a serious
    0:33:23 one that major
    0:33:25 results are not
    0:33:25 replicating.
    0:33:28 The reason why is
    0:33:29 because we weren’t
    0:33:30 serious about
    0:33:31 replication in the
    0:33:31 first place.
    0:33:33 So, this sort of puts
    0:33:34 the onus on
    0:33:35 policymakers and
    0:33:37 funding agencies in
    0:33:37 a sense of saying,
    0:33:39 we need to change the
    0:33:39 equilibrium.
    0:33:42 So, that
    0:33:43 suggests that
    0:33:45 policymakers or
    0:33:46 decision makers, they
    0:33:47 are being, what,
    0:33:50 overeager, premature in
    0:33:52 accepting a finding that
    0:33:53 looks good to them and
    0:33:54 want to rush it into
    0:33:54 play?
    0:33:56 Or is it that the
    0:33:57 researchers are
    0:33:59 overconfident themselves
    0:34:00 or maybe pushing this
    0:34:01 research too hard?
    0:34:02 Where is this failure
    0:34:03 really happening?
    0:34:04 Well, I think it’s sort
    0:34:05 of a mix.
    0:34:07 I think it’s fair to
    0:34:08 say that some
    0:34:10 policymakers are out
    0:34:11 looking for evidence
    0:34:13 to base their
    0:34:14 preferred program on.
    0:34:15 What this will do is
    0:34:16 slow that down.
    0:34:18 if you have a
    0:34:19 pet project that
    0:34:19 you want to get
    0:34:21 through, fund the
    0:34:22 replications and
    0:34:23 let’s make sure the
    0:34:24 science is correct.
    0:34:25 We think we should
    0:34:26 actually be rewarding
    0:34:28 scholars for
    0:34:29 attempting to
    0:34:29 replicate.
    0:34:31 You know, right now
    0:34:33 in my community, if I
    0:34:33 try to replicate
    0:34:35 someone else, guess
    0:34:35 what I’ve just
    0:34:36 made?
    0:34:38 I’ve just made a
    0:34:39 mortal enemy for
    0:34:39 life.
    0:34:41 If you find a
    0:34:42 publishable result,
    0:34:43 what result is that?
    0:34:43 you’re refuting
    0:34:45 previous research.
    0:34:47 Now I’ve doubled
    0:34:48 down on my
    0:34:48 enemy.
    0:34:50 So that’s like a
    0:34:52 first step in
    0:34:53 terms of rewarding
    0:34:55 scholars who are
    0:34:55 attempting to
    0:34:56 replicate.
    0:34:58 Now, to
    0:34:59 complement that, I
    0:34:59 think we should
    0:35:01 also reward
    0:35:02 scholars who
    0:35:02 have produced
    0:35:04 results that are
    0:35:05 independently
    0:35:06 replicated.
    0:35:07 You know, and I’m
    0:35:08 talking about tying
    0:35:09 tenure decisions,
    0:35:11 grant money, and the
    0:35:13 like to people who
    0:35:14 have given us
    0:35:15 credible research
    0:35:16 that replicates.
    0:35:20 After the break,
    0:35:21 how can researchers
    0:35:22 make sure that the
    0:35:23 science they are
    0:35:24 replicating works
    0:35:25 when it scales up?
    0:35:40 Before the break, we
    0:35:41 were talking with the
    0:35:41 University of Chicago
    0:35:43 economist John List
    0:35:44 about the challenges
    0:35:45 of turning good
    0:35:46 research into good
    0:35:46 policy.
    0:35:48 One challenge is
    0:35:49 making sure that the
    0:35:50 research findings are
    0:35:52 in fact robust enough
    0:35:53 to scale up.
    0:35:54 Say I’m doing an
    0:35:55 experiment in Chicago
    0:35:57 Heights on early
    0:35:59 childhood, and I find
    0:36:01 a great result, how
    0:36:03 confident should I be
    0:36:05 that when we take that
    0:36:06 result to all of
    0:36:07 Illinois or all of the
    0:36:09 Midwest or all of
    0:36:10 America, is that
    0:36:12 result still going
    0:36:14 to find that
    0:36:15 important benefit
    0:36:17 cost profile that
    0:36:18 we found in
    0:36:18 Chicago Heights?
    0:36:20 We need to know
    0:36:21 what is the magic
    0:36:22 sauce.
    0:36:23 Was it the 20
    0:36:25 teachers you hired
    0:36:26 down in Chicago
    0:36:28 Heights where if we
    0:36:29 go nationally, we
    0:36:30 need 20,000?
    0:36:33 So it should
    0:36:34 behoove me as an
    0:36:36 original researcher
    0:36:37 teacher to say,
    0:36:38 look, if this
    0:36:40 scales up, we’re
    0:36:41 going to need many
    0:36:42 more teachers.
    0:36:44 I know teachers are
    0:36:45 an important input.
    0:36:47 Is the average
    0:36:48 teacher in the
    0:36:51 20,000 the same
    0:36:52 as the average
    0:36:53 teacher in the
    0:36:53 20?
    0:36:55 This is the dreaded
    0:36:56 voltage drop that
    0:36:57 implementation
    0:36:58 scientists talk
    0:36:58 about.
    0:36:59 And the
    0:36:59 implementation
    0:37:01 scientists have
    0:37:02 focused on
    0:37:04 fidelity as a core
    0:37:05 component behind
    0:37:06 the voltage
    0:37:06 drop.
    0:37:08 Fidelity
    0:37:09 meaning that the
    0:37:10 scaled up program
    0:37:10 reflects the
    0:37:11 integrity of the
    0:37:12 original program.
    0:37:13 Measures of
    0:37:14 fidelity.
    0:37:15 That’s a really
    0:37:16 critical part of
    0:37:17 the implementation
    0:37:18 process.
    0:37:19 That, again, is
    0:37:20 Patty Chamberlain,
    0:37:21 founder of
    0:37:21 Treatment Foster
    0:37:22 Care Oregon.
    0:37:23 You’ve got to be
    0:37:24 able to measure,
    0:37:26 is this thing
    0:37:27 that’s down in the
    0:37:28 real world the
    0:37:29 same, you know,
    0:37:30 does it have the
    0:37:31 same components
    0:37:32 that produce the
    0:37:33 outcomes in the
    0:37:33 RCTs.
    0:37:35 Remember, it was
    0:37:35 Chamberlain’s good
    0:37:37 outcomes with young
    0:37:37 people in foster
    0:37:38 care that made
    0:37:39 federal officials want
    0:37:40 to scale up her
    0:37:41 program in the first
    0:37:41 place.
    0:37:43 We got this call
    0:37:44 saying, we want you
    0:37:45 to implement your
    0:37:47 program in 15
    0:37:48 sites.
    0:37:49 She found the
    0:37:50 scaling up initially
    0:37:51 very challenging.
    0:37:52 It wasn’t the
    0:37:54 kumbaya moment that
    0:37:54 we thought it was
    0:37:55 going to be.
    0:37:56 But in time,
    0:37:57 Treatment Foster
    0:37:58 Care Oregon became
    0:37:59 a very well-regarded
    0:38:00 program.
    0:38:00 It’s been around for
    0:38:02 roughly 30 years
    0:38:03 now, and the
    0:38:04 model has spread
    0:38:05 well beyond Oregon.
    0:38:06 One key to this
    0:38:07 success has been
    0:38:08 developing fidelity
    0:38:09 standards.
    0:38:10 So the way that we
    0:38:11 do it is we have
    0:38:12 people upload all of
    0:38:13 their sessions onto
    0:38:14 a HIPAA secure
    0:38:15 website, and then
    0:38:16 we code those.
    0:38:18 And if they’re not
    0:38:18 meeting the fidelity
    0:38:20 standards, then we
    0:38:21 offer a fidelity
    0:38:22 recovery plan.
    0:38:23 You know, we
    0:38:24 haven’t had to drop
    0:38:24 a site, but we
    0:38:26 have had to have
    0:38:27 some of the people
    0:38:28 in the site
    0:38:30 retrained or not
    0:38:31 continue.
    0:38:32 being able to
    0:38:33 measure fidelity
    0:38:34 well from afar
    0:38:35 provides another
    0:38:37 benefit to scaling
    0:38:37 up.
    0:38:38 It allows the
    0:38:39 people who
    0:38:39 developed the
    0:38:40 original program
    0:38:41 to ultimately
    0:38:43 step back, so
    0:38:44 they don’t become
    0:38:45 a bottleneck, which
    0:38:45 is a common
    0:38:46 scaling problem.
    0:38:47 There can be
    0:38:48 sort of an
    0:38:49 orderly process
    0:38:50 whereby you
    0:38:52 step back in
    0:38:53 increments as
    0:38:54 people become
    0:38:55 more and more
    0:38:56 competent doing
    0:38:56 what they’re
    0:38:57 doing.
    0:38:57 And that’s
    0:38:58 what you want
    0:38:58 because you
    0:38:59 don’t want to
    0:38:59 have this tied to
    0:39:00 the developer
    0:39:00 forever.
    0:39:01 Otherwise, you
    0:39:02 can’t get any
    0:39:03 kind of reasonable
    0:39:03 reach.
    0:39:05 That said, you
    0:39:06 also need to
    0:39:06 have some
    0:39:07 humility.
    0:39:08 When you’re
    0:39:08 scaling up, you
    0:39:09 shouldn’t assume
    0:39:10 your original
    0:39:11 program was
    0:39:12 perfect, that it
    0:39:13 won’t need
    0:39:14 adjustment, and
    0:39:15 you need to be
    0:39:15 willing to make
    0:39:16 adjustments.
    0:39:18 For example, we
    0:39:19 recognized that
    0:39:20 when we were in
    0:39:21 real-world
    0:39:23 communities, kids
    0:39:23 needed something
    0:39:24 that wasn’t
    0:39:25 therapy, per se.
    0:39:26 they needed
    0:39:28 skills because
    0:39:29 the kids had
    0:39:30 often been
    0:39:31 excluded from
    0:39:32 normal socializing
    0:39:33 sort of things
    0:39:34 like sports
    0:39:35 teams and
    0:39:36 clubs.
    0:39:37 And so we
    0:39:38 needed what
    0:39:39 we call a
    0:39:39 skills coach
    0:39:41 to help
    0:39:42 those kids
    0:39:42 learn the
    0:39:43 moves that
    0:39:44 they needed
    0:39:44 to be able
    0:39:45 to participate
    0:39:47 in these
    0:39:47 pro-social
    0:39:49 activities that
    0:39:49 are normal
    0:39:50 kind of things.
    0:39:51 So you have
    0:39:51 research, you
    0:39:52 have a theory,
    0:39:52 and then you
    0:39:53 have the
    0:39:54 implementation, and
    0:39:54 that feeds
    0:39:55 into more
    0:39:55 research, more
    0:39:56 theory, more
    0:39:56 implementation.
    0:40:01 Look, everybody’s
    0:40:01 motivation at the
    0:40:02 end of the day is
    0:40:03 about trying to
    0:40:04 do good for
    0:40:05 the people they
    0:40:05 serve.
    0:40:07 Dana Susskind
    0:40:07 again.
    0:40:08 There are many
    0:40:09 children out there,
    0:40:10 and there are a
    0:40:11 lot of injustices,
    0:40:12 so we need to
    0:40:13 move, but I
    0:40:14 don’t know.
    0:40:14 The science is
    0:40:15 slower than
    0:40:16 you’d like.
    0:40:17 People have
    0:40:18 wanted things
    0:40:19 before I thought
    0:40:19 they were ready,
    0:40:21 and finding a
    0:40:22 way to deal
    0:40:23 with that dance
    0:40:24 of people wanting
    0:40:26 information, but
    0:40:27 also wanting to
    0:40:28 continue to build
    0:40:28 the evidence.
    0:40:29 I think we can
    0:40:30 figure out how
    0:40:31 to do it.
    0:40:31 I think that’s
    0:40:32 exactly right.
    0:40:33 And John List
    0:40:34 again.
    0:40:35 I think too
    0:40:36 many times,
    0:40:38 whether it’s
    0:40:39 in public
    0:40:40 policy, whether
    0:40:41 it’s a for-profit
    0:40:43 or a not-for-profit,
    0:40:44 we tend to
    0:40:45 only focus on
    0:40:46 one side of
    0:40:47 the market when
    0:40:47 we have
    0:40:49 problems, and
    0:40:50 you really need
    0:40:51 to take account
    0:40:52 of both sides
    0:40:52 because your
    0:40:53 optimal solutions,
    0:40:54 the best
    0:40:55 solutions, are
    0:40:55 only going to
    0:40:56 come when you
    0:40:56 look at both
    0:40:57 sides of the
    0:40:57 market.
    0:40:58 I’m probably
    0:40:59 getting this
    0:40:59 wrong, or at
    0:41:00 least being way
    0:41:00 too reductive,
    0:41:01 but to me it
    0:41:01 sounds like the
    0:41:03 chief barrier to
    0:41:04 scaling up programs
    0:41:05 to help people
    0:41:07 is people, that
    0:41:08 people are the
    0:41:08 problem.
    0:41:10 Yeah, so I do
    0:41:11 think inherently
    0:41:12 it is about
    0:41:13 people.
    0:41:15 That said, this
    0:41:17 is not a fatal
    0:41:20 flaw that causes
    0:41:21 us to throw up
    0:41:22 our arms and
    0:41:23 say, well, this
    0:41:24 isn’t physics,
    0:41:24 this isn’t
    0:41:25 chemistry, we
    0:41:26 have to deal
    0:41:27 with people, so
    0:41:28 we can’t use
    0:41:28 science.
    0:41:29 I think that’s
    0:41:30 wrong, because
    0:41:30 there are some
    0:41:32 very, very neat
    0:41:34 advantages of
    0:41:35 scaling.
    0:41:36 Think about on
    0:41:37 the cost side,
    0:41:38 economists always
    0:41:39 talk about, you
    0:41:39 know, when
    0:41:40 things get bigger
    0:41:42 and bigger, guess
    0:41:42 what happens?
    0:41:44 The per-unit cost
    0:41:45 goes down.
    0:41:46 It’s called
    0:41:47 increasing returns
    0:41:48 to scale.
    0:41:49 The problem that
    0:41:50 kind of we’re
    0:41:51 thinking about is
    0:41:52 let’s make sure
    0:41:53 that those
    0:41:54 policymakers who
    0:41:55 really want to
    0:41:56 do the right
    0:41:57 thing in use
    0:41:58 science, let’s
    0:41:59 make sure that
    0:42:00 they have the
    0:42:01 right programs to
    0:42:01 implement.
    0:42:03 So one of your
    0:42:04 papers includes
    0:42:05 this quote from
    0:42:06 Bill Clinton, or
    0:42:06 at least something
    0:42:07 that Clinton may
    0:42:07 have said, which
    0:42:08 is essentially
    0:42:09 that nearly
    0:42:10 every problem
    0:42:11 has been solved
    0:42:12 by someone
    0:42:13 somewhere, but
    0:42:13 we just can’t
    0:42:14 seem to replicate
    0:42:15 those solutions
    0:42:16 anywhere else.
    0:42:18 So what makes
    0:42:19 you think that
    0:42:20 you’ve got the
    0:42:21 keys to success
    0:42:21 here where
    0:42:22 others may not
    0:42:23 have been able
    0:42:23 to do it?
    0:42:25 You know, I
    0:42:26 view what we’ve
    0:42:27 done is put
    0:42:29 forward a set
    0:42:29 of modest
    0:42:31 proposals as
    0:42:32 only a start
    0:42:34 to tackle what
    0:42:35 I think is the
    0:42:36 most vexing
    0:42:37 problem in
    0:42:38 evidence-based
    0:42:39 policymaking,
    0:42:39 which is
    0:42:39 scaling.
    0:42:40 I think we’re
    0:42:41 just taking
    0:42:42 some small
    0:42:44 steps theoretically
    0:42:45 and empirically,
    0:42:46 but I do think
    0:42:47 that these first
    0:42:49 set of steps
    0:42:49 are important
    0:42:51 because if
    0:42:52 you go in the
    0:42:53 right direction,
    0:42:54 what I’ve
    0:42:54 learned is that
    0:42:55 literature will
    0:42:56 follow that
    0:42:56 direction.
    0:42:58 If you go in
    0:42:58 the wrong
    0:42:58 direction,
    0:43:00 sometimes the
    0:43:01 literature follows
    0:43:02 that wrong
    0:43:02 direction for
    0:43:03 several years,
    0:43:04 and we
    0:43:05 really don’t
    0:43:05 have the
    0:43:06 time.
    0:43:07 Right now,
    0:43:08 the opportunity
    0:43:09 cost of time
    0:43:10 is very high.
    0:43:13 You know, in
    0:43:14 the end, I
    0:43:14 want policy
    0:43:15 science not
    0:43:15 to be an
    0:43:16 oxymoron,
    0:43:17 and I think
    0:43:18 that’s what this
    0:43:19 research agenda
    0:43:19 is about.
    0:43:21 The way that I
    0:43:21 would view it
    0:43:23 is that the
    0:43:24 world is
    0:43:25 imperfect because
    0:43:26 we haven’t
    0:43:27 used science
    0:43:28 in policymaking,
    0:43:30 and if we
    0:43:31 add science
    0:43:31 to it,
    0:43:33 we have a
    0:43:34 chance to
    0:43:34 make an
    0:43:35 imperfect world
    0:43:36 a little bit
    0:43:37 more perfect.
    0:43:42 If you want
    0:43:42 to read the
    0:43:43 papers that
    0:43:44 John List and
    0:43:45 Dana Susskind
    0:43:45 and their
    0:43:46 collaborators
    0:43:46 have been
    0:43:47 working on,
    0:43:47 you will find
    0:43:48 links on
    0:43:49 Freakonomics.com
    0:43:50 as well as
    0:43:51 links to
    0:43:51 Patty Chamberlain’s
    0:43:52 work with
    0:43:53 Treatment Foster
    0:43:53 Care Oregon
    0:43:55 and much more,
    0:43:56 including, as
    0:43:56 always, a
    0:43:57 complete transcript
    0:43:58 of this episode.
    0:43:59 And we will
    0:44:00 be back soon
    0:44:00 with another
    0:44:01 new episode
    0:44:02 of Freakonomics
    0:44:03 Radio.
    0:44:03 Until then,
    0:44:04 take care of
    0:44:04 yourself.
    0:44:05 And if you
    0:44:06 can, someone
    0:44:07 else, too.
    0:44:09 Freakonomics
    0:44:09 Radio is produced
    0:44:10 by Stitcher
    0:44:11 and Renbud
    0:44:11 Radio.
    0:44:12 You can find
    0:44:13 our entire
    0:44:14 archive on
    0:44:14 any podcast
    0:44:15 app, also
    0:44:17 at Freakonomics.com
    0:44:18 where we publish
    0:44:19 transcripts and
    0:44:19 show notes.
    0:44:21 This episode was
    0:44:21 produced by
    0:44:22 Matt Hickey
    0:44:23 with an update
    0:44:24 by Augusta
    0:44:24 Chapman.
    0:44:25 The Freakonomics
    0:44:26 Radio network
    0:44:27 staff also includes
    0:44:28 Alina Cullman,
    0:44:28 Dalvin
    0:44:29 Abuaji,
    0:44:30 Eleanor Osborne,
    0:44:31 Ellen Frankman,
    0:44:31 Elsa Hernandez,
    0:44:32 Gabriel Roth,
    0:44:33 Greg Rippon,
    0:44:34 Jasmine Klinger,
    0:44:35 Jeremy Johnston,
    0:44:35 John Schnarz,
    0:44:36 Morgan Levy,
    0:44:37 Neil Carruth,
    0:44:38 Sarah Lilly,
    0:44:39 Tao Jacobs,
    0:44:40 and Zach Lipinski.
    0:44:41 Our theme song
    0:44:42 is Mr. Fortune
    0:44:43 by the Hitchhikers
    0:44:43 and our composer
    0:44:45 is Luis Guerra.
    0:44:46 As always,
    0:44:47 thanks for listening.
    0:44:56 So you want to
    0:44:56 talk scaling?
    0:44:57 Wow,
    0:44:57 it’s a heavy
    0:44:58 paper, right?
    0:44:58 It’s great.
    0:44:59 I thought it
    0:45:00 was about
    0:45:01 scaling fish
    0:45:01 initially,
    0:45:03 so that was
    0:45:04 all my
    0:45:05 background reading.
    0:45:05 Yeah,
    0:45:06 so I don’t
    0:45:06 know anything
    0:45:07 about what
    0:45:07 we’re going
    0:45:08 to talk about
    0:45:08 today.
    0:45:10 Neither do I,
    0:45:10 so we can
    0:45:11 just both
    0:45:11 wing it.
    0:45:17 The Freakonomics
    0:45:18 Radio Network,
    0:45:19 the hidden
    0:45:19 side of
    0:45:20 everything.
    0:45:24 Stitcher.

    Why do so many promising solutions in education, medicine, and criminal justice fail to scale up into great policy? And can a new breed of “implementation scientists” crack the code?

     

    • SOURCES:
      • Patti Chamberlain, senior research scientist at the Oregon Social Learning Center.
      • John List, professor of economics at the University of Chicago.
      • Lauren Supplee, former deputy chief operating officer at Child Trends.
      • Dana L. Suskind, professor of surgery at the University of Chicago.

     

     

  • The Future of Drone Warfare

    AI transcript
    0:00:04 The industrial capacity of China is fearsome.
    0:00:08 Being able to deploy highly autonomous AI-driven drones at scale
    0:00:10 is still a domain that we can win in.
    0:00:14 I think the technologies that matter most to the future of war
    0:00:17 are right there in front of us.
    0:00:20 I think a modern conflict becomes basically like a software writing fight.
    0:00:22 It will be the pace of deployment
    0:00:27 that is the make or break for militaries around the world.
    0:00:30 The game theory here is just as simple and obvious as it can be.
    0:00:35 Unless we find a way for industry and government to work together,
    0:00:39 we will find ourselves in a very tough situation.
    0:00:43 Will Durant once said, quote,
    0:00:46 war is one of the constants of history, unquote.
    0:00:49 And while the presence of war has not changed,
    0:00:51 the way it’s conducted has.
    0:00:55 It is technology, whether steel, gunpowder, radio, GPS,
    0:00:59 or nuclear weapons, which have defined conflicts over the eras.
    0:01:02 And while the images of tanks or machine guns
    0:01:04 dominate the visuals we have of war,
    0:01:07 these are not the decisive technologies of the future.
    0:01:09 And here’s the thing.
    0:01:11 The future of warfare isn’t coming.
    0:01:12 It’s already here.
    0:01:16 It’s fought in the skies over Ukraine, Israel, and beyond
    0:01:18 by AI-empowered drones.
    0:01:22 These drones have become a crucial weapon of war with asymmetric capability,
    0:01:26 where a handful of drones costing hundreds or thousands of dollars
    0:01:30 can disable equipment like tanks or aircraft that cost orders of magnitude more.
    0:01:33 We’re literally off by like three orders of magnitude.
    0:01:37 Within a few short years, drones have gone from a reconnaissance tool
    0:01:40 to one that ensures aerial and battlefield dominance.
    0:01:43 The army with more drones has a decisive advantage.
    0:01:48 So, with China dominating about 80% of the global drone market,
    0:01:52 what does that say about our national security in the growing power competition?
    0:01:54 Why have we fallen so far behind?
    0:01:56 And what will it take to build up our domestic drone industry?
    0:02:00 Plus, where should autonomy play a role in military decision-making,
    0:02:03 when lives are literally on the line?
    0:02:07 In today’s episode, recorded live at our third annual American Dynamism Summit
    0:02:09 in the heart of Washington, D.C.,
    0:02:13 A16Z’s Senior National Security Advisor, Matt Cronin,
    0:02:16 sits down with two people who have been thinking about these questions
    0:02:21 and building solutions dating all the way back to when this industry was full of hobbyists.
    0:02:24 That is Ryan Tseng, co-founder and CEO of Shield AI,
    0:02:28 and Adam Breit, co-founder and CEO of Skydio.
    0:02:33 Shield has been building intelligent systems like AI-powered fighter pilots
    0:02:35 and drones since 2015,
    0:02:40 while Skydio has been manufacturing drones for use in the battlefield since 2014,
    0:02:44 and today is the largest U.S. drone manufacturer by volume.
    0:02:47 So who will command the skies in the years to come?
    0:02:49 Listen in to find out.
    0:02:54 As a reminder, the content here is for informational purposes only,
    0:02:57 should not be taken as legal, business, tax, or investment advice,
    0:03:00 or be used to evaluate any investment or security,
    0:03:02 and is not directed at any investors or potential investors
    0:03:04 in any A16Z fund.
    0:03:07 Please note that A16Z and its affiliates
    0:03:10 may also maintain investments in the companies discussed in this podcast.
    0:03:13 For more details, including a link to our investments,
    0:03:15 please see A16Z.com slash disclosures.
    0:03:26 We’re here to chat about drones, autonomy, and great power conflict.
    0:03:31 Now, both of you have started incredibly successful and innovative drone-focused companies.
    0:03:35 At the same time, both of you started when the industry was truly nascent.
    0:03:40 It was seen as a field for hobbyists rather than something that could actually shape the future of warfare.
    0:03:46 So what led you to be interested in this field and to spend so much of your time,
    0:03:50 blood, sweat, and tears, on this now incredibly important industry?
    0:03:53 I had started and sold a company to Qualcomm.
    0:03:59 And I’ve always been somebody that has had an intense passion to compete, fight, win at whatever I was doing.
    0:04:02 And in my last year, Qualcomm was there for about four years.
    0:04:05 I didn’t have that fire in the belly that I had had throughout my life.
    0:04:09 And so I started thinking about what it was that would really motivate me,
    0:04:11 not for the next five years, but for the next 50.
    0:04:15 And I decided that if I could find the intersection of three things,
    0:04:18 a noble mission, the chance to work with extraordinary people,
    0:04:21 and a chance to define the possible, I’d have that fire in the belly.
    0:04:26 And my brother went on to become a Navy SEAL, so a totally different track in life.
    0:04:29 And he was getting ready to go to business school.
    0:04:32 I encouraged him to think about what he wanted to do in life.
    0:04:35 And he came to me with the idea of bringing the best of what was going on
    0:04:39 in the autonomous driving sector to the mission of protecting service members and civilians.
    0:04:45 He felt like if somebody would do that, it would have brought home a lot of his friends safely to their families.
    0:04:50 And he felt like it would be a pillar for the future of American military dominance.
    0:04:52 I thought it was an extraordinary mission.
    0:04:54 I told you earlier, I thought it was a stupid business.
    0:05:00 And so I wished him luck and suggested that he come up with a better business to make mission impact.
    0:05:03 But SEALs are very persistent people.
    0:05:04 My brother is no exception.
    0:05:09 And for a long story short, I started to spend more time with him learning about the challenges.
    0:05:13 And I was just shocked by the scope of the problem and how little was being done about it.
    0:05:19 And I’ve been proud and humbled every day for the last 10 years to have an opportunity to contribute to a mission that I think is so important.
    0:05:19 Incredible.
    0:05:21 Well, I’m glad SEALs are persistent in general.
    0:05:25 And I’m especially glad that your brother, the SEAL, dragged you along in this important mission.
    0:05:26 Yes.
    0:05:26 What about you?
    0:05:29 Yes, we come at this from very different places.
    0:05:42 First, I think that S.H.I.E.L.D. deserves just enormous credit for 10-plus years ago recognizing the need and the opportunity for what we now think of as defense tech at a time when that really did not exist.
    0:05:44 And people thought they were crazy for even trying.
    0:05:47 Candidly, I don’t think we get that same kind of credit.
    0:05:49 So I grew up flying radio-controlled airplanes.
    0:05:51 I’ve basically been doing grown stuff my whole life.
    0:06:03 And that led me to be a grad student at MIT in the late 2000s, early 2010s, when you could basically take radio-controlled airplanes and put computers and sensors on them and write software to get them to do smart stuff.
    0:06:10 So I really became obsessed with trying to build AI systems that could fly better than people could and wasn’t thinking so much about applications at the time.
    0:06:18 But in 2013, 2014, my lab mate and I started to look out and see there were interesting things starting to happen with small light quadcopters.
    0:06:23 And we felt like the applications and implications of the technology could be enormous.
    0:06:27 But needing to have an expert pilot there flying it was just sort of a fundamental restriction.
    0:06:35 So the big bet that we made when we started Skydio was that AI and autonomy built into a small light quadcopter was going to be very powerful for a wide range of industries.
    0:06:40 And the government and enterprise applications were always part of the vision.
    0:06:47 But we explicitly decided to start with a consumer product because we thought that something like light, integrated, easy to use would be a really good platform for this other stuff.
    0:06:52 And for me personally, when I was in grad school, I was wrestling with, you know, I love this technology.
    0:06:54 I wonder if I want to keep working on it.
    0:06:56 Am I going to have to go work for a defense contractor?
    0:07:02 I deeply believe in like the mission of the U.S. military, but the idea of working at a traditional defense contractor was very unappealing to me.
    0:07:05 And so we started with a consumer product.
    0:07:11 And it’s reflective of kind of the trajectory of the space that very quickly, you know, like 2018, 2019 timeframe.
    0:07:19 And I think to the credit of the U.S. military, they realized that these small light civilian quadcopters had enormous value on the battlefield.
    0:07:30 And the technology from consumer to enterprise to military is so tight that in the span of a year, basically, we won our program of record, the Army Short Range Reconnaissance Program.
    0:07:34 And I think it was officially announced in 2021, but it really started in 2018, 2019.
    0:07:38 And that was the beginning for us of expanding to serve a much broader set of markets.
    0:07:40 Adam was also part of my early story.
    0:07:43 I don’t know if you know this, Adam, but I read all of your papers.
    0:07:44 We’ve learned a lot since then.
    0:07:46 Who’s the foundation?
    0:07:46 Yeah, yeah.
    0:07:50 No, I mean, look, a lot of the ideas that we use at Skydio came from the research community.
    0:07:53 Some of the research that we had done, some that other folks had done.
    0:08:05 So one of the things I think has most shocked many and fascinated military strategists is how drones have reshaped the nature of warfare in the past decade, particularly in the past three years.
    0:08:08 There’s just been huge proliferation, right?
    0:08:11 Far more mass being brought to the battlefield via drones.
    0:08:19 And it’s enabling much more distributed, decentralized, and lethal force structures, right?
    0:08:29 Anybody running around in a truck can pop out with a drone that might go 1,000 nautical miles or a collection of drones that might go 1,000 nautical miles and hit who knows what far off into the distance.
    0:08:36 There used to be much more concentration of forces, dependence on large, exquisite assets to deliver capabilities.
    0:08:40 So it’s been just a complete transformation.
    0:08:44 And the world has seen a lot of that unfold in the conflict in Ukraine.
    0:08:50 I think one of the major questions is how can the United States and her allies adopt those lessons?
    0:08:53 Because the things that we’re doing have a lot of merit.
    0:08:56 It has been a force structure that’s dominated the last several decades.
    0:09:01 But our adversaries have spent a long time thinking about how to counter what we’re doing.
    0:09:12 And I think that it’s going to be important for us to think about how we can embrace 100 times more systems to empower our service members to be lethal and effective, to come home safely to their families.
    0:09:15 And I think drones, in many ways, are the future of war.
    0:09:16 Absolutely.
    0:09:30 And Adam, one of the things that particularly shocked those observing, the Ukraine conflict in particular, is you would see, as writers are referring to, say a commercial quadcopter like Escadio in some instances, they’ll go and take out an exquisite system like a tank.
    0:09:35 Two or three of those packed with sufficient munitions could cripple a large armored vehicle.
    0:09:41 So how have you seen that sort of shift impact how military looks at dual-use commercial drones?
    0:09:46 To be honest, I think that’s a question that the U.S. military has not fully digested yet.
    0:09:52 And one of the realities of drones is that it just creates this massive asymmetry, right?
    0:09:57 A system that costs a few thousand dollars can take out a system that costs a few million dollars.
    0:10:01 And I don’t think we’ve fully grappled with that yet, to be honest.
    0:10:04 I think that we’re seeing evidence of this in Ukraine.
    0:10:14 And the Ukrainians, largely out of necessity, and really just incredibly impressive ingenuity, they have a broad array of ground robots or drones, air drones, sea drones.
    0:10:16 They’re building them at an incredible rate.
    0:10:17 They’re iterating very quickly.
    0:10:19 And it’s very scrappy.
    0:10:23 The U.S. military may not be subject to the same kind of constraints that the Ukrainians are.
    0:10:29 But I think that we need to understand that the possibility for that asymmetry is real.
    0:10:37 Like, the possibility of building very low-cost systems that are very capable and capable of delivering strikes or capable of maintaining surveillance is very real.
    0:10:40 And our adversaries are likely to take advantage of it.
    0:10:43 That’s a journey that we still need to go on to some extent.
    0:10:46 There’s still quite a bit of inertia and momentum in our military.
    0:10:49 I don’t know what your experience has been towards, like, larger, more traditional, exquisite systems.
    0:10:51 I think that is starting to pivot.
    0:10:54 But that’s a real question for us for the future.
    0:10:58 There’s a phrase used in the military context often.
    0:11:00 It’s that quantity has a quality all its own.
    0:11:08 And there is an incredible disparity between what our chief adversary, the People’s Republic of China, can produce in a month for drones.
    0:11:12 Whether it’s going to be formal military-style drones or dual-use commercial drones.
    0:11:22 And that scale, I think a lot of people are wondering what that means in order to deter a future conflict or, God forbid, whether it would be a conflict in the Taiwan Strait or elsewhere.
    0:11:33 Adam, would you mind just talking about the sort of differences in scale, just roughly speaking, between the production capabilities of both countries and why that is, particularly on a regulatory basis?
    0:11:35 So I mentioned I grew up flying radio-controlled airplanes.
    0:11:39 So basically all radio-controlled airplanes were made in China in the 90s and 2000s.
    0:11:41 And nobody was thinking too hard about that.
    0:11:42 They still are, right?
    0:11:43 They probably still are, yeah.
    0:11:47 And, you know, that didn’t seem like a national security issue at the time.
    0:11:55 But if you think about what a drone is, it’s basically like the combination of radio-controlled airplane-type stuff, motors, and consumer electronics.
    0:11:57 A lot of the same stuff that goes into a phone.
    0:12:01 And, you know, as a country, we basically outsource manufacturing to China.
    0:12:06 And I think that that was a series of policy decisions, expediency on the part of business.
    0:12:19 That was a mistake from a military standpoint because one of the major themes is that the gap between kind of civilian technology and consumer technology and military technology is closing in many of these domains.
    0:12:31 The general manufacturing capacity in China for low-cost, capable compute systems, which are really now becoming robotic systems, not just drones, but other kinds of robots, is substantial.
    0:12:32 It’s a whole ecosystem.
    0:12:35 And it’s not about cost either at this point.
    0:12:46 It’s really about, like, technical expertise, built capacity in terms of all the different things that it takes to, like, mold and machine and build PCBs, the circuit boards, and place components on them.
    0:12:49 So I don’t think this is something that we can solve overnight.
    0:12:59 The ultimate thing in TBD on if this is attainable, I always say, like, wherever they’re building iPhones, they’re going to have a really good ecosystem for building drones and other kinds of electronics.
    0:13:06 And so the real prize is, can we bring that level of scaled manufacturing back to the U.S.?
    0:13:09 I don’t know if we can, to be honest, but I think it’s worth a shot.
    0:13:12 And from the outset, as a company, we’ve been manufacturing our drones in the U.S.
    0:13:18 And I would say that when we started doing that in 2016, it felt like we were swimming upstream into, like, a fast-flowing river.
    0:13:26 I would say that, like, we’re maybe starting to get some, like, signs of tailwinds, especially with the new administration, which, you know, is cause for optimism.
    0:13:31 But I think this is one that we just can’t give up on because robots are going to become more and more important.
    0:13:39 They’re going to be using the same kind of ingredients from consumer electronics and other kinds of, like, relatively low-cost systems.
    0:13:41 Cars are going in this direction as well.
    0:13:45 Cars are starting to look more like laptops and phones in terms of the components that are in them.
    0:13:49 This is a combination of industry and policy and all of us working together.
    0:13:51 And I think there’s a cause for optimism over the last couple of years.
    0:13:55 There’s still a lot of work to do, but I don’t think it’s an insurmountable hill for us.
    0:13:58 I’ve got a, I guess, good news, bad news take.
    0:14:00 I think the bad news, you sort of led with it.
    0:14:04 The industrial capacity of China is fearsome.
    0:14:10 And try as we might, I think that’s going to be a very difficult thing to close out in one year and even a decade.
    0:14:16 And I’m an optimist and always like to believe that there’s a way, but it is a substantial gap.
    0:14:23 And I think we’re sort of approaching, to use a rocket term, a Max-Q moment from a national security perspective where we’re undergoing a forced transformation.
    0:14:27 Huge technology changes are afoot.
    0:14:29 Things like AI are coming into play.
    0:14:36 We’ve got an adversary that’s become extremely wealthy, huge industrial capacity, also investing massively in their military capabilities.
    0:14:39 And so the question then becomes, what’s the right play?
    0:14:49 And I think that history has shown, World War I, World War II, Vietnam, tremendous amounts of mass were brought to the battlefield.
    0:15:00 And in some cases, despite tremendous amounts of mass being brought to sections of the battlefield, it just turned into grinded out, war of attrition without a lot of movement on either side.
    0:15:02 And we start to see some of that in the Russia-Ukraine conflict as well.
    0:15:07 Tremendous amounts of mass being brought to the battlefield, but front lines that are extremely difficult to move.
    0:15:12 And I think a reason for that is there’s a difference between mass and effect.
    0:15:20 And simply, the world is big, and targets are relatively small compared to the scale of the world.
    0:15:25 And it turns out that just throwing a bunch of mass downrange, it can still be pretty hard to hit the things that matter.
    0:15:33 And where the United States has dominated over the last couple decades is the software prowess, the AI, the autonomy.
    0:15:45 And so I think that if we can combine the fantastic work that’s going to re-industrialize the United States to build more mass, to build more capability, we have to do that.
    0:16:01 But if we can combine it with the software and autonomy capabilities, if we can close, like, the OODA loop so that we can push software updates at a moment’s notice and make every ounce of charge, every minute of flight time, maximally effective, I think that’s how we can compete.
    0:16:06 There’s a second wave that hasn’t really broken yet, which is really AI and autonomy.
    0:16:10 The vast majority of what’s happening in Ukraine is still basically one-to-one.
    0:16:17 You’ve got these FPV pilots who are expert operators who are flying the thing, or they’re flying drones with very limited, pretty simple mission.
    0:16:20 Go to these coordinates and deliver a strike.
    0:16:31 And I think that we are going to see over the next decade another fundamental change as rather than having these drones animated by an operator on the ground or by, like, relatively simple algorithms on board,
    0:16:35 they become animated by really advanced autonomy, and they can communicate with each other.
    0:16:42 The implications of that, I think, are probably going to be even more significant than the first step to just having these things be unmanned.
    0:16:47 And I do think that’s an area where, as a country, it plays more to our strengths.
    0:16:51 And you can’t forget about the hardware and the manufacturing capacity.
    0:16:52 You need to be able to do that stuff.
    0:16:57 But winning on the AI front, I think, is even more important.
    0:17:01 So to ensure we dominate, to ensure we win, Vice President Vance,
    0:17:05 just spoke a few moments ago at the American Dynamism Summit, and he said,
    0:17:11 our goal is to essentially remake the economy, to fix that mistake that you guys were referencing earlier,
    0:17:15 about we just offshored all of our manufacturing, we can’t make things anymore.
    0:17:21 And there are leading members in Congress committed to that, and also committed to defense procurement reform.
    0:17:27 So if you had an opportunity to sit down with anyone, the leaders in the executive branch, leaders in the legislative branch,
    0:17:31 or anyone who would be listening right now, those leaders or staffers for them,
    0:17:37 like if you just fix one or two really pivot points, and if those were resolved, we could do so much more,
    0:17:41 either for drones in particular, manufacturing generally, unleashing AI.
    0:17:43 What sort of recommendations would you give?
    0:17:47 Number one, I would ask that they continue to do what they’ve been doing,
    0:17:52 which is reinforce their belief and investments in the incredible people that sign up to serve.
    0:17:58 The one thing that I think we’ve got absolutely right is we have brilliant people that are brave.
    0:18:03 They believe in the values of this country, and they just go and do extraordinary things.
    0:18:05 And it’s been a privilege of mine to be able to meet them.
    0:18:10 My brother drew me into that universe, and I just think it is such a gift to the nation
    0:18:12 that we have these people that are willing to do what they do.
    0:18:17 Now, we strive to contribute to make sure they can be as effective as possible
    0:18:19 to fight when the Tura come home safely to their families,
    0:18:22 and to provide them the best possible tools.
    0:18:24 And look, what do I know?
    0:18:27 The administration has hard jobs, so let me put that disclaimer up front.
    0:18:31 I think the technologies that matter most to the future of war,
    0:18:34 that matter most to the future of this country,
    0:18:38 and all of our allies around the world are right there in front of us.
    0:18:46 And I think the security challenge of our time is whether or not we can mobilize the bureaucracy
    0:18:50 to go reach out and pick up what’s on the table.
    0:18:54 Everybody already knows is one of the most important capabilities to the future,
    0:18:59 and that is, like, taking and making real the large-scale deployment
    0:19:02 and operationalization of autonomy technologies.
    0:19:07 If you look in the kind of space that we play, which are small, light quadcopters
    0:19:09 that weigh a few pounds that are soldier-carried,
    0:19:13 the Ukrainians today are using these things at the rate of millions per year,
    0:19:15 literally millions per year.
    0:19:18 It’s the primary method through which they’re delivering strikes and surveilling the battlefield.
    0:19:24 The U.S. military, I think, to their credit, has programs generally pointed in this direction,
    0:19:28 but those programs are operating at the scale of thousands, like single-digit thousands.
    0:19:32 So we’re literally off by three orders of magnitude, I would argue,
    0:19:38 relative to what evidence suggests the modern battlefield demands.
    0:19:40 There are similar trends in Israel as well.
    0:19:44 I mean, Israel has rapidly adopted these class of systems at substantial scale.
    0:19:47 And this is an area, to the point about re-industrialization,
    0:19:51 where military purchasing power makes a massive difference.
    0:19:58 The consumer-civilian quadcopter markets are measured in the scale of single-digit billions.
    0:20:01 That’s, like, comparable, I would argue, to what the military should be spending in the space.
    0:20:04 They’re not spending anywhere close to that today for this class of system.
    0:20:11 And so I think that’s a pretty obvious lever that has a bunch of benefits from a national security perspective.
    0:20:15 I mean, one, you’re equipping our soldiers with modern, relevant technology.
    0:20:19 But two, the purchasing power that the military can bring to bear there is significant enough
    0:20:24 to actually move the needle from an industrial-based standpoint for this class of technology
    0:20:25 that serves other markets.
    0:20:28 Not just us, but, like, the companies in our space serve public safety
    0:20:32 and critical infrastructure inspection, and there’s huge technology overlap
    0:20:35 between the products that you use to do that
    0:20:37 and the products that folks use on the battlefield.
    0:20:40 I think if you just went down all the quantities of everything
    0:20:42 and just added a zero behind all of them,
    0:20:44 and then, of course, there’s a cost challenge,
    0:20:46 and we have to figure out how to do that.
    0:20:48 Yeah, I mean, I think there’s some things that you can delete,
    0:20:49 and deleting those things is also painful.
    0:20:51 Well, maybe not a zero.
    0:20:52 Sometimes a zero in front of something.
    0:20:58 Yeah, yeah, no, I think that’s the trade-off that needs to be made.
    0:21:02 Buying legacy exquisite systems
    0:21:04 that cost hundreds of millions of dollars,
    0:21:05 some cases billions of dollars,
    0:21:08 there’s a trade-off between one or two of something
    0:21:11 or 10,000 or 100,000 or a million drones.
    0:21:13 And if you just look into the future,
    0:21:15 which of those things is going to be more powerful?
    0:21:18 I think, like, large quantities of AI-driven drones is,
    0:21:20 it’s not the only thing that you need,
    0:21:22 but I think in many situations is the right answer.
    0:21:23 Well, let’s dive into it a little bit more.
    0:21:26 So, as you both noted a few moments ago,
    0:21:29 the U.S. military, starting around the 1980s, 1990s,
    0:21:33 really went all in on highly expensive, exquisite systems,
    0:21:36 small numbers, and that worked fine for us.
    0:21:38 But our now near-peer adversary,
    0:21:42 China, developed an asymmetric military designed to counter that.
    0:21:45 So, they have a carrier-strike missile.
    0:21:47 So, for a cost of, say, 100 million,
    0:21:50 they take out tens of billions, if not more, on our side,
    0:21:52 plus the horrible lives we lost.
    0:21:55 So, there’s been a move within the DoD
    0:21:57 to counter the counter,
    0:21:59 to have a project replicator.
    0:22:02 Let’s presume even if we move those decimal points,
    0:22:03 so that all of a sudden,
    0:22:05 the older systems has less procurement,
    0:22:10 and then the newer systems have more dollars allotted to procure.
    0:22:13 China, some would argue,
    0:22:14 still have certain advantages,
    0:22:17 perhaps because they’ve had more time working on it,
    0:22:18 perhaps because they’re more subsidized.
    0:22:20 One of them, people would argue,
    0:22:22 and I welcome you to say that this is wrong,
    0:22:24 would be in the area of swarm technology.
    0:22:25 So, you can see the light shows
    0:22:28 over Shenzhen and other cities
    0:22:30 for the Chinese New Year,
    0:22:32 where it was just extraordinary shows
    0:22:33 with hundreds of thousands
    0:22:36 that broke the Guinness World Record book again this year.
    0:22:39 Do we have that level of sophistication,
    0:22:40 and why or why not?
    0:22:42 And what can we do to make sure
    0:22:43 that we not only achieve parity if we have not,
    0:22:45 but achieve superiority in that space?
    0:22:48 So, this is an area that we’re quite focused on.
    0:22:50 I mean, swarm is sort of one of these terms
    0:22:52 that can mean a lot of different things, right?
    0:22:53 And it’s really like,
    0:22:55 what tasks are you trying to accomplish?
    0:22:57 So, when you see the drone light shows,
    0:22:59 they’re 100% relying on GPS,
    0:23:01 so they’re using GPS to figure out where they are
    0:23:03 and to position themselves precisely,
    0:23:07 and they’re 100% reliant on a comms link
    0:23:08 between all the drones all the time.
    0:23:10 And both of those technologies
    0:23:13 are basically irrelevant on the modern battlefield.
    0:23:15 It’s very easy to jam GPS,
    0:23:17 and comms is always contested.
    0:23:19 And so, I think that those are representative
    0:23:21 of their ability to build a bunch of drones
    0:23:22 and get them into the air,
    0:23:24 which is certainly part of the equation,
    0:23:26 but from a core technology standpoint,
    0:23:29 they’re less relevant for the things
    0:23:32 that I think would be impactful on the battlefield.
    0:23:34 And we have the great joy of competing
    0:23:36 against the leading Chinese drone company, DJI,
    0:23:39 in civilian markets that are unregulated,
    0:23:40 where customers can buy everything.
    0:23:42 And our biggest advantage competing against them
    0:23:44 is AI and autonomy capability.
    0:23:45 The stuff that we’ve been able to build into our drones
    0:23:49 is far more advanced than what DJI has been able to do
    0:23:50 in terms of being able to sense the environment
    0:23:52 in real time, respond to it,
    0:23:54 automate complex tasks and missions.
    0:23:56 And the advantage that we have,
    0:23:59 I think, is reflective of our strengths as a country.
    0:24:02 We were talking about building on academic research.
    0:24:04 The technology of Skydio is reflective
    0:24:05 of a lot of smart investments
    0:24:08 that the government has made over the years.
    0:24:10 So, I think that being able to deploy
    0:24:12 highly autonomous AI-driven drones at scale
    0:24:15 is still a domain that we can win in,
    0:24:17 and, in my view, is still up for grabs
    0:24:19 and is something that we’re quite focused on as a company.
    0:24:22 Right, and your company has also invested heavily
    0:24:23 in autonomy.
    0:24:25 Yeah, so our investments on autonomy,
    0:24:28 kind of going back to one of my earlier statements,
    0:24:29 is sort of predicated on this belief
    0:24:31 that to make math effective,
    0:24:33 it has to be intelligent, right?
    0:24:34 That’s the difference between
    0:24:37 grinded out, trenched situations
    0:24:39 and being able to assert dominance in a space
    0:24:43 is if you can find, fix, and finish targets at scale.
    0:24:46 So, we just think that’s fundamentally important.
    0:24:47 And the first 10 years of our journey
    0:24:48 was defined by,
    0:24:51 let’s strive to make the world’s best AI pilot
    0:24:53 and climb the aviation food chain,
    0:24:56 which culminated in us doing some work on F-16s
    0:24:58 that has circulated the internet.
    0:25:00 When we thought about the next 10 years,
    0:25:01 the question was,
    0:25:03 is the future really about Shield AI
    0:25:04 building the world’s best AI pilot,
    0:25:06 or is it about making a contribution
    0:25:07 to the industrial base
    0:25:10 so that everybody that’s building these systems
    0:25:11 across America,
    0:25:14 building sort of incredibly sophisticated machines,
    0:25:16 striving to do it at larger scale,
    0:25:19 can now deploy the best possible AI pilot
    0:25:20 for their vehicles
    0:25:22 for the customer’s missions.
    0:25:24 And we think our best and highest contribution
    0:25:26 is to enable the industrial base
    0:25:29 to fast forward the large-scale deployment
    0:25:32 of the world’s best AI pilots.
    0:25:34 In Ukraine, you see drones come up,
    0:25:35 drones come down,
    0:25:36 oftentimes they’re DJI drones
    0:25:38 in a matter of minutes or seconds,
    0:25:39 many times failing in the mission.
    0:25:42 How have you thought through
    0:25:44 how your drones can operate
    0:25:45 in a battlefield
    0:25:46 where there is a high degree
    0:25:47 of electronic warfare?
    0:25:48 So, you do not have access to GPS,
    0:25:50 you don’t have access to reliable signals
    0:25:51 to the controller and operators.
    0:25:53 When we started the company,
    0:25:55 we made a huge bet on computer vision
    0:25:58 as the right technology for AI and autonomy.
    0:25:59 And this was back in 2014
    0:26:01 when it was like much less clear
    0:26:02 than it is today.
    0:26:05 So, just sort of natively,
    0:26:06 our drones have a bunch of cameras,
    0:26:07 they look out and see the world,
    0:26:09 they use that to figure out where they are
    0:26:09 and how they’re moving
    0:26:12 and what’s interesting and important around them.
    0:26:13 We come from a place
    0:26:15 of not being reliant on GPS,
    0:26:16 having more of an ability
    0:26:17 to do onboard things.
    0:26:19 Having said that, candidly,
    0:26:20 like our first round of drones
    0:26:22 in Ukraine basically failed.
    0:26:25 And we had built the system
    0:26:28 largely informed by the U.S. Army’s requirements
    0:26:30 for what they thought was important
    0:26:31 for a quadcopter in this space.
    0:26:32 And electronic warfare
    0:26:34 was just nowhere on that list.
    0:26:36 And so, from a radio standpoint
    0:26:38 and from a navigation standpoint,
    0:26:40 our first generation system
    0:26:42 was just not set up for success.
    0:26:44 And it was a painful process for us.
    0:26:46 So, I’ve been to Ukraine twice myself.
    0:26:48 We had a bunch of folks on our team
    0:26:49 spend a bunch of time there
    0:26:50 and we learned a bunch of hard lessons
    0:26:51 about what it takes
    0:26:53 to really operate in this environment.
    0:26:56 And that really started to drive our development.
    0:26:58 And more so, honestly,
    0:27:00 than the requirements coming from the U.S. military,
    0:27:02 we made an explicit decision as a company
    0:27:05 that we think this is the real world situation
    0:27:07 that matters both from an immediate impact standpoint,
    0:27:09 but also whether or not they realize it,
    0:27:11 this is what everybody else is going to need as well.
    0:27:13 And so, there was a process for us
    0:27:14 that really took a couple of years
    0:27:18 of adapting the sort of native vision AI primitives
    0:27:20 to work in this environment,
    0:27:21 which we’ve now gotten to
    0:27:23 with pretty phenomenal results
    0:27:24 where the drone is incredibly resilient
    0:27:26 and capable from both a comm standpoint
    0:27:29 and from a GPS-denied navigation standpoint
    0:27:31 in extremely harsh environments.
    0:27:35 But it’s a real technology hurdle to get across.
    0:27:36 And we’re now seeing this.
    0:27:38 The bet that we made is in many ways paying off.
    0:27:41 We’re seeing this with other militaries around the world.
    0:27:42 They’re starting to come around
    0:27:43 that this is the kind of thing that matters
    0:27:45 and our systems are performing.
    0:27:46 Now, electronic warfare testing
    0:27:48 is becoming part of the evaluation protocol
    0:27:49 for a lot of purchases.
    0:27:51 I think Adam hit the nail on the head.
    0:27:53 The effectiveness of your systems in an EW environment
    0:27:56 is the difference between whether they’re relevant or not.
    0:27:58 And a lot of people will say
    0:28:00 that our next generation product
    0:28:01 is going to work in an EW environment.
    0:28:03 But I think that’s hard to say
    0:28:05 unless you’re actually doing it.
    0:28:06 I mean, that’s a pretty tall claim.
    0:28:08 Another element of effectiveness
    0:28:10 in these complex battlefields,
    0:28:12 I think it’s just the adaptability
    0:28:15 of your capabilities of your software.
    0:28:17 So, anecdote from Ukraine,
    0:28:19 to solve for this environment,
    0:28:20 which is constantly changing,
    0:28:21 we were going to go out.
    0:28:22 We have a product called the V-Bad.
    0:28:23 It’s a plane.
    0:28:25 It’s 12 feet tall, 12-foot wingspan.
    0:28:27 I can fly for about 12 hours.
    0:28:30 We got sort of the brief from the Ukrainians
    0:28:31 and the situation we expected.
    0:28:32 Here’s the situation.
    0:28:34 You guys are going to take off.
    0:28:35 You’re going to go this way,
    0:28:37 maybe 70, 80, 90 nautical miles.
    0:28:39 You’ll have GPS on the ground.
    0:28:40 As soon as you get to 200 feet,
    0:28:41 the jammer’s going to hit you.
    0:28:43 And you have to have no GPS
    0:28:44 from that point forward.
    0:28:46 So, engineer team’s like,
    0:28:46 great, got it, good.
    0:28:48 We’ll get the fix on the ground.
    0:28:49 We’ll take off.
    0:28:50 We’ll go take care of business.
    0:28:50 It’s going to be sick.
    0:28:52 We go out there,
    0:28:53 and at three feet,
    0:28:55 the airplane loses GPS.
    0:28:57 And so, the Ukrainians
    0:28:58 are constantly jamming to us.
    0:28:59 The Russians, Ukrainians,
    0:29:00 because the Ukrainians stop jamming,
    0:29:02 they find themselves at risk.
    0:29:02 Even if you’re launching
    0:29:03 on the friendly side,
    0:29:04 there’s intense jamming
    0:29:05 at three feet off the ground.
    0:29:08 So, the airplane takes off
    0:29:09 and it just starts flying
    0:29:10 the other direction.
    0:29:11 I don’t know if you’ve seen
    0:29:12 the movie Interstellar,
    0:29:13 where he’s going through the cornfield,
    0:29:14 the dude has a laptop,
    0:29:15 and he brings down the drone.
    0:29:18 Our team and the Ukrainians
    0:29:19 just took off in a truck
    0:29:21 and chased it for about two hours
    0:29:22 and found it orbiting
    0:29:23 over a cornfield
    0:29:25 about, I think,
    0:29:26 80 kilometers away.
    0:29:29 And in the span of 24 hours,
    0:29:30 the engineering team
    0:29:31 re-architected the stack
    0:29:33 to not use GPS
    0:29:34 at any point in the mission.
    0:29:36 We validated it
    0:29:37 at our Texas facilities,
    0:29:38 and then we pushed it forward,
    0:29:39 and 24 hours later,
    0:29:40 the team took off
    0:29:41 and then conducted
    0:29:42 a demonstration
    0:29:43 that was written about
    0:29:44 in the Wall Street Journal,
    0:29:46 where the outcome of it
    0:29:46 was ultimately
    0:29:47 a Russian SA-11
    0:29:48 getting found,
    0:29:49 getting fixed,
    0:29:49 and finished
    0:29:50 by a high Mars.
    0:29:52 I tell that anecdote
    0:29:53 because it will be
    0:29:55 the pace of deployment
    0:29:56 that is the make
    0:29:57 or break
    0:29:58 for militaries
    0:29:59 around the world.
    0:30:00 If we want to make
    0:30:01 a software change
    0:30:02 in some of our programs,
    0:30:03 it can take up to a year,
    0:30:04 right?
    0:30:05 it is considered
    0:30:07 a big deal
    0:30:08 to change the software.
    0:30:10 When we took
    0:30:11 that aircraft forward
    0:30:11 to Ukraine,
    0:30:12 we wrote new software
    0:30:13 in 24 hours,
    0:30:14 we pushed it,
    0:30:15 and then we needed
    0:30:16 24 hours
    0:30:17 to properly plan
    0:30:18 the operation.
    0:30:19 And unless
    0:30:21 we find a way
    0:30:21 for industry
    0:30:22 and government
    0:30:23 to work together
    0:30:24 to deploy
    0:30:25 software capabilities
    0:30:26 at that pace,
    0:30:27 we will find ourselves
    0:30:29 in a very tough
    0:30:30 situation.
    0:30:31 In a world
    0:30:32 where you’re using
    0:30:33 autonomous systems,
    0:30:34 everything is basically
    0:30:35 software-defined,
    0:30:35 right?
    0:30:36 The entire behavior
    0:30:37 capability of the system
    0:30:37 is software-defined.
    0:30:39 Electronic warfare
    0:30:40 is also software-defined.
    0:30:41 The behavior
    0:30:41 of the jammers
    0:30:42 and everything
    0:30:42 is coming largely
    0:30:43 through software.
    0:30:44 And so in many ways,
    0:30:45 I think a modern conflict,
    0:30:46 and you see this in Ukraine,
    0:30:47 becomes basically
    0:30:48 like a software writing fight.
    0:30:49 And the speed
    0:30:50 at which you can write it
    0:30:51 and deploy it
    0:30:51 really matters.
    0:30:52 And I have no choice
    0:30:53 but to be hopeful
    0:30:54 and optimistic here
    0:30:54 because we’ve had
    0:30:55 the same experience
    0:30:56 where sometimes
    0:30:57 it’s taken us
    0:30:57 two years
    0:30:59 to push new software
    0:31:01 into deployed systems.
    0:31:02 I would like to think
    0:31:02 that if we were
    0:31:03 actually in a conflict,
    0:31:04 that would just evaporate
    0:31:05 and we could do it
    0:31:06 in a day,
    0:31:07 maybe that’s overly
    0:31:08 optimistic and hopeful.
    0:31:09 But I think that’s one
    0:31:09 where the status quo
    0:31:10 is unacceptable.
    0:31:12 Two exceptional stories.
    0:31:14 So we’re discussing autonomy.
    0:31:16 And one of the concerns
    0:31:18 either of you may hear
    0:31:19 from time to time
    0:31:20 is that,
    0:31:20 well,
    0:31:21 if you have autonomous drones,
    0:31:22 that means humans
    0:31:23 are not in the loop.
    0:31:24 And that means
    0:31:25 we’re ceding our authority
    0:31:26 to essentially software.
    0:31:28 is that an accurate assessment?
    0:31:30 What does autonomy mean
    0:31:31 in terms of humans
    0:31:32 actually having control
    0:31:34 over the end state
    0:31:35 of what the drones
    0:31:35 are doing?
    0:31:37 And how do we best
    0:31:38 configure the military
    0:31:39 and also civil society
    0:31:40 to a future
    0:31:42 where autonomous drones
    0:31:43 are not only available
    0:31:43 but cheap,
    0:31:44 widely adopted?
    0:31:46 The first thing to say,
    0:31:47 and this is really,
    0:31:48 I think,
    0:31:48 the important backdrop
    0:31:49 for all of this
    0:31:51 is that this is
    0:31:52 terrible stuff, right?
    0:31:52 I mean,
    0:31:53 we’re talking about
    0:31:53 weapon systems
    0:31:54 that kill people
    0:31:56 and create immense suffering
    0:31:57 and that is the reality
    0:31:58 of war.
    0:31:59 The real goal
    0:32:00 for everything
    0:32:02 is to act as a deterrent
    0:32:04 and to make conflict
    0:32:04 less likely
    0:32:05 and to make it such
    0:32:07 that if conflict does happen,
    0:32:08 you can be maximally targeted
    0:32:08 and precise
    0:32:10 and minimize human suffering.
    0:32:11 I think that’s just
    0:32:12 sort of like
    0:32:13 an important backdrop
    0:32:13 for all of this
    0:32:14 when we’re thinking
    0:32:15 about what we do
    0:32:16 and the kinds of systems
    0:32:17 that we’re building.
    0:32:18 Now,
    0:32:19 I think there’s a bunch
    0:32:20 of legitimate concerns
    0:32:21 about what does it mean
    0:32:22 to have these AI-driven robots
    0:32:23 and how automated
    0:32:24 are they going to be
    0:32:25 and how much authority
    0:32:26 are we going to delegate to them?
    0:32:27 I think the thing
    0:32:28 that we have to also
    0:32:29 keep in mind
    0:32:29 is unfortunately
    0:32:31 the game theory here
    0:32:32 is just as simple
    0:32:33 and obvious as it can be,
    0:32:33 right?
    0:32:34 Nobody thinks
    0:32:35 nuclear weapons
    0:32:36 are good for humanity
    0:32:38 on an individual level.
    0:32:39 Deploying a nuclear weapon
    0:32:40 is like a miserable,
    0:32:41 terrible, terrible thing,
    0:32:43 but the only world
    0:32:44 worse than one
    0:32:44 where like you
    0:32:45 and your adversary
    0:32:46 have nuclear weapons
    0:32:47 is one where only
    0:32:48 your adversary does,
    0:32:48 right?
    0:32:49 So I think
    0:32:50 one of our strengths
    0:32:51 as a country
    0:32:52 is our values
    0:32:52 and the way
    0:32:53 that we try
    0:32:54 to conduct conflict
    0:32:56 with a high ethical standard
    0:32:57 and I think
    0:32:57 that it’s important
    0:32:58 to maintain that.
    0:32:58 One of the things
    0:32:59 that I’ve been impressed by
    0:33:01 is really the level
    0:33:01 of sophistication
    0:33:02 within the military
    0:33:03 on these issues.
    0:33:03 I mean,
    0:33:04 there’s people
    0:33:05 whose job it is
    0:33:06 to think deeply
    0:33:07 about the implications
    0:33:08 of different kinds
    0:33:08 of weapon systems
    0:33:09 and how authority
    0:33:10 is delegated.
    0:33:11 there’s very robust
    0:33:12 controls in place
    0:33:13 for how the military
    0:33:14 thinks about these systems.
    0:33:15 Those things are evolving
    0:33:17 as the technology evolves,
    0:33:18 but this is something
    0:33:19 I’ve thought quite a bit
    0:33:19 personally about.
    0:33:20 The more that I’ve thought
    0:33:20 about it,
    0:33:21 the more time I’ve spent
    0:33:21 with the military,
    0:33:23 the U.S. military in particular,
    0:33:24 the more comfortable
    0:33:24 I’ve gotten
    0:33:26 that this is a robust organization
    0:33:27 that cares about
    0:33:27 doing the right thing,
    0:33:28 is thinking deeply
    0:33:29 about the implications
    0:33:30 of technology.
    0:33:32 And my general view,
    0:33:33 which I think is shared
    0:33:34 by the U.S. military
    0:33:35 policy and doctrine,
    0:33:36 is that ultimately
    0:33:36 human judgment
    0:33:37 is really important.
    0:33:39 A human exercising judgment
    0:33:41 in how force should be used
    0:33:42 is super important.
    0:33:43 But the other thing
    0:33:44 that people have to understand
    0:33:45 is that the status quo
    0:33:46 is not great.
    0:33:46 Oftentimes,
    0:33:47 your choice,
    0:33:48 if you want to take out
    0:33:48 a target,
    0:33:49 is dropping a 500
    0:33:51 or 2,000-pound bomb,
    0:33:52 which is going to cause
    0:33:53 widespread destruction
    0:33:55 and a lot of collateral damage.
    0:33:56 And so,
    0:33:57 an AI system
    0:33:58 that might be
    0:33:59 using autonomy
    0:34:01 to a pretty intense degree
    0:34:01 to figure out
    0:34:02 where something is
    0:34:03 and what to do about it
    0:34:04 is probably better
    0:34:05 than dropping
    0:34:07 a 2,000-pound bomb
    0:34:07 and blowing up
    0:34:08 a whole city block.
    0:34:09 And so,
    0:34:10 I think you’ve always
    0:34:10 got to be thinking about
    0:34:11 what’s the status quo
    0:34:13 and can we use AI
    0:34:14 and autonomy
    0:34:15 to better,
    0:34:16 more precisely,
    0:34:17 accomplish the thing
    0:34:18 that we care about
    0:34:19 while inducing
    0:34:20 as little human suffering
    0:34:21 as possible.
    0:34:23 Maybe the good
    0:34:25 and true news
    0:34:25 right now
    0:34:26 is that
    0:34:27 I think human-machine teams
    0:34:28 are far more effective
    0:34:29 than machine-only teams
    0:34:30 right now
    0:34:31 and for the next
    0:34:32 several years
    0:34:33 that’ll continue
    0:34:33 to be true.
    0:34:34 And maybe it’s true
    0:34:35 for longer than that.
    0:34:36 And the frameworks
    0:34:37 that we have in place,
    0:34:39 people are in the loop
    0:34:40 on the decisions,
    0:34:41 make a tremendous amount
    0:34:41 of sense.
    0:34:43 I think Adam brings out
    0:34:43 an excellent point.
    0:34:44 The game theory
    0:34:45 is one that
    0:34:46 if somebody finds
    0:34:47 that a machine-only team
    0:34:49 is the most effective team
    0:34:50 in certain missions
    0:34:50 and circumstances,
    0:34:52 the question is
    0:34:52 why and when
    0:34:53 would that be used
    0:34:54 and what do you do
    0:34:54 about it?
    0:34:55 And how do you make sure
    0:34:56 that you’re ready for it?
    0:34:57 And so I don’t think
    0:34:58 you can live in a world
    0:34:59 where you have blinders.
    0:35:00 I was speaking
    0:35:01 to a senior military leader
    0:35:02 that had a nice framing,
    0:35:03 which is his expectation
    0:35:04 is the more defensive
    0:35:06 you end up being,
    0:35:07 the more likely you are
    0:35:07 to turn things over
    0:35:08 to machine control
    0:35:10 to get the very fast reactions
    0:35:11 and the dominance
    0:35:11 that you need
    0:35:12 to come out
    0:35:13 of that situation.
    0:35:14 And so a great,
    0:35:15 very practical example
    0:35:15 of that today
    0:35:17 is the phalanx gun system
    0:35:18 that protects ships, right?
    0:35:19 If you come into
    0:35:20 that thing’s weapon
    0:35:20 engagement zone
    0:35:21 and it’s turned on,
    0:35:22 it will kill it, right?
    0:35:23 And so you can think
    0:35:24 about that
    0:35:25 on a larger scale.
    0:35:26 If a force is pressing
    0:35:27 on another force,
    0:35:28 they find themselves
    0:35:29 in a defensive situation
    0:35:30 and they flip the switch
    0:35:31 and they go full auto,
    0:35:33 what does that mean, right?
    0:35:34 And how does that get
    0:35:35 put back in the box?
    0:35:35 And tactically,
    0:35:36 what does that mean
    0:35:36 for your forces?
    0:35:37 Were they trained
    0:35:38 to face something
    0:35:39 that had that level
    0:35:40 of capability
    0:35:41 and that level
    0:35:41 of discretion,
    0:35:42 that level of speed?
    0:35:43 And then how does it
    0:35:44 play forward from there?
    0:35:45 And so I think
    0:35:45 that there are a lot
    0:35:46 of hard questions.
    0:35:47 I think that like
    0:35:48 the convenient answer,
    0:35:48 the easy answer
    0:35:49 is that humans
    0:35:50 are always going
    0:35:50 to be on the loop.
    0:35:51 It’s going to be fine.
    0:35:52 Don’t worry about it.
    0:35:53 But I think that
    0:35:53 the world is a little bit
    0:35:54 more complicated than that.
    0:35:56 I don’t have answers for it
    0:35:57 other than
    0:35:58 a strong conviction
    0:36:00 that America needs to lead.
    0:36:01 So no matter
    0:36:02 what you believe
    0:36:03 about any of this,
    0:36:04 whether you have conviction
    0:36:05 on one side or the other
    0:36:06 or you’re just uncertain
    0:36:07 what the future
    0:36:08 is going to be
    0:36:08 and I think there’s
    0:36:09 a lot of uncertainty
    0:36:10 about how the future
    0:36:11 will play out,
    0:36:12 American leadership
    0:36:12 is the answer.
    0:36:14 At the conceptual level,
    0:36:15 a lot of these things
    0:36:16 are less new
    0:36:17 than they seem.
    0:36:18 So like dropping a bomb
    0:36:19 in World War II,
    0:36:19 like once that thing
    0:36:20 leaves the bomber,
    0:36:21 it’s autonomous.
    0:36:22 It’s pretty dumb autonomy,
    0:36:23 but human judgment
    0:36:24 is over, right?
    0:36:25 That thing is falling
    0:36:26 and it’s guided
    0:36:26 by gravity
    0:36:27 and wind
    0:36:27 and physics
    0:36:28 and other things
    0:36:30 and at some point
    0:36:30 in that trajectory,
    0:36:32 if it starts heading
    0:36:32 towards the wrong place
    0:36:33 or you realize
    0:36:34 that it was the wrong target,
    0:36:34 there’s nothing
    0:36:35 you can do about it.
    0:36:36 We are used
    0:36:37 to relinquishing control
    0:36:39 over the end outcome
    0:36:40 and usually that results
    0:36:41 in much less precision
    0:36:42 and much less ability
    0:36:44 to actually accomplish
    0:36:44 the thing
    0:36:45 that you care about
    0:36:47 and AI fundamentally
    0:36:48 changes that equation,
    0:36:49 but I don’t think
    0:36:50 that the concept
    0:36:50 of the human
    0:36:51 relinquishing control
    0:36:53 over the ultimate
    0:36:54 thing that happens
    0:36:55 is actually new.
    0:36:57 both of you
    0:36:57 questioned the premise
    0:36:59 that this is a new thing
    0:37:00 that’s never happened before.
    0:37:01 It is not the case.
    0:37:01 And second,
    0:37:02 both of you
    0:37:02 zoomed out
    0:37:03 and said,
    0:37:04 listen,
    0:37:05 we are not
    0:37:06 at the end of history.
    0:37:07 Fukuyama was wrong.
    0:37:08 There are rival systems
    0:37:09 of government,
    0:37:10 totalitarian states
    0:37:11 and free states
    0:37:13 and we have to make sure
    0:37:13 and we have to make sure
    0:37:14 whether your concerns
    0:37:16 may be in a conscientious,
    0:37:17 thorough and democratic manner,
    0:37:19 we ensure that we are the ones
    0:37:19 who have dominated
    0:37:20 this technology
    0:37:21 to ensure deterrence.
    0:37:22 Now let’s imagine
    0:37:23 two different futures
    0:37:24 10 years from now.
    0:37:25 One,
    0:37:27 where totalitarian states,
    0:37:28 China,
    0:37:28 Russia,
    0:37:29 et cetera,
    0:37:30 has dominance
    0:37:31 over this technology.
    0:37:32 They have not only
    0:37:33 in terms of procurement,
    0:37:33 in terms of production,
    0:37:34 but also in terms of technology.
    0:37:35 And another,
    0:37:37 where we maintained
    0:37:39 and then advanced
    0:37:40 our technological advancement
    0:37:41 and lead over them
    0:37:42 and also achieved
    0:37:43 manufacturing,
    0:37:43 at least parity
    0:37:44 if not superiority.
    0:37:46 What do those two futures
    0:37:47 look like?
    0:37:49 It’s hard to predict exactly,
    0:37:50 but I think AI
    0:37:51 is the most important technology
    0:37:52 really in the history
    0:37:52 of humanity.
    0:37:54 And a world
    0:37:55 where our adversaries
    0:37:57 have it and we don’t
    0:37:57 is not a good one.
    0:37:58 I mean,
    0:37:58 a world where like
    0:37:59 the Soviet Union
    0:38:00 had nuclear weapons
    0:38:01 and we didn’t.
    0:38:02 A world where
    0:38:02 Nazi Germany
    0:38:03 had nuclear weapons
    0:38:04 and we didn’t.
    0:38:04 I mean,
    0:38:05 these are not pleasant things
    0:38:06 to think about.
    0:38:06 And so,
    0:38:07 in my standpoint,
    0:38:08 I think we should just view that
    0:38:10 as an unacceptable outcome,
    0:38:12 like one that we cannot allow.
    0:38:14 And some of this plays out
    0:38:15 like beyond
    0:38:16 the military domain,
    0:38:17 but I think there is
    0:38:18 in AI,
    0:38:19 and I think this is changing rapidly
    0:38:20 for the better
    0:38:21 with the new administration,
    0:38:23 there was a lot of talk
    0:38:24 of like safety
    0:38:24 and regulation
    0:38:25 and we can’t do this
    0:38:26 and we can’t have
    0:38:26 this many parameters
    0:38:27 and if you use more
    0:38:28 than this much compute,
    0:38:29 it’s not allowed.
    0:38:30 If you’re sitting
    0:38:31 in Beijing or Moscow,
    0:38:32 I mean,
    0:38:32 that’s just got to be
    0:38:33 music to your ears,
    0:38:33 right?
    0:38:34 Of please,
    0:38:34 yes,
    0:38:35 slow down.
    0:38:35 It’s not to say
    0:38:36 that we shouldn’t be
    0:38:36 thoughtful,
    0:38:37 but like,
    0:38:38 we’ve got to be real
    0:38:39 about the game theory
    0:38:40 dynamics here
    0:38:41 and the implications
    0:38:42 for this technology.
    0:38:44 So, to close out,
    0:38:45 there are, I’m sure,
    0:38:46 a number of founders
    0:38:48 watching online
    0:38:49 and I’m sure many of them
    0:38:50 would be interested
    0:38:50 in getting into
    0:38:51 public safety,
    0:38:52 defense tech,
    0:38:53 want to make a difference.
    0:38:54 They see the mission,
    0:38:56 they see the need.
    0:38:57 Perhaps they’re scared,
    0:38:57 they’re afraid,
    0:38:58 they’re afraid of failure,
    0:38:59 they don’t know how to start.
    0:39:00 So, if you were to give
    0:39:02 just one piece of advice
    0:39:03 to these founders
    0:39:03 that would be founders,
    0:39:04 what would it be?
    0:39:05 Number one,
    0:39:07 the mission is worth it.
    0:39:08 I don’t think that there is
    0:39:10 a thing in the world
    0:39:11 that you can care about
    0:39:11 that doesn’t build
    0:39:12 from a foundation
    0:39:13 of security and stability.
    0:39:15 And I wake up every day
    0:39:18 just incredibly excited
    0:39:19 and honored to have
    0:39:19 the opportunity
    0:39:20 to contribute
    0:39:21 to something that I think
    0:39:22 is so important.
    0:39:23 The other thing I would say
    0:39:23 is this stuff
    0:39:25 is just extremely challenging
    0:39:25 and I think that
    0:39:26 especially if you’re
    0:39:27 building hardware
    0:39:27 and you’re serving
    0:39:28 these critical industries
    0:39:29 where the stakes
    0:39:30 are really high,
    0:39:31 you should just expect
    0:39:32 it to be really hard.
    0:39:33 And it’s hard
    0:39:34 in different ways
    0:39:35 at different points in time.
    0:39:37 But at some level,
    0:39:38 I think to be successful
    0:39:39 at it over the long term,
    0:39:40 you’ve got to kind of love that.
    0:39:41 And I think probably
    0:39:41 both of us,
    0:39:42 different things
    0:39:43 in our backgrounds, we do.
    0:39:44 I love the challenge
    0:39:45 of trying to solve
    0:39:46 these difficult problems
    0:39:47 and I think probably
    0:39:48 both our companies
    0:39:48 tend to attract people
    0:39:50 who want to be really pushed
    0:39:51 and challenged
    0:39:53 and work on hard problems
    0:39:54 and struggle with things
    0:39:55 over days, months, years.
    0:39:56 And that is definitely
    0:39:57 part of the journey here.
    0:39:59 If you want to get to something
    0:40:00 that is going to have
    0:40:00 real impact,
    0:40:01 you know, you’ve got to
    0:40:02 embrace the struggle.
    0:40:03 100%.
    0:40:04 Wise words.
    0:40:07 Now, if you made it this far,
    0:40:09 a reminder that this
    0:40:10 was recorded live
    0:40:11 at our third annual
    0:40:12 American Dynamism Summit
    0:40:14 in the heart of Washington, D.C.
    0:40:15 And if you’d like to see
    0:40:16 more exclusive content
    0:40:17 from the summit,
    0:40:18 head on over to
    0:40:19 a16z.com
    0:40:20 slash American
    0:40:21 dash dynamism
    0:40:22 dash summit
    0:40:24 or you can click the link
    0:40:25 in our description.

    War has always been shaped by technology—from steel and gunpowder to GPS and nuclear weapons. But the decisive technologies of tomorrow aren’t coming—they’re already here.

    In this episode, recorded live at our third annual American Dynamism Summit, a16z’s Senior National Security Advisor Matt Cronin sits down with Ryan Tseng (cofounder & CEO, Shield AI) and Adam Bry (cofounder & CEO, Skydio) to discuss the rise of autonomous drones, AI-driven warfare, and the escalating great power competition with China. They cover:

    • Why drones are reshaping the battlefield in Ukraine, Israel, and beyond
    • The asymmetry of $1,000 drones taking out $10M tanks
    • Why U.S. drone production lags China—and how to catch up
    • The ethical and tactical implications of autonomy in combat
    • What it will take to reindustrialize America and maintain deterrence

    If the future of warfare is software-defined, who writes that software—and who deploys it first—matters more than ever.

     

    Resources: 

    Find Adam on X: https://x.com/adampbry

    Find Ryan on LinkedIn: https://www.linkedin.com/in/ryantseng/

    Find Matt on LinkedIn: https://www.linkedin.com/in/matt-cronin-8b88811/

    See more from the American Dynamism Summit: www. a16z.com/american-dynamism-summit

     

    Stay Updated: 

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    Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

  • Building Ultimate Trust: Volleyball Coach John Cook’s Championship Formula

    AI transcript
    0:00:04 In my years of entrepreneurship, I’ve seen countless startups.
    0:00:06 And here’s the truth.
    0:00:12 Smart spending drives growth, which is something Brex has championed.
    0:00:14 Brex isn’t just a corporate credit card.
    0:00:19 It’s a strategic tool to help your company achieve peak performance.
    0:00:22 Corporate cards, banking, expense management,
    0:00:30 all integrated on an AI-powered platform that turns every dollar into opportunity.
    0:00:35 In fact, 30,000 companies are trusting Brex to help them win.
    0:00:39 Go to brex.com slash grow to learn more.
    0:00:43 And one of the things that I ask them is what their why is.
    0:00:47 And sometimes it takes them a couple of years to figure out what their why is,
    0:00:47 why they’re playing.
    0:00:50 Deep down inside, what is it they’re really getting from this?
    0:00:53 Is it because I can get more social media followers?
    0:00:54 Do I like the color red?
    0:00:56 I hear it all.
    0:00:58 But at some point, they’re going to dig down.
    0:01:00 Why am I really playing this game?
    0:01:02 Is it for the love of my teammates?
    0:01:03 I want to win championships.
    0:01:06 I want to be the best player I possibly can be.
    0:01:09 So at some point, they got to figure out their why.
    0:01:13 And I think the ones that do that, they’re the ones that can go to the next level.
    0:01:20 I’m Guy Kawasaki.
    0:01:22 This is the Remarkable People Podcast.
    0:01:25 And we are on a mission to make you remarkable.
    0:01:28 And what we do is we find remarkable people.
    0:01:30 And we found one today.
    0:01:32 This is John Cook.
    0:01:38 He is the four-time national champion Nebraska volleyball coach.
    0:01:41 And he was a dominant coach.
    0:01:45 You want more Division I games than any other volleyball coach.
    0:01:46 Is that true?
    0:01:47 Yes.
    0:01:48 Or this century.
    0:01:49 That’s correct.
    0:01:52 There was women’s volleyball in the previous century?
    0:01:53 Yes, there was.
    0:01:58 It started in the 70s and the early 80s is when it really got going.
    0:02:04 So is it accurate for me to say you’re like the John Wooden of women’s volleyball?
    0:02:07 You can say whatever you want.
    0:02:13 But I’ve actually tried to be the Tom Osborne of Nebraska for volleyball because he was a role model for me, a mentor.
    0:02:16 He was my athletic director for a while.
    0:02:19 And I watched him as a kid coaching.
    0:02:23 And so he’s somebody I’ve tried to emulate being in Nebraska.
    0:02:28 And he is the John Wooden of football probably in this country.
    0:02:30 So similar type of guys.
    0:02:32 Okay.
    0:02:32 All righty.
    0:02:35 So first of all, just give us a little update.
    0:02:37 How is retirement going?
    0:02:39 You’ve been retired a couple months now.
    0:02:44 I’ve had all these opportunities like this one have come out of the woodwork.
    0:02:47 So I’ve had lots of requests to do things.
    0:02:48 I’ve turned down a lot.
    0:02:51 I had a lot of commitments that I’d committed to.
    0:02:53 So I’m still fulfilling those.
    0:02:55 So it doesn’t feel much like retirement.
    0:03:00 I’ve actually probably been traveling more right now than I normally would recruiting at this time.
    0:03:02 But busy hands make happy hearts.
    0:03:03 But I’m just doing different things.
    0:03:05 And the great thing is you don’t wake up every morning.
    0:03:10 I’m worried about 14 players and what they’re doing, how we’re going to get better today.
    0:03:13 It’s more of what adventure are we going on today?
    0:03:15 So let me ask you then.
    0:03:21 So how and why did you accept this request to be on my podcast?
    0:03:24 It was because of the picture of Madison at your camp?
    0:03:25 Or what was it?
    0:03:27 That was a big part.
    0:03:30 And we have a saying here, once a Husker, always a Husker.
    0:03:34 And you’re really smart to hire kids from Nebraska to work for you.
    0:03:35 They’re great.
    0:03:37 It’s a great state.
    0:03:41 And you sent me a great information page to check out.
    0:03:42 I looked to you hat on.
    0:03:43 I read about you.
    0:03:49 And I just thought, I love your idea of remarkable because our saying is dream big every day.
    0:03:51 And you’re doing a remarkable person podcast.
    0:03:53 And that really caught my attention.
    0:03:55 I think it’s a really cool idea.
    0:04:00 And that’s how we learn is we study other people and share things with other people that have been successful.
    0:04:03 You’ve been on my podcast before.
    0:04:05 You’ve been on Joe Rogan’s, I bet.
    0:04:05 Yeah.
    0:04:08 I’d love to go on Joe Rogan’s.
    0:04:17 In one part of your book, you mentioned these three questions you asked yourself every day as a coach.
    0:04:24 So I would like you to explain the three questions you used to ask yourself every day as a coach.
    0:04:25 Yeah.
    0:04:27 And I do quite a bit of speaking.
    0:04:36 And this is something that I share with people that if you’re a leader or working with teams or even your family, you’ve got to ask yourself these three questions.
    0:04:38 And you’ve got to answer them every day.
    0:04:40 So when you go to bed at night, you’ve got to be able to answer them.
    0:04:41 And the first one is, who needs me today?
    0:04:46 And I’ve always thought, working with a team, somebody’s going to need me today.
    0:04:48 Somebody’s going to have a crisis.
    0:04:49 Somebody’s going to need something.
    0:04:50 Is it my family?
    0:04:51 Is it my team?
    0:04:52 Is it my staff?
    0:04:53 All the people that work in our program.
    0:04:55 But somebody’s going to need you today.
    0:05:01 So if you could start off the day and figure that out and be proactive as opposed to waiting until something happens.
    0:05:03 So that’s the first question.
    0:05:09 The second question is, would I be hired again if they were hiring for the Nebraska Volleyball Coach?
    0:05:12 Did I do a good enough job today that they would want to hire me back?
    0:05:16 And then the third question is, would I want to be coached by me?
    0:05:28 And this is the one that is the toughest as a coach because you want those players to want to come back and work hard and be excited to come to practice the next day and keep building towards something.
    0:05:38 But if I have a bad day or I’m too negative or didn’t run a good practice or didn’t create a great mindset, we didn’t feel like we got better.
    0:05:40 That’s the tough question to answer.
    0:05:44 And there’s a lot of days, I’m sure, our players, they don’t want to see me the next day.
    0:05:49 But it helps you stay centered on what is really, really important.
    0:05:56 And as a Nebraska Volleyball Coach, working with this program, those are three key questions that I had to answer every day.
    0:06:09 Related to the third question, and you mentioned about maybe the Nebraska players didn’t like you every day, but do you think that coaching comes from a position of love or anger?
    0:06:12 Because there are some different coaching styles right there.
    0:06:18 There’s the love your player, there’s the scared the shit out of your player, Vince Lombardi, I don’t know, Bobby Knight.
    0:06:24 Although we interviewed Tara Vandermeer, and she said Bobby Knight was not what you see on ESPN.
    0:06:28 So let’s talk about love versus anger as a coach.
    0:06:30 It’s a really good question.
    0:06:42 So my background was I coached football, I played football, and all my models in the 70s was football coaches who were tough guys, and they coached with anger.
    0:06:48 They were in your face, they got after you, they ran guys till they quit, stuff like that.
    0:06:59 And I started off coaching that way, and then my early years at Nebraska and Wisconsin, I was a tough coach, and I was on them all the time.
    0:07:11 And then I realized about 2012 to 2014, when social media came in and iPhones came in, all of a sudden, these players couldn’t take it.
    0:07:16 And I became very frustrated as a coach, and I wasn’t connecting with them.
    0:07:21 And so I realized I had to make a change, and that’s exactly what I call it, is you got to coach with love.
    0:07:29 And so each day when I went in, I always had that mindset, I’m going to coach with love today, very similar to raising kids.
    0:07:33 I’m going to challenge you, push you, but I’m going to do that with a lot of love.
    0:07:35 And did I do perfect every day?
    0:07:41 No, but that is the mindset, because I don’t think this generation handles failure very well.
    0:07:46 They live in this perfect world on these phones and social media.
    0:07:48 Everybody’s perfect, everybody looks great.
    0:07:52 They post pictures, I love you, you look so cute, blah, blah, blah.
    0:07:57 So they just have a different mindset about them and how the world works.
    0:08:00 And that was a big adjustment I had to make.
    0:08:05 And when I made that adjustment, I went on the best run of my coaching career, which is the last 10 years.
    0:08:13 I’ve had more success, won more games, played for more championships, won more championships than I did in the previous two decades before that.
    0:08:20 So it was a major change for me and something that I had to do, and I did it, and it worked well.
    0:08:33 So the irony is that what you describe as iCentered, as opposed to iPhone or iPod or iPad, but iCentered made you into a better coach.
    0:08:38 Yeah, that technology and those things definitely made a difference.
    0:08:53 But here’s the downside of that: all of my older players, who all have kids now and they’re getting ready to play college volleyball and high school volleyball, every time they see me or they come watch practice, the first thing they say is, “You’re so soft.”
    0:09:04 I can’t believe how you treat these guys because what the world they came from and the way I coached the last 10 years here was completely different.
    0:09:06 They get after me quite a bit.
    0:09:16 Would you say that if iPhones didn’t exist and social media didn’t exist, do you think a person like Harper Murray would have had all the issues she had?
    0:09:21 But were her issues at least partially caused by this kind of social media world?
    0:09:31 Most definitely, and there’s tremendous pressure that social media is putting on high-performing athletes or high performers.
    0:09:36 People can say anything and anything they want.
    0:09:40 And again, I’ve been criticized on social media, but it doesn’t bother me because I don’t care.
    0:09:41 People can say whatever they want.
    0:09:52 But when you’re 18 years old and you’re trying to please everybody and you’re trying to be this person that kids look up to and people are saying awful things about you in social media, they believe it.
    0:10:13 So that’s why every athletic department now, there’s a shortage of trying to hire mental health professionals because they have to handle, that’s why you’ve seen this in college athletics, this explosion of mental health issues and talking about mental health, because they just cannot handle that side of social media.
    0:10:15 And you might say, “Hey, just don’t listen to them.”
    0:10:16 But remember, they’re on their phones.
    0:10:17 That’s their world now.
    0:10:19 They’re connected to that.
    0:10:23 And they’re doing things, social media, to build their brand.
    0:10:25 And they’re making money off of it.
    0:10:31 And they’re using that as a platform, whether it’s political or being a leader or helping kids.
    0:10:33 But again, that negative stuff’s going to come in.
    0:10:37 And some of the stuff I saw that was written was sickening.
    0:10:39 But again, they believe it.
    0:10:41 And that’s all that really matters.
    0:10:48 As an antidote to this, didn’t you take away their phones and put them into small groups of four or five,
    0:10:50 as opposed to 25 at a dinner.
    0:10:56 And you set up these small groups and the players just rejoiced in that, that they had these personal
    0:11:04 interactions instead of constantly looking, what does lonelyboy15 on Instagram think about my play or whatever?
    0:11:07 Yeah, those are the two great stories on that.
    0:11:09 We were in China in 2014.
    0:11:10 Again, this is when all this is happening.
    0:11:12 And we weren’t playing very well.
    0:11:14 We went on an international trip.
    0:11:16 So I got mad at them.
    0:11:17 So I walked down the bus.
    0:11:19 I said, throw all your phones in this bag.
    0:11:20 You’re not getting your phones back.
    0:11:22 Until we start playing better.
    0:11:24 So I kept them.
    0:11:29 And for a couple of days, and all of a sudden we went and knocked off this professional team.
    0:11:29 Played great.
    0:11:31 I gave them back.
    0:11:34 And one of the players, she goes, coach, I don’t want my phone back.
    0:11:37 I really enjoy not having my phone.
    0:11:41 So that was a big telltale sign for me.
    0:11:43 And then the other thing is you’re from Hawaii.
    0:11:46 I gave you a hard time about not going to Kamei Kamei.
    0:11:49 Because my assistant coach Jalen is Kamei.
    0:11:51 He’s got it on his license plate in Nebraska.
    0:11:53 He’s very proud of that.
    0:11:57 So when we go to Hawaii with the team, every spring break in March,
    0:12:04 we divide up into small groups and we go to some hole in the wall restaurant,
    0:12:09 something would not that where the tourists go, not a cheesecake factory or something like that.
    0:12:16 And one of the places I go is this Japanese place and everything’s in Japanese and they barely speak English.
    0:12:19 So our players have to figure out how to order and what to order.
    0:12:20 So it’s an adventure.
    0:12:23 But what I noticed was being in small groups.
    0:12:25 And again, their phones are off.
    0:12:30 It goes three, four hours for dinner because they’re not distracted.
    0:12:33 So they actually sit down and have a conversation and they love it.
    0:12:37 I just sit there and listen and they just talk and talk and talk.
    0:12:39 Because whenever in their life, do they ever do that?
    0:12:40 Everything’s on the go.
    0:12:41 They’re on their phones.
    0:12:42 Everything’s boom, boom, boom.
    0:12:43 Got to go to this class.
    0:12:44 Got to go do this.
    0:12:47 So they really, really enjoy it.
    0:12:48 And that’s something I’ve noticed.
    0:12:52 I think they crave that social interaction, but they just don’t do it.
    0:12:56 In the old days, I’m sure you and I both had family dinners and everybody was there.
    0:12:59 And we didn’t leave till dinner was over.
    0:13:02 It’s something that’s missing for this generation.
    0:13:03 Okay.
    0:13:05 I got to know because I’m about to go to Hawaii.
    0:13:08 So what is the name of that Japanese restaurant?
    0:13:09 I know right where it is.
    0:13:11 It’s a Japanese name.
    0:13:16 It’s in Waikiki and it’s very close to the Cheesecake Factory, but it’s in a back alleyway.
    0:13:24 Just look up best Japanese restaurant down there in Waikiki because we stay in Waikiki so we can walk to it.
    0:13:25 But the players love it.
    0:13:27 Of course, they don’t even know what they’re ordering half the time.
    0:13:34 Oh, before I forget, I got to tell you, I prepared for this interview by reading your book.
    0:13:38 And I want to know, do you have another book in you now that you’re in retirement?
    0:13:39 Yes.
    0:13:41 I submitted an outline to Nebraska Press.
    0:13:43 They’re reviewing it right now.
    0:13:49 Again, after I wrote that first book and that book came to me when I go for bike rides, all of a sudden,
    0:13:55 it just started coming in my head. I can’t explain why. I just like that would be a great chapter to
    0:14:02 write about. And now that I’m finished and retired, I have another set of chapters and things I want
    0:14:09 to write about and not so much about my life or Nebraska volleyball. This is more about what I’ve
    0:14:15 learned about coaching and working with people and building teams. And the really big thing is culture.
    0:14:22 And that’s the one thing I see is failing in a lot of places is how do you maintain a great culture?
    0:14:27 Everybody’s complaining about it. It’s really hard now, especially coaches. And I think leaders and
    0:14:33 people working with teams, how do you build a great culture? It’s what I did for the last 30 years is
    0:14:38 how am I going to build a great team culture and keep these guys together. And now with in college
    0:14:44 volleyball and college athletics, with the portal, money coming in, it’s going to be even harder to
    0:14:49 have a great culture. So I’ve got some great wisdom I would love to share. And so I’m just waiting to hear
    0:14:51 back how we’re going to do it, when we’re going to go.
    0:14:58 Well, how about giving us two or three pointers while you open that door? I mean, geez, give us one.
    0:15:05 For a great culture, to start off, you have to get everybody seeing the same vision and goal
    0:15:12 that you want for your program. And do they commit to whatever it’s going to take? And do they trust
    0:15:18 the leader and the leaders that we’re going to stay with us and get there? And to me, that’s
    0:15:24 the first thing on culture, are we all in? Or are you thinking about something else? And is Madison
    0:15:28 thinking about something else? Am I thinking about something else? Are we just all over the place?
    0:15:34 And then do you have other things that are more important than the team? And again, I think it
    0:15:40 comes, can you see a picture? It’s about something bigger than yourself. And to me, if you don’t have
    0:15:49 that going, you don’t have a chance.
    0:15:57 Every business is under pressure to save money. But if you want to be a business leader, you need to do
    0:16:04 more to win. You need to create momentum and unlock potential, which is where Brex comes in.
    0:16:10 Brex isn’t just another corporate credit card. It’s a modern finance platform. That’s like having a
    0:16:16 international superhero in your back pocket. Think credit cards, banking, expense management,
    0:16:24 and travel all integrated into one smart solution. More than 30,000 companies use Brex to make every
    0:16:31 dollar count towards their mission and you can join them. Get the modern finance platform that works as
    0:16:39 hard as hard as you do at brex.com/grow. I have a question that as I was reading your book and I read
    0:16:46 about the story I’m about to reference, I just was like, how do you handle that? How do you make a
    0:16:52 transition? So the story, of course, I’m talking about is, so you were the coach of Wisconsin, you lose to
    0:16:58 Wisconsin, you have dinner with the opposing coach, and he approaches you to become an assistant coach for
    0:17:04 the next year. So now you’ve been building this culture with your Wisconsin team. And how do you
    0:17:10 tell those people, all those girls that you recruited and all that, guess what gang, I’m going to Nebraska.
    0:17:16 How do you navigate something as difficult as that? I did not navigate it very well. And that’s one of
    0:17:24 my regrets in my coaching career is how I handled that. If I had it to do over again, I would have done it much
    0:17:32 differently. But it was just so bang bang. And I didn’t have great support from the leadership at
    0:17:38 the time at Wisconsin. So after we met and we started talking through some things, I was really disappointed
    0:17:44 because I wasn’t sure I was going to go. I wanted to talk to them about in Wisconsin about what can we
    0:17:51 do now to make this program like what Nebraska wants to do. And I just got shut down. So it’s one of my
    0:17:57 my regrets. But what the great thing about a guy is, and this is sometimes you get second chances in life.
    0:18:03 Two of the players on that team that I recruited, I both hired as coaches later on and coached with me
    0:18:10 at Nebraska. And so for me, I knew they came back around and I worked hard to make that happen.
    0:18:15 And because, again, they wouldn’t even shake my hand after the match. We won in five that next year,
    0:18:21 we beat them. We played Wisconsin, my team for the national championship. And we won in five games.
    0:18:25 And they wouldn’t even shake my hand afterward. And we had an undefeated season that year,
    0:18:30 my first year in Nebraska. So it was a magical year, but it was no joy in winning the national
    0:18:36 championship. I felt so bad for those Wisconsin players. And I was just glad to get it over with.
    0:18:42 And so it’s one of my, in my regret chapter, that’ll be one of the things I didn’t handle it great.
    0:18:49 And you even mentioned, you tried to take a player with you from Wisconsin to Nebraska and
    0:18:53 you couldn’t do it. Right. Right. Is she one of the players that became a coach for you?
    0:18:57 Yes. Charissa Livingston. She later, she went and played professionally,
    0:19:03 came back down to coaching. She was kind of lost and wandering around. I called her and I said,
    0:19:08 you’re going to come to Nebraska. I want to hire you as a coach. And she came there and coached a
    0:19:12 couple of years and then went on and has been a head coach at a couple of different universities.
    0:19:19 it was somewhat of a validation for me that I did some good stuff. I just didn’t handle it very well.
    0:19:25 And it was a way for me to make it up to them. I hope I didn’t cause PTSD by asking that question.
    0:19:34 I still have dreams guy. And I talked to another coach who was a hall of fame coach who retired.
    0:19:41 And I asked her, I said, I’m still having dreams. She goes, they’ll never go away. They’ll never go away.
    0:19:49 You’re so consumed in your life and coaching. It’s a 24/7, 365 days a year. And so those things stay with
    0:19:49 you.
    0:19:57 You say that the more you coach, the less you think, you know, now, why is that?
    0:20:02 Like I said, my first year at Nebraska, we went undefeated and won the national championship.
    0:20:11 So I thought I’m pretty good. I got this figured out. And so if you start thinking that way,
    0:20:17 you’re never going to get better. And I was thinking that way. And then all of a sudden I was getting
    0:20:21 frustrated because we weren’t getting better and we weren’t performing at the highest level over the
    0:20:27 next couple of years. And then so one of my mentors told me like, you got to get a growth mindset and
    0:20:32 keep learning and getting better because you’re just stuck. And so that’s the saying I would tell
    0:20:37 myself, the longer I coach, the less I know. And if you have that open mindset and that growth mindset,
    0:20:45 you are going to continue to learn over and over and continue to learn more and more. And one of the
    0:20:51 things I say is I learned more about coaching and Harper’s situation, some other things last year,
    0:20:56 than I did in the previous 24 years combined. That’s how it feels. And that’s how the mindset I have,
    0:21:01 I’m going to continue to learn and grow and continue to get better. And because there’s things that are
    0:21:06 going to come up that there’s no coaching one-on-one, how to deal with what happened with Harper Murray.
    0:21:12 There’s no preparation for that. So I had to really have a great growth mindset on how to handle that
    0:21:19 one specific thing. Oh, Carol Dweck is smiling someplace at Stanford right now. She’s been a guest
    0:21:27 twice and we love Carol Dweck. So yeah, I’m sure she’s happy now. Yeah. So what do you think is the
    0:21:35 most rewarding thing about coaching? Is it the glory and the fame and the money? I mean, why? What is that?
    0:21:42 That’s an easy, easy question to answer. For me personally, it’s, if you look at my coaching tree
    0:21:49 and how many former players have gone into coaching and I’ve got, I just hired the one who replaced me in
    0:21:56 Nebraska as a former player coach. But to me, that’s the most rewarding thing is, is seeing a young woman go
    0:22:03 through our program and then wanting to stay in coaching. And whether it’s high school, junior high,
    0:22:09 club volleyball. I have a former player that started a club in Lincoln. She has a 12 court facility she’s
    0:22:15 built. And just seeing these former players stay in coaching and want to be difference makers. And to me,
    0:22:20 that’s what it’s all about that validates. I did something pretty good. They’re developing those
    0:22:25 guys that they want to stay with us. And they have the passion for the sport and the passion for working
    0:22:32 with people. And especially the ones that work with younger kids, but just to be a devil’s advocate for a
    0:22:39 second here, I understand what you just said. And I like, I am impressed. And I think it’s wonderful, but
    0:22:47 isn’t it a luxury that only someone like you can say that because you have in fact won four national
    0:22:53 championships. If you were a middle of the road coach, could you still say that? Would you still say that?
    0:23:01 Yes, I would. And for me to be able to hire Danny Buspoon Kelly to come back to coach Nebraska and be a
    0:23:11 female coach coaching women’s volleyball, that by far overshadows any national championship or great
    0:23:18 wins or great moments. The stadium match we did where we broke the world record, that overshadows any national
    0:23:23 championship. I’ll trade that for four national championships, for example, and same with hiring
    0:23:29 Danny and some of these other ones. So those national championships, those great wins, those are moments
    0:23:37 and they’re there. It’s exciting. And then it goes away. It’s the things that stay with you that to me are
    0:23:38 why you coach.
    0:23:44 So I am a parent and I’m not a parent of a professional athlete or college athlete, but
    0:23:52 I would love to hear what you say about what’s your advice to parents of kids who are interested
    0:23:59 in playing volleyball or any real sport, actually, at such a high level. What’s your advice to these
    0:24:05 parents? Are they supposed to be screaming on the sideline? Are you focusing on one sport, multi-sport,
    0:24:08 send them away to camp? What’s your advice to these parents?
    0:24:14 My advice is your kids growing up should play every sport that they want to play.
    0:24:21 And I see so many times, I’ve just seen this over the years, they think they got to specialize in one
    0:24:26 sport. And by the time they get to high school or college, they’re burned out. Our best players at
    0:24:32 Nebraska, and if you look at our all American wall, they were all multi-sport athletes. Now, eventually,
    0:24:38 they started playing volleyball or got involved in a club. Usually it was in high school.
    0:24:45 So I think when you, for example, a lot of our players at Nebraska, because they come from small
    0:24:51 towns, they do everything. And I remember my first year recruiting there, I go and see this player.
    0:24:56 She’s warming up for a volleyball game. This is a little gym like in the movie Hoosiers.
    0:25:02 She runs over and plays the flute in the national anthem, runs back and plays the volleyball match.
    0:25:09 That’s doing everything. But I’ve heard them talk about like when they run track,
    0:25:15 that running the 800 and track is the hardest thing they’ve ever done mentally and physically,
    0:25:21 because you’re going till you think you can’t go anymore. And so that develops a toughness playing
    0:25:26 golf or tennis. A lot of our players have played tennis. There’s a lot of transfer over to volleyball,
    0:25:30 but it’s a different game. It’s a different mindset. Everything’s a little different,
    0:25:35 but I think that’s how they develop athletically. And then eventually where’s their passion and then
    0:25:36 turn them loose on that.
    0:25:43 And should parents like, look at, we got to send them to this elite sports camp. We got to do all
    0:25:49 this kind of stuff or just let them grow up, just let them grow up. And maybe once a summer or something,
    0:25:54 go to a camp. Cause I think there’s other things they can gain from camp, being around other kids,
    0:25:59 seeing the other kids, a different experience, learning how to function. These aren’t my players
    0:26:05 I play with every day or my friends. Maybe they go to a camp and, oh, I got all these new kids I got
    0:26:12 to meet and learn how to interact with. So I think there’s a lot to going to some camps and things like
    0:26:17 that. But to send them to camps all summer, to me, I see that’s a lot of, I don’t want to get my kids
    0:26:22 out of the house. I don’t have to babysit them. But I think there is some advantages to going to some
    0:26:26 camps and being a part of that. And then eventually getting into club programs.
    0:26:36 So from the flip side, I read that like, how do you possibly make a judgment about a 14 or 15 year old
    0:26:43 girl to make her commit to a program? This is three, four years before she graduates. How do you figure that
    0:26:50 out about a recruit? And is that a good practice that you’re getting kids to commit at 14 or 15?
    0:26:56 They actually made some rules to take that away now. So the old days or two years ago,
    0:27:05 we could do that. And I hated it. It’s not good. And when you’re in ninth grade, going into ninth grade,
    0:27:14 you’re really not ready to make a decision. And so you see a lot of bad choices and then kids will change
    0:27:18 later on. And then they’d have to transfer and all that, which is hard to go through. But two years ago,
    0:27:25 they changed the rules. Now they can’t commit till they’re a junior in high school. So it’s been a big
    0:27:31 change now. And I think that was a really positive move that the NCAA did because we can’t even talk to
    0:27:37 or we can go watch ninth graders, but we can’t talk to them or recruit them or do anything like that. So
    0:27:43 that part has changed. But most of our players committed going into ninth grade up to the last
    0:27:43 couple of years.
    0:27:52 So what do you look for in a recruit, how athletic they are, how good an arm swing they have for
    0:27:57 volleyball. You got a major league baseball player, you can either throw the ball or you can’t hard,
    0:28:03 you know, and so can they swing if they’re an attacker. And then how do they move? How athletic
    0:28:09 are they? How do they compete? But those things are really hard to judge in ninth grade. Some of those
    0:28:14 level of play, they’re just trying to get the ball over the net. So it’s really, really hard. And again,
    0:28:21 I think, Guy, what I’ve learned in reading and studying, once you see so many athletes over and
    0:28:27 over, over, your mind can pick it out. I can pick out things that I’ve seen that will translate to a
    0:28:33 player projecting them to be pretty good, but it’s not a hundred percent, but you just get an eye for it.
    0:28:39 And I think anybody in anything that’s seen it enough and seen enough video or watch things enough,
    0:28:46 they can just get that sense, kind of that sixth sense. So talking about that sixth sense. So then
    0:28:55 as you look back, how do you allocate athletic success? Is it talent? Is it luck? Is it grit?
    0:29:02 What are the major components and what’s most important? I think in this generation talking about now,
    0:29:10 it’s being able to, first of all, can they focus on what they need to do to be great?
    0:29:17 How many outside distractions and how do they manage that? The second thing is there’s definitely a grit
    0:29:25 factor, a perseverance factor. It’s moving on from failure because most of the players that get to the
    0:29:31 Nebraska level, they’ve had very little failure in their life. And when you get to that level,
    0:29:35 you’re going to fail, you’re going to lose, you’re going to get beat out because everybody has come
    0:29:40 from that same background. So how do you handle that? Are you going to complain? Is it you’re
    0:29:44 going to blame somebody else? Are you going to get, look in the mirror and start working hard or ask what
    0:29:50 you need to do to be better and how to get there? So those are two areas that I think are really important.
    0:29:58 And then I think you can call it heart, desire, you know, what drives them. And one of the things that I
    0:30:02 ask them is what their why is. And sometimes it takes them a couple of years to figure out what
    0:30:08 their why is, why they’re playing. Deep down inside, what is it they’re really getting from this? Is it
    0:30:14 because I can get more social media followers? Do I like the color red? I hear it all. But at some point,
    0:30:19 they’re going to dig down. Why am I really playing this game? Is it for the love of my teammates?
    0:30:25 I want to win championships. I want to be the best player I possibly can be. So at some point you got
    0:30:30 to, they got to figure out their why. And I think the ones that do that, they’re the ones that can go to
    0:30:37 the next level. As I was reading your book, I saw a lot of parallels in business practices. So these are,
    0:30:44 this is a business person asking you these questions to see if I can gain insight into business. All right.
    0:30:52 First question is, how do you pick a team captain? Wow, that’s actually one of the chapters of my new
    0:30:59 book. We have tried everything. And what we’ve done the last two years, and I think we’ve had the
    0:31:05 best leadership. And again, you have to have great leaders to maintain your culture because I’m not
    0:31:10 with them in the locker room. I’m not with them in the back of the bus. I’m not with them when we’re
    0:31:15 in hotel rooms and they’re hanging out or they go to, you know, coffee and hang out. You’ve got to have
    0:31:21 leadership there. And so the captains to us are very, very important. So what we decided to do,
    0:31:28 because I was in a coach’s meeting a few years ago and the question came up right there. How do you let
    0:31:33 captains and every coach said, I don’t know. We have no leaders anymore. These kids don’t come
    0:31:40 in here with the leadership mindset. What do we do? So what I did is I have read books and studied
    0:31:48 leadership. We start every January and once a week, we have a leadership class with our team and anybody
    0:31:55 can go to it. Guess what? They all show up and we talk about leadership. And at the end of the semester,
    0:32:00 we let them choose. They, the ones that are interested in being a captain, they get up and
    0:32:07 talk in front of the team. Okay. This is why I want to be a captain. And I’ve, I’ve heard some incredible
    0:32:13 things and I’ve also seen some that really, really struggled with being able to verbalize why they
    0:32:20 should be captain. And then we let the team elect them or vote on them. And that’s how we’ve done it. And
    0:32:28 it’s actually been about the last three or four years we’ve had amazing leaders and that’s how we
    0:32:34 have filtered them out. And they’ve taken ownership of wanting to go through that process. So that would
    0:32:42 be my best number one way to select captains. All right. But in a sense in business, we never do it that way.
    0:32:48 Right. We make this false assumption that if you are good in sales, you should be VP of sales,
    0:32:55 which, and if you’re a good engineer, you should be VP of engineering, which both of those examples
    0:33:00 don’t make sense. Yeah. It’s the same in the university world. Somebody’s
    0:33:05 a good professor has been there a long time. All of a sudden they’re in a leadership position,
    0:33:13 same in athletics. And you’re like, wait a second. I’ve seen a lot of people bomb out in the athletic
    0:33:19 departments, trying going into leadership positions. How about how to pick who to redshirt, which in
    0:33:26 business is like asking some people to not get promoted, stay in their lane, continue to do what
    0:33:32 they’re doing or step aside. Well, how do you tell somebody, how do you pick somebody to redshirt when
    0:33:38 they’re at the University of Nebraska? They’re all American, something, they’re all MVP, something,
    0:33:43 right? How do you pick the redshirts? Up until the last couple of years,
    0:33:50 it was a lot easier because if somebody knew they weren’t going to play, the talk was, hey, your fifth
    0:33:54 year, you’re going to be a lot better in your fourth year. You could stay your fifth year and get your
    0:34:01 master’s degree. So from a business perspective, it was a no brainer. If you could just get over not
    0:34:07 playing your freshman year. In this day and age with the portal, forget it. There’s going to be very
    0:34:13 little redshirting. I would never even try to redshirt anybody because I got burned two years ago. We
    0:34:19 have this player. We had a great plan for her. We recruited her. She bought into the plan. She comes and
    0:34:27 sits for a year, not happy, transfers. Because now they can transfer so easy. So after that, we said, we’re not
    0:34:33 redshirting anybody anymore. It’s because it’s so easy for them to transfer. A few years ago before the
    0:34:38 portal, they would have to potentially sit out a year if they transferred. So there was a consequence
    0:34:46 for leaving or breaking a commitment. Now there’s no more of that. So the redshirt days are gone.
    0:34:54 In a sense, the portal has had a completely unexpected result, right? That wasn’t the point
    0:34:59 of the portal was like, go where you’re appreciated or something, but you could in fact hurt your career.
    0:35:07 Yes. I understand the portal, but again, I told you, it’s going to be really hard to culture and develop
    0:35:13 loyalty. And it’s so easy to leave. And then you’ve got the poaching going on. So if you’re a business guy,
    0:35:17 and you got somebody, your redshirt, and I think they got a lot of talent, I’m going to go out for
    0:35:21 more money and take them away from you. And that’s what’s happening in college athletics.
    0:35:26 Oh, you’re not playing very much. Hey, I’m going to, I’ll pay you to come to our program. You’ll start
    0:35:34 here. So that is constantly going on right now. And there’s no way to manage it or to combat it.
    0:35:39 So you’re going to have to figure out in college athletics. Now, how am I going to keep this group
    0:35:45 connected? And those guys, they’ve got to believe that there’s no better option for them
    0:35:52 if they were to leave and knock on wood this year, you know, I retired, we had nobody leave. And again,
    0:35:58 it still could happen at the end of the year, but that’s one of the reasons I retired when I did,
    0:36:02 because I felt like I got this group and they’re going to stick together. I really believe that in my
    0:36:08 heart. And so far it’s worked out because I wanted Danny, the new coach to come in a great situation.
    0:36:12 Because, you know, the coach leaves all of a sudden, there’s an excuse to go somewhere else.
    0:36:16 Here’s other coaches coming in. Hey, your coach left. You want to come to our program?
    0:36:23 And so I felt my heart, the timing, this group is going to stick together. And it is a great group.
    0:36:32 First of all, surround yourself with people that are going to help you figure out who can help me
    0:36:39 be the best and develop and grow. And as a coach, is it other coaches? Is it somebody in business?
    0:36:45 Is it somebody from your church? Who is it? Surround yourself with mentors. Second thing is listen to
    0:36:59 podcasts. Read, study other people, study successful people and study other successful teams.
    0:37:06 AI’s impact on the environment is one of the most pressing issues facing the tech industry today.
    0:37:13 People want to know, what’s the carbon footprint of a chat GPT query? What does it mean to innovate
    0:37:19 sustainably? And can AI actually be used to solve the climate crisis?
    0:37:26 I’m Rana El-Khalyubi. On my podcast, Pioneers of AI, we bring questions like this to some of the
    0:37:32 leading thinkers and builders working in AI. Join me each week as we explore how this technology
    0:37:42 is leaving its mark on humanity and our planet. Find Pioneers of AI on Apple Podcasts, Spotify, YouTube,
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    0:38:03 If you find our show valuable, please do us a favor and subscribe, rate, and review it.
    0:38:08 Even better, forward it to a friend. A big mahalo to you for doing this.
    0:38:23 Okay, coach, so I want an explanation of something you hear in football and basketball and hockey all the time.
    0:38:29 And I don’t know anything about volleyball. So I just want to know, does defense or offense win games?
    0:38:39 I’ve always was a defensive minded coach because I felt like defensively, if we were really good, defense is a lot of effort and mindset.
    0:38:44 So you could bring that every night. Offense sometimes, and again, I’ll equate it to basketball.
    0:38:50 You may not be hitting your shots. You may be off a little bit hitting your shots. Same thing in volleyball.
    0:38:55 The center may be a little bit off. The hitters might be a little bit off. The passers could be off.
    0:39:00 So your offense may not be doing great, but you can always play defense.
    0:39:06 And so we’ve always thought if we can be a great defensive team, we’re always going to have a chance to win.
    0:39:10 And again, remember, I started off as a football coach and I was on the defensive side.
    0:39:17 And if you can stop your opponent, you got a chance to win. And so I think that’s the most important thing.
    0:39:23 But when it comes down to winning the close matches and the big matches, who’s going to step up and take it over?
    0:39:30 And typically they’re going to do that offensively, but you can’t always plan for that and always doesn’t go well.
    0:39:34 And maybe your best players have an off night and somebody else steps up.
    0:39:44 And if you’re a coach, is it harder to prepare to play a great offensive team or a great defensive team?
    0:39:51 In volleyball, a great offensive team, you can match them by they side out, you can side out.
    0:39:54 So we’re basically washing each other out.
    0:40:00 What I found in my early years playing against Penn State and Russ Rose, who’s the Hall of Fame coach,
    0:40:04 they were always a great defensive team. And this is in the 90s.
    0:40:10 And every time we played them, I could see how frustrated our players would get because they couldn’t put the ball away.
    0:40:14 They couldn’t pass their serves. They couldn’t hit around their block.
    0:40:23 And it’s deflating. And again, another great example would be a football team that can’t run the ball and they just have to throw.
    0:40:28 And all of a sudden now you’ve got them. The defense is more frustrating and harder to play against.
    0:40:32 And it’ll take away people’s confidence faster than anything.
    0:40:47 I have a very tactical question that I am very curious about that I picked up in your book, which is I want you to tell me the three best motivational movies to show to players.
    0:40:58 This year, Gladiator 1 would be one of them, which I actually, before I retired, I have a clip about mindset for this year.
    0:41:05 So Gladiator was one of them. I really believe, and I actually made a coaching video from the first Top Gun.
    0:41:15 There’s so many analogies in there about teams and about performance, about sticking with it, about performing under pressure.
    0:41:22 And I actually took a bunch of edits and made a coaching video that I would show at coaches clinics about,
    0:41:28 this is all the things we can apply in Top Gun to coaching. And so that would be the second one.
    0:41:35 So those are older movies. Of course, some of our players, they would never seen Gladiator, but when Gladiator 2 came out, they all went and saw it.
    0:41:43 So I’m like, now you got to go watch Gladiator 1. And I think the third movie that I’ve actually used quite a bit is Hoosiers.
    0:41:52 And Hoosiers is another really good coaching movie and a team building movie and a lot of great lessons in there.
    0:41:57 I can see everybody going on Netflix right now and writing those three.
    0:42:08 You have very interesting mantra about two points better, which I think the gist of it is two points is all it takes to win these matches, right?
    0:42:08 Yeah.
    0:42:19 And so, first of all, correct me if I’m wrong. Like what’s the implication of a mantra that two points better is the key?
    0:42:26 Yeah. So if you look at our match, we lost to end the season. We won the first two games by more than two points.
    0:42:36 We lost the last three by two points each. So that’s how close we were. All we needed was to get two more points and we would have won that match and play for the national championship.
    0:42:43 A few years ago, we had a frustrating season. This is about four or five years ago.
    0:42:53 And so I was looking back and studying and analyzing and I looked at how many two point games we won and how many two point games we lost.
    0:43:05 And it was three to one that we lost all these close games. So we put this big poster in our weight room and said two point wins, two point losses.
    0:43:11 We got to find a way to start winning closer games. And that’s where that win by two points came out.
    0:43:19 And so we need a little lesson in mantras here because most companies have mission statements that are 50 words long.
    0:43:33 Like we endeavor to create world-class products, increase customer satisfaction, enable our employees to self-actualize their goals while maintaining a reasonable return for our shareholders and kill as few dolphins as possible.
    0:43:39 So that’s your typical McKinsey mission statement. So what is it that makes a mantra magical?
    0:43:49 Well, you call it a mantra. We call it a theme. And I’ve always believed we have to have themes because again, you just went on and on and on there, right?
    0:44:02 Remember these kids today, their attention spans about seven seconds. So you have to have a theme and we want to put it on a shirt, in their notebooks, on their bag tags, in their lockers.
    0:44:07 So this is our theme or themes that we’re working on and we’re going to use this year.
    0:44:12 And that would actually be another chapter in the book is all the themes that we’ve used over the years.
    0:44:16 We’ve come up with some good ones. I actually have a patent on one of them.
    0:44:21 I got a patent in Nebraska. It’s called wheel feel with each other for each other.
    0:44:29 And we grabbed that and actually Adidas used it one year in their marketing campaign with each other for each other.
    0:44:31 So you call them mantras. I call them themes.
    0:44:36 I’m beginning to sense that this is going to be a really good book.
    0:44:42 When you talk to the University of Nebraska Press, have you already signed with them?
    0:44:44 My first book was with them. Yes.
    0:44:52 And so I don’t know if I’m technically signed with them or not, but they’re very selective on what books they print.
    0:44:57 They’re not a New York publishing firm, but they’re very selective. It’s prestigious to get with them.
    0:45:06 If you get Madison’s grandparents’ tickets to this game, we’ll introduce you to a real New York publisher that would, you know.
    0:45:07 Okay.
    0:45:08 All right.
    0:45:09 What’s that for a deal?
    0:45:16 I’ll tell you what. I’ll give them my two tickets for one of them. There’s two Madison’s. I’ll give them my two tickets for one of them. So they’re covered, Madison.
    0:45:18 Okay. Two more questions.
    0:45:26 First of all, you introduced this concept of ultimate trust. So what is ultimate trust and how do you develop it?
    0:45:34 Ultimate trust is something we talk about, but it’s very hard to define and you have to be able to see it.
    0:45:42 Our best teams played with ultimate trust. And guy, you have to remember, volleyball is six people, 900 square feet.
    0:45:48 The ball’s moving over. I actually had a physicist chart this out.
    0:45:57 Because volleyball, when the ball’s spiked by the hardest hitters and the defensive players are so close, it’s the equivalent of 160 mile an hour fastball.
    0:46:06 That’s how fast it’s getting to those diggers. So things are happening very fast. So there has to be tremendous trust among the six people out there.
    0:46:16 The teams that have that magical feel and everything just flows, we call that ultimate trust. And the best way I can define it for you is this.
    0:46:26 I have two pictures I show our team. One is the first test way back in the early 1900s of the first bulletproof vest.
    0:46:31 So the guy taking the bullet had ultimate trust that that vest was going to work.
    0:46:38 And then the other picture we have in our locker room, we blew it up, is a dog leading a blind dog.
    0:46:44 And they really connect with that because all of our players love dogs. Many of them have dogs.
    0:46:50 And if they can play like that dog leading the blind dog, that’s ultimate trust.
    0:46:56 And so, like I said, it’s hard to define, but when it happens, it’s beautiful and you can see it.
    0:47:04 And our last two years, if you watched our team play, and I’ve had so many people just say, I love watching your team.
    0:47:10 These aren’t even Nebraska fans. That’s the greatest compliment because they see that ultimate trust that they’re playing with.
    0:47:14 I lied. I have two more questions. Okay. I just thought of this.
    0:47:19 I have watched my kids play volleyball and I want to show total ignorance of volleyball.
    0:47:29 But what is the like theoretical perfect amount of serves that are out or hit the net?
    0:47:33 If you make it so that every serve goes in, probably those serves are too easy to return.
    0:47:39 But if you hit the ball out or into the net, then the pressure was always on you.
    0:47:41 They didn’t have to return it at all.
    0:47:53 So there must be some number where you say, we would like to have a certain percentage of serves not successful because at least we’re trying to attack with our serve.
    0:48:01 Yeah. So we were the number one serving and passing team in the country last year after they did all the stats at the end of the year.
    0:48:07 Our error percentage, the best, it’s about 8%.
    0:48:18 If you can serve tough with about 8% error, so think 9 out of 10 go in and you’re really attacking the ball, you’re going to be at a really high level, at our level.
    0:48:23 There’s teams that were in the final four that had a higher error percentage than that.
    0:48:26 In high school, it may even be lower than that.
    0:48:30 Maybe it’s 8 out of 10 misses or 7 out of 10 just because of the skill level.
    0:48:37 Of course, if you go down to fourth and fifth grade, you’re hoping maybe one out of five go in over the net.
    0:48:39 So it just depends on the levels.
    0:48:41 And then men’s volleyball is completely different.
    0:48:50 They’ll accept 30% error, no problem, because they have to go for it because the men are so good at siding out and they’re so hard to stop.
    0:48:52 So they have to take more risk.
    0:48:59 But again, you asked me, defense or offense, if we’re a great defensive team, we want to give our defense a chance.
    0:49:02 And so 9 out of 10 is a great ratio.
    0:49:07 And if you can do that with good serves, you’re going to have a really good chance to win.
    0:49:15 So what is your advice to a young coach listening to this about how to develop as a good coach?
    0:49:21 First of all, surround yourself with people that are going to help you.
    0:49:25 Figure out who can help me be the best and develop and grow as a coach.
    0:49:27 Is it other coaches?
    0:49:29 Is it somebody in business?
    0:49:30 Is it somebody from your church?
    0:49:31 Who is it?
    0:49:32 Surround yourself with mentors.
    0:49:40 Second thing is, listen to podcasts, read, study other people, study successful people and study other successful teams.
    0:49:45 One team that I’ve studied, and again, I grew up in Chula Vista, California.
    0:49:53 And our beach that we would go to, and Chula Vista is right on the border across from Tijuana, we would go over to Coronado.
    0:49:56 As a kid, guess who I saw running down the Coronado Beach?
    0:49:57 The Navy SEALs.
    0:49:58 That’s where their headquarters are.
    0:50:04 So I became infatuated with the Navy SEALs and how they train and what they do.
    0:50:17 And I actually, in about 2010, a former Navy SEAL commander retired in Lincoln, and I went and got him and had him work with our team and work with me and work with our leaders because they’re doing it at the highest level.
    0:50:19 So that’s just one example.
    0:50:21 Can we study those other teams?
    0:50:24 And the third thing I would say is you’ve got to have a hobby.
    0:50:32 And this is the thing that people that are driven and consumed, and again, in coaching, it’s 24-7, 365.
    0:50:37 But at some point, you’ve got to let your mind go and you’ve got to think about something else.
    0:50:38 So what hobby do you have?
    0:50:39 Is it hitting golf balls?
    0:50:41 I went and got my pilot’s license.
    0:50:45 When I fly planes, I’m not thinking about Nebraska volleyball.
    0:50:47 I hope not.
    0:50:48 You’re thinking about flying the plane.
    0:50:55 So I really think you have to have some type of hobby, distraction, something else.
    0:51:01 But also with that, when I learned how to fly, I was getting coached by somebody else.
    0:51:04 So I figured out what helps me learn.
    0:51:12 How do I want this guy to interact with me on learning how to fly a plane is very stressful?
    0:51:16 So how can I stay calm, trust my training?
    0:51:18 How do I want to be coached?
    0:51:21 So it really helped me figure out how do I want to coach my team?
    0:51:26 And so those are three things right at the top of my head of what advice I would give.
    0:51:27 Okay.
    0:51:29 I promise you, this is really the last question.
    0:51:30 I promise.
    0:51:37 I just want to know, looking back at hindsight, with all that you figured out, if you were back
    0:51:43 in 2023 and you were playing against Texas and Texas just scored nine string points against you
    0:51:48 and you called timeout, what would you say to your team if you could do it all over again?
    0:51:50 The same thing I told them.
    0:51:53 Hey, take a deep breath.
    0:51:53 We’re okay.
    0:51:56 We’re going to focus on winning the next point.
    0:51:58 And again, that’s a mantra for us.
    0:52:03 We’re just going to focus on winning the next point because remember I told you, they don’t
    0:52:07 handle failure very well and that starts building and then they lose their confidence.
    0:52:09 And that’s what I’ve always tried to do.
    0:52:13 In the biggest matches, you have to be the most calm and reassuring.
    0:52:18 Now, there’s other times when we’re playing a team that we should be beating, we’re better
    0:52:20 than, and I haven’t done that.
    0:52:24 I’ve gone the other route of getting after them and getting in their face a little bit.
    0:52:29 But in the big matches, on the biggest stage, you’ve got to be the most calm and the most
    0:52:30 supportive and make them believe.
    0:52:35 Which comes back to this concept of coaching from love as opposed to anger.
    0:52:36 Exactly.
    0:52:39 And I, over my career, I got a lot better at it.
    0:52:44 And it’s something that I’ve worked really hard at, but I knew I had to do it.
    0:52:51 And I think, again, go online and Merit Beeson, who just graduated and is playing pro volleyball.
    0:52:53 She does a great job of explaining.
    0:53:00 She was one of our captains of explaining how I adapted to her teams the last two years.
    0:53:03 And I think that would be another thing in a young coach.
    0:53:06 You’ve got to be willing to adapt to whatever you need.
    0:53:07 Like, guys, I don’t know.
    0:53:09 I made TikTok videos with our players.
    0:53:11 To me, it’s nuts.
    0:53:12 Yeah.
    0:53:14 Oh, they get over 5 million views.
    0:53:15 Yeah.
    0:53:18 But they just take my phone.
    0:53:19 They do something.
    0:53:20 They edit it.
    0:53:22 And I get approval.
    0:53:23 Like, is this okay?
    0:53:27 But, again, it’s way out of my comfort zone.
    0:53:32 But you have to be able to adapt to whatever group you’re coaching.
    0:53:36 John Cook, thank you so much for spending this hour with us.
    0:53:39 It’s been just delightful and remarkable.
    0:53:45 And there are many lessons in your book that apply to life and business in general.
    0:53:48 And I really enjoyed this.
    0:53:50 And we have some Nebraska fans listening.
    0:53:54 And I’m sure they’re just doing backflips right now.
    0:53:58 So, Grandpa and Madison, why don’t you come on and say goodbye to John Cook?
    0:54:00 All right.
    0:54:00 All right.
    0:54:02 You guys, you doing okay?
    0:54:04 That was amazing.
    0:54:05 It was amazing.
    0:54:06 Yeah.
    0:54:08 You got two tickets out of it.
    0:54:14 So, I’ll just cruise around and sit in my seats.
    0:54:15 I’ll just be right across for yours, Art.
    0:54:17 You keep those.
    0:54:20 I’ll pay some extra money off of the secondary market.
    0:54:20 I’ll be there.
    0:54:22 I’ll be there.
    0:54:25 Anyway, Madison, we’ll stay in touch as we get closer.
    0:54:26 Okay.
    0:54:30 I’m going to get ribeyes from Nebraska for life for that.
    0:54:31 Wow.
    0:54:37 All right, John Cook, thank you very much.
    0:54:38 Thank you, John.
    0:54:40 This is a Remarkable People podcast.
    0:54:43 And thank you from all of us here.
    0:54:47 And I want to thank, of course, Madison Neismar for making this happen.
    0:54:50 And Grandpa for helping make this happen.
    0:54:55 Tessa Neismar, who is another girl from Nebraska who helps us with this.
    0:54:59 And our sound design is Shannon Hernandez and Jeff C.
    0:55:01 This is the Remarkable People podcast.
    0:55:06 And go Big Red, I guess, is the way to sign this off.
    0:55:13 This is Remarkable People.

    Can a volleyball coach’s mindset transform your leadership approach? In this captivating episode of Remarkable People, Guy Kawasaki talks with legendary volleyball coach John Cook, who led Nebraska to four national championships and more Division I wins than any other coach this century. Cook shares his coaching philosophy centered on “coaching with love” rather than anger, and reveals how social media has dramatically changed athlete psychology. Discover his three essential questions every leader should ask daily, his insights on building ultimate trust, and why having a clear “theme” drives team success better than lengthy mission statements. Whether you’re building a business or mentoring others, Cook’s wisdom on creating exceptional culture will transform your approach.

    —

    Guy Kawasaki is on a mission to make you remarkable. His Remarkable People podcast features interviews with remarkable people such as Jane Goodall, Marc Benioff, Woz, Kristi Yamaguchi, and Bob Cialdini. Every episode will make you more remarkable.

    With his decades of experience in Silicon Valley as a Venture Capitalist and advisor to the top entrepreneurs in the world, Guy’s questions come from a place of curiosity and passion for technology, start-ups, entrepreneurship, and marketing. If you love society and culture, documentaries, and business podcasts, take a second to follow Remarkable People.

    Listeners of the Remarkable People podcast will learn from some of the most successful people in the world with practical tips and inspiring stories that will help you be more remarkable.

    Episodes of Remarkable People organized by topic: https://bit.ly/rptopology

    Listen to Remarkable People here: **https://podcasts.apple.com/us/podcast/guy-kawasakis-remarkable-people/id1483081827**

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  • Is Elon Musk Failing Shareholders? Teaching Kids About Money, and Scott’s Wildest Fan Encounter

    AI transcript
    0:00:04 There’s over 500,000 small businesses in B.C. and no two are alike.
    0:00:05 I’m a carpenter.
    0:00:06 I’m a graphic designer.
    0:00:08 I sell dog socks online.
    0:00:12 That’s why BCAA created One Size Doesn’t Fit All Insurance.
    0:00:15 It’s customizable based on your unique needs.
    0:00:18 So whether you manage rental properties or paint pet portraits,
    0:00:23 you can protect your small business with B.C.’s most trusted insurance brand.
    0:00:28 Visit bcaa.com slash smallbusiness and use promo code radio to receive $50 off.
    0:00:29 Conditions apply.
    0:00:32 Scientists find weird kinds of life all the time.
    0:00:35 And normally they can run experiments.
    0:00:38 If I hypothesize, life can live in bleach.
    0:00:41 Well, I can get bleach and see if life lives in it.
    0:00:47 But what if the weird thing about the life they find is that it lives for millions of years?
    0:00:48 Time!
    0:00:50 I don’t have any control over that.
    0:00:52 I can literally do nothing with time.
    0:00:54 This week on Unexplainable,
    0:00:56 Intra-Terrestrials.
    0:00:58 Aliens on Earth.
    0:01:00 Deep beneath the seafloor.
    0:01:04 Follow Unexplainable for new episodes every Wednesday.
    0:01:14 Buying a house has long been considered the best way to build wealth and move into true adulting.
    0:01:15 Isn’t it?
    0:01:17 I mean, at least that’s what society wants us to think.
    0:01:20 Got to get a Birkin, got to get a home, you know.
    0:01:24 Okay, the handbag you can probably manage without.
    0:01:26 But what about a house?
    0:01:30 Surely that’s actually good, right?
    0:01:34 We’re going to find out this week on Explain It To Me.
    0:01:38 New episodes every Sunday morning, wherever you get your podcasts.
    0:01:44 Welcome to Office Hours with Prof G.
    0:01:48 This is the part of the show where we answer your questions about business, big tech, entrepreneurship,
    0:01:49 and whatever else is on your mind.
    0:01:52 Today, we have two great listener questions lined up.
    0:01:56 And then after the break, we’re continuing our new segment, the Reddit hotline,
    0:01:58 where we pull questions straight from Reddit.
    0:02:02 If you’d like to submit a question for next time, you can send a voice recording to
    0:02:03 officehours at profgmedia.com.
    0:02:07 Again, that’s officehours at profgmedia.com.
    0:02:11 Or if you prefer to ask on Reddit, post your question on the Scott Galloway subreddit.
    0:02:14 Jesus Christ, that’s something I never thought I would say, Scott Galloway subreddit.
    0:02:17 And we just might feature it in our next episode.
    0:02:18 What a thrill!
    0:02:20 Let’s bust right into it.
    0:02:23 First question, I have not heard or seen these questions.
    0:02:26 Hey, Prof G.
    0:02:32 Given the recent performance of the stock and his dual roles as the CEO of Tesla and
    0:02:38 Chief Doge Doge Doge, is there a point where Elon’s divided attention and or ever-increasing
    0:02:43 negative public perception become a violation of his fiduciary responsibility to shareholders?
    0:02:44 Thanks for all you do.
    0:02:45 Jacob in Kansas City.
    0:02:47 Jacob in Kansas City.
    0:02:50 So yeah, but he doesn’t have a board.
    0:02:52 So this is how corporate governance works.
    0:02:53 Shareholders elect the board.
    0:02:56 And the board really has two jobs.
    0:02:58 They’re supposed to, one, what does it mean to be a fiduciary?
    0:03:00 I love the word fiduciary.
    0:03:01 I think it’s a fantastic word.
    0:03:05 And that is once you have your deal, once you say, okay, I’m getting paid X,
    0:03:09 your job is then as a fiduciary to represent the interests of others.
    0:03:11 I love that.
    0:03:15 When someone asks you to be their fiduciary for their estate, that’s a real compliment
    0:03:18 because what they’re saying is, I think you can represent other people.
    0:03:20 I think you have the skills to represent somebody else.
    0:03:26 And also you have the integrity to think about or look at the lens of decision-making through other people.
    0:03:27 It’s very difficult.
    0:03:31 Other than instincts, one of the things that separates us from the animals is that we have
    0:03:33 the ability to be just better fiduciaries.
    0:03:34 We can represent society.
    0:03:36 We can represent people that we will never meet.
    0:03:39 I mean, I guess the board really has two jobs other than being good fiduciaries.
    0:03:43 And then where you become a good fiduciary is you kind of have two things.
    0:03:47 One, the chair of the audit committee just needs to make sure there’s not fraud, no surprises.
    0:03:51 And then the other two real responsibilities of the board are one, when to hire and fire
    0:03:55 the CEO and two, when to sell and if and when to sell the company.
    0:03:59 But your primary job is to make sure you have the right guy or gal running the company.
    0:04:01 That’s the most important decision they make.
    0:04:05 I have found with almost every company I’m with, if you have the right guy or gal, things
    0:04:06 work out.
    0:04:08 If you don’t have the right guy or gal, it’s just not going to go well.
    0:04:11 A good board can’t save a bad CEO.
    0:04:14 A bad board can probably fuck up a good CEO.
    0:04:21 But it really is about finding the right person and then having the sober conversation with other
    0:04:27 board members who the CEO tends to weaponize and play golf with and help get their kids into
    0:04:28 the school he went to.
    0:04:31 And before you know it, you have a board that is just their buddies.
    0:04:33 I have a four-year statute of limitations.
    0:04:38 I leave every board within four years because I want to do a series of different things.
    0:04:40 And I also find that boards need churn.
    0:04:44 And on every board I’ve been on, there’s at least one or two people, if not three or four,
    0:04:48 that are golfing buddies with the CEO and just don’t objectively evaluate the CEO.
    0:04:51 And the CEO likes that because they don’t want to be objectively evaluated.
    0:04:55 They want a board that’s going to continue to increase their compensation and agree with
    0:04:56 them and provide advice.
    0:04:57 Is that fair?
    0:04:58 Some CEOs do like pushback.
    0:05:00 Probably somewhere in the middle.
    0:05:05 Anyways, in the case of Tesla, the board there has made so much fucking money.
    0:05:11 They would let him, I don’t know, kill puppies on live TV.
    0:05:13 And they’d say, oh, he’s a genius.
    0:05:16 That’s a strategy around autonomous driving.
    0:05:19 The chairman of the board, I think, has made $150 or $200 million.
    0:05:23 So her job is just to make excuses for his behavior.
    0:05:28 He basically engaged in market manipulation or securities fraud.
    0:05:33 He said that he was taking the company private at $420 a share and that the funding was secured.
    0:05:35 And the stock skyrocketed.
    0:05:38 And then it ended up that, what do you know, he was probably on ketamine.
    0:05:39 That was a lie.
    0:05:41 And then the stock fell.
    0:05:47 So anyone who bought the stock thinking that was credible information is owed money by Tesla,
    0:05:50 whose CEO was lying to them.
    0:05:54 And what does the board do of 499 of the S&P 500 companies?
    0:05:56 They fire this area, boss, you’re in trouble.
    0:06:00 At a minimum, they would say, you need to go into rehab and maybe we give you a job back, but unlikely.
    0:06:03 Anyways, they have not done their job.
    0:06:04 They’re not fiduciaries.
    0:06:08 They made so much money that they’re basically the worst board now.
    0:06:09 He could absolutely leave Tesla.
    0:06:10 Why?
    0:06:14 Because to his credit, he does what a good CEO is supposed to do.
    0:06:17 And that is he builds a really strong team.
    0:06:23 Supposedly, the way they describe him at Neuralink or at SpaceX now is he shows up,
    0:06:26 gives a bunch of speeches, kicks over a bunch of trash cans,
    0:06:28 and then he leaves and they get back to work.
    0:06:29 He’s clearly a visionary.
    0:06:33 But what people don’t appreciate or what he doesn’t get the credit he deserves for
    0:06:38 is that he clearly can put together really talented management teams such that even when he’s off,
    0:06:43 impregnating anyone he can find at a conservative CPAC conference
    0:06:45 such that they can give birth to someone he will ignore
    0:06:48 and then ultimately get sued for sole custody of that child because he hasn’t seen them,
    0:06:52 that, all right, he’s able to do all these different things
    0:06:54 because he’s put in place competent management teams.
    0:06:57 Now, having said that, typically what happens in competitive markets
    0:07:02 is that a company establishes differentiation based on technology or distribution channel,
    0:07:06 which translates to margin power, which translates to above market shareholder gains,
    0:07:10 and then the rest of the market goes, huh, maybe electric cars are the thing, huh,
    0:07:12 maybe tune-ups over the airwaves are something, huh,
    0:07:16 maybe we can come up with better battery technology, see above BYD,
    0:07:19 and the differentiation gets reverse engineered out,
    0:07:23 the margins or kind of irrational margins get starched out,
    0:07:26 and the company just trades within a normal trading range.
    0:07:28 The majority of car companies traded somewhere between, I don’t know,
    0:07:31 six or eight and 20 times earnings,
    0:07:33 and this company, see above, is trading at 144,
    0:07:37 and that’s what I think we’re about to see over the next 12 months.
    0:07:38 This is not financial advice.
    0:07:40 I have been wrong on Tesla over and over and over,
    0:07:43 but if this stock was trading at the same multiple as other car companies,
    0:07:45 it’d be trading at about 14 bucks a share,
    0:07:47 and I think today it’s at 260 or 270.
    0:07:51 So, yes, they have violated their fiduciary duty.
    0:07:52 Thanks for the question.
    0:07:54 Question number two.
    0:07:57 Hello, my name is Clara.
    0:07:58 I’m from California.
    0:08:05 I have a 15-year-old grandson who is desperate to learn something about money.
    0:08:07 He wants to learn about money.
    0:08:14 Unfortunately, neither his parents nor I have a clue as to how to guide him.
    0:08:18 His school is of no use in that regard.
    0:08:20 What do you suggest?
    0:08:22 Thank you.
    0:08:23 Clara, I love this question.
    0:08:24 15.
    0:08:27 So, my son is 14, and he’s constantly asking me,
    0:08:28 how can I make some money, Dad?
    0:08:32 And I sit down with him, and I say, all right, let’s go to ChatGPT,
    0:08:35 and let’s say, all right, I’m a 14-year-old.
    0:08:36 I have a little bit of money.
    0:08:37 I’m good with technology.
    0:08:41 I’m super into football, soccer.
    0:08:42 How can I make extra money?
    0:08:48 And it comes back with five ideas, including buying shirts, unprinted shirts, football shirts,
    0:08:53 and then figuring out a way to embroider them or put cool stuff on them, using Shopify for the back end.
    0:08:55 And we’ll do this together.
    0:09:02 And then another idea was the AI suggested that we buy sneakers during sneaker drops at Nike
    0:09:04 and then try and resell them.
    0:09:05 And you have to have some money.
    0:09:08 Some of the times the sneaker drops are $150 or $200.
    0:09:10 But he learns about it.
    0:09:11 He learns about taking risks.
    0:09:16 He learns about trying to examine the market, look at data, customer service.
    0:09:18 You’ve got to ship those shoes once you get them to a buyer.
    0:09:20 How much money did he make?
    0:09:21 Well, it ends up with shipping and everything in taxes.
    0:09:24 We lost a little bit of money, but that’s a life lesson, too.
    0:09:28 So I think the way you learn about money to a certain extent, if you’re talking about money,
    0:09:30 is you start a little bit of a small business.
    0:09:31 I’ve been an entrepreneur my whole life.
    0:09:34 I started walking dogs when I was his age to try and make some money.
    0:09:38 Now, in terms of learning the markets for money when you’re 15,
    0:09:43 I don’t know if it’s too early for him to be reading the front page of the Wall Street Journal,
    0:09:47 but I love that you’re involved in your grandson’s life.
    0:09:48 What a gift.
    0:09:51 What a gift for you and what a gift for him that you’re thinking this way.
    0:09:56 So to the extent that he’s willing, read the front page of the Wall Street Journal,
    0:10:01 those little bullets, and then discuss them with him and say, you know, what does it mean?
    0:10:02 What are earnings?
    0:10:05 Also, I think an interesting way to learn about the markets.
    0:10:07 When I was 13, I wasn’t very popular.
    0:10:08 I know that’s hard to believe.
    0:10:09 I know that’s hard to believe.
    0:10:13 And my mom’s boyfriend gave me $200.
    0:10:15 By the way, he had his own family.
    0:10:18 We were the second family, but that’s an entirely different story.
    0:10:23 But he gave me $200 and said, if I get back next weekend and you haven’t bought stock,
    0:10:26 go to one of those fancy brokerages in Westwood, I want my $200 back.
    0:10:31 So I went down to Dean Witter Reynolds, I think it was, and I met this guy named Cy Saro.
    0:10:33 This was literally 45 years ago.
    0:10:36 And I walked in with $200 and I ended up buying.
    0:10:38 He came out, introduced himself.
    0:10:41 We bought 12 shares or 16 shares in Columbia Pictures.
    0:10:46 And I used to call him from the Emerson Junior High phone booth and ask what the stock had
    0:10:46 done that day.
    0:10:49 And then he would give me a lesson on why the stock moved.
    0:10:51 He said, okay, there’s buyers and there’s sellers.
    0:10:56 When there’s more buyers one day than there are sellers, the sellers get to increase the
    0:10:57 price or the buyers.
    0:11:02 Actually, the buyers have to increase their bid such that they can attract more sellers
    0:11:03 into the market to sell those shares.
    0:11:05 And then where they two meet is the market.
    0:11:07 And these platforms are market makers.
    0:11:08 That would be a lesson.
    0:11:11 And then he would say, okay, I would say, why is the stock up today?
    0:11:14 He said, well, Close Encounters of the Third Kind is a hit.
    0:11:15 I’m dating myself.
    0:11:16 Why is the stock down today?
    0:11:17 Well, Casey’s shadow is a bomb.
    0:11:19 And I started learning about the market.
    0:11:24 So maybe reading together the front page of the Wall Street Journal and discussing it,
    0:11:30 maybe buying a couple of shares in stock, download the public app and buy a couple of
    0:11:33 shares in companies and say, why do you want to buy this company and discuss it with them?
    0:11:38 I’ve made a good amount of money starting and selling businesses, but where I’ve made a crazy
    0:11:40 amount of money is investing in stocks.
    0:11:46 And I go back to that moment where I walked into a brokerage and I used to call Cy from
    0:11:48 Emerson Junior High School and talk about the markets.
    0:11:52 To give me a love for the markets, it got me thinking about investing early and often.
    0:11:56 And just a quick side note, Cy and I are still in touch.
    0:12:02 45 years later, every Christmas, my birthday, Father’s Day, he sends me a nice text message
    0:12:05 to say that he hopes I am doing well.
    0:12:07 Anyways, Clara from California.
    0:12:07 Thanks for the question.
    0:12:10 We have one quick break.
    0:12:14 And when we’re back, we’re diving into the depths of Reddit, the bowels of Reddit.
    0:12:21 Support for Prop G comes from LinkedIn.
    0:12:25 One of the hardest parts about B2B marketing is reaching the right audience.
    0:12:30 And sometimes it feels like the only solution is posting everywhere, paying exorbitant amounts
    0:12:32 of money just to get one company to notice you.
    0:12:36 It’s time for a new strategy so your ads don’t get lost in the noise.
    0:12:40 LinkedIn ads can help by ensuring your message makes it to the right audience.
    0:12:45 LinkedIn has grown to a network of over 1 billion professionals, making it stand apart from
    0:12:46 other ad buys.
    0:12:50 You can target your buyers by job title, industry, company role, seniority skills, and company
    0:12:50 revenue.
    0:12:54 LinkedIn has all the professionals you need to reach in one place.
    0:12:59 So stop wasting budget on the wrong audience and start targeting the right professionals only
    0:13:00 on LinkedIn ads.
    0:13:05 LinkedIn will even give you a $100 credit on your next campaign so you can try it yourself.
    0:13:09 Just go to linkedin.com slash scott.
    0:13:11 That’s linkedin.com slash scott.
    0:13:15 Terms and conditions apply only on LinkedIn ads.
    0:13:23 Support for Prof G comes from Masterclass.
    0:13:27 It’s one thing to be curious, but you really set yourself apart from the crowd by turning
    0:13:30 your curiosity into action and growing for the better.
    0:13:33 With Masterclass, you can learn from the best to become your best.
    0:13:37 You can think like a boss and live like a legend with Martha Stewart.
    0:13:41 You can learn how to invest in the stock markets with investor Ray Dalio and some of Wall Street’s
    0:13:41 best.
    0:13:46 You can learn leadership skills from former CEOs, including Inja Noye, Howard Schultz, Rosalind
    0:13:49 Brewer, Whitney Wolfherd, and more.
    0:13:51 I checked out Masterclass.
    0:13:56 I saw the one with Bob Iger and Martha Stewart and enjoyed them both and learned about the
    0:13:59 creative process for just $10 a month billed annually.
    0:14:04 A membership with Masterclass gets you unlimited access to every instructor, and you can access
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    0:14:13 Right now, our listeners get an additional 15% off any annual membership at masterclass.com
    0:14:14 slash prop G.
    0:14:19 That’s 15% off at masterclass.com slash prop G.
    0:14:21 Masterclass.com slash prop G.
    0:14:28 Support for the show comes from Betterment.
    0:14:32 When investing your money starts to feel like a second job, Betterment steps in with a little
    0:14:33 work-life balance.
    0:14:37 They’re an automated investing and savings app, which means they do the work.
    0:14:41 While they build and manage your portfolio, you build and manage your weekend plans.
    0:14:45 While they make it easy to invest for what matters, you just get to enjoy what matters.
    0:14:50 Their automated tools simplify the complex and put your money to work, optimizing day after
    0:14:52 day and again and again.
    0:14:57 So go ahead, take your time to rest and recharge, because while your money doesn’t need a work-life
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    0:15:01 Make your money hustle with Betterment.
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    0:15:10 Investing involves risk, performance not guaranteed.
    0:15:13 Welcome back.
    0:15:15 We asked and Reddit delivered.
    0:15:16 Let’s bust right into it.
    0:15:22 Our first question comes from Pancake Gold E, as in Goldie Hawn.
    0:15:23 That’s weird.
    0:15:28 They say, okay, what is the craziest fan encounter you’ve ever had?
    0:15:30 Generally speaking, they’re not crazy.
    0:15:35 Generally speaking, they’re super lovely.
    0:15:38 I don’t know if this is a good or a bad thing.
    0:15:41 I had breakfast today and we were talking about addictions.
    0:15:43 I think that every person has a certain level of addiction.
    0:15:46 It’s just important to be in touch with it.
    0:15:47 Addictions are for survival.
    0:15:51 You’re supposed to be addicted to gorging food because there was an absence of salty,
    0:15:52 sugar, and fattery food.
    0:15:56 But now that industrial production has vastly outpaced our instincts, we gorge even when we
    0:15:56 don’t need to.
    0:15:58 But there’s a reason for addictions.
    0:16:03 I’m addicted to the affirmation of others, specifically strangers.
    0:16:04 But I can modulate it.
    0:16:08 I can realize that, okay, what’s really important is to have your friends and family love you.
    0:16:09 That’s like job number one.
    0:16:16 And if strangers don’t like you on threads or on blue sky, you know, is that relevant?
    0:16:17 Yeah, is it meaningful?
    0:16:18 Maybe is it profound?
    0:16:19 Absolutely not.
    0:16:20 I’ve gotten much better at that.
    0:16:23 I don’t get as rattled by strangers’ comments anymore.
    0:16:26 I actually kind of stopped reading the comments.
    0:16:31 I want to write and act as if no one’s reading or as if no one’s commenting.
    0:16:36 It’s like I’ve always thought the people I’ve just so admired, the people at restaurants where
    0:16:40 I’m on vacation and a good song comes on and they get up and they dance on the table like
    0:16:41 no one’s watching them.
    0:16:43 I think you want to live your life that way.
    0:16:46 I think the most successful people in the world have one thing in common, and that is they dance
    0:16:47 as if no one’s watching them.
    0:16:49 I try and remember that when I dance.
    0:16:51 I try and think, well, what happens when I’m home alone?
    0:16:56 Like after when Trump was elected, I literally the next morning spent the entire day, entire
    0:17:01 day experimenting with Xanax and Peroni, or as I call it, my panics method.
    0:17:06 And I found out that I am an outstanding dancer to 80s music.
    0:17:08 The dogs are totally freaked out.
    0:17:09 Didn’t matter.
    0:17:14 I have incredible moves to the straight line beats of George Michael, Tom Petty, R.E.M.
    0:17:19 I mean, I can just fucking, I can just burn up the rug in my pajamas with a Peronian hand
    0:17:25 and with a little bit of Xanax kind of helping daddy, helping daddy give a little bit of that
    0:17:30 rhythm that every 49-year-old white guy has.
    0:17:31 Is that racist?
    0:17:32 Is that racist?
    0:17:34 Anyways, probably, hey, Graham.
    0:17:37 So when I get fan encounters, they’re really nice.
    0:17:38 People come up to me.
    0:17:39 They’re super wonderful.
    0:17:40 They want to take photos.
    0:17:47 Even when they disagree with me, they go, the most common comment I get, hands down, is
    0:17:48 the best comment, and it’s the following.
    0:17:50 And people say the following.
    0:17:53 While I don’t always agree with you, you make me think.
    0:17:55 And that’s, folks, that’s why I’m here.
    0:18:02 If I’m just reinforcing your current beliefs, then I’m just fucking Fox News, tickling your
    0:18:03 censors and dividing America.
    0:18:08 The whole idea is to occasionally, and I do this sometimes, sometimes I’ll say something
    0:18:12 I know coming out of my mouth, I don’t know if that’s right, but it’s provocative and I
    0:18:13 want people to think.
    0:18:17 I want us to look at the issue from all different angles and think, are we getting this right
    0:18:21 and wrong such that we can express different viewpoints and hopefully craft better solutions
    0:18:25 and learn and stretch and strain and damage the muscle in between our ears such that it
    0:18:26 grows back stronger.
    0:18:30 Anyways, I can’t tell you how lovely the encounters are.
    0:18:30 The craziest ones.
    0:18:32 One comes to mind.
    0:18:39 I was a can and this guy came up, this nice guy in kind of a leather jacket.
    0:18:40 And I was saying, why is he in a leather jacket?
    0:18:41 It’s so warm.
    0:18:42 And he came in and he’s like, I love your videos.
    0:18:44 And I had a line actually develop.
    0:18:46 I’m kind of a big deal at can.
    0:18:49 Anyways, and there was a line of people who wanted to say hi or get their book signed.
    0:18:50 So he said, I love your video.
    0:18:50 I’m like, great.
    0:18:51 Do you want to get a picture?
    0:18:52 And he was sort of like, well, okay.
    0:18:54 And we took a quick picture and I said, boss, big line.
    0:18:55 He started asking me a question.
    0:18:58 I cut him off and said, I really apologize, but I got to get through the rest of the line.
    0:18:58 He said, oh, I understand.
    0:18:59 And he laughed.
    0:19:03 And then the next person comes up and goes, you know, that was Jensen Huang, right?
    0:19:07 That’s the CEO of NVIDIA who’s built an amazing company that’s worth more than Intel.
    0:19:07 And I didn’t know what to do.
    0:19:10 And I turned around and went, Jensen, Jensen.
    0:19:11 He’d already walked off.
    0:19:13 He didn’t need to hang out with me.
    0:19:15 Anyways, that was sort of funny.
    0:19:16 Is there another one?
    0:19:18 Is there another one?
    0:19:22 Someone, when I was in, where was I?
    0:19:23 I was in LA.
    0:19:24 I said, wait for a minute.
    0:19:24 Wait for a minute.
    0:19:28 And she pulled her kid out of the car and it was like a three-year-old.
    0:19:33 And she held the baby in her arms and she said, Johnny, Johnny, what do you say?
    0:19:35 What do you say when the radio comes on?
    0:19:38 And he went, go, go, go.
    0:19:43 Because I guess they listen to my podcast in the car and his favorite part is when I say go, go, go.
    0:19:47 So in general, they haven’t been that crazy, but what they have been is lovely.
    0:19:59 And I’ve often said, or I believe that LLM, it’s a shame that LLMs are crawling the online world because I think the online world is, while fascinating and interesting and can be inspiring, there’s a lot of toxicity.
    0:20:00 And it is a bit of a cesspool.
    0:20:04 But on the whole, I find in general, people out in the real world are really nice.
    0:20:06 And I don’t always find that online.
    0:20:10 But nothing that crazy, just mostly really lovely.
    0:20:13 And I really do appreciate when people come up and say hi.
    0:20:14 It’s really nice to me, really rewarding.
    0:20:15 Thanks for the question.
    0:20:25 Next and last question, a faceless void reads, before having kids, where did you find meaning in your life?
    0:20:26 Whoa.
    0:20:31 Before kids, where did you find meaning in your life?
    0:20:35 I don’t think I did.
    0:20:40 I had motivation.
    0:20:44 I grew up without money or without enough money.
    0:20:55 And I quickly learned in a capitalist society that your ability to take care, I would say the reason I’m rich is because of, I mean, there’s a lot of reasons.
    0:21:05 The generosity and vision of California taxpayers and the reason I’m rich in California that led unremarkable kids into UCLA, 76% admissions rate, 2.27 GPA at UCLA, then got into Berkeley.
    0:21:08 So public, I’m a product of public, I’m a product of big government.
    0:21:22 So United States government, being born in America, the United States and all of the sacrifice and commitment people have made to build this incredible infrastructure, this incredible public works, including the University of California, is a key component of my success.
    0:21:30 Two, or maybe it’s the irrational passion for my well-being of my mother, but every day in small and big ways, she told me she loved me.
    0:21:34 And if you do that with a child, I think over time, they start to believe you.
    0:21:35 And I think that’s really important.
    0:21:42 The thing that was most motivating for me was women, specifically two types of women.
    0:21:45 And one’s a hallmark story and the other’s like kind of crass.
    0:21:48 The first is I wanted to take care of my mom.
    0:22:01 I got, my mom got very sick when I was in graduate school and it was the most kind of rattling, traumatizing thing I’ve ever been through because I didn’t, I wasn’t capable of taking care of her because I was in graduate school.
    0:22:10 I didn’t have any money and she was discharged from the hospital early and it was just a very ugly situation and I didn’t know what to do and I felt powerless.
    0:22:17 And that was the first time I really felt this sort of masculine instinct kicking in that I, I’m the man of the house.
    0:22:18 It’s me and my mom.
    0:22:23 I am supposed to take care of my mom who took great care of me and I couldn’t.
    0:22:25 And it was just incredibly humiliating and upsetting.
    0:22:35 And I thought, all right, there’s a lot of things outside of your control in terms of making money, but the things that are in your control, getting your shit together, being disciplined, working really hard, none of which I had done to that point.
    0:22:36 I decided, all right, that’s it.
    0:22:40 I’m going to, I got to get my act together because I got to take care of my mom.
    0:22:50 And from that point forward, quite frankly, I just worked all the fucking time, took a lot of risks and really tried hard to, to make money.
    0:22:51 Money was my motivation.
    0:22:54 And then the second thing also involves women.
    0:22:58 And that is I wanted women in my life.
    0:23:00 I wanted romantic and sexual partners in my life.
    0:23:07 And I noticed I did the math pretty early that women seem to be attracted to guys who had their shit together and could signal resources.
    0:23:14 So for me, my motivation, I don’t really think I had meaning, was making money.
    0:23:17 And I’m not proud of that, but that’s the truth.
    0:23:29 Up until the age of 45, my identity and my motivation was to be economically secure so I could take care of my mom and be more attractive to potential mates and also just be relevant to other men and be interesting.
    0:23:31 It wasn’t even about living well.
    0:23:33 Like I wasn’t addicted to things.
    0:23:37 I didn’t, you know, I never like had fat cars or anything like that.
    0:23:38 I lived well.
    0:23:40 I didn’t deny myself of anything, but it wasn’t, that wasn’t the motivation.
    0:23:48 The motivation for me was relationships specifically to take care of loved ones and to be, you know, to find a great mate.
    0:23:49 That was my motivation.
    0:23:52 I did not have purpose in my life.
    0:23:53 And I kind of knew it.
    0:23:55 I kind of knew, like, I don’t know what this is all about.
    0:23:57 I can’t get enough of anything.
    0:23:59 I can’t get enough of experiences.
    0:24:00 I can’t get enough of money.
    0:24:02 I can’t get enough of dating.
    0:24:03 I can’t get enough of anything.
    0:24:06 I recognized that I was like a hunger monster.
    0:24:16 And the, when I had kids, which I didn’t want to have, I didn’t want to have, I was with a partner who was, you know, higher character, much hotter than me.
    0:24:18 And she basically said, I want to have kids.
    0:24:22 And I said, well, I don’t want to have kids because I don’t want to get married.
    0:24:24 And she said, I don’t need to get married to call my bluff to have kids.
    0:24:28 And boom, we pulled the goalie for like three minutes and she was pregnant.
    0:24:31 The dog is fertile.
    0:24:32 That’s right.
    0:24:38 Anyways, when that kid came rotating out of my girlfriend, I felt tremendous anxiety at first.
    0:24:41 Not like, oh, I have purpose because I, it was a rough time for me economically.
    0:24:44 And I felt shame that I already wasn’t taking care of him.
    0:24:45 Jesus Christ.
    0:24:51 I think about the two hardest moments coming home, not being able to take care of my mom and feeling as if I failed my brand new son.
    0:25:00 But slowly but surely, and Fraser Crane said this on the Fraser show, that you don’t, you don’t immediately love your kids, but you fall in love with them.
    0:25:01 I found that to be true.
    0:25:09 And I have found that caring more about these kids than I care about myself is an unlock.
    0:25:09 It’s relaxing.
    0:25:11 What are you doing this weekend?
    0:25:12 Shit, I don’t know.
    0:25:12 It’s Friday.
    0:25:13 I need to be fucking fabulous.
    0:25:14 I’m a baller.
    0:25:15 I’m living in New York.
    0:25:15 I’m a tech guy.
    0:25:25 I need to be around super interesting people at super interesting venues and be with super hot women and do incredible things.
    0:25:27 And then, okay, next weekend, how do we raise the bar?
    0:25:29 Once you have kids, oh, what are you doing?
    0:25:30 I’m going to soccer practice.
    0:25:30 What are you doing?
    0:25:31 Yeah, it’s the same soccer practice.
    0:25:36 And I’m going to some lame birthday party on Sunday with all the other dads to give their partners a break.
    0:25:39 At first, it’s like, this fucking sucks.
    0:25:41 And then it becomes sort of relaxing.
    0:25:44 It’s like, okay, that’s what I do.
    0:25:45 I’m dad.
    0:25:46 That’s what I do.
    0:25:54 And being involved or caring more about someone else than you care about yourself has been incredibly cathartic for me.
    0:25:56 And I do finally have my purpose.
    0:26:01 And my purpose is to raise loving, patriotic men.
    0:26:04 And I want boys who will find a good partner.
    0:26:07 I try to be really good to their mother.
    0:26:07 I model that.
    0:26:19 I try to motivate them to a variety of things, and I want them to be patriotic, not only to their country, but to the world, to think about the human race, to be good, kind to others, build a better world, and absolutely build a better country.
    0:26:21 And that’s my purpose.
    0:26:25 That will be the last thing I think that will run through my mind is that I want, have I done that?
    0:26:28 Have I tried to model that behavior?
    0:26:29 Have I given them the resources?
    0:26:35 So it’s really nice to feel a sense of purpose in a weird way just to get existential.
    0:26:40 I’m not ready to die, but I feel like it’s no longer going to be a tragedy.
    0:26:42 I feel like I’ve mostly accomplished that.
    0:26:43 We’ll see.
    0:26:44 They’re still young.
    0:26:49 But it just makes the idea, when I was young, the idea of dying was just terrifying to me, as it should be.
    0:27:00 And now I’d like to be around for another 50 years, but if I’m not, I’ve kind of checked the box, and that is I have produced offspring that I think will add more value to the world than I’ve added.
    0:27:08 And at the end of the day, that is really our core and only mission here is to produce, in my view, or make the world a better place.
    0:27:13 And one way you can do that is by producing offspring that are smarter, stronger, faster than you.
    0:27:16 And it’s so nice to have that purpose.
    0:27:20 I’m not saying everyone needs that to have purpose, but that was where I found mine.
    0:27:24 But pre-kids, I didn’t have meaning.
    0:27:25 What I had was motivation.
    0:27:29 And my motivation in a capitalist society was, quite frankly, was money.
    0:27:30 Thanks for the question.
    0:27:34 That’s all for this episode.
    0:27:38 If you’d like to submit a question, please email a voice recording to officehours at proptamedia.com.
    0:27:41 Again, that’s officehours at proptamedia.com.
    0:27:48 Or, if you prefer to ask Reddit, just post your question on the Scott Galloway subreddit, and we just might feature it in our next Reddit hotline segment.
    0:27:58 This episode was produced by Jennifer Sanchez.
    0:27:59 Our intern is Dan Shallon.
    0:28:01 Drew Burrows is our technical director.
    0:28:04 Thank you for listening to the Prop G pod from the Vox Media Podcast Network.
    0:28:09 We will catch you on Saturday for No Mercy, No Malice, as read by George Hahn.
    0:28:15 And please follow our Prop G Markets pod wherever you get your pods for new episodes every Monday and Thursday.

    Scott breaks down whether Elon Musk poses a fiduciary risk to Tesla shareholders. He then offers advice to a grandmother trying to help her 15-year-old grandson understand money.

    In our Reddit Hotline segment, Scott shares the craziest fan encounter he’s ever had and reflects on where he found meaning in life before becoming a father.

    Want to be featured in a future episode? Send a voice recording to officehours@profgmedia.com, or drop your question in the r/ScottGalloway subreddit.

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  • #222 Outliers: Cornelius Vanderbilt — The First Tycoon

    #222 Outliers: Cornelius Vanderbilt — The First Tycoon
    #222 Outliers: Cornelius Vanderbilt — The First Tycoon
    AI transcript
    0:00:04 “Gentlemen, you have undertaken to cheat me.
    0:00:07 I won’t sue for the laws too slow.
    0:00:08 I’ll ruin you.”
    0:00:11 Yours truly, Cornelius Vanderbilt.
    0:00:14 And that quote is the embodiment of the man
    0:00:17 we’re gonna talk about today, Cornelius Vanderbilt.
    0:00:33 – Welcome to The Knowledge Project.
    0:00:35 I’m your host, Shane Parrish.
    0:00:41 If you wanna take your learning to the next level,
    0:00:43 consider joining our membership program
    0:00:45 at fs.blog/membership.
    0:00:48 As a member, you’ll get early access to episodes,
    0:00:51 no ads, including this, exclusive content,
    0:00:54 hand-edited transcripts, and so much more.
    0:00:56 Check out the link in the show notes for more.
    0:01:05 – He began as a teenage ferry captain,
    0:01:08 battling storms in New York Harbor for pennies.
    0:01:11 From these humble beginnings, he clawed his way
    0:01:15 to become America’s most feared and admired magnet.
    0:01:18 Cornelius Vanderbilt didn’t just dominate one industry,
    0:01:22 he conquered three: ferries, steamships, and railroads.
    0:01:27 Rivals called him ruthless, passengers called him unstoppable.
    0:01:29 But Vanderbilt didn’t just build businesses,
    0:01:32 he rewrote the rules, laying the foundation
    0:01:34 for the modern corporation.
    0:01:36 This is his story.
    0:01:38 If you think today’s tycoons are tough,
    0:01:40 wait till you meet the Commodore.
    0:01:43 Most of the research for this episode
    0:01:46 came from reading “The First Tycoon” by TJ Stiles,
    0:01:50 and “Tycoon’s War” by Stefan Dando Collins.
    0:01:51 Remember to stick around at the end of the show
    0:01:54 for my reflections and afterthoughts,
    0:01:57 as well as the lessons you can learn from Vanderbilt.
    0:02:01 If you want to read my highlights from “Tycoon’s War” or “The First Tycoon”,
    0:02:05 you can sign up below fs.blog/membership.
    0:02:09 Just look in the description for this podcast, you can find a link.
    0:02:11 It’s time to listen and learn.
    0:02:15 This podcast is for entertainment and informational purposes only.
    0:02:23 It’s January 4th, 1877.
    0:02:25 A winter wind cuts through New York City,
    0:02:28 where a crowd gathers outside the Church of Strangers.
    0:02:33 Inside, elites await the reading of Cornelius Vanderbilt’s will,
    0:02:37 rumored to be worth $100 million.
    0:02:40 One twentieth of all U.S. currency.
    0:02:44 His legendary willpower and ruthless business tactics
    0:02:47 had forced rivals to pay him to go away.
    0:02:51 Now, the entire city wants to see how the man who once dominated ferries,
    0:02:55 steamboats, and railroads chose to dispense his vast fortune.
    0:03:00 His early life was filled with fistfights, high-speed steamboat duels,
    0:03:04 engine explosions, and numerous near-death experiences.
    0:03:09 His latter days with daredevil harness races and high-stakes financial confrontations
    0:03:11 that even extended to the international scene.
    0:03:17 By the time of his death, virtually every American had paid tribute to his treasury.
    0:03:21 They had passed through his Grand Central Depot on 42nd Street,
    0:03:25 crossed the bridges over the sunken tracks along 4th Avenue,
    0:03:29 or traveled one of the countless ferries, steamboats, or railroads
    0:03:31 he had controlled during his 60-year career.
    0:03:35 Vanderbilt was the precursor to a class of men
    0:03:38 who would wield power within the state so great
    0:03:40 that it would rival the state itself.
    0:03:45 Rockefeller, Carnegie, Mellon, Gould, Morgan,
    0:03:49 all were just beginning their careers when Vanderbilt stood at his zenith.
    0:03:52 They studied him and they followed his example,
    0:03:55 though few would match his impact.
    0:03:58 His admirers called him the finest example of the common man
    0:04:00 rising through hard work and ability.
    0:04:02 His critics, on the other hand, called him ruthless,
    0:04:07 an unelected king who never pretended to rule for his people.
    0:04:11 But Vanderbilt’s significance was more complex, more contradictory,
    0:04:14 than either of his admirers or critics fully grasped.
    0:04:20 His life spanned from the days of George Washington to those of John D. Rockefeller,
    0:04:26 from a rural, agricultural, essentially colonial society in which the term businessman was unknown
    0:04:31 to a corporate, industrial economy that would define America’s future.
    0:04:39 Vanderbilt didn’t just experience that changing time period and watch as the American economy rose around him.
    0:04:43 He was possibly the largest force at the time responsible for the building of it.
    0:04:49 He was the trailblazer in the corporate world and reimagined what a corporation could do.
    0:04:54 Now, to truly understand the Commodore and that world, we must go back to the beginning,
    0:05:04 to Stanton Island, May 27th, 1794, when Phoebe Vanderbilt gave birth to her fourth child naming him Cornelius after his father,
    0:05:07 Although they called the boy Cornelius.
    0:05:20 The Vanderbilt’s descended from Jean Arsene Vanderbilt, who had come to the New World to farm generations earlier.
    0:05:25 On Stanton Island, most of the original Dutch settlers led an inward-looking rural life.
    0:05:30 Americans of British descent often viewed them with distaste.
    0:05:39 As one traveler observed in the 1790s, nothing can exceed the state of indolence and ignorance in which these Dutchmen are described to live.
    0:05:44 Many of them are supposed to live and die without having been five miles from their own houses.
    0:05:51 But there was one distinctive feature of the rural Dutch that would profoundly shape young Cornelius Vanderbilt:
    0:05:53 they farmed for profit.
    0:05:58 While many English-speaking farmers in the region devoted much of their efforts to subsidence,
    0:06:04 Dutch farming was market-oriented and derived its distinct characteristics from Dutch tradition.
    0:06:09 The rural Dutch shared the commercial consciousness of their urban brethren.
    0:06:14 They clattered their wagons into Albany, New Brunswick, and New York to sell their produce
    0:06:16 with a savvy that became their custom.
    0:06:21 When a cobbler refused to return a man’s shoes until he made full payment,
    0:06:25 the frustrated customer wrote in his diary, “He is too Dutch by half.”
    0:06:29 Young Cornelius was born into this tradition of commerce.
    0:06:34 His parents created a household where, far earlier than in remote communities,
    0:06:37 the marketplace strode right through the door every day.
    0:06:43 While farmers living up the Hudson may make just one delivery of crops to riverside merchants,
    0:06:49 in an entire year at harvest time, the Vanderbilt house pulsed constantly with buying and selling,
    0:06:52 borrowing and lending, earnings and debt.
    0:07:00 Cornelius’ father had built or bought a prerogar, a specialized two-masted vessel deployed by the
    0:07:06 dutch for trading and began ferrying neighbors and their produce along the bay in Manhattan.
    0:07:10 Phoebe, Cornelius’ mother, was equally entrepreneurial.
    0:07:16 She was not only the family oracle one 19th century writer declared, she was the oracle of the neighborhood
    0:07:20 whose advice was sought in all sorts of dilemmas and whose judgment had weight.
    0:07:27 Court records show she lent money at commercial rates of interest and once even foreclosed on a
    0:07:34 widow’s mortgage. The widow being her own daughter. Unlike most farmers, they actually lived within
    0:07:40 sight of New York City, where from their Stanton Island home, they could literally see the mass of
    0:07:43 ships in the harbor bringing goods and ideas from around the world.
    0:07:50 The United States that Cornelius was born into was a very young nation.
    0:07:56 Keep in mind that when Vanderbilt was born, George Washington was still early in his second term of the
    0:08:03 presidency. Only five cities held more than 10,000 residents at this time and the percentage of the
    0:08:09 the nation’s four million citizens who lived in towns of at least 2,500 people languished in the
    0:08:15 single digits and would linger there for decades to come. Most Americans live scattered along the
    0:08:19 Atlantic coast with only the bravest pioneers venturing west to the Appalachians.
    0:08:26 By his early teens, Vanderbilt was totally drawn to the water, leaving the fields behind.
    0:08:33 Accounts on this vary, but by the age of 16, he was running his own ferry that was either owned by
    0:08:38 his parents or one that he purchased with a loan from them, promising to help pay the family’s $1,000
    0:08:45 mortgage. His plan to do so? Operate the boat as a ferry between Stanton Island and the growing
    0:08:52 Manhattan. The teenager launched himself into this venture with intensity, charging a shilling each
    0:08:58 way, 12 and a half cents that accumulated with glacial slowness in a vessel that seated just 20
    0:09:04 passengers. Vanderbilt discovered in those daily handfuls of silver, a hunger for money that would
    0:09:11 shape his life from then on. What distinguished his small ferry service from the competition was
    0:09:17 predictability. While other boatmen waited until their crafts filled to their capacity, the teenage
    0:09:24 entrepreneur introduced something uncommon to the New York Harbor, a schedule. His ferry departed at fixed
    0:09:29 times regardless of the passenger count, a self-imposed discipline that transformed water
    0:09:36 transit from casual to reliable service, earning him loyalty and repeat business. This was an extension
    0:09:41 of how he lived his life, with one contemporary at the time saying his life was regulated by self-imposed
    0:09:50 rules, with a fixedness of purpose as invariable as the sun in its circuit. That reliability for service
    0:09:56 extended to the winter months as well. When ice choked the harbor, he was often the only ferryman willing to
    0:10:02 make the crossing. He studied the tides, winds and currents relentlessly and mastered the natural forces
    0:10:10 that other boatmen merely accommodated. In simple terms, he worked harder. During the blinding storms,
    0:10:15 when sleet and snow crashed across the bay, Pearl Street merchants would seek out the gangly team,
    0:10:21 trusting him alone to deliver urgent messages to vessels anchored in the harbor, earning Vanderbilt
    0:10:28 additional income. Not only was he reliable, in these months he was often the only option for
    0:10:32 passengers. And although there was no recorded earnings from this time, presumably he could charge
    0:10:39 a premium for his services. In fair weather, it was a different story and he competed aggressively on
    0:10:44 price, undercutting established operators as a rule. If other ferrymen charged 18 cents,
    0:10:50 Vanderbilt would charge 12. If they dropped to 10, he’d go to 8. This approach, price competition to
    0:10:55 the point of driving out rivals, would become his signature strategy throughout his career, regardless
    0:11:02 of scale or industry. It’s worth pausing here for a second to talk about this. Isidore Sharp,
    0:11:08 the founder of the Four Seasons, said something that has long struck with me. He said, “Excellence is the
    0:11:15 capacity to take pain.” One of the things that sets Vanderbilt and other outliers apart is their
    0:11:22 willingness to tolerate pain. Most people have no capacity to endure pain, financial or psychological.
    0:11:28 And if you can, you can gain a real advantage. It hurt Vanderbilt to cut prices, but it hurt his
    0:11:33 competitors more and he knew it. It hurt Vanderbilt to run his service when he couldn’t see or the
    0:11:40 weather was bad, but he knew it hurt his competitors so much they wouldn’t even operate. It’s hard
    0:11:45 to compete with someone who can tolerate more pain than you. There’s over 500,000 small businesses in
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    0:12:03 needs. So whether you manage rental properties or paint pet portraits, you can protect your small
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    0:12:17 radio to receive $50 off. Conditions apply. Okay, back to our story. Within a year, legend has it
    0:12:22 that teenage Vanderbilt had not only paid off his loan, but had given his parents several hundred
    0:12:28 dollars towards their mortgage. And it is here where we see Vanderbilt evolve from just a hard worker
    0:12:34 into something much more. If his parents had taught him anything, it was that business thrived on
    0:12:39 relationships. Though his hands grew calloused from twisting wooden tillers, the harbour brought him not
    0:12:45 just physical strain, but valuable connections. As he accumulated his modest portion of the ferry’s
    0:12:53 earnings through 1810, 1811, and 1812, Vanderbilt made a pivotal decision: to purchase shares in other
    0:13:01 boats. Shares whose profits he deliberately kept separate from his family obligations. Vanderbilt transformed
    0:13:07 himself from a mere labourer, somebody who worked harder than anyone else, into an investor, or to
    0:13:12 put it plainly, a capitalist. His money was now making money for him, expanding his reach beyond
    0:13:19 what only his two hands could accomplish in a day’s work. As war with Britain loomed and then erupted in
    0:13:25 1812, these investments positioned him perfectly to capitalize on the shifting trade patterns. With a
    0:13:31 British blockade due to the war forcing coastal shipping to rout through Stanton Island and overland
    0:13:36 across New Jersey, Vanderbilt’s position between Manhattan and Stanton Island suddenly became
    0:13:43 a commercial choke point and one where he could take advantage. By 1813, with war raging and his
    0:13:50 reputation growing, Vanderbilt took two decisive steps towards independence. First, he commissioned his very
    0:13:56 own custom-built ferry in New Jersey, funded by his carefully hoarded and growing savings. On Sundays,
    0:14:02 he would sail up the Pasek River to inspect the boat’s progress accompanied by his cousin Sophia Johnson,
    0:14:08 the woman who would become both his bride and his business partner in his life’s enterprise. The boat
    0:14:13 represented his commercial ambitions, while the marriage his declaration of financial independence
    0:14:30 and moved into a small house near the ferry dock. This wasn’t merely a personal milestone but a commercial
    0:14:37 one, the final step in his evolution from wage-earning boatmen to an entrepreneurial force. By his early 20s,
    0:14:43 he had expanded from a single ferry to multiple, including larger schooners used for coastal shipping.
    0:14:48 The locals no longer called him Cornille. He was now widely known as Captain Vanderbilt.
    0:14:57 The War of 1812 accelerated Vanderbilt’s rise. With military necessity came opportunity. In 1814,
    0:15:03 as the United States stood on the brink of defeat with Washington captured and New York bracing for an attack,
    0:15:08 one account says that Vanderbilt secured a lucrative military contract to carry supplies to the city’s
    0:15:14 defensive fortifications. When asked why he won the bid, despite not offering the lowest price,
    0:15:19 the military officer’s response crystallized Vanderbilt’s growing reputation. “Don’t you
    0:15:25 know why we have given the contract to you? It’s because we want this business done and we know you’ll do it.”
    0:15:31 Following the war’s end in 1815, Vanderbilt made a strategic decision that revealed his commercial
    0:15:37 instincts. He moved his young wife from Stanton Island to 93 Broad Street in Manhattan, settling
    0:15:44 into an artisan’s boarding house. The relocation placed him deliberately at the center of commerce
    0:15:49 and information where newspapers published shipping schedules and commodity prices, where auctions
    0:15:56 and exchanges operated daily and were reputations, the true currency of business in that era, were established
    0:16:03 or broken. At his new home in Manhattan, Vanderbilt studied the city’s other merchants carefully. He noted that
    0:16:08 despite the energy and innovations of artisans and small businessmen, New York’s wealthiest citizens were
    0:16:16 primarily general merchants who traded in international cargoes of all types. When the government needed to sell war bonds,
    0:16:22 it turned to merchant financers like John Astor. Vanderbilt internalized this lesson realizing that
    0:16:29 the wealth he desired would require him to trade in cargoes and not just provide its transportation. He
    0:16:35 would have to expand. With the war over, New York erupted in commercial activity and Vanderbilt jumped in
    0:16:42 with both feet. His approach showed both boldness and shrewdness. Rather than remaining in the familiar
    0:16:48 harbor waters, the now 20-year-old entrepreneur reached out to distant ports along the Atlantic coast.
    0:16:53 More importantly, he began seeking partners with greater expertise and capital than he had.
    0:16:59 He joined forces with his brother-in-law, John DeForest, an experienced mariner who commanded the schooner
    0:17:04 Charlotte. Vanderbilt purchased a share in this vessel using it to transport goods between New York
    0:17:10 and the Southern ports before eventually buying full ownership. Vanderbilt also partnered with
    0:17:16 his father and others to finance even larger ferries suitable for open water. These vessels cost approximately
    0:17:24 $750 each. With a small but growing fleet, Vanderbilt aggressively began seeking out market opportunities.
    0:17:29 He raced competitors to Virginia’s oyster grounds, sailed up the Delaware River to purchase shad,
    0:17:36 and ventured up to New Jersey’s Raritan River, hiring horsemen to spread word of his available fish.
    0:17:42 His commercial operations extended to paying boatmen to meet incoming ships while he negotiated cargo sales
    0:17:47 on South Street. His business practices here displayed the same forceful determination that
    0:17:54 characterized his ferry operations. Court records from 1816 and 17 showed that Vanderbilt pursued debts through
    0:18:00 legal channels, though judges frequently determined he had overstated what was owed. This commercial
    0:18:07 aggressiveness served him in America’s rapidly changing economy. The post-war era brought dramatic
    0:18:13 transformations to American commerce. Manufacturing, which had developed during wartime isolation, continued
    0:18:19 expanding. Banking proliferated, especially in New York, and the number of American banks increased from
    0:18:29 208 to 246 in 1815 alone, while currency in circulation jumped from 46 to 68 million. Vanderbilt’s rise
    0:18:35 paralleled these developments completely as he sought opportunities in the growing domestic markets.
    0:18:41 Transportation barriers remained a critical limitation in commerce in early America. In the
    0:18:46 era before railroads, moving goods 30 miles on land could cost as much as shipping them across the
    0:18:52 entire Atlantic Ocean. Even coast-wise shipping in small schooners had limited capacity. Meanwhile,
    0:18:58 up-river journeys against currents could take days or prove impossible, and cargoes would often be lost or
    0:19:04 damaged. The speed of transportation also limited the speed of information and thereby constrained long-distance
    0:19:11 commerce and financial markets. America needed a transportation revolution, and it was coming.
    0:19:17 In 1817, New York State began constructing the Erie Canal, which would connect the Great Lakes to the
    0:19:23 Atlantic Ocean, while steamboats at the same time appeared on the Hudson River, offering transportation
    0:19:31 independent of wind, muscle, or current. By December 1817, the 23-year-old Vanderbilt had substantial assets
    0:19:36 now under his command. He was following the path of other successful merchants he had observed. He was a
    0:19:42 maritime merchant rising through the ranks of America’s most vibrant port city, and specializing in cargo
    0:19:47 trading. And he might well have made a fine living as just another successful merchant in
    0:19:57 Manhattan if it were not for a chance encounter on November 24th, 1817.
    0:20:05 On that November day, Vanderbilt turned at the sound of his name and saw a well-dressed 60-year-old
    0:20:12 man looking at him with sharp, hard eyes. The man introduced himself as Thomas Gibbons, a staggeringly
    0:20:18 rich rice planter from the state of Georgia, now residing in Elizabethtown, New Jersey. Empathetic and
    0:20:23 direct, Gibbons explained that the captain of his steamboat had suddenly left my employ, creating what he
    0:20:29 called my present embarrassment. He needed someone to take charge of the boat on this day, and I expect
    0:20:35 for a few days to come. Would Vanderbilt do it? The vessel in question was named Stowdinger, nicknamed
    0:20:41 the Mouse of the Mountain, or simply Mouse, as it was second-hand and a small craft, smaller at 47 feet
    0:20:47 than even Vanderbilt’s own small boats. But there was one crucial difference between the mouse and Vanderbilt’s
    0:20:55 own boats. It ran on steam. Vanderbilt understood that the steam engine was the most dramatic
    0:21:01 technological breakthrough since the printing press. To move on water at will against the wind or current
    0:21:07 was to transform a fundamental fact of life at this time. A practical education in the steamboat
    0:21:13 business would be worth more than a few days of taking orders from someone else. The engagement with
    0:21:17 Gibbons seemed to set him back a step in his own plans, which surprised his friends and acquaintances.
    0:21:24 Vanderbilt aspired to be more and more, and he was using his own boats to embark on the only obvious
    0:21:30 voyage to wealth that he could see, setting up as a general merchant. As he stepped aboard the mouse
    0:21:35 and inspected its copper boiler, he kept his own boats plying between Stanton Island and Manhattan with
    0:21:41 passengers and produce while his schooners nosed along coastal waters with cargoes of fish and woolens,
    0:21:47 but it’s clear that he saw the advantages of this new connection. What he did not reckon on was how
    0:21:52 well he would get on with Gibbons. “I always thought Thomas Gibbons a very strong-minded man,
    0:21:58 the strongest I ever knew,” Vanderbilt said later. “I don’t believe any human could control him. He was
    0:22:03 the man that could not be led. He could just as easily have been describing himself when he said that.”
    0:22:08 There’s another reason that Vanderbilt may have chosen to run a steamboat for a few days,
    0:22:13 and that was because of the excitement. Running a steamboat at the time would make him the focus of a
    0:22:21 very interesting legal and business war that was the talk of the waterfront. In 1798, Chancellor Robert
    0:22:27 Livingston, who was a powerful New York aristocrat, convinced his friends in the legislature to give him
    0:22:34 a monopoly on steamboat ships in the New York state waters. In 1807, after partnering with inventor Robert
    0:22:40 Fulton, they launched the first commercially successful steamboat service on the Hudson River. Not only did they
    0:22:45 have a monopoly, the New York legislature went so far as to give them the right to seize steamboats that
    0:22:52 entered the New York waters from other states. Enter Thomas Gibbons, the man who would hire Vanderbilt.
    0:22:59 Cunning and commanding, Gibbons’ daughter dryly noted he had a peculiar and singular mode of doing business.
    0:23:08 Gibbons was, in a word, intense. Perhaps an example will help illustrate this point. Once he became embroiled in a
    0:23:14 dispute with his neighbor, a man named Aaron Ogden, what began as a fight over a leased peer escalated
    0:23:20 when Ogden involved himself in Gibbons’ ongoing family disputes. Their conflict reached a breaking
    0:23:28 point when Ogden had Gibbons arrested for an unpaid debt. For Gibbons, this had become an affair of honor,
    0:23:35 and on July 25th, 1816, he stormed over to Ogden’s house, horsewhip in hand. He pounded on the door as
    0:23:41 Ogden ran out the back and scrambled over a fence. Gibbons tacked up a challenge that read, “Sir, I
    0:23:46 understand that you have interfered in a dispute between Miss Gibbons and myself. My friend General
    0:23:52 Dayton will arrange with you the time and place of our meeting.” He later testified in court that if he had
    0:23:56 found him at home he meant to have whipped him, within an inch of his life, in his own house,
    0:24:02 for he knew he was a coward. Ogden, who had no intention of exchanging shots at Don,
    0:24:08 had Gibbons arrested for trespass and for dueling. Gibbons decided to get revenge another way. He would
    0:24:15 drive Ogden out of the steamboat business. There was just one problem. Ogden had become an ally of the
    0:24:21 Livingston Fulton monopoly, receiving a license to operate his steamboat services between New Jersey
    0:24:28 and New York. Taking on Ogden meant taking on the most powerful monopoly in New York State. When Gibbons
    0:24:33 launched his steamboat service in defiance of that monopoly, he needed a ship captain who wouldn’t back
    0:24:40 down under pressure. He needed someone with technical skill, physical courage, and a defiant nature. And he
    0:24:45 found that man in Cornelius Vanderbilt. The Vanderbilt who stepped aboard the mouse
    0:24:50 that November day was walking into a fight bigger than himself, which is saying something because he
    0:24:57 was a very big man. The Livingston Fulton monopoly represented the old economic order. It was a system
    0:25:04 where political power and economic privilege were inextricably linked. What began as a temporary job
    0:25:09 for a few days quickly became a permanent position as Gibbons recognized Vanderbilt’s exceptional
    0:25:14 abilities. Vanderbilt quickly mastered the technical aspects of steam, but more importantly,
    0:25:20 he shared Gibbons’ combative nature and willingness to challenge authority. Indeed,
    0:25:25 the authority that he was challenging was the remnants of what might have been the last aristocratic
    0:25:31 generation in America, one that felt it in their inherited privilege to give themselves state-granted
    0:25:37 monopolies as they saw fit. The monopoly holders did not take this challenge from Gibbons and his
    0:25:43 steamboat captain lightly. They used every legal maneuver at their disposal to shut down the
    0:25:48 operation. They had Vanderbilt arrested repeatedly, they tried to seize the mouse, and they even attempted
    0:25:54 to bribe him to abandon Gibbons. But Vanderbilt remained loyal to Gibbons, partly because of their
    0:25:59 shared temperament, but also because he recognized the larger principle that was at stake. The monopoly
    0:26:05 wasn’t just unfair to competitors and customers, it was slowing the very adoption of steam technology
    0:26:11 that could revolutionize transportation for everyone. Vanderbilt had grown up in a market-oriented
    0:26:17 world and represented the new individualistic American citizen, one that was not guided by honor and
    0:26:24 respecting others’ rights to a legal monopoly. When Ogden’s men physically blocked Vanderbilt from docking in
    0:26:29 New York, he responded in kind, and as Gibbons’ case against the monopoly wound its way through the
    0:26:35 courts, Vanderbilt took charge of the fight on the water. He developed a reputation for daring and
    0:26:40 ingenuity. When monopoly agents came to arrest him, he would sometimes hide in the ship’s engine room, other
    0:26:46 times he would simply outrun them, pushing the steamboat’s engines to their limits and relishing the fight.
    0:26:52 The legal battle culminated in a landmark Supreme Court case called Gibbons versus Ogden.
    0:26:57 And remember, this just started as a neighborly dispute, and now we’re in the Supreme Court.
    0:27:03 In 1824, Chief Justice John Marshall ruled in Gibbons’ favor, striking down the New York steamboat
    0:27:09 monopoly. The decision established that the federal Congress alone had the power to regulate interstate
    0:27:14 commerce. This ruling affirmed that the federal government has the authority to regulate interstate
    0:27:20 commerce and put a limit on state authority. The steamboat monopoly was dead. Vanderbilt and Gibbons
    0:27:26 had won. The victory, however, came with a tragic footnote. Gibbons, Vanderbilt’s cantankerous mentor,
    0:27:31 died shortly after the ruling, leaving Vanderbilt at a crossroads. Would he remain to manage
    0:27:35 Gibbons’ steamboat line for his heirs, or would he strike out on his own?
    0:27:42 After working for Gibbons’ son and heir William for a few years, Vanderbilt decided his apprenticeship was
    0:27:48 over in 1829. He had mastered steam navigation, knew the business inside and out, and earned a
    0:27:53 reputation as one of the country’s top steamboat operators. But it was time to become his own
    0:27:59 master again. Launching his first independent steamboat venture on the busy New York-Philadelphia route,
    0:28:04 he went head-to-head with his former employers. He named his vessel, fittingly, the Independents.
    0:28:10 Vanderbilt’s business model was straightforward, echoing his teenage fairy days: better service,
    0:28:15 lower prices. He cut fares dramatically from three dollars to just one dollar, forcing rivals to
    0:28:21 match or lose customers. By upgrading food and accommodations and running on razor-thin margins
    0:28:27 others couldn’t sustain, Vanderbilt gained an upper hand. He practically lived aboard his boats,
    0:28:34 rarely seeing his wife and family fully committed to his ambitious vision. Critics accused him of ruinous
    0:28:40 competition and cutthroat capitalism. The New York Times later described Vanderbilt accusing him of
    0:28:46 pursuing competition for competition’s sake. During this era, many companies still operated under the
    0:28:53 unspoken code of respecting each other’s territory. But Vanderbilt ignored this tradition entirely. Passengers
    0:28:59 eagerly flocked to his boats, attracted by his unbeatable fares and dependable service. Competitors,
    0:29:04 however, struggled to grasp his simple but revolutionary insight: increased volume could
    0:29:10 more than offset slim or even negative profit margins. By drastically reducing prices, Vanderbilt
    0:29:16 significantly boosted demand, a phenomenon economists now recognize as Jevons paradox, where improved
    0:29:22 efficiency and reduced costs lead to greater overall consumption. And we’re going to get into this a
    0:29:27 little later, but here’s a little foreshadowing for you. He would often get paid not to compete. So when
    0:29:32 I say he’s operating at negative profit margins, it sounds like, well, that doesn’t work. But in effect,
    0:29:38 what would happen is he does get paid not to compete. So it does work. Travel time between New York and
    0:29:44 Philadelphia shrank dramatically to just 10 hours, driven by Vanderbilt’s obsession with speed. This
    0:29:51 didn’t merely make the trip affordable, it made it exciting. What began as basic transportation soon
    0:29:57 transformed into theatrical spectacle. The waters of Raritan Bay became an arena. It’s steamboats,
    0:30:03 the gladiators, and the passengers saw themselves not just as travelers, but as active participants in a
    0:30:09 thrilling competition that embodied the spirit of the age. They didn’t merely seek passage,
    0:30:15 they craved victory and Vanderbilt was determined to deliver. Vanderbilt’s ship, the thistle, sliced
    0:30:20 through the waters aggressively, mirroring its captain’s relentless spirit. To Vanderbilt this
    0:30:27 battle wasn’t solely about profit or winning, it was about domination. This competitive drive defined
    0:30:32 Vanderbilt’s character. Historical author Stephen Dando Collins notes that Vanderbilt was driven by two
    0:30:39 things, making money and winning. He had an instatiable thirst for conquest, often temporarily
    0:30:45 sacrificing profit to achieve victory. Once he triumphed though, Vanderbilt rarely lingered. Instead,
    0:30:51 he swiftly sought out his next challenge. A more modern parallel to Vanderbilt’s relentless competitiveness
    0:30:57 can be seen in athletes like Michael Jordan, who famously manufactured slights to fuel his competitive
    0:31:02 edge. For many, including Vanderbilt, the impossible challenge wasn’t a bug,
    0:31:07 it was the feature. Victory itself was less fulfilling than the next mountain to climb,
    0:31:13 the next unwinnable fight. The Evening Post observed how the sport arising from boat racing
    0:31:18 had captured public imagination. The New York-Philadelphia route attracted passionate fans who
    0:31:24 cheered their champions. English actress Anne Royal described her boats as “our heroine” recounting
    0:31:29 dramatically how the rival vessel drew up alongside somewhat boldly and sometimes had the presumption
    0:31:35 to run ahead. Her account read less like a travel diary and more like a sports reporter’s play by
    0:31:42 play. The races compressed time and space, combining speed, affordability, and spectacle to create new
    0:31:49 markets. Vanderbilt applied what economists later termed price elasticity of demand, demonstrating that
    0:31:56 transportation wasn’t a fixed need but one stimulated by accessibility and excitement. As fares dropped,
    0:32:01 more people traveled reshaping commerce by enabling merchants to access distant markets rapidly and
    0:32:08 allowing stock market speculators to profit from faster information flow. Leisure travel expanded, forcing
    0:32:15 aristocrats to mingle with those who were considered social inferiors at the time. Vanderbilt’s competitive
    0:32:21 success earned him the nickname Commodore, a title usually reserved for naval commanders. The irony wasn’t lost on
    0:32:27 Vanderbilt who had begun his career challenging monopolies. Now he commanded a fleet himself.
    0:32:33 Despite his ruthlessness in business, he lived pretty frugally with tremendous self-control and personal
    0:32:40 expenditure, but none when pursuing competition and business expansion. The confrontation began
    0:32:47 dramatically when three angry representatives from the Hudson River Steamboat Association stormed Vanderbilt’s office.
    0:32:53 They accused him of secretly owning the Westchester, a steamboat running the Albany line at a scandalously
    0:33:00 low fare of $2, undercutting the established $3 price. This powerful association was an alliance between
    0:33:05 the Hudson River, North River, and Troy steamboat companies, and it had previously paid rival Robert
    0:33:14 Stevens $80,000 to withdraw from competition for 10 years to secure their monopoly. Vanderbilt recognized this
    0:33:19 this was a dangerous moment. He truthfully insisted that he no longer had ties to the Westchester,
    0:33:25 and it even declined profitable offers to avoid involvement in any rivalry, but the monopoly leaders
    0:33:33 didn’t believe him. War was inevitable. The ensuing Hudson River rate war exposed a striking contradiction
    0:33:39 with Vanderbilt. When the monopoly retaliated by targeting one of his own profitable roots, Vanderbilt exploded with
    0:33:47 fury. In a dramatic public declaration in the New York Evening Post. He positioned his people’s line as a
    0:33:53 champion against the great triangular monopoly, appealing directly to public support. What Vanderbilt
    0:34:01 conveniently omitted was his own history of enforcing monopolies without hesitation. Yet as the underdog,
    0:34:06 Vanderbilt captivated public imagination. He deployed his ships with the Nimrod and the champion,
    0:34:14 slashing fares at first to $1 and then an astonishingly low $0.50. With relentless intensity,
    0:34:21 he expanded service-launching, aggressive advertising campaigns proclaiming “no monopoly” and winning public
    0:34:27 acclaim at every dock. However, when spring thawed the Hudson River after winter, passengers found fares
    0:34:34 back at $3 and Vanderbilt’s ships gone. Years later, a New York Herald investigation revealed he had been
    0:34:44 paid off by the original monopoly, $100,000 plus an annual $5,000 fee to withdraw. Vanderbilt’s brutal but
    0:34:51 effective strategy became clear: identify lucrative routes, wage devastating fair wars, use anti-monopolistic
    0:34:58 rhetoric to rally public support and a bit of showmanship and demand large payoffs to end the
    0:35:04 competition. This is something he would repeat over and over again. His focus shifted from steamboats to
    0:35:10 the promising railroad, a safer and faster connection between New York and Boston, bypassing the notoriously
    0:35:17 rough seas at Point Judith. Vanderbilt recognized the railroad’s potential immediately, later remarking
    0:35:22 to its chief engineer, “There’s nothing like it. The first time I ever traveled on this Stonington, I made up my mind.”
    0:35:36 In 1837, Cornelius Vanderbilt saw an opportunity marking his first significant step into railroads
    0:35:43 with the financially troubled Stonington Railroad. Saddled with $2.6 million in debt, Vanderbilt realized
    0:35:49 controlling travel along Long Island Sound required balancing both rail and steamboat operations.
    0:35:55 At home, Vanderbilt’s relationship with his 16-year-old son William was strained by his demanding nature.
    0:36:01 Frustrated by Billy’s passive demeanor, Vanderbilt placed him under Wall Street broker Daniel Drew,
    0:36:08 forging an uneasy partnership. Drew accepted Billy as a clerk, but demanded a favor in return:
    0:36:13 access to one of Vanderbilt’s new steamboats for his Hudson River operations. While they had once been
    0:36:21 rivals, they had, at least for the time, transformed it into an uneasy partnership, each respecting the
    0:36:25 other’s ruthlessness while never fully trusting one another. Vanderbilt faced
    0:36:32 renewed competition on his Boston-to-Main steamboat route, responding with fierce public anti-monopoly
    0:36:38 rhetoric. By now, his reputation alone often intimidated rivals into paying him off, instead
    0:36:44 of competing. The transportation company, for instance, purchased Vanderbilt’s Lexington steamer
    0:36:53 for $60,000 plus a $10,000 bonus, well above its real value, just to remove it from competition. Vanderbilt
    0:37:00 skillfully recouped his investment while shifting his focus to railroads. Unlike steamboats, the Commodore
    0:37:07 couldn’t easily compete with railways, so he needed a new strategy. In 1840, when the struggling Stonington
    0:37:13 Railroad sought Vanderbilt’s help, he cryptically remarked, “If I owned the road, I’d know how to make
    0:37:19 it profitable.” Though initially dismissed, Vanderbilt quietly joined the Long Island Railroad Board,
    0:37:25 redirecting business away from the Stonington. Shareholders panicked at the high debt and reduced
    0:37:32 traffic. Its stock plummeted, allowing Vanderbilt and his allies, including Drew, to secretly acquire a
    0:37:39 controlling share. By 1847, Vanderbilt became president of the Stonington, the first stop toward railroad
    0:37:47 dominance. By 1848, Vanderbilt had firmly established his transportation empire, becoming the monopoly he
    0:37:53 once opposed. Though his methods contradicted his anti-monopoly rhetoric, Vanderbilt democratized
    0:37:57 transportation, forever transforming the industry. And he was only just beginning.
    0:38:06 In early 1848, far from Vanderbilt’s bustling New York, two men carried gold nuggets into an adobe
    0:38:13 building in California, triggering one of history’s greatest economic frenzies: the California gold rush.
    0:38:20 Vanderbilt’s eye was always drawn to major transportation routes. Now, watching tens of
    0:38:26 thousands head to San Francisco each month, he saw a bigger opportunity than anything he’d tackled before.
    0:38:33 This is why the Commodore went to see Secretary of State John M. Clayton with Joseph L. White,
    0:38:38 a former politician who knew his way around Washington. Vanderbilt explained the situation
    0:38:43 clearly. Without a transcontinental railroad, only a fraction of the Americans and European
    0:38:48 immigrants rushing west could travel by covered wagon. The overland route was dangerous, passing
    0:38:53 through hostile Native American territories and treacherous mountain ranges, often taking up to six
    0:39:00 months. Hundreds died annually from accidents, exposure, starvation, or attacks. The alternative,
    0:39:06 sailing around Cape Horn at the tip of South America was faster. Roughly 90 days, but expensive at around
    0:39:14 $600 per passenger in the lowest class. “I can improve on that,” Vanderbilt told Clayton. “I can
    0:39:20 make money at $300 by crossing through Nicaragua.” But Vanderbilt wasn’t primarily after passengers,
    0:39:27 he wanted the lucrative U.S. mail contracts. Vanderbilt proposed to Clayton exclusive rights from the
    0:39:32 Nicaraguan government to build a canal. In the meantime, he planned to operate steamships from New
    0:39:38 York to Nicaragua and onward to San Francisco. Passengers would travel up the San Juan River,
    0:39:43 cross Lake Nicaragua by steamship, and then journey the remaining 12 miles by mule.
    0:39:50 Clayton favored Vanderbilt’s proposal as an American-built canal would exclude British
    0:39:56 interests and expand American influence. White successfully secured an initial contract from
    0:40:01 Nicaragua, but the company soon faced controversy when Britain protested, asserting control over
    0:40:08 strategic points in Nicaragua. Despite a British blockade and international crisis, Vanderbilt pressed
    0:40:15 forward, commissioning new ships, including the Prometheus, at 1,200 tons, the largest and fastest
    0:40:22 of its kind at the time. In 1850, Vanderbilt traveled to London to secure critical British financing,
    0:40:28 only to find the financial elite skeptical about the ambitious canal project. Matters worsened when a
    0:40:33 scathing press report accused Vanderbilt’s enterprise of being an experiment in which a few
    0:40:38 lawyers in Wall Street were the principal movers, their original purpose being to obtain a charter
    0:40:45 and afterwards dispose of it at any good price. It was a low point in Vanderbilt’s career. Seen as
    0:40:52 a fraud, he became determined to prove his critics wrong. In late 1850, undaunted by setbacks in London,
    0:40:57 Vanderbilt shifted his focus from a challenging canal project toward a practical transportation route
    0:41:04 across Nicaragua. Unlike the canal enterprise, which depended on numerous investors for the large capital
    0:41:11 project, his new steamship line was his personal venture. The Prometheus became the first ocean-going
    0:41:17 steamship entirely owned by one man. Vanderbilt’s assistant recalled, “When she started out there was
    0:41:22 not a cent owing on her,” he remarking that he wanted her to go out on her own bottom.
    0:41:31 In December 1850, Vanderbilt, now 57, boarded the Prometheus for his first of three crucial voyages
    0:41:37 to Nicaragua. His journey was so secretive due to fears of alerting rivals that even his wife didn’t
    0:41:44 know his whereabouts. Upon arrival, Vanderbilt found troubling news. His captain in Nicaragua, Colonel David
    0:41:51 White, reported the steamer Orris wrecked on the rapids of the San Juan River, deeming the rapids impassable.
    0:41:57 White’s repeated attempts with another steamer the director failed. Vanderbilt refused to accept defeat,
    0:42:04 declaring, “I’m going up to the lake without any more fooling.” Taking command himself, he pushed the
    0:42:12 little steamer through the rapids with brute force, defying his engineers’ warnings. Returning triumphantly
    0:42:18 to New York, Vanderbilt boasted of the Prometheus’ unmatched speed and fuel efficiency. Ever the showman,
    0:42:26 he even offered a $100,000 wager that no existing ship could surpass its performance. His innovative
    0:42:31 walking-beam engine design, contrary to conventional wisdom, proved more efficient and lighter than
    0:42:38 competitors’ side-lever engines. Despite significant challenges, George Law’s rival Panama route,
    0:42:46 and Nicaraguan political instability, Vanderbilt established the Accessory Transit Company in
    0:42:53 summer 1851. His Nicaragua transit route rapidly gained popularity thanks to his aggressive improvements,
    0:42:59 competitive pricing, and sheer determination. He overcame logistical hurdles by building specialized
    0:43:04 river boats, blasting through rapids, constructing a plank road, and adding more ocean-going ships.
    0:43:11 Vanderbilt’s Nicaragua venture shows his remarkable ability to execute complex operations before
    0:43:17 modern communications and management systems existed. Remember that this was over 170 years ago,
    0:43:23 and he was coordinating ocean-going vessels, river steamboats, and overland transportation across
    0:43:28 multiple countries, dealing with foreign governments and international rivals, all without telephones,
    0:43:33 computers, or even telegraphs to much of his operation. It’s a reminder of how much business
    0:43:40 success in this era depended on judgment, foresight, and an almost intuitive understanding of logistics.
    0:43:47 In November 1851, Vanderbilt’s third Nicaragua voyage nearly ended disastrously when a British warship
    0:43:54 fired warning shots, forcing him to pay a port fee. The incident triggered a diplomatic crisis resulting
    0:43:59 in a formal apology from London. Once labeled a fraud, Vanderbilt was now hailed by the press,
    0:44:06 which praised his indomitable resolution. Not just known for being rich, Vanderbilt was now becoming an
    0:44:14 international sensation. By 1852, Vanderbilt’s Nicaraguan shipping route exploded with profits.
    0:44:22 After a shipwreck near Mexico exposed his cold focus on revenue over rescue, tensions grew when his ex-partner
    0:44:28 Joseph White faked British financing. Vanderbilt retaliated. He drove down Accessory Transit’s stock,
    0:44:35 snapped it up cheap, then sold his fleet for $1.35 million, trapping short sellers and making a fortune.
    0:44:42 Sensing his own mortality, the Commodore decided on a rare vacation in Europe, leaving trusted
    0:44:47 associates in charge. Little did he know betrayal awaited while he was gone. He had never taken a
    0:44:54 vacation before and he’d never take one again. His yacht, the North Star, was the largest and most
    0:45:02 luxurious private vessel of its time, built at Vanderbilt’s own shipyard. At 270 feet and 2,500 tons with twin
    0:45:08 walking beam engines of his own design, it featured opulent interiors decorated with rosewood furniture,
    0:45:14 marble paneling, lace curtains, and ceilings adorned with paintings of American heroes.
    0:45:22 Vanderbilt’s motivations for the voyage were multifaceted. After decades of relentless work, he genuinely wanted a
    0:45:29 vacation. He also sought to repair strained family relationships by inviting extended family members.
    0:45:34 Additionally, he viewed the trip as a chance to showcase American engineering prowess abroad.
    0:45:40 As he told Senator Hamilton Fish: “I have a little pride as an American to sail over the
    0:45:45 waters of England and France with such a vessel as will give credit to the enterprise of our country.”
    0:45:52 Before departing, Vanderbilt carefully arranged his affairs. He removed Joseph White and his allies from
    0:45:57 the Accessory Transit Company’s board, replacing them with trusted associates including Nelson
    0:46:02 Robinson Robinson and Charles Morgan. However, while Vanderbilt toured Europe receiving widespread acclaim,
    0:46:09 betrayal was brewing at home. Charles Morgan, whom Vanderbilt himself had appointed to the
    0:46:16 Accessory Transit Board, secretly began buying up shares and aligned with Vanderbilt’s old enemy Joseph
    0:46:23 White. In July 1853, they staged a coup electing themselves directors and removing Vanderbilt as the
    0:46:29 company’s agent, cutting off his lucrative commissions by falsely claiming Vanderbilt was indebted to the company.
    0:46:36 When Vanderbilt returned to New York in September 1853 aboard the North Star, he discovered the betrayal.
    0:46:44 The Herald had already reported Morgan and White’s actions, noting ominously, “trouble is anticipated upon
    0:46:50 the return of Commodore Vanderbilt.” This betrayal allegedly produced what has become one of the most famous
    0:46:57 letters in American business history. Now listen to this: “Upon learning of Morgan and White’s treachery,
    0:47:04 Vanderbilt supposedly penned these immortal words, ‘Gentlemen, you have undertaken to cheat me. I won’t
    0:47:11 sue for the law is too slow. I’ll ruin you. Yours truly, Cornelius Vanderbilt.” The letter perfectly
    0:47:19 capturing Vanderbilt’s fierce reputation, bias toward action, and blunt style is likely apocryphal.
    0:47:24 Historians point out it first appeared decades later in Vanderbilt’s obituary without evidence
    0:47:29 and the language used doesn’t match Vanderbilt’s usual correspondence. True or false, it captured the
    0:47:36 essence of the man. Vanderbilt, known for frequent litigation since 1816, immediately filed lawsuits
    0:47:42 against Morgan and White, contradicting the letter’s claim that he would not sue. The letter may be
    0:47:48 myth, but Vanderbilt’s actual response was no less dramatic. He fired off a blistering public letter to
    0:47:54 James Gordon Bennett, editor of the Herald, denouncing the cowardice which, in my absence in a foreign country,
    0:48:01 dictated the columnist statement and calling it utterly false that he owed the company money. Far from eschewing
    0:48:07 legal action, he concluded with this warning, “My rights against the company will be determined
    0:48:13 in due time by the judgment of the legal tribunals.” When Morgan suggested arbitration but failed to
    0:48:20 follow through, Vanderbilt escalated his retaliation with financial warfare. On January 5th, 1854,
    0:48:27 he began aggressively short-selling accessory transit stock, selling thousands of shares he didn’t own
    0:48:33 at $25, betting prices would collapse. Morgan, recognizing the threat, desperately bought stock
    0:48:39 to maintain its price. The New York Times described the clash as a fierce contest of bull and bear,
    0:48:44 pitting two seasoned, wealthy financiers against each other in a ruthless battle for control.
    0:48:52 Vanderbilt doesn’t want to win, he wants to dominate. Twelve days later, Vanderbilt sprung his trap.
    0:48:59 New line of steamships to San Francisco, announced the Times. While Morgan was busy buying transit stock,
    0:49:05 Vanderbilt had been secretly refitting his luxury private yacht, the North Star, as a passenger liner
    0:49:11 to compete directly with accessory transit. And of course, when he put it on the market,
    0:49:17 the resulting fare war was unprecedented. Vanderbilt undercut the transit fare, slashing the price to
    0:49:23 less than a third of the going rate. Passengers flocked to what became known as the independent
    0:49:30 line, though conditions reflected the ruthless cost-cutting. The bloodletting extended beyond
    0:49:37 Morgan and White. Pacific Mail and U.S. Mail steamship companies, which had long dominated the California
    0:49:43 route, saw their revenues collapse as they tried to match Vanderbilt’s prices. All companies were
    0:49:48 hemorrhaging money, even Vanderbilt. One of his partners who had come in on the business with him
    0:49:54 actually went bankrupt, but Vanderbilt could take more pain than anyone else. By August 1854,
    0:49:59 Morgan and his allies capitulated. They purchased Vanderbilt’s steamships for $800,000,
    0:50:09 price far exceeding their original cost. Accessory Transit agreed to pay Vanderbilt an additional $115,000
    0:50:15 for his claims of every sort. The companies quickly restored fares to their previous high levels:
    0:50:24 $300 for first cabin, $250 for second, and $150 for steerage. But those who knew Vanderbilt’s history
    0:50:30 remained skeptical of his promise to stay away from the California trade. After signing Morgan and his
    0:50:37 allies breathed a sigh of relief. It was over. Or so they thought. In reality, it was just beginning.
    0:50:51 In November 1855, Cornelius Vanderbilt orchestrated a stunning financial coup. While Morgan and his
    0:50:56 allies thought the battle was over, the Commodore was buying his way back in and doing it at a discount.
    0:51:04 Only, typical to him, people didn’t yet know it was him. Wall Street buzzed with news that mysterious brokers
    0:51:11 were accumulating quantities of transit stock: 25,000 shares, nearly a third of all shares in existence.
    0:51:18 Behind this movement, as such operations were called at the time, stood Vanderbilt and two unlikely allies:
    0:51:24 Marshall Roberts of the U.S. Mail Steamship and William Aspinwall of Pacific Mail. These titans of shipping,
    0:51:30 men who considered themselves Vanderbilt’s social superiors, had decided to place their fortunes in
    0:51:38 his hand. Their plan was audacious: acquire accessory transit, get rid of Morgan and his allies, use Vanderbilt’s
    0:51:44 cost-cutting expertise to make it profitable again, then consolidate it with U.S. Mail and create a monopoly
    0:51:51 over all passenger traffic between New York and California. Roberts and Aspinwall would pocket
    0:51:57 millions while Vanderbilt would rule the most vital transportation corridor in America. The Commodore,
    0:52:02 who less than nine months earlier had publicly proclaimed his belief in unfettered trade and
    0:52:10 unrestrained competition, was now plotting to establish the very monopoly he claimed to despise. And it’s not
    0:52:14 like he was telling people what he was going to do. He was doing it through obfuscation,
    0:52:21 through silence and misdirection. Little did he know that 3,500 miles away, a small man with intense
    0:52:29 gray eyes was about to upend everything. On November 8th, soldiers executed General Corll,
    0:52:35 a respected military commander in the central plaza of Granada, Nicaragua. The man behind his death was
    0:52:40 William Walker, a slight, freckle-faced American whose penetrating gray eyes captivated everyone he
    0:52:45 met. Though he looked more like a priest than a revolutionary, Walker had just seized control of
    0:52:51 Nicaragua. Walker was a filibuster, not in the parliamentary sense of the word. He was a private
    0:52:58 citizen leading armed invasions of foreign lands under the guise of patriotic expansion. Born in Nashville
    0:53:05 and trained as a doctor and lawyer, he previously led a failed invasion of Mexico. Undeterred, Walker
    0:53:11 arrived in Nicaragua in 1855 with only 56 men, initially hired as mercenaries to fight for the
    0:53:18 liberal faction in Nicaragua’s civil war. His victory hinged on one brilliant maneuver:
    0:53:24 commandeering an accessory transit steamboat on Lake Nicaragua and capturing the conservative
    0:53:29 stronghold at Granada from behind. By taking prominent conservative families hostage,
    0:53:36 Walker forced General Corll into surrender. Walker established a puppet government headed by Patricio
    0:53:42 Rivas, a weak leader he easily controlled. Shortly after, Walker accused Corll of treason,
    0:53:47 orchestrated a quick trial by his own men, and executed him publicly, consolidating his power.
    0:53:53 Yet Walker knew his survival depended on reinforcements from the United States, making
    0:53:59 him dangerously reliant on Vanderbilt’s accessory transit company, a company Vanderbilt was determined
    0:54:05 to reclaim. Walker’s initial attempts to negotiate with transit executives Joseph White and Cornelius
    0:54:12 Garrison failed. Enter Edmund Randolph, a Virginia aristocrat and friend of Walker who proposed a daring
    0:54:19 conspiracy to Cornelius Garrison, transit San Francisco agent. Randolph intended to revoke the existing
    0:54:26 charter and grant a new charter to himself, cutting Vanderbilt out entirely. After initial hesitation,
    0:54:33 Garrison sent his son William to Nicaragua to secure Walker’s agreement. Walker enthusiastically accepted
    0:54:39 Randolph’s scheme, providing legal justification for cancelling Transit’s charter. By late December,
    0:54:44 Walker awarded the new transit rights to Randolph who immediately sold them to Garrison and Morgan.
    0:54:48 In return, they promised Transports Walker reinforcements at no cost.
    0:54:55 Meanwhile, Vanderbilt continued buying accessory transit stock, oblivious to the conspiracy forming
    0:55:01 behind him. Walker represented a threat Vanderbilt had never faced, someone wielding an armed force
    0:55:07 behind the reach of Vanderbilt’s usual tactics of financial warfare or intimidation. Vanderbilt was forced
    0:55:10 to enter the dangerous realm of international intrigue.
    0:55:17 Sylvana Spencer, a sailor known for his toughness and intimate knowledge of Nicaragua’s transit routes,
    0:55:23 became Vanderbilt’s unlikely hero. Armed with Vanderbilt’s backing and $40,000, Spencer traveled to Costa Rica
    0:55:32 and convinced President Juan Rafael Mora to help sever Walker’s supply lines. On December 23rd, 1856,
    0:55:37 Spencer’s troops silently overtook the filibuster garrison at Hips Point and captured Walker’s steamboats
    0:55:44 one by one, using Spencer’s expert knowledge to avoid suspicion. Spencer’s campaign culminated at San
    0:55:49 Carlos, a strategic fortress where he secured a bloodless surrender, crippling Walker’s supply lines.
    0:55:57 With his resources cut off, Walker’s position rapidly deteriorated. Following a prolonged siege,
    0:56:05 Walker surrendered on May 1st, 1857 and was safely escorted from Nicaragua. But Walker didn’t abandon his
    0:56:11 ambitions. In November, he attempted another invasion but was swiftly captured and forced to surrender by
    0:56:17 the U.S. Navy. His final expedition ended tragically when the British forces captured him and delivered
    0:56:23 him to the Honduran authorities, who executed him by firing squad on September 12th, 1860.
    0:56:31 Vanderbilt’s enemies fell one by one. After breaking Walker, he turned his focus to exacting revenge on
    0:56:38 Garrison and Morgan. When Garrison faced arrest in New York for alleged frauds exceeding half a million
    0:56:43 dollars during his tenure as transit company agent, he attempted reconciliation with Vanderbilt.
    0:56:47 Visiting Vanderbilt, Garrison proposed collaborating on the Walker grant.
    0:56:53 Vanderbilt sternly refused, emphasizing his actions were solely for the benefit of transit
    0:56:59 company and its shareholders, not for personal gain. Simultaneously, Vanderbilt intensified his
    0:57:05 dominance of the California steamship traffic and boldly targeted the Atlantic shipping lanes.
    0:57:11 On December 10th, 1855, thousands watched at Simpson’s shipyard as Vanderbilt launched the
    0:57:19 largest steamship of its era, aptly named the Vanderbilt. At 335 feet long with massive 42-foot
    0:57:27 diameter side wheels and five decks, it cost over $900,000, a clear demonstration of Vanderbilt’s wealth
    0:57:31 and determination to defeat the heavily subsidized Collin and Canard lines.
    0:57:38 The Vanderbilt’s maiden voyage in May 1857 was a stunning success, reaching England in
    0:57:44 record time. Newspapers lauded its unprecedented speed and luxury, prompting Vanderbilt to aggressively
    0:57:51 cut fares and strategically timed departures to undermine competitors. By 1860, the strategy had
    0:57:58 dismantled the Collins Line, forcing it to sell off its ships. In 1860, Vanderbilt also achieved total
    0:58:03 control of the California steamship traffic through an agreement with William Aspinwall,
    0:58:07 dividing the business between Vanderbilt’s Atlantic and Pacific steamship company
    0:58:14 on the Atlantic side and Pacific Mail on the Pacific side. Despite years spent dismantling monopolies
    0:58:20 through fierce competition, Vanderbilt pragmatically created his own monopoly once victorious, stabilizing
    0:58:26 fares and securing consistent profits. Vanderbilt’s influence was so immense that when the California
    0:58:32 postal contract ended in 1860, he boldly refused to carry the mail, threatening communication between
    0:58:39 coasts. This prompted President Buchanan’s personal intervention, promising Vanderbilt retroactive
    0:58:42 payment from Congress to maintain essential services.
    0:58:48 Even as Vanderbilt dominated ocean transportation, he began extending his reach into railroads. His
    0:58:55 involvement with the New York and Harlem Railroad began in 1857 when he joined its board alongside his son-in-law,
    0:59:01 Horace Clark and financier Daniel Drew. The company faced bankruptcy with large debts due amid the
    0:59:09 panic of 1857. Vanderbilt’s code of honor distinguished him in a crisis. When Drew refused
    0:59:15 to endorse the renewal of the railroad’s notes despite having accepted a commission, Vanderbilt confronted him
    0:59:22 directly. Mr. Drew, Vanderbilt declared, are you going to sign all these acceptances? Not one of them,
    0:59:28 Drew objected. Are you crazy? I’m going to sign all these and you are too, Vanderbilt insisted.
    0:59:34 Where’s the money to come from to pay for it, Drew asked. You and I will pay for it if no one else does,
    0:59:40 Vanderbilt replied firmly. I’ll do it if it takes the code off my back. I always honor my commitments.
    0:59:47 Drew eventually signed, nearly in tears. Vanderbilt later recounted with satisfaction,
    0:59:53 he did not dare cheat me. This incident highlighted Vanderbilt’s growing authority and strict adherence
    0:59:57 to business commitments, qualities that would prove vital as he expanded further into railroads.
    1:00:05 Drew would soon want revenge, but for the moment he waited. By 1860, Vanderbilt had achieved immense
    1:00:11 influence, described by the Chicago Tribune as almost knightly power. He controlled the Atlantic steamship
    1:00:17 traffic. He was the largest shareholder in Pacific mail and held prominent positions in New York’s Harlem and Erie
    1:00:24 railroads. His estimated wealth of around 11 million likely made him America’s richest private citizen.
    1:00:30 Vanderbilt represented something new in American business, a man whose wealth and power transcended the
    1:00:37 categories of the past. Beyond historical analysis, men followed this demanding profane titan out of
    1:00:45 genuine respect. When Captain Ludlow of the aerial died at sea during a fierce storm in December 1859,
    1:00:49 his final words were, “Tell the Commodore I died at the post of duty.”
    1:00:57 This loyalty reflected Vanderbilt’s core qualities. No one understood steamships better,
    1:01:00 took greater personal risks, or was more committed to keeping his word.
    1:01:06 As America entered the turbulent 1860s, Vanderbilt stood unmatched in power.
    1:01:15 He was hated, admired, resented, yet always respected, even by his enemies.
    1:01:25 As Cornelius Vanderbilt approached 70 in 1864, most men his age were considering retirement.
    1:01:31 Vanderbilt, however, was not most men. He’s an outlier and he was planning his greatest conquest
    1:01:38 yet. After decades dominating American waterways, the Commodore was now transforming himself into
    1:01:45 the railroad king. The stakes were immense. In an era before automobiles, highways, or air travel,
    1:01:51 railroads were essential to American commerce. The rail network represented far more than transportation.
    1:01:57 It was, according to a contemporary observer, “the most tremendous and far-reaching engine of social
    1:02:05 revolution which has ever either blessed or cursed the earth.” Just as steamboats had reshaped America
    1:02:12 decades earlier, railways now dramatically altered geography, economy, and society. Cities like Chicago
    1:02:21 grew exponentially, increasing from 43,000 people in 1850 to 400,000 people by 1870, driven primarily by its
    1:02:32 the most critical rail corridor in America. Vanderbilt’s railroad empire began with
    1:02:39 its purchase of the struggling New York and Harlem Railroad in 1863, a small line critics dismissed as
    1:02:45 “the only good for wrapping paper.” Vanderbilt, however, saw potential where others saw failure,
    1:02:51 aiming to create the only direct rail connection between New York City and the industrial heartland
    1:02:57 of New England. Driven by pride and a relentless pursuit of efficiency, Vanderbilt acquired the Hudson River
    1:03:06 Railroad in 1864, securing control over the only railways entering Manhattan. Yet, this is merely the starting point.
    1:03:13 To access the lucrative Midwest markets, Vanderbilt needed cooperation from New York Central Railroad,
    1:03:18 a major trunk line running from Albany to Buffalo. For three years, Vanderbilt patiently
    1:03:24 negotiated with the Central’s presidents, Ernest Corning and then Dean Richmond. Frustrated by the
    1:03:30 Central’s practice of diverting his railcars to competing steamboat lines, Vanderbilt repeatedly
    1:03:37 voiced his complaints. Though Vanderbilt preferred diplomacy to confrontation, a proposed merger between
    1:03:43 the railroads faced strong opposition. His lieutenant, Horace Clark, warned that such a move would shake the
    1:03:51 state to its center, appearing as an attempt to strengthen railroad monopolies. In August 1866,
    1:03:57 the landscape changed suddenly when Richmond died. Henry Keep, the Central’s new president, was a secret
    1:04:05 strategic thinker openly hostile to Vanderbilt. Keep vowed privately to take revenge against Vanderbilt,
    1:04:13 even if it cost him half his wealth. The breaking point came in December 1866, when Keep abruptly revoked
    1:04:19 a hard-fought agreement to pay the Hudson $100,000 annually for handling the New York Central traffic
    1:04:26 into New York City. So on December 29th, Vanderbilt met with Keep and his allies. William Vanderbilt
    1:04:32 repeatedly questioned Keep asking, “Gentlemen, you have repudiated our contracts and disrupted our
    1:04:39 established agreement. Do you have any alternative to propose?” Keep responded coldly, offering only minimal
    1:04:44 compensation. After five hours of fruitless discussion, Vanderbilt drove Corning, a more
    1:04:50 reasonable central director, to his hotel. “Mr. Corning, I’m very sorry we cannot get along together
    1:04:56 in this manner,” Vanderbilt remarked. “I am too,” Corning replied. “If it was left to just you and me,
    1:05:02 we could fix it in a little while. I believe we could,” the Commodore agreed. But this brief exchange
    1:05:07 clarified for Vanderbilt that Corning had no real authority. Keep held all the power, making further
    1:05:17 negotiations pointless. Soon after, Vanderbilt unleashed his most powerful weapon. On January 14th, 1867, the
    1:05:23 boards of the Hudson River and Harlem Railroads voted to sever all ties with the New York Central Railroad.
    1:05:29 They refused to accept any tickets or freight from the Central and halted all train crossings over the
    1:05:37 Albany Bridge. This decision amounted to a declaration of economic war with immediate and severe consequences.
    1:05:42 “It would place the New York Central Railroad in a position where it could no longer claim to be a
    1:05:49 primary trunk line between New York and Buffalo,” explained William Vanderbilt. “Amid a fierce snowstorm that
    1:05:55 paralyzed New York State, passengers were forced to cross the frozen Hudson River at Albany on foot or hire
    1:06:01 sleighs for transportation. Freight shipments from the west piled up, and alternative railroad connections
    1:06:08 dependent on unreliable ferry crossings due to severe weather failed to meet the demand.” Effectively,
    1:06:13 what’s happened here is Vanderbilt has isolated New York City from the rest of the world. He literally
    1:06:20 controls the ingress and egress through railroad lines, and he’s said no. The Brooklyn Eagle accused
    1:06:25 Vanderbilt of placing the city under strict blockade, describing his actions as criminal and deserving,
    1:06:31 exemplary punishment. When called before a legislative committee, Vanderbilt bluntly defended himself.
    1:06:37 When you talk about legally, I suppose your next question will be why didn’t you prosecute them?
    1:06:43 It is not according to my mode of doing things to bring a suit against a man that I have the power in
    1:06:50 my own hands to punish. The law as I view it goes too slow for me when I have the remedy in my own hands.”
    1:06:57 This was a striking display of private power overriding public interest. One man’s ability
    1:07:04 to disrupt a major city’s commerce for personal corporate objectives. If you were to judge this
    1:07:09 solely on its effectiveness, then you would conclude it worked and it worked quickly.
    1:07:15 Keep quickly capitulated. On January 17th, he urgently telegraphed Corning what is to be done.
    1:07:21 He handed full authority to settle the dispute to Corning and two other directors more inclined
    1:07:28 towards compromise. The trio promptly visited Vanderbilt’s office to negotiate peace. One director
    1:07:34 later acknowledged that Vanderbilt was the most eager to resolve the situation. On January 19th,
    1:07:38 a new agreement was finalized. The Central Railroad promised to deliver as much freight to the Hudson
    1:07:43 River Railroad as it received from them, ensuring no more empty cars returned from Albany. It also
    1:07:51 agreed to cover a portion of the terminal expenses. Keep was publicly humiliated. John M. Davidson wrote
    1:07:56 to Corning that Keep and Lockwood are large sellers. They flooded the market with stock. The swearing against
    1:08:01 Keep by the stockholders is intent. They’re considering a meeting to demand his resignation.
    1:08:07 So basically, Keep gets outmaneuvered by Vanderbilt. He dumps the shares. Davidson is telling us that
    1:08:12 Keep and Lockwood are both just flooding the market with their shares. So the share price is sinking.
    1:08:18 What Davidson did not yet realize, but Vanderbilt clearly saw, was the exceptional opportunity the
    1:08:25 crisis presented. As Keep and his partner dump their shares, Vanderbilt starts acquiring them.
    1:08:32 This marked a decisive shift in his tactics. After three years of diplomacy, he now aggressively moved
    1:08:38 to secure ownership, taking advantage of Keep’s weakening position. By the Central’s annual meeting
    1:08:45 in December 1867, Vanderbilt had acquired enough stock to seize control. His allies, including William
    1:08:50 Clark and Schell, were elected to the board. Keep’s presidency ended, and when Corning nominated
    1:08:57 Vanderbilt as president, the outcome was inevitable. In just four years, Vanderbilt had progressed from
    1:09:03 controlling the modest Harlem Railroad to dominating one of America’s major rail networks.
    1:09:10 The event underscores a critical lesson about leverage and power. Keep had mistakenly believed that his title
    1:09:15 as president granted him control, but Vanderbilt understood that true power came from ownership.
    1:09:22 Titles alone didn’t guarantee authority. CEOs holding little equity can be removed by boards or investors,
    1:09:27 whereas those with controlling stakes can dictate strategy and vision despite opposition.
    1:09:32 I just want to recap here for a second. Keep and Vanderbilt get into this fight.
    1:09:37 So Vanderbilt effectively humiliates Keep. Keep dumps the shares, because he’s like,
    1:09:43 he knows he’s getting ousted. So he starts dumping all his shares. Then Vanderbilt buys the shares. So the
    1:09:49 shares at depressed prices. So he effectively causes the share price to lower, and then buys the shares and
    1:09:56 takes control. This isn’t the first time he used this play bug. So with control of both the Hudson River and
    1:10:02 New York Central Railroads, Vanderbilt revived his earlier proposal for consolidation. Persistent
    1:10:07 conflicts between these interconnected lines had proven impossible to resolve through contractual
    1:10:12 agreements alone. A merger would align their interests and eliminate structural inefficiencies.
    1:10:19 So in 1869, Vanderbilt successfully secured legislation permitting the merger. The resulting
    1:10:24 New York Central and Hudson River Railroad was among America’s largest enterprises, boasting a
    1:10:30 capitalization of approximately $90 million in dominating the crucial transportation corridor from
    1:10:36 New York to Buffalo. This consolidation represented more than just another business transaction.
    1:10:43 It marked a fundamental transformation of American capitalism. Vanderbilt helped pioneer the concept
    1:10:48 of the giant corporation distinct from the individual ownership or management. By merging older
    1:10:54 railroad companies into a single entity focused on efficiency and profitability, he also shifted
    1:11:01 their original public service missions. The concentration of economic power raised concerns.
    1:11:06 It’s not a pleasant reflection, wrote Harper’s Weekly, that the great thoroughfare between the East
    1:11:13 and the West is in the hands of the Vanderbilt family. Despite these anxieties, Vanderbilt’s consolidation
    1:11:19 brought clear economic advantages. Resolving conflicts between the Central and Hudson River lines reduced delays,
    1:11:23 reduced delays, simplified operations, and significantly lowered costs.
    1:11:26 By 1870,
    1:11:31 Cornelius Vanderbilt had fundamentally reshaped American business. In just four years, he transformed himself from
    1:11:38 the Commodore of the Waterways into the Railroad King, building one of America’s most significant business empires.
    1:11:44 Vanderbilt’s railroad consolidation mirrored broader economic trends, the emergence of large
    1:11:50 corporate enterprises, increasingly abstract ownership structures, and concentrated economic power.
    1:11:56 He demonstrated both the benefits and the risks of this new corporate model. His railroad empire achieved
    1:12:03 economies of scale that lowered transportation costs and boosted economic growth. However, his blockade of New York
    1:12:09 raised troubling concerns about the extent of private influence within a democratic society.
    1:12:16 As Vanderbilt, now 75, surveyed his empire, he saw a nation transformed, not merely by railroads,
    1:12:20 but by the corporate structures he pioneered to build and manage them.
    1:12:27 William Lloyd Garrison had envisioned that improved communication and connections would unify societies,
    1:12:29 promoting common values and shared prosperity.
    1:12:36 Vanderbilt indeed united distant regions with iron rails, but the legacy of his work was not
    1:12:40 democracy or equality. It was the modern corporation itself.
    1:12:52 A statue of Cornelius Vanderbilt stands at the entrance of New York City’s Grand Central Terminal,
    1:12:58 overlooking Park Avenue South and the empire that he created. The infrastructure he built remains essential,
    1:13:05 even as the original corporation and dynasty established has faded. Yet, as Vanderbilt’s railroad
    1:13:10 directors noted after his death, this work will go on, though the master workman is gone.
    1:13:17 Cornelius Vanderbilt is often portrayed as a ruthless Robert Barron, but the reality is more nuanced. He
    1:13:23 embodied fascinating contradictions, a fierce competitor who preferred diplomacy, a tough negotiator who
    1:13:29 carefully balanced aggression with patience. His railroad empire consolidating the Harlem, Hudson River,
    1:13:36 and New York Central railroads was built only after repeated attempts at peaceful negotiation failed.
    1:13:42 Vanderbilt’s mergers produced a corporate giant from Manhattan to Lake Erie, ushering in bureaucracy,
    1:13:49 delegation, and unprecedented efficiency. By his death, he reportedly held one-ninth or one-twentieth,
    1:13:53 depending on how you count it, of all U.S. currencies, sparking debate over his wealth,
    1:14:00 democracy, and the unsettling power of massive corporations. America had entered its era of great
    1:14:07 fortunes, and Vanderbilt had led the way. Vanderbilt’s legacy is nearly entirely heroic nor villainous,
    1:14:12 but reflects America’s shift from rural merchants and farmers to corporate industrialism. He was an
    1:14:19 individualist who built institutions, a practical navigator who pioneered an abstract economy. He was
    1:14:25 at the forefront of the industrial revolution. His relentless drive and continual reinvention
    1:14:33 defined not just his life but also the nation’s transition into modern times. As his railroad directors
    1:14:38 aptly summarized Vanderbilt’s greatest monument was the lasting framework he established. The work will go
    1:14:48 on, though the master workman is gone.
    1:14:54 All right, I want to cover a few reflections before we get into the lessons that you can take away from
    1:15:01 this. This guy is such a badass. He’s done so many things. So there’s so many points in this story,
    1:15:06 like this is a skeleton of his life. We missed a whole bunch of things. I’m going to tell you a few
    1:15:11 of them. But each of these things could be their own episodes, like each of these little battles on
    1:15:18 the seas or the Nicaragua thing or the railroad battles. Like this is crazy. We didn’t even cover
    1:15:24 the Erie battle with Jay Gould and Daniel Drew comes back for that one. And we just didn’t have time
    1:15:28 because I want to keep these reasonably short. So we’ll cover some of this more in the future. But I
    1:15:34 wanted to give you a sketch of the person and some of the things you can learn from him. Now we didn’t
    1:15:41 have time to get into this either. But Vanderbilt was not a role model as a parent or partner. I mean,
    1:15:47 he applied a lot of the same tactics that he championed in business. He applied at home and
    1:15:52 they had a much different result. Instead of leading to success, it led to disaster.
    1:15:57 Another fascinating angle to this that we’ll maybe cover in another episode in the future is
    1:16:04 how his heirs managed to squander the world’s largest fortune in only a few generations. It’s a
    1:16:11 story that involves New York society, royalty, lavish parties, wives trying to one up each other,
    1:16:16 legal battles and so much more. Interestingly, Anderson Cooper wrote a book on this and he
    1:16:23 would know because he is after all a Vanderbilt. You can take your learning to the next level for
    1:16:28 these two. Like one of the things that people email me and they’re like, what book did you use? So we
    1:16:34 highlight the books we used for this. It was Tycoon’s War and we used the first tycoon. We also put my
    1:16:39 highlights into the repository that you can access. So if you’re a member at Farnham Street, if you go
    1:16:45 to fs.blog/membership, you can actually search all my highlights from all the books and all the episodes
    1:16:51 and see what I underline when I was reading the book. Okay, I want to get into some of the lessons that we
    1:16:58 can take away from Vanderbilt and how we can use them and maybe apply them in life. So the first lesson
    1:17:03 is ride the wave. When a better technology came along, Vanderbilt jumped on it. He went from
    1:17:08 various to steamships to railroads without trying to cling to the past. The guy did not think about
    1:17:13 sunk costs. He went all in and I admire that about him. A lot of people would have been trying to
    1:17:20 sort of walk that line between both, but not Vanderbilt. He went all in. Two, patience. He didn’t need
    1:17:25 quick wins. He wasn’t looking for validation from other people. He’d rather take a temporary loss if it
    1:17:31 meant owning the market later. He would always do what was in the long-term interests of himself and not
    1:17:36 the short-term interests. Which leads me to three, which is his ability to take pain. Physical
    1:17:41 pain from powering through blizzards when no other ferry captain would. Financial pain from slashing
    1:17:48 prices to zero profit and sometimes at a loss just to drive out rivals. Psychological pain threatening to
    1:17:53 block rail traffic to and from New York City and then doing it. And he survived all these scenarios and
    1:17:59 crushed everyone else. The man could take more pain than almost anybody I’ve studied.
    1:18:05 Four, control. Vanderbilt wasn’t really interested in being a passive shareholder. He wanted control
    1:18:09 or nothing. He didn’t want to sit on the sidelines and watch somebody else do it. He wanted to lead.
    1:18:15 Five, showmanship. It’s easy to root for the underdog and Vanderbilt often positioned himself that way
    1:18:20 against a big monopoly. Then once he beat them, he essentially became the monopoly. But by that point,
    1:18:26 he already had public sympathy on his side. And he was also a bit of a showman too. Whether racing
    1:18:33 steamboats or blocking the rails, he made business feel like high drama. And that’s part of why his legend
    1:18:37 endures today. He was always the talk of the town, even when he was the one pulling the strings behind
    1:18:45 the scenes. Six, go all in. If you cross Vanderbilt, it wasn’t enough for him to beat you. He wanted to
    1:18:52 finish you financially or otherwise. He didn’t just win, he dominated. Seven, positioning. Vanderbilt operated
    1:18:59 with very little leverage so he could always take advantage of his rivals’ misfortunes. He also would cause
    1:19:03 poor positioning in other people. He knew when people were selling or buying, he would often force
    1:19:08 them to sell and he would short the stock. They would sell, it would drive down the price and then
    1:19:13 he’d buy it back and he’d either take control. He was always in a position to capitalize on the
    1:19:20 circumstances that forced other people into bad hands. Eight, move in silence. Yes, he might say I’m
    1:19:26 going to ruin you, but he rarely telegraphed how he’d do it. He made clandestine stock purchases,
    1:19:32 he turned enemies into allies and he pulled off secret deals. By the time rivals realized it was
    1:19:38 too late. Nine, make money. Vanderbilt made money in many ways. It reminds me of a quote by John D.
    1:19:43 Rockefeller who said, “I have ways of making money that you know nothing of.” Vanderbilt made money on
    1:19:49 competing. He made money from not competing. He made money from monopolizing and he understood where the
    1:19:53 real money was, like the lucrative mail contracts from New York to San Francisco.
    1:19:59 And that’s the lessons you can take away from this. Thank you for listening and tune in next time.
    1:20:17 Thanks for listening and learning with us. For a complete list of episodes, show notes,
    1:20:23 transcripts, transcripts, and more, go to fs.blog/podcast or just Google the Knowledge Project.
    1:20:27 The Farnham Street blog is also where you can learn more about my new book,
    1:20:33 Clear Thinking: Turning Ordinary Moments into Extraordinary Results. It’s a transformative
    1:20:40 guide that hands you the tools to master your fate, sharpen your decision-making, and set yourself up for
    1:21:00 unparalleled success. Learn more at fs.blog/clear. Until next time.

    Cornelius Vanderbilt was a force in 19th century America, playing a pivotal role in transitioning the U.S. economy from rural mercantilism to industrial corporate capitalism. Vanderbilt didn’t just compete—he dominated; and didn’t just dominate one industry—he conquered three: ferries, steamships, and railroads. He understood that power lay in controlling infrastructure and not just operating within it. His cutthroat tactics were both feared and admired but his vision for what the economy could be was undeniable. 

    This is the story of how Vanderbilt turned calculated aggression into an art form, how he endured more pain than his competitors, and how he built systems that outlived him. 

    Learn the mindset, strategies, and brutal lessons behind his dominance; the game of business hasn’t changed as much as you think. 

    (02:20) Prologue

    (05:12) PART 1 – The Dutch Inheritance

    (08:21) The Young Boatman

    (12:30) Capitalizing on War

    (15:27) General Merchant of the Sea

    (19:29) PART 2 – The Meeting That Changed Everything

    (21:48) The Steamboat Wars

    (24:12) The Anti-Monopoly Crusader

    (27:06) The Rise of the Commodore

    (32:08) The Monopolist’s Nemesis

    (34:58) PART 3 – Sole Control

    (37:28) Prometheus

    (40:18) Star of the West

    (44:06) Europe and Betrayal

    (48:15) The Independent Line

    (50:13) PART 4 – The Commodore’s Return

    (51:55) Gray Eyed Man of Destiny

    (53:36) The Conspiracy

    (54:41) Finishing Walker

    (55:54) Conquering the Seas

    (58:13) America’s Wealthiest Citizen

    (60:47) PART 5 – Vanderbilt’s Railroad Dominance

    (01:01:59) The Path to Confrontation

    (01:03:37) The Breaking Point

    (01:04:43) The Power to Punish

    (01:06:32) The Collapse

    (01:07:50) The Silent Conquest

    (01:08:57) The Consolidation

    (01:10:54) The Legacy

    (01:12:15) FINAL PART – Vanderbilt: The Architect of Modern American Business

    (01:14:19) Reflections

    This episode is for informational purposes only and most of the research came from reading The First Tycoon by T.J. Stiles and Tycoon’s War by Stephen Dando-Collins.

    Check out highlights from these books in our repository, and find key lessons from Cornelius Vanderbilt here — fs.blog/knowledge-project-podcast/outliers-cornelius-vanderbilt

    Upgrade — If you want to hear my thoughts and reflections at the end of all episodes, join our membership: ⁠⁠⁠⁠⁠⁠⁠fs.blog/membership⁠⁠ and get your own private feed.

    Newsletter — The Brain Food newsletter delivers actionable insights and thoughtful ideas every Sunday. It takes 5 minutes to read, and it’s completely free. Learn more and sign up at fs.blog/newsletter

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  • Raging Moderates: The Price of Trump’s Trade War

    AI transcript
    0:00:04 There’s over 500,000 small businesses in B.C. and no two are alike.
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    0:01:16 Hey, this is Peter Kafka.
    0:01:20 I’m the host of Channels, a podcast about technology and media
    0:01:23 and how they’re both changing all the time.
    0:01:27 And this week, I’m trying to figure out how Donald Trump is changing the media
    0:01:30 in Washington, in the courtroom, and in the boardroom.
    0:01:33 On to help me figure it all out is Sarah Fisher,
    0:01:37 the excellent Washington-based media reporter for Axios.
    0:01:42 That’s this week on Channels, wherever you get your favorite podcasts.
    0:01:49 Welcome to Raging Moderates.
    0:01:50 I’m Scott Galloway.
    0:01:51 And I’m Jessica Tarla.
    0:01:52 Should I run for president, Jess?
    0:01:54 It seems like the internets want you to.
    0:01:55 Yeah?
    0:01:56 Stack ranking.
    0:01:58 Give me the pluses and the minuses.
    0:01:59 The dog.
    0:02:00 Dog 28.
    0:02:01 A chicken in every pot.
    0:02:03 A Cialis in every cupboard.
    0:02:04 We’re going off script here.
    0:02:06 I’m coming in hot.
    0:02:07 What do you think?
    0:02:08 Give me the negatives first.
    0:02:09 I can take it.
    0:02:11 I’m thick-skinned, although I may never speak to you again the rest of my life.
    0:02:11 Go ahead.
    0:02:12 Oh, well, that would be a bummer.
    0:02:15 I would hope that you could at least give me a low-level campaign position.
    0:02:17 You’re going to be ambassador.
    0:02:20 No, you’re going to head up, what is it, American Free Radio?
    0:02:23 To the islands with the penguins that got a big tariff?
    0:02:24 No, the one they just canceled.
    0:02:26 You’re going to be my Carrie Lake.
    0:02:29 I’ll send you to Voice of America and then fire you the next day.
    0:02:31 I’ll put you in charge of Doge.
    0:02:35 Just use sub-congressional authority and ramp up the spending on everything.
    0:02:37 I like that.
    0:02:39 I won’t be in it for that.
    0:02:40 I’ll think about what position I would want.
    0:02:45 But the big negative, I would think, would be your family.
    0:02:48 I do not feel like they would be excited about this.
    0:02:49 100% agree.
    0:02:51 My son brought it up.
    0:02:53 My 17-year-old came home, panicked, and said, are you running for president?
    0:02:54 I said, no.
    0:02:58 And my partner said, the only thing, she listed off about 15 reasons I shouldn’t.
    0:03:02 Then the one she liked said, I’d like the outfits.
    0:03:06 And see, that almost compensated for the 15 negatives.
    0:03:08 Yeah, the outfits are pretty serious.
    0:03:19 And if they’re now even dressing first ladies that they don’t like, imagine what she would have access to if the couture were available to her.
    0:03:22 I mean, saving the country seems like a good thing.
    0:03:32 I feel like we’re moving into a world in which a traditional politician is going to have a much harder time winning a primary.
    0:03:41 At least there’s going to be a need for some pushback of someone with, like, real-world experience, also understands media very well, which you do.
    0:03:48 And not to be too positive, I guess, at the beginning of the show, because I don’t want you to get too big for your britches in minute three.
    0:03:50 It’s happened already.
    0:03:51 I’m already there.
    0:03:52 We’ve already arrived.
    0:03:53 Dad, when are we getting there?
    0:03:54 We’re here.
    0:03:55 We are here.
    0:04:07 But it does feel as if an abundance Democrat, because now we’re a cohort, is going to have, I don’t want to say a very easy time, because I think the primary is going to be super fierce.
    0:04:20 But if you’re looking at where the electorate is and how people moved in 2024, it feels like someone that thinks like you slash Ezra Klein could do very well.
    0:04:22 So, I mean, I’m excited.
    0:04:26 And once I figure out what it is that I want to do for the campaign, that’s going to be very important.
    0:04:28 Well, you’re clearly, at this point, you’re comms director.
    0:04:30 If you bring up Ukraine, you’ll be secretary of defense.
    0:04:32 We’re going to pivot back and forth.
    0:04:33 This is all inspired.
    0:04:39 I was on a college tour this week, and it was a really, really lovely and emotional week for me.
    0:04:45 And I spoke at the University of Chicago at the Institute of Politics, run by David Axelrod, and he interviewed me.
    0:04:48 And he said, what should Democrats do?
    0:04:51 And I said, this is the platform Democrats should adopt.
    0:04:55 And then everyone weighed in and said, you should run.
    0:05:04 And then, over the weekend, I heard from all people or organizations, a guy who runs a law firm who’s very involved in democratic politics.
    0:05:06 And this is how the sausage gets made, I guess.
    0:05:09 And he said, I slash we will put in $10 million.
    0:05:18 If you put in $10 million, that’ll get you the name recognition you need and get your ideas out there.
    0:05:20 And we’ll see how you do, and we’ll go from there.
    0:05:23 And I thought, that’s really interesting.
    0:05:33 They clearly want people with money, but there’s a machine out there kind of behind the curtain working to identify candidates.
    0:05:46 And it’s also sort of a signal of just how desperate things have become when they reach this far into the barrel that someone is willing to give me $10 million to announce I’m running for president and start putting my ideas out there.
    0:06:01 Well, I think, and I don’t want to diminish the importance or the weight of this early ask, but I think that people are pretty smart about the fact that we’re going to spend several billion dollars on doing this.
    0:06:08 Again, if it’s possible that they’re going to hit a winner or someone who at least can get the conversation started.
    0:06:14 Remember when we were on the bulwark with Tim Miller and he said, like, why doesn’t someone just start running for president now?
    0:06:22 And you could be that person because you’re not the governor of Pennsylvania right at this particular moment.
    0:06:33 So I think a $10 million investment makes a lot of sense when they, A, could have someone who could go on and do it, and B, looking at the total haul that’s going to be spent on this.
    0:06:34 So are you taking the money?
    0:06:35 Are we going?
    0:06:39 Well, first I’ll tell you what my platform is because I made it up on the spot.
    0:06:42 I think our platform, I don’t think we can go back.
    0:06:48 I think the current platform of we need to return to normal is not what Americans are looking for.
    0:06:56 The right is coarse and mean and stupid, but the left is corporate elitists who tell us what we want to hear and then just continue in this grift that’s not helping Americans.
    0:06:57 Can’t go back.
    0:07:05 And my campaign or my platform that I thought the Democrats should adopt is very simple.
    0:07:08 We all do this because we want to have purpose in our life.
    0:07:09 We want to have meaning.
    0:07:17 And the thing that is the source of that purpose and meaning, I think for most people, is the ability to partner with someone and raise children.
    0:07:23 And I don’t think you have to do that to be happy, but I think that’s table stakes for the most prosperous nation in the world.
    0:07:33 And so I would reverse engineer every policy up to this great unifying theory of everything, and that is young people should have the opportunity to meet and fall in love.
    0:07:34 What does that mean?
    0:07:49 National service, more freshman seats, more mental health, starting kids later, boys later, getting more funding in pre-K and education, putting a lot more money in their pockets, tax subsidies for third places.
    0:07:59 Give more young people the opportunities, break up big tech, tax the shit, hold them liable for things that radicalize young men and sequester them for society.
    0:08:02 Give people the chance to meet and fall in love.
    0:08:07 And we don’t like to talk about it because it sounds sexist or weird, but not enough young people are getting together.
    0:08:36 And then should they decide to have kids, table stakes, 7 million manufactured homes, which are 30% to 50% less expensive than homes built on site within six years, minimum wage of $25 an hour, every state that’s done it has grown their economy, stop the bullshit that it’s going to destroy jobs, tax holiday for people under the age of 40, such that young people, should they decide, can start the most meaningful, purposeful voyage in life.
    0:08:43 And that is to have a family and should they decide not to have kids and spend that money on brunch and St. Bart’s more power to you.
    0:08:49 But for me, that’s table stakes, and we should reverse engineer any policy that gets in the way of love.
    0:08:56 If you can’t be in an emergency room or in a hospital room with someone because you’re the same sex, we do away with that law.
    0:09:04 If you’re putting a family in poverty because you’re forcing a 16-year-old to carry a baby to term, no, we’re not going to lie.
    0:09:09 We’re going to redo family court to try and get dads more involved in people’s lives.
    0:09:11 And also, I’m a war hawk.
    0:09:20 I’m just a big believer that the far left doesn’t understand that the moment people have the ability to take our Netflix and espresso away with violence, they will.
    0:09:23 Anyways, love and prosperity, 2028.
    0:09:24 What do you think?
    0:09:24 I’m in.
    0:09:26 I’m living it.
    0:09:28 Well, a little bit less on the prosperity.
    0:09:29 I see a vice president.
    0:09:31 I see a vice president.
    0:09:33 Oh, now I get upgraded?
    0:09:34 Why not?
    0:09:37 You’re literally more qualified than half the cabinet right now.
    0:09:37 I mean.
    0:09:38 You are.
    0:09:39 Low bar, but thank you also.
    0:09:41 I’m not sure about that.
    0:09:42 Well, I don’t know.
    0:09:44 I am a little bit sure of that at this particular moment.
    0:09:55 But I’m looking forward to talking about your platform for the Democratic Party with our special guest of the live show, which is coming up next week.
    0:09:56 He should run.
    0:09:56 It’s Hakeem Jeffries.
    0:09:57 Oh, Hakeem.
    0:09:59 I was thinking Mark Cuban.
    0:10:00 No.
    0:10:01 He’d be amazing.
    0:10:06 I think Mark Cuban has kind of the sauce in terms of witty banter and fleet of foot.
    0:10:09 And also, he has the money, which unfortunately is hugely important.
    0:10:11 Do you think Hakeem Jeffries should run for president?
    0:10:18 I think Hakeem Jeffries is pretty busy right now, but I think it’s important that we tell our listeners that we have this amazing guest.
    0:10:18 Oh, yeah.
    0:10:19 He’s coming on with us.
    0:10:20 Talk about that.
    0:10:23 Well, we have our live show coming up on April 17th.
    0:10:23 Sold out.
    0:10:24 Sorry, folks.
    0:10:24 You’re out.
    0:10:25 Wait outside.
    0:10:27 I might sign.
    0:10:28 I might sign something.
    0:10:31 I might say hi on the way out as I dash into my suburban.
    0:10:33 Is that like a thing that people do at the Y?
    0:10:35 They wait to sign autographs?
    0:10:40 I’m expecting a suburban with one of the Kardashian sisters waiting inside adoringly for me.
    0:10:45 That’s, you know, potential number, what am I, 48?
    0:10:47 I should be dating a Kardashian.
    0:10:50 You are the Benjamin Button of the podcasting world, huh?
    0:10:51 That’s right.
    0:10:53 I’m aging in reverse, 100%.
    0:10:57 But Mark Cuban and Hakeem Jeffries are both probably in the top 10.
    0:11:00 I haven’t heard Hakeem’s name a lot.
    0:11:00 Really?
    0:11:02 Is that because you’re not paying attention to politics?
    0:11:03 Is that right?
    0:11:06 Is that because I’m ignorant to all of this?
    0:11:09 Yeah, the minority leader, the leader of the Democratic Party.
    0:11:11 You’re not hearing a lot about him.
    0:11:14 Is anyone hearing a lot from any Democrat right now?
    0:11:15 Well, you were on school tour.
    0:11:18 But anyway, we’re very excited to have him with us.
    0:11:21 And it’s a big deal that he wanted to do it.
    0:11:26 And we’ll see for those of you who can be there on the 17th, but you can also watch online.
    0:11:31 There’s an option to buy tickets for that on the 92nd Street Y site.
    0:11:32 Okay, Secretary of Commerce.
    0:11:33 Oh, I’m sorry.
    0:11:34 Be careful.
    0:11:35 Pretty soon you’re going to be Secretary of Transportation.
    0:11:37 Although you look great this morning.
    0:11:38 I’m going to make you director of HHS.
    0:11:40 Is that the good looking one?
    0:11:46 Well, you got to think someone who’s in good shape and looks good should be, would be better than some anti-vax weirdo.
    0:11:51 I mean, sure, but RFK Jr. is like not hot to me anymore, like post kids dying of measles.
    0:11:55 I’m like, it’s overriding my attraction to him.
    0:11:57 I mean, he looks great for his age, but all right, get into it.
    0:11:58 He’s a good looking guy.
    0:12:00 All right, let’s get into it.
    0:12:05 Last week, President Trump shook global markets by announcing sweeping tariffs on nearly everything the U.S. imports.
    0:12:15 In a Rose Garden appearance, he imposed a 10% baseline tariff on all countries and with much deeper rates for countries he called the worst offenders, including China, the EU, Japan, and South Korea.
    0:12:20 Basically, our largest trading partners, if not our strongest allies.
    0:12:26 China alone was hit with a 54% tariff and quickly responded with 34% of retaliatory tariffs.
    0:12:28 Markets tanked.
    0:12:35 The Dow posted its biggest back-to-back losses since March 2020 when the pandemic created, obviously, incredible uncertainty.
    0:12:42 So it’s just important to note, we are now have the same level of uncertainty about what’s going to happen as when a global pandemic was killing,
    0:12:47 when they had to have makeshift refrigerator trucks as morgues outside of Langone here in New York.
    0:12:54 The S&P 500 entered bear market territory on Monday, and tech giants, including Apple and NVIDIA, were hit hard.
    0:13:00 On top of that, Jess, a separate 25% tariff on foreign-made cars also took effect Thursday.
    0:13:09 The Fed warns this could fuel inflation, and a Yale study estimates it’ll cost the average U.S. household over $2,000 a year, with low-income families hit hardest.
    0:13:12 That’s usually U.S. economic policy.
    0:13:19 And Congress is now pushing back with a bipartisan bill to limit the president’s power to impose tariffs without approval.
    0:13:22 Jess, Trump has urged patience.
    0:13:29 That’s the party line, that somehow he’s playing 40-chest, and we should just wait, and we’re taking some pain, but the pain will be worth it.
    0:13:36 However, China’s slapping on a 34% retaliatory tariff, and the EU gearing up for its own responses.
    0:13:39 Are we officially, do you think, in a full-blown trade war?
    0:13:44 It feels, I mean, I’m looking forward to listening to your markets coverage again.
    0:13:48 With that, I thought you guys did a great job on that episode that came out this morning.
    0:13:55 It feels like someone formally has to declare that, someone more important than me, as I’m only the HHS secretary.
    0:14:08 But it feels like we’re inching closer to it, and this level of whiplash is not comfortable for anyone but the true, true, truest of believers.
    0:14:13 And it seems like even some of those people are just not having it anymore.
    0:14:17 You kind of see top Trump defenders from the finance community.
    0:14:22 Like, Bill Ackman has been having a meltdown on social media for the last few days.
    0:14:28 He’s going after Lutnick, which I find that part pretty amusing in all of this.
    0:14:38 But you have people like Stan Druckenmiller who tweeted, I guess this is not a regular occurrence for him, that he does not support tariffs in excess of 10%.
    0:14:48 The Wall Street Journal wrote an article even about the fact that he had a tiny tweet just to say that people are taking his words out of context when he talked about the potential benefits of tariffs.
    0:14:54 And, I mean, there were so many reactions to this that I found interesting.
    0:15:05 But one in particular was the Singaporean prime minister who did a direct-to-camera, two-and-a-half, three-minute speech about what’s going on here.
    0:15:12 And how this is a total shakeup to the global order and that this is not so bad for Singapore at this particular moment.
    0:15:23 But as a country that relies heavily on trade, that they know now that we are entering a new, what do you say, arbitrary and protectionist phase, that America is not a reliable partner.
    0:15:37 And he brought up the fact that when we did have a trade war in the 1930s, we ended up in armed conflict leading to World War II, which was a pretty scary forecasting to be hearing in all of this.
    0:15:46 And, I mean, some things as a political strategist with, you know, I have a Ph.D. in political economy, but definitely politics more my business.
    0:15:48 Remember, you’re going to be Secretary of State.
    0:15:49 Oh, my God.
    0:15:53 By the end of this, maybe I’ll be president and you can be my VP.
    0:15:54 I’ll be comms director.
    0:15:55 You just want to be comms director?
    0:15:56 OK.
    0:15:59 No, I’ll be like Billy Carter, just embarrassing you if you go.
    0:16:03 No, you never embarrass me, or at least I wouldn’t admit that in such a public forum.
    0:16:09 But I keep thinking about the job of government.
    0:16:19 And we’ve this has been a theme in our conversations as, you know, we’ve gone the last six months and that the parties have a different vantage point on what role government is supposed to have in your life.
    0:16:23 But fundamentally, we all agree that the government is supposed to protect us.
    0:16:33 And Trump ran on that with the border, right, saying Biden opened it up and Harris was even the borders are and they did nothing about this policy.
    0:16:36 And millions of people got in here.
    0:16:42 Americans are dead as a result of undocumented people that came across the border during the Biden years, et cetera.
    0:16:53 And I’m looking at an administration that tells you to keep holding the line when 62 percent of Americans own stocks or mutual funds.
    0:16:57 This isn’t a problem only for the top 10 percent or something.
    0:17:04 Scott Besant gave a big interview to Tucker Carlson and was making this out to be a rich person’s problem.
    0:17:06 He did the same thing on the Sunday shows.
    0:17:07 That is not true at all.
    0:17:13 This is a middle class and upper middle class and certainly upper upper class problem.
    0:17:27 But this collective F you to the majority of Americans who are not too dumb to understand what’s going on and are aware of the fact that also this calculation was based on a mistake.
    0:17:33 There’s an incredible article from the conservative think tank, the American Enterprise Institute,
    0:17:45 where they were the ones that figured out that they had done the equation improperly in determining what the tariffs are that led to that chart that they paraded out there that Ed was talking about on Prof G markets.
    0:17:52 And people know it is not just about Nike doing more here or more auto plants here.
    0:17:54 Like this is a small business problem.
    0:18:08 The amount of small businesses in America that run off of cheap imports is in the millions and they can’t afford to pay a $500,000 tariff in order to stay in business.
    0:18:16 This is going to wreck every sector of the economy if it keeps up like this.
    0:18:19 And this morning there was and we should say it’s 11 a.m.
    0:18:21 On Monday morning that we’re recording.
    0:18:30 There was a rumor that Kevin Hassett from the president’s team had said that there was going to be a 90 day pause or they’re exploring a 90 day pause on everyone.
    0:18:32 But China market jumped up.
    0:18:35 Then the White House confirmed that that wasn’t the case.
    0:18:37 And now the market is back down.
    0:18:40 And how are we supposed to survive this?
    0:18:46 Yeah, just to the point about playing the populist argument of like a small number of people.
    0:18:54 I think the Dow and the Nasdaq are in fact terrible metrics because they give the illusion of prosperity and that everything’s fine.
    0:18:58 So life expectancy has gone down for the last five years.
    0:19:00 Our kids are more obese and anxious.
    0:19:12 But we don’t have these really elegant metrics to track that that everybody talks about every day at the beginning of every news program if it goes up a lot or down a lot.
    0:19:15 So we’ve decided that that’s more important.
    0:19:18 And the argument they’re making is one they’ve never made before.
    0:19:20 They didn’t make it until it was convenient.
    0:19:27 But I do believe that the Dow and the Nasdaq are not an accurate reflection on the health and well-being of America.
    0:19:27 They’re an indicator.
    0:19:28 They’re a signal.
    0:19:31 But people think, oh, market’s up 2%.
    0:19:33 That means America is 2% better today.
    0:19:34 No, that’s not true.
    0:19:42 And there are reasons you would probably want to sacrifice or justify the long-term investment or tradeoff of the markets going down.
    0:20:05 If we were to decide that people in the most prosperous nation in the world should not live in poverty if they work full-time and we need to create incentives for work and we need to buttress the American brand, which includes central to that, that we work, Americans work hard, and we raise minimum wage to $25 an hour, where it would be naturally if it had kept pace with productivity or inflation.
    0:20:13 And McDonald’s and Walmart stock went down, and so did Chipotle, because they’re dependent upon labor at $12 or $15 an hour.
    0:20:14 I think that would be worth it.
    0:20:16 I think you could make the argument.
    0:20:35 If we were to say we’re going to – these deficits are out of control and we need to be more responsible and stop taxing young people in the future and we’re going to substantially increase the tax rate on corporations, which are paying their lowest taxes since 1929, okay, I get it.
    0:20:38 And the market would go down because I remember being on the board.
    0:20:39 I was on the board of Dex Media.
    0:20:43 I remember one quarter of them saying, we beat earnings by 30%.
    0:20:44 And everyone was like, what?
    0:20:45 What happened?
    0:20:47 What good thing happened?
    0:20:50 And they said, well, the Trump tax cuts just took effect.
    0:20:51 And our stock went up.
    0:21:05 So if you created a more progressive tax structure and took corporations back to, say, not even the median of what they’ve usually paid, but in the 30th percentile versus the lowest, which is where they are now, I think that’s worth it.
    0:21:07 This is an own goal.
    0:21:13 This is shooting yourself in the feet and then after recognizing what you’ve done, you take the gun and you put it in your mouth.
    0:21:18 Lumber comes across or steel and aluminum comes across the border from Canada.
    0:21:19 We tax it.
    0:21:20 We collect some revenue.
    0:21:27 But it makes it more expensive for our cars, which supposedly are going to go up $10,000 to $12,000 per car.
    0:21:35 And aluminum that goes into everything, including a workout bench, including steel that you build in buildings, everything goes up in price.
    0:21:39 So the demand for those products goes down so people can have less of them.
    0:21:42 And then the other nation puts on reciprocal tariffs.
    0:21:51 And Jack Daniels or Brown Foreman, one of the biggest companies, I think, in Kentucky, sells less and they make less money.
    0:21:53 So our prices go up.
    0:21:55 We make less money.
    0:21:58 And the demand for our products goes down.
    0:22:05 And the amount of money collected in the tariff is dwarfed by the loss in capital.
    0:22:10 So this is just the oldest, this is the biggest un-goal or own goal since Brexit.
    0:22:12 It’s an economy that’s $25 trillion.
    0:22:15 So we’re taking everyone else down with us.
    0:22:17 And it’s the definition of stupid.
    0:22:20 Smart people help themselves while helping others.
    0:22:23 That’s the basis of capitalism and, quite frankly, free trade.
    0:22:26 We don’t want these manufacturing jobs back.
    0:22:29 We are the second largest manufacturer in the world.
    0:22:38 People don’t want to go back into a manufacturing facility and put on a hazmat suit or work with a robot doing repetitive tasks.
    0:22:43 They would rather be in higher-paid service work or very high-end finished goods manufacturing.
    0:22:45 We have purposely made these trade-offs.
    0:22:48 Now, have we done a good job protecting the people outplaced?
    0:22:49 No.
    0:22:59 But this is a conscious decision to move to more higher-end, higher-margin, higher-paying lines of business in the service economy or very high-end manufacturing.
    0:23:08 The other thing that people aren’t recognizing is that these tariffs are especially punishing on us.
    0:23:09 And this is what people are missing.
    0:23:13 Toyota trades at 0.6 times revenue.
    0:23:16 Their market cap is equivalent to 0.6 times their top-line revenue.
    0:23:19 Tesla trades at 8 times revenue.
    0:23:26 So assume these tariffs sort of diminish each company’s revenues by a billion dollars.
    0:23:32 That means the market capitalization that Toyota loses is $70 million.
    0:23:36 The market cap that Tesla loses is $8 billion.
    0:23:44 So just the general level of prosperity and wealth is massively decreased in America relative to other countries
    0:23:48 because when they bring in a Mercedes, it trades at 0.23 times revenues.
    0:23:54 When we export Meta or NVIDIA, NVIDIA trades at 24 times revenues.
    0:24:05 So free trade is overly accretive to us because we’re selling them high-margin products and we’re importing low-margin products.
    0:24:18 Every dollar we increase from selling our products abroad, we recognize a much greater increase in market capitalization and value and prosperity than when they lose a dollar.
    0:24:29 This is the biggest own goal in history, maybe since we tightened the fiscal programs of 1929 and 30 and just made things worse.
    0:24:30 What’s your thoughts?
    0:24:31 I agree.
    0:24:47 I mean, it all sounds reasonable to me the way that you’re putting it and it just runs completely counter to what Trump is saying, the quote-unquote goal of this, which is no more trade deficits, which is a complete impossibility.
    0:24:59 We’re not set up anyway to be the kind of economy that you would need to be, a manufacturing economy, not less because we have to take, you know, two to three years to even build a factory.
    0:25:06 But Larry Fink from BlackRock was speaking at the tariffs and he said, we don’t have the workers for this and we don’t have the interest in it.
    0:25:25 Like to your point about people are not dying to go get an assembly line and you have the commerce secretary out there talking about how people can be the ones putting the screws in the iPhone, like as if that’s the goal for everybody who’s feeling like they’re getting a bad deal with the way the global economy is structured.
    0:25:42 And I think as usual, I don’t want to say the fundamental problem, but at least a fundamental problem in what’s going on with this tariff strategy implementation is that nobody who’s speaking on behalf of the administration has the same story.
    0:25:44 So Lutnik saying we want to reshore everything.
    0:25:46 Besant wants more free trade deals.
    0:25:48 Navarro wants to burn it all down.
    0:25:53 Now Elon wants this robust free trade zone, right, an EU free trade zone.
    0:25:56 So which official has the actual policy?
    0:26:07 And, you know, I’m going back to the fact that it was all predicated on a mathematical error and the economists who they even whose paper is being cited in this have even now spoken out.
    0:26:15 There’s have an op-ed in the New York Times saying they completely missed the point, which doesn’t surprise me at all about this administration and the kind of clown car that we have going on.
    0:26:18 But what is the policy?
    0:26:36 And if you were setting it, I’m curious as to what you would be doing about China, because I’ve been reading a lot, particularly in the FT, about how much China is pumping their products through other Asian countries,
    0:26:44 particularly Vietnam, and that that’s the way that they’re going to try to get around any tariffing, also looking even to Mexico.
    0:26:50 So they can kind of huff and puff and President Xi can say we’re going to have a retaliatory tariff to 34 percent.
    0:27:00 But if China is the big dog problem in all of this for the U.S., how can they address that problem in particular?
    0:27:05 Let’s use China as an example to bring it home down to a ground level.
    0:27:06 Get this, Jess.
    0:27:09 Seventy-seven percent of toys imported in the U.S. are imported from China.
    0:27:14 As a result of the new tariffs, toy prices could jump as much as 50 percent.
    0:27:15 Think about this.
    0:27:20 Four-fifths of America is going to have half as many toys under the Christmas tree.
    0:27:21 That’s what this president means.
    0:27:23 You don’t think your kids are going to notice that?
    0:27:27 Half as many toys this holiday.
    0:27:29 And they do pay some tariffs.
    0:27:30 I think it’s like 10 or 12 percent.
    0:27:34 This is going to take those tariffs to 34 percent.
    0:27:46 And the clowns of the Trump administration or Peter Navarro will say, well, actually, the company that we are exporting from that is sending their products into the U.S. will absorb those costs.
    0:27:56 That would make them a monopsonist, meaning that they have so much power that essentially they’ve been able to dictate prices to this point.
    0:28:03 And they can easily take the price down and still make a lot of profits if they could have if that were, in fact, the case, they would have already done it.
    0:28:07 They would have already raised their prices and captured that additional revenue.
    0:28:11 They will basically not have as much demand.
    0:28:13 They will not lower their prices because they can’t.
    0:28:14 They have to make a profit.
    0:28:18 So the prices will go up and there’ll be less demand.
    0:28:21 Now, 70 percent of people have hand to mouth.
    0:28:24 So they’re not going to go, oh, toys are a little bit more.
    0:28:26 But Trump has this master plan.
    0:28:27 No biggie.
    0:28:39 They will have to buy 20 percent less toys to meet the same Christmas budget, meaning that every kid in America, the family, is going to have eight toys under the Christmas tree, not 10.
    0:28:43 This immediately impacts American consumers.
    0:28:46 In addition, 40 million jobs in the United States.
    0:28:49 And you think, well, it’s a big country, 350 million.
    0:28:49 That’s not as bad.
    0:28:52 Only 150 million people work.
    0:28:58 So a quarter of workers, their jobs are directly linked, if not dependent, upon trade.
    0:29:06 So this will have huge impact on unemployment, prosperity, inflation, prices.
    0:29:08 And this leads me to one place.
    0:29:15 And it sounds, I realize I’m going to sound a little bit Laura Loomerish, but just free your mind.
    0:29:25 And that is, if somehow we had elected Putin as president and Xi as vice president, and they said, okay, what do we need to do with America to help us?
    0:29:27 We need them out of Ukraine.
    0:29:36 We need to fragment the alliances between the largest economies in the world that believe in democracies and pushing back on autocrats.
    0:29:44 We need to thrust every large economy and trading partner into the arms of China, of me, Mr. Xi.
    0:30:05 What would be different about the major policies that the Trump administration has implemented since inauguration than the policies that the Putin-Xi administration would be implementing had they been elected president and vice president of the United States?
    0:30:35 And effectively, I believe, and I’m paranoid, it doesn’t mean I’m wrong, but he either has such weird acolytes and a cultish group around him that have bought into this cult of Trump, and he is really, really stupid, or they have taken advantage, Putin and Xi, of this ultimate grift, the Trump coin, where they essentially opened a Swiss banking account that people can put money in.
    0:30:47 And it’s not disclosed and have called Trump and said, hey, I need you to figure out a way to get out of Ukraine, I’m losing, I’m losing, I’ve lost 800,000 people, it’s draining my military, it’s weakening me.
    0:30:55 And Xi to say, I’d love for Japan and South Korea to get over their differences with us and start trading with us.
    0:31:04 What would be different about America if it had been the Putin-Xi ticket than the Trump-Vance ticket?
    0:31:09 It’s just, I mean, we don’t like to say that because we don’t want to be the pizza gate guys.
    0:31:17 There is more legitimacy to the conspiracy I am outlining than any conspiracy they have put forward.
    0:31:23 What would they do differently had they been elected president and vice president?
    0:31:23 Your thoughts?
    0:31:52 Well, I think that you’re touching on something that has become kind of omnipresent, I guess, in democratic circles or in buyer’s remorse circles as well about President Trump, which is the number of people, including first and foremost Hillary Clinton, who was on record, essentially calling every single thing that was going to happen through the first and beginnings of the second administration, save for the Abraham Accords, which I do think are a bright shining light in this.
    0:32:01 And I’m not sure that President Xi and Vladimir Putin would have been as into that as Trump administration 1.0.
    0:32:08 But you see, like, video circulating now of Mitt Romney calling it, saying we’re going to have a recession.
    0:32:11 Kamala Harris calling it, saying we’re going to have a recession.
    0:32:15 They were predicting by the summer, I think Kamala said.
    0:32:16 So this could come a little early.
    0:32:19 I think Goldman has it up to, what, a 60 percent chance.
    0:32:27 And if the tariffs, the April 9th tariffs go through in full, they’ll say it’s immediately up to the 100 percent likelihood of a recession.
    0:32:29 So I think your point is well taken.
    0:32:37 But it gets to this larger issue of how do you say that without seeming like someone who needs to be wearing a tinfoil hat?
    0:32:39 Because we’ve gone through this before.
    0:32:46 People have been chicken little screaming for years about, you know, he’s a Russian agent.
    0:32:48 He’s not on the side of America.
    0:32:53 He’s on, you know, the side of the most terrible authoritarians that are out there.
    0:32:59 And the American electorate rejected that argument at the ballot box.
    0:33:10 And so now we are stuck with this mess and also in finding a way to articulate a cogent counterargument to it.
    0:33:16 And I already mentioned a few minutes ago that I was listening to Scott Besson on with Tucker Carlson.
    0:33:18 What do people say?
    0:33:20 Like, I listen so you don’t have to.
    0:33:23 But it was actually, you know, a pretty thoughtful conversation.
    0:33:32 But I was stuck on, you know, the number of times that Besson says we’re heading for a financial calamity because of our debt.
    0:33:35 Not something that you say the same, right?
    0:33:36 He’s talking about wealth inequality.
    0:33:38 He sounds like an economic populace.
    0:33:40 He sounds like Bernie Sanders or Donald Trump.
    0:33:47 And then the problem is, is that their solution does nothing to get us out of that jam.
    0:33:51 You know, talking about lowering the corporate tax rate, benefits accruing to the richest people,
    0:33:57 cuts to food stamps, cuts to food stamps are part of their policy, $880 billion in cuts to Medicaid.
    0:34:01 And society will not be better off.
    0:34:02 It will not be more balanced.
    0:34:12 It will not be freer or fairer under the Republican legislation that they’re trying to get through, the big reconciliation bill, or frankly, under their leadership.
    0:34:26 And, you know, you’re seeing because it has to do with money and money brings Republicans out of hiding and you’re seeing this pushback on whether Trump even has the right to do this in the first place.
    0:34:36 So there are seven Republicans in the Senate that are signing on to this bill, which is a bipartisan co-sponsored bill, to limit his tariff ability.
    0:34:46 But, like, I was watching Rand Paul on the floor give this speech, a completely historical speech, looking back, saying, like, this is why we left.
    0:34:48 There is no taxation without representation.
    0:34:52 The Constitution is clear that the president cannot do this.
    0:34:56 And the power to tariff goes through Congress.
    0:34:58 Ted Cruz has said something similar.
    0:35:05 James Lankford was on TV last night, very conservative senator, talking about it as well, that this is going to be an issue for the courts.
    0:35:07 There’s going to be a House bill.
    0:35:12 Congressman Don Bacon, who’s a Republican, is bringing up on the House.
    0:35:13 Obviously, all the Dems are going to sign on.
    0:35:18 I don’t know how many Republicans will feel the same.
    0:35:25 But he’s told us who he is more times than I can count.
    0:35:32 You know, this is someone who gets to the Supreme Court, which ends up granting him a level of immunity that you never expected.
    0:35:39 But this is someone who wants the ability to shoot someone on Fifth Avenue and for nobody to care and for there to be no penalties about it.
    0:36:00 And I feel, you know, borderline despondent about this, not only because of the economic consequences of it, but this feels like a constant affirmation of the fact that this man has no limits and that society may not be structured in such a way that we can push back effectively and make a real change.
    0:36:01 I’ll show a couple of things there.
    0:36:02 Let’s talk about the deficit.
    0:36:04 We’re a family.
    0:36:07 If we’re a household, we make $50,000 a year.
    0:36:11 We spend $70,000 and we have debt of $320,000.
    0:36:15 And unfortunately, this debt, our kids will get it.
    0:36:17 Our kids may not want it, but they’ll get it.
    0:36:20 So mom and dad are going to Cancun and doing tequila shots.
    0:36:26 And they have this amazing credit card and they keep getting more and more offers because they’ve always paid their debts.
    0:36:28 And they continue to live above their means.
    0:36:32 There is a really solid argument that we need to get our fiscal house in order.
    0:36:36 I think the Democrats should become dead hawks.
    0:36:40 Having said that, there’s two ways you address the deficit.
    0:36:43 The first is to cut spending and raise taxes.
    0:36:44 You just have to.
    0:36:48 But you also need to continue to grow.
    0:36:54 So if you were to, for example, massively raise taxes and cut spending the way they’re cutting.
    0:36:57 People stop spending money.
    0:37:03 And we’re not going to solve the deficit if we don’t continue, if we shrink the top line.
    0:37:07 And the reason we’re headed, in my opinion, towards a deflationary economy.
    0:37:09 I think inflation is going to come down and they’ll claim victory.
    0:37:11 But I think we’re going to go deflationary.
    0:37:21 And a deflationary economy is especially bad because what happens is if you have a mortgage or credit card debt or student loan debt, you end up paying with more valuable dollars.
    0:37:23 You have less money, more valuable dollars.
    0:37:29 Inflation, in some ways, is good for people with large fixed debt because they’re paying with less valuable dollars.
    0:37:34 So you have to, you can’t put the economy into a coma.
    0:37:39 If you were to cut Social Security or I don’t want to cut it, I want to means test it.
    0:37:40 I want to move the age limit up.
    0:37:44 It’s now three people for everyone supporting someone on Social Security.
    0:37:45 It used to be 12 to 1.
    0:37:48 It’s absolutely the thing we don’t talk about on Social Security.
    0:37:49 It’s an aggressive tax.
    0:37:53 Someone making $160,000 on my team pays $9,000.
    0:37:58 If I make $10 million in a year selling stock or companies, I pay, wait for it, $9,000.
    0:38:03 So we tax the middle class and lower class households full freight on Social Security.
    0:38:04 But rich people, it’s capped.
    0:38:08 But you wouldn’t want to cut it too much.
    0:38:18 You wouldn’t want to cut spending too much because the last thing you want to do is diminish the second weapon of getting us out of the deficit, and that is growing.
    0:38:29 If we just grow 3.6% a year GDP growth, which is real growth, that means in 10 years the economy grows by 50%, which should grow our tax base 50%.
    0:38:38 And who knows, if we’re thoughtful and even keep spending flat, maybe take it down, we substantially reduce the deficit.
    0:38:46 But you can’t just come in and start cutting and put the economy into a coma thinking you’re being responsible.
    0:38:47 You’re not.
    0:38:49 There are two sides to debt reduction.
    0:38:50 Growth.
    0:38:53 Growth isn’t everything, but it’s mostly everything, as is productivity.
    0:38:55 You have to have both.
    0:39:06 So attempting to, quote unquote, use it as an excuse to put the economy in a coma, that is cutting off your nose to spite your face.
    0:39:09 It absolutely makes no sense.
    0:39:25 The other thing we should just keep in mind here is that, and this is the reason why I believe that U.S. stocks are going to vastly underperform the rest of the world, is that there’s two components to a stock price.
    0:39:33 There’s the earnings that represents the underlying innovation, culture, industry they’re in of a company, right?
    0:39:38 The profits, and then the stock is a function of the profits times the earnings multiple.
    0:39:47 The earnings multiple on the S&P 500, all 500 of our biggest, best companies, is around 28 until Wednesday.
    0:39:48 Now it’s about 26.
    0:39:52 The S&P multiple in the German stock market is 22.
    0:39:53 Japan, 18.
    0:39:55 China, 14.
    0:40:03 Now, why do great companies trade as a whole on different multiples when they’re under the umbrella of a different nation or a different index?
    0:40:05 And that’s why everyone wants to go public on U.S. exchanges.
    0:40:11 It’s because we have certain attributes and features that people and investors all over the world really like.
    0:40:13 America is more risk-aggressive.
    0:40:14 It has more risk capital.
    0:40:15 It has great universities.
    0:40:16 It has great IP.
    0:40:18 It’s really flexible.
    0:40:19 It’s agile.
    0:40:21 And it has rule of law.
    0:40:26 No one’s going to come and just, China put Didi out of business.
    0:40:28 We don’t like your practices around information.
    0:40:29 You’re out of business.
    0:40:31 They can put a company out of business.
    0:40:35 Jack Ma, you’re talking, you’re a little too big for your britches.
    0:40:36 We don’t like what you’re saying.
    0:40:39 We’re going to disappear you for six months.
    0:40:41 The U.S. has rule of law.
    0:40:45 So companies feel somewhat safe that when they’re investing, they’re protected.
    0:40:47 Their money is protected.
    0:40:49 Two, we’re seen as consistent partners.
    0:40:51 They’re not stupid.
    0:40:52 They’re not sclerotic.
    0:40:53 They don’t have these epileptic seizures.
    0:40:57 They don’t just weigh in and decide not to do business with certain people or kick these
    0:40:59 nations out or kick these companies out.
    0:41:01 They respect rule of law.
    0:41:07 We haven’t seized the $300-plus billion in Russian assets, despite the fact they’ve invaded
    0:41:11 a neighbor, because we have rule of law.
    0:41:17 Because we no longer in the last two months have those associations with the American brand
    0:41:21 of rule of law and consistency, I believe you’re going to see a re-rating of the price earnings
    0:41:26 multiple on the S&P down from 28 to more like what China’s at at 14.
    0:41:30 And this is what that means and why it’s so important to investors.
    0:41:42 You can’t outrun multiple contraction if the multiple on U.S. stocks gets cut in half, which it very well could, and it’s happened before.
    0:41:46 You know, all of these nations had traded at higher multiples in the U.S. before.
    0:41:56 Then a company like Meta, a company like P&G could grow its earnings 18% a year, which is incredible.
    0:41:59 And in five years, its stock would still be down.
    0:42:03 In Latin America, it didn’t matter how good your company was.
    0:42:12 The flows of capital out of Latin America because of different policies, inconsistent governments, unreliability, lack of rule of law,
    0:42:18 have taken their multiple consistently down for the last 10 or 20 years, and no one’s made any money.
    0:42:35 So the multiple contraction we’re about to endure in U.S. stocks because of the puncturing, because of the exit of this notion of this brand America that includes rule of law and consistency,
    0:42:47 is going to result in a massive contraction in market capitalization in the U.S., which will trickle down to U.S. households, prosperity, tax revenue.
    0:42:53 This is, I mean, you never like to predict a recession because people like me have predicted nine of the last three recessions.
    0:43:05 But if you see this kind of multiple contraction that I think we’re about to incur because of the puncturing and erosion of the brand U.S. right now,
    0:43:13 you’re going to see just a massive, you’re going to see companies overperform, hit all of their earnings, and their stocks are going to go down over the next couple of years.
    0:43:14 That’s an uplifting story. Thank you.
    0:43:15 Right?
    0:43:15 Yeah.
    0:43:17 There you go. I should write children’s novels.
    0:43:20 Oh, my God. Your children’s novels would be so weird.
    0:43:22 Dog, 2028. Things are about to get worse.
    0:43:29 Sometimes, sometimes it’s darkest just, sometimes it’s darkest just before it’s pitch black.
    0:43:31 How’s that on a bumper sticker?
    0:43:37 I mean, I’ve seen worse ones, but that would be up there for sure.
    0:43:40 But it feels like the mood that folks are in right now.
    0:43:43 But we have to take a quick break. Stay with us.
    0:43:49 Support for the show comes from NetSuite.
    0:43:51 Nobody knows what the future will bring.
    0:43:56 Sure, you can keep an eye on trends and cross your fingers, but rates will always rise and fall and rise again.
    0:44:00 The bear market will change to a bull market and back again.
    0:44:04 And until they invent a crystal ball, your next best bet is NetSuite by Oracle.
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    0:45:06 It’s been a rough week for your retirement account, your friend who imports products from China for the TikTok shop, and also Hooters.
    0:45:10 Hooters has now filed for bankruptcy, but they say they are not going anywhere.
    0:45:15 Last year, Hooters closed dozens of restaurants because of rising food and labor costs.
    0:45:23 Hooters is shifting away from its iconic skimpy waitress outfits and bikini days, instead opting for a family-friendly vibe.
    0:45:30 They’re vowing to improve the food and ingredients, and staff is now being urged to greet women first when groups arrive.
    0:45:33 Maybe in April of 2025, you’re thinking, good riddance?
    0:45:37 Does the world still really need this chain of restaurants?
    0:45:44 But then we were surprised to learn of who exactly was mourning the potential loss of Hooters.
    0:45:50 Straight guys who like chicken, sure, but also a bunch of gay guys who like chicken?
    0:45:55 Check out today’s Explained to find out why exactly that is, won’t ya?
    0:46:05 So we want to introduce you to another show from our network and your next favorite money podcast, for ours of course, Net Worth and Chill.
    0:46:09 Host Vivian Tu is a former Wall Street trader turned finance expert and entrepreneur.
    0:46:14 She shares common financial struggles and gives actionable tips and advice on how to make the most of your money.
    0:46:27 Past guests include Nicole Yoder, a leading fertility doctor who breaks down the complex world of reproductive medicine and the financial costs of those treatments, and divorce attorney Jackie Combs, who talks about love and divorce and why everyone should have a prenup.
    0:46:30 Episodes of Net Worth and Chill are released every Wednesday.
    0:46:34 Listen wherever you get your podcasts or watch full episodes on YouTube.
    0:46:35 By the way, I absolutely love Vivian Tu.
    0:46:37 I think she does a great job.
    0:46:41 Welcome back.
    0:46:46 According to Politico, Trump told cabinet members that Elon Musk will be stepping down from his role in the White House by the end of May.
    0:46:50 Musk had been working under a special government employee designation.
    0:46:52 It’s called the mail abandonment designation.
    0:46:58 Anyways, which limits him to 130 days per year, and that clock runs out soon.
    0:47:01 But the possible exit comes at a very tense moment.
    0:47:12 Musk’s Doge team, the Department of Government Efficiency, has been pushing aggressive federal budget cuts, and his unpredictable behavior, especially on X, has reportedly frustrated cabinet officials.
    0:47:24 Critics say his entire billionaire mindset clashes with the realities of governing, especially after a major GOP loss in the Wisconsin Supreme Court race, a race Musk spent over $23 million trying to win.
    0:47:30 The news follows a new Marquette poll showing just 41% approve of his working government, with 58% disapproving.
    0:47:38 And while Trump publicly praised Musk early on, some insiders say may now be using him as a scapegoat for recent political missteps.
    0:47:47 Meanwhile, Tesla is also feeling the heat, with Q1 sales down 13%, raising questions about whether Musk’s political ambitions are hurting his business.
    0:47:49 Jess, what do you think?
    0:47:50 Do you think Doge is coming to an end?
    0:47:51 Do you think Elon’s leaving?
    0:47:52 I think those are two separate questions.
    0:48:07 So I think Elon is leaving, and, you know, he talked about being a special government employee, which has this 130-day limit on it, in his interview with some of the Dogettes with Brett Baer a couple weeks ago.
    0:48:25 So that was the expectation, but it couldn’t come at a better time for how tense things are in D.C. between Musk and the other cabinet secretaries who are trying to do their job or at least trying to do their job less bad.
    0:48:34 And Musk keeps getting in the way of it, because every time that he makes a cut from somewhere that has to be reinstated, that’s something that these cabinet secretaries need to deal with.
    0:48:40 And there’s been public reporting about pushbacks in meetings and complaints.
    0:48:49 Secretary Rubio and Secretary Duffy, in particular, have been very frustrated by Musk’s outsized role in this, and apparently Trump himself.
    0:48:57 And, you know, you could see that something was changing by the content of Musk’s tweets.
    0:49:02 So the last kind of week, he doesn’t talk about DEI much anymore.
    0:49:05 He doesn’t talk about massive fraud.
    0:49:13 He’s actually talking about business again and going to space and Tesla and Neuralink and Starlink.
    0:49:20 And, yeah, he’s still red-pilled and getting into some of the conspiracies, but he was so far off the reservation that you can tell that something has changed.
    0:49:31 And I think that this moment where he is advocating for a free trade zone at the time when Trump is putting forward these crazy tariffs,
    0:49:33 And he’s going straight at Peter Navarro.
    0:49:37 Those two seem to have a bit of a war going on.
    0:49:43 I think Navarro called him not a carmaker, but a car assembler on CNBC this morning.
    0:49:45 So that’s Monday morning.
    0:49:48 You know that there was massive trouble in paradise.
    0:49:57 And I don’t know if you caught it because you were on school tour last week, which was way more important than this, but Musk joined the five.
    0:50:08 And I got to ask him a question last Tuesday, the day of the Wisconsin Supreme Court race, which he spent more than anybody else, you know, dwarfing George Soros, who loves to buy off elections.
    0:50:12 Musk spent $23 million it was versus Soros spent $2 million, I think.
    0:50:17 But I had just a couple hours to figure out what I wanted to ask him.
    0:50:22 And there are so many things when you have the opportunity to talk to someone that is doing these other cool things.
    0:50:23 Like, I’d love to talk about Neuralink.
    0:50:28 I’d love to talk about global population trends with him and things like that.
    0:50:29 But I was like, you know what?
    0:50:35 I need to talk to or at least try to get him to answer something about these conflicts of interest that he has.
    0:50:48 And so I brought up him firing all of the folks that are looking into his companies and then he keeps getting government contracts and he keeps accepting these subsidies for Tesla, even though he’s advocated against subsidies.
    0:50:51 And he totally dodged, which is what I expected.
    0:50:53 And I lured him in.
    0:50:59 I was friendly and I smiled and I made a joke about being a liberal Tesla lover and not throwing a Molotov cocktail myself.
    0:51:18 And that dichotomy between what he says that he’s doing, that this is a totally transparent process and they’re just trying to make government more efficient and what they are actually doing is what is animating and kind of setting the Democratic base on fire as of late.
    0:51:24 I mean, Elon Musk is the most unpopular figure in government by far and away.
    0:51:44 And I think that Trump saw the writing on the wall with all this, looked at the results of the Wisconsin Supreme Court seat, but also these special elections in Florida where Democrats overperformed 15, 16 points and just said, like, it cannot come soon enough for you to leave.
    0:51:45 Maybe he’ll continue to be an advisor.
    0:51:55 I’m sure they’ll have their alliance for time to come, but he can’t be someone that’s in every press conference and became a political liability for them.
    0:52:01 And likewise, the government became a liability for Musk to continue to be a successful businessman.
    0:52:09 I predicted, I think, three weeks ago on Pivot that Musk would fade to black and leave within 30 days.
    0:52:24 And the reason why is if you want to understand anything or you want to understand behavior across big tech and for the most part across almost every individual in America, just reverse engineer to what decisions, what motivations would give them more money.
    0:52:35 America used to be about your character, your partner, your reputation indicated a decent amount of what your life was like.
    0:52:37 Now, you can have none of those things.
    0:52:40 And if you have money, you can have an amazing life and you can have all of those things.
    0:52:44 And if you don’t have a lot of money, you can have almost none, you know, none of the prosperity.
    0:52:49 So all incentives are do whatever you can to make a lot of money.
    0:52:57 And when I think it was the province of Alberta announced that they were canceling their start their SpaceX contract for Starlink.
    0:52:58 I’m like, that’s it.
    0:52:58 He’s out.
    0:53:00 All he cares about is money.
    0:53:01 He’s not trying to help America.
    0:53:05 He’s trying to remove inspectors so he can get his autonomous out there faster.
    0:53:13 He’s trying to remove any sort of oversight around merging X and XAI and data privacy.
    0:53:19 He hates subsidies after he’s taken, you know, after he’s taken 15 to 50 billion.
    0:53:22 These guys are all about money.
    0:53:23 And there’s a few lessons in here.
    0:53:28 The Democrats need to move away from identity politics.
    0:53:36 There’s a very loud minority of people who see at Ivy League universities and in the media and cultural elites who see everything through the lens of race.
    0:53:44 America, the people who decide the president, see everything through the lens of money.
    0:53:47 Who’s going to put the most money in my pocket?
    0:53:50 Yeah, I’ll give some lip service to women’s rights.
    0:53:53 I’ll give some lip service to Ukraine.
    0:53:58 But for the most part, I’ll vote for the criminal because I’m under the impression he’s going to put more money in my pocket.
    0:54:01 So and it’s also the most dynamic issue.
    0:54:07 If you’re a fierce, if you’re one of those voters that just votes on bodily autonomy, you know which party you’re going with.
    0:54:11 But the swing voters basically vote on the economy.
    0:54:15 And the economy is dynamic in the sense that sometimes Democrats get the benefit of the doubt around the economy.
    0:54:23 If anyone does any homework and realizes over the last 50 years, Democratic administrations have created 40 million jobs and Republican administrations have created 1 million.
    0:54:29 Or they say this guy is not a failed businessman and reality TV game show host.
    0:54:30 He’s actual businessman.
    0:54:38 And Biden and Harris were trying to tell us when we couldn’t afford to put food on the table that everything’s great, not to worry about it.
    0:54:41 They went Republican, but it’s about the economy.
    0:54:45 And this is what is so disappointing in my view.
    0:54:49 Even though I know this is happening, but it shocks me when it happens.
    0:54:52 What have we had in the last couple of months?
    0:54:57 We’ve had a reversal in the global order.
    0:55:09 We’ve said we used to use our massive tax base and military to support democracies and nations that believe in civil rights, women’s rights, trying to do the right thing.
    0:55:10 We’ve torn it up and said, you know what?
    0:55:14 Let’s pretend that Russia won the Cold War.
    0:55:16 Let’s not support Democratic allies.
    0:55:19 Let’s support murderous autocrats.
    0:55:34 Let’s create an environment where we have judges where if a woman is bleeding out or dying from sepsis in an emergency room parking lot because people, doctors are so scared to treat her, you know, her pregnancy gone wrong.
    0:55:36 Let’s create that environment.
    0:55:57 Let’s decide that a woman who is a conspiracy theory fucking loon, Laura Loomer, that she gets to fire General Hawk, who is arguably one of the brightest generals, government men in the world who’s responsible for intercepting signals and making sure our troops are safe overseas.
    0:56:09 Let’s hire a group of people that are so incompetent that they’re sharing attack plans on an unsecure phone, on an app, and then start calling the reporter they invited in a loser.
    0:56:17 You know, let’s do everything we can to tear up 90-year alliances.
    0:56:26 Let’s start picking up people with the wrong tattoo, shipping them to El Salvadorian prisons, not even where they’re from, and then claim we can’t get them back.
    0:56:29 When that’s just a bald-faced lie, one call they could get this person back.
    0:56:34 All of these things in my mind are well ahead.
    0:56:38 I lost several million dollars on Thursday and Friday, which is the bad news.
    0:56:42 The good news is if you lose several million dollars, it means you’re already wealthy and you’re doing just fine, and I am.
    0:56:42 I’m doing just fine.
    0:56:48 I was much less triggered by the markets than I have been about these other things.
    0:57:00 And so if it took the markets going down 10% to convince you that this ass clown was the wrong person to be overseeing the greatest experiment in history, you still have your head up your ass.
    0:57:04 I was kind of like, this is what it took to get people upset?
    0:57:09 All of a sudden, the tech bros, the most powerful people in the world, are decided they need to go down to Mar-a-Lago.
    0:57:19 Okay, when a 14-year-old girl in Mississippi has to carry a baby to term after being raped, you know, okay, that’s bad.
    0:57:23 But when my stock goes down, I’m on a plane.
    0:57:30 So what I know, but it always disappoints me, is the following.
    0:57:32 Look what money has done to us.
    0:57:42 We forgive a guy and idolize a guy that’s being sued concurrently by two women for abandonment, for not seeing his kids.
    0:57:45 I mean, look what money has done to us.
    0:57:53 And the thing we get outraged about, the thing that is the call to arms, is the markets go down fucking 10%.
    0:57:59 This is number 15 on the reasons why we elect our leaders.
    0:58:03 So I actually found this week, it didn’t trigger me at all.
    0:58:04 I was on the college tour.
    0:58:06 I was emotional for a lot of reasons.
    0:58:06 I didn’t care.
    0:58:14 I, you know, whether I’m worth X million or, you know, whatever, 0.95X, it doesn’t fucking make any difference.
    0:58:23 What makes a difference is your kids growing up in a nation that’s no longer America, where, you know, their friends who aren’t them, who aren’t in the top 1%,
    0:58:28 don’t have the same rights we had, don’t have a shot at getting out of the bottom 90.
    0:58:37 So I find what’s happened in the last week disappointing because, not disappointing because Trump makes a stupid decision.
    0:58:37 He’s been making them.
    0:58:42 I actually am disappointed that this is what it took to get people into the streets of America.
    0:58:48 That, okay, the war on poor women, well, okay, that’s too bad.
    0:58:52 But, you know, what’s profound is if the NASDAQ goes down.
    0:58:53 Your thoughts?
    0:58:54 Depressing, but expected.
    0:58:58 And that has been the lesson of the last few cycles.
    0:59:06 And to your point about wanting to get away from identity politics, this is the game that we have to play.
    0:59:19 And that the most compelling stories are going to be the ones that are rooted in the oligarch class taking something away from people with a lot less.
    0:59:23 Man, that does come down to Social Security payments.
    0:59:24 That’s been resonating.
    0:59:24 That’s money.
    0:59:26 How do you put food on your table?
    0:59:27 Right?
    0:59:28 Your Medicaid.
    0:59:32 That’s money that you need to spend on your health care.
    0:59:33 And they want to come for that.
    0:59:40 And I do think that the Democrats are getting hip to the fact that those are the types of arguments that are going to resonate.
    0:59:43 And that they’re the most powerful motivators.
    0:59:54 And I still feel like heart’s in the right place, of course, to want to amplify those incredibly depressing stories that you’re talking about.
    1:00:00 You know, young women being forced to carry children that they don’t want to term.
    1:00:12 And what’s going on to the going on with the global order and how we’re treating our allies versus our enemies and that the world is moving on without us, whether we like it or not.
    1:00:22 And, you know, we’re stuck here with this administration that feels like isolationism is the way to go.
    1:00:26 But nothing is going to move the needle unless it’s bank accounts.
    1:00:31 And I don’t want anyone to suffer.
    1:00:33 I have said this multiple times.
    1:00:36 And also, obviously, I’m fine financially.
    1:00:46 But the stock market change makes a big difference in my life, thinking about how I’m going to send my girls to college and what retirement looks like and all of that.
    1:00:57 But it feels like this was the fuck around and find out moment for a lot of people, those 62 percent that are invested in the market.
    1:01:10 And I’m not sure that that’s such a bad thing for us to have this reminder this early in the Trump administration that he doesn’t care about anyone but himself and a few select friends.
    1:01:11 And that number is dwindling by the day.
    1:01:13 Let’s take one more quick break.
    1:01:14 Stay with us.
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    1:02:26 That’s Nutraful.com, promo code PROFG.
    1:02:33 Welcome back.
    1:02:38 Before we go, we wanted to briefly touch on the record-breaking speech from Senator Cory Booker last week.
    1:02:41 He spoke on the Senate floor for over 25 hours straight.
    1:02:47 The longest speech in congressional history urging lawmakers to reconsider proposed GOP cuts to Social Security.
    1:02:54 He called on Democrats to unify and fight harder, even as the party continues to debate how aggressively to take on Trump’s policies.
    1:03:01 This speech was followed by a massive hands-off protest in all 50 states over the weekend.
    1:03:05 Groups including Indivisible and MoveOn organized around a simple message.
    1:03:12 Trump’s economic policies, including cuts to healthcare, education, and Social Security, are hurting everyday Americans while helping the wealthy.
    1:03:13 Actually, I should correct myself.
    1:03:16 A lot of those protests weren’t just solely a function of the market decline.
    1:03:19 They had been planned around some of these other issues.
    1:03:21 So that was not fair to me.
    1:03:24 Booker’s marathon moment wasn’t just about policy.
    1:03:28 It’s also being read as a potential turning point in democratic leadership and strategy.
    1:03:35 Jess, what are your thoughts on Booker’s Senate floor, diatribe speech, marathon?
    1:03:37 We have a pulse.
    1:03:45 I feel a little alive in terms of being a strong opposition force.
    1:03:47 And there are going to be detractors.
    1:03:56 And I work with a lot of them who were calling him Spartacus and mocking the whole scene of it all.
    1:04:00 But it animated people.
    1:04:06 And he made a lot of points that I think are really important to take stock of.
    1:04:11 That, you know, we’re in a moral moment in American history.
    1:04:15 And our coalition does not look like it used to.
    1:04:18 Well, also, we used to win and now we don’t win as much.
    1:04:19 So that’s part of it.
    1:04:23 But he said, I’m standing here because of a national crisis that’s growing.
    1:04:24 We talked about Social Security.
    1:04:25 We talked about health care.
    1:04:26 We talked about education.
    1:04:28 This is a crisis for us.
    1:04:36 And you’ve taken responsibility for going too hard about the market because those are things that are causing people to show up at town halls.
    1:04:39 That are causing millions to go to these hands-off protests.
    1:04:53 I saw that the Google searches for the protests this weekend rivaled the Women’s March in 2017, which I remember and participated in the New York one.
    1:04:59 So I feel like we have a pulse.
    1:05:03 And Musk has been the linchpin, I think.
    1:05:12 And it’s the money component of Musk that he continues profiting off of us while trying to fire us and steal from us.
    1:05:18 But Musk was a huge gift and something that I wanted to ask you about that occurred to me.
    1:05:30 Do you think actually now looking at what’s happened in the few weeks since the continuing resolution passed that actually Chuck Schumer was right to make sure that we keep the government open?
    1:05:35 Because now the Republicans own absolutely everything that’s going on.
    1:05:41 There’s no way to blame a Democrat, right, for any of the cuts that are happening, for what’s happening in the market.
    1:05:47 Like, you pick your pet issue, and we are nowhere near any position of power.
    1:06:04 And I am feeling a little bit wrong that I was so hard on Schumer because I think it is a good thing that we can just point squarely to Mike Johnson, Donald Trump, Senator Thune, and say, Musk, you know, you did this.
    1:06:05 So I love what the military does.
    1:06:10 Whenever they have an operation, so the best performing organization in history is the U.S. military.
    1:06:18 And a lot of corporations take their cues from military procedures and best practices because they’re so good at what they do.
    1:06:35 And one of their best practices is that they review every combat operation, what went right, what went wrong, how can we improve the culture, the chain of command, the weapons, the intelligence, such that we iterate to become a more lethal force moving forward.
    1:06:42 And one of the key sort of paradigms or lens through which they look at decisions is they say, it’s not about the outcome.
    1:06:48 It’s about, did that officer, what did that officer know at that moment?
    1:06:54 And did he or she make the best decision based on the available information at that point?
    1:07:01 And given what’s happened in the market with these tariffs, you’re right.
    1:07:07 It worked out well because they can’t say, well, if the Democrats hadn’t shut down the government, this wouldn’t have happened.
    1:07:19 So it’s ended up, I think we would rather be here right now with the government open and then not having a, you know, a punching bag or a voodoo doll to blame this all on.
    1:07:24 So it, the, the, the, the outcome here is good.
    1:07:29 Having said that, given what we knew back then, I think it was still the wrong decision.
    1:07:43 So I hear what you’re saying and I wouldn’t change anything now, but it doesn’t excuse the fact that based on what we knew then, he should have gone upstream and shut it down and said this, you no longer believe in government.
    1:07:56 Fine, we’re no longer going to fund something that is nothing but a vehicle for you to raise, raise taxes on the young, uh, bypass congressional authority, make budget cuts that are supposed to have congressional approval.
    1:07:58 We’re done.
    1:08:02 We’re no longer going to fund your clown car, but I agree with you.
    1:08:06 This is actually ended up turning to Democrats advantage.
    1:08:23 I think we could judge it a little and we can meet in the middle and just say maybe he just, he should have messaged everything better, which we were talking about at the time and had a plan to keep the government open from the Democratic side.
    1:08:35 And tried to do some negotiating, Nancy Pelosi styles, um, and maybe then we could have won on all, on all fronts, but I feel a bit better.
    1:08:37 Like we’re a real opposition party.
    1:08:41 I feel like 2026 could go well for us.
    1:08:44 I already mentioned the overperformance in those Florida special elections.
    1:08:56 One was by 16 points, one was by 22 and Elise Stefanik, who was headed to the UN to be our ambassador, was called back to her New York district, which is a Trump plus 21.
    1:09:02 So, and they’re clearly afraid of the fact that they could possibly lose that or it would get so close.
    1:09:09 That’d be another, you know, big moment of embarrassment for the Republican party and a harbinger of things to come.
    1:09:22 So, you know, the country is in turmoil, but on the politics side of it, I, I feel like we’re getting our ducks in a row and I have not felt that way for several months.
    1:09:24 There was so much I loved about Senator Booker’s speech.
    1:09:25 You’re right.
    1:09:27 It, it pulses the right word.
    1:09:28 I love that.
    1:09:33 And just because I like to trigger our listeners, I thought it was a very masculine thing to do.
    1:09:38 Uh, I think the Democrats lack a certain level of aspirational masculinity.
    1:09:44 I think showing that sort of physical strength and endurance and people will say, well, could a woman do it?
    1:09:45 Not as easily.
    1:09:46 Men are stronger.
    1:09:47 We definitely have to pee.
    1:09:50 Women can endure more pain because they have to endure childbirth.
    1:10:02 Notice how I, my politically correct instincts moved in and I had to say something nice about when you say, or if you say something nice about women, you don’t feel a need to compensate with saying something nice about men, but we need to say something nice about women.
    1:10:14 But anyways, I think the Democratic Party is lacking aspirational masculinity and demonstrating that sort of strength and endurance demonstrates some of that masculine energy, which I think we’re desperate for.
    1:10:17 So hats off to you, Senator Booker.
    1:10:27 It also convinces me that he’s going to get married or engaged in the next two years because these guys realize it’s like, who is that?
    1:10:30 Who is the senator?
    1:10:31 Is he a senator from?
    1:10:32 Tim Scott?
    1:10:37 Yeah, who was making a run for VP, so decided to get engaged in the middle of his run for VP.
    1:10:42 These guys, they’ve all figured out you can’t be president as a single man.
    1:10:43 I’ll be like, that would be a good rap.
    1:10:44 What do you do?
    1:10:45 I’m president.
    1:10:48 Hey, you want to go back and, you know, watch some TV?
    1:10:51 That would be a good rap, right?
    1:10:53 Well, I mean, nothing better than the American president.
    1:10:55 He was a widow, widower.
    1:10:56 That was such a fantasy.
    1:10:59 A nice Democrat who’s single.
    1:11:01 That was so ridiculous.
    1:11:02 It’s the best.
    1:11:04 Annette Bening, God, she’s hot.
    1:11:05 She’s really hot.
    1:11:05 Yeah.
    1:11:14 Andrew Shepard, though, he is, I think he usually is the top for fictional presidents on all of those lists.
    1:11:16 She was great in Bugsy.
    1:11:18 I mean, she’s great in everything.
    1:11:22 And she makes that haircut so hot, like the little pixie cut.
    1:11:24 Yeah, I hadn’t thought about that.
    1:11:30 She was in one of my favorite movies, which is how I would describe almost every tech bro right now.
    1:11:32 Did you ever see Grifters, The Grifters with Angela?
    1:11:33 No.
    1:11:34 Oh, gosh, what’s her name?
    1:11:35 Am I saying the movie right?
    1:11:39 The woman who was dating John Cusack, Annette Bening.
    1:11:41 It’s a fantastic film.
    1:11:45 And the woman, Angela Houston, that’s her name.
    1:11:48 That is a fantastic film.
    1:11:53 Well, I need something to watch because I saw the White Lotus finale, and I am not pleased.
    1:11:54 We shouldn’t talk about it.
    1:11:54 No spoilers.
    1:11:54 Don’t say anything.
    1:11:55 You don’t know?
    1:11:56 You’re the star of White Lotus?
    1:11:57 You don’t know what happened?
    1:11:59 Well, I’m glad you recognize I’ve carried the season.
    1:12:01 It’s pretty obvious.
    1:12:02 It’s pretty obvious.
    1:12:12 No, but I think Cory Booker basically is now one of the top three or four contenders for the Democratic nomination for president.
    1:12:14 All of these guys want to be president.
    1:12:16 So, one.
    1:12:16 Mostly you.
    1:12:18 I’m probably the most recalcitrant.
    1:12:19 I’d have to give abettables.
    1:12:22 I just don’t think I could be president.
    1:12:22 Would you?
    1:12:23 To be president?
    1:12:25 Look what’s going on right now.
    1:12:25 Would I?
    1:12:27 I mean, he’s sober, but he’s.
    1:12:29 Yeah, but it’s a different standard.
    1:12:30 It’s a different standard.
    1:12:33 I’d have to go so fucking crazy like him that nothing mattered.
    1:12:37 Edibles would be the least of your issues, I think, if you were.
    1:12:37 Yeah.
    1:12:39 No, I love the idea of running.
    1:12:41 I just don’t like the idea of actually being president.
    1:12:44 It’s like the key to happiness is to be rich but anonymous.
    1:12:46 I’d rather.
    1:12:48 I’m going to fade into anonymity.
    1:12:57 Anyways, but my prediction here is that Cory Booker, in the next 24 months, as he makes a move towards announcing his presidency, is he going to realize he needs to be married.
    1:13:02 So, ladies, if you like the Senator Booker, he is looking right now.
    1:13:05 Because, you know, marriage, as I coach young people, I’m really going off script.
    1:13:08 I’m like, it’s not about meeting the one.
    1:13:09 There is no such thing as the one.
    1:13:14 It’s about where you are and where they are at that point in their lives.
    1:13:24 So, but he’s at that point because he realizes that he actually has a shot at doing what he’s wanted to do his whole life, be president.
    1:13:26 But he needs to be married.
    1:13:28 Anyways, kind of an odd prediction, but I’m standing by it.
    1:13:31 First person to ever say timing is everything, huh, Scott?
    1:13:32 That hurts.
    1:13:35 A chicken in every pot of sea.
    1:13:36 Alice in every cupboard.
    1:13:36 All right.
    1:13:38 That’s all for this episode.
    1:13:40 Thanks for listening to Raging Moderates.
    1:13:42 Our producers are David Toledo and Chinenye Onike.
    1:13:45 Our technical director is Drew Burroughs.
    1:13:47 You can now find Raging Moderates on its own feed every Tuesday.
    1:13:48 That’s right.
    1:13:49 Its own feed.
    1:13:50 What a thrill.
    1:14:04 That means exclusive interviews, including the one with the person who will come in number two in Iowa, Mark Cuban, with sharp political minds you won’t hear anywhere else except every other fucking place in the world as they’re all whores running for president.
    1:14:07 But yeah, just exclusively on Raging Moderates.
    1:14:09 Unless you got a mic and they’ll speak there.
    1:14:12 This week, as we said, we’ll be speaking with Mark Cuban.
    1:14:15 Make sure to follow us wherever you get your podcasts.
    1:14:16 You don’t miss an episode.

    Scott and Jessica break down Trump’s sweeping new trade war that’s tanking the markets, Elon Musk’s rumored White House exit after a rocky tenure as Trump’s government efficiency czar, and a fiery 25-hour speech from Senator Cory Booker that’s lighting a fire under Democrats — and may signal a new chapter in the resistance.

    Follow Jessica Tarlov, @JessicaTarlov. 

    Follow Prof G, @profgalloway.

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  • Nvidia’s AI Chief: “AI Agents Will Solve the World’s Biggest Problems”

    AI transcript
    0:00:04 Hey, welcome to the Next Wave Podcast.
    0:00:08 I’m Matt Wolfe, and in today’s episode, we’re talking about AI agents.
    0:00:15 I had the opportunity to go spend a few days with NVIDIA out at the NVIDIA GTC conference.
    0:00:20 And in this episode, I’m going to deep dive with Amanda Saunders from NVIDIA.
    0:00:26 She’s been at the heart of the AI agent revolution that’s happening right now while working with
    0:00:30 NVIDIA, the company that’s enabling the AI revolution.
    0:00:36 In this episode, we’ll unpack exactly what agentic AI is and how it’s reshaping industries
    0:00:41 from healthcare and telecom to sports coaching, as well as why that’s just scratching the surface
    0:00:42 of what’s possible.
    0:00:48 You’ll also discover NVIDIA’s secret blueprint for easily building powerful AI agents, as
    0:00:54 well as get into some of the fears that people have around AI agents, like is an AI agent going
    0:00:56 to take your job and replace you?
    0:00:57 Yeah, we’re going to go there.
    0:01:01 And after the interview, I’m going to share some cool clips from NVIDIA’s GTC, where I
    0:01:09 got Bob Petty, also from NVIDIA, to break down exactly what NVIDIA’s DGX Spark is and how
    0:01:14 they’re going to be putting AI supercomputers in normal people’s homes.
    0:01:18 You can have an AI supercomputer in your home by this time next year.
    0:01:20 We’re going to get into that in today’s episode.
    0:01:25 So without further ado, here’s my discussion with Amanda Saunders, followed by my tour of the
    0:01:27 EGX Spark with Bob Petty.
    0:01:28 Enjoy.
    0:01:33 Marketing in 2025 is wild.
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    0:01:42 Privacy changes make ad targeting a nightmare, and everyone needs more content than ever.
    0:01:46 That’s why you have to have HubSpot’s new Marketing Trends Report.
    0:01:51 It doesn’t just show you what’s changing, it shows you exactly how to deal with it.
    0:01:55 Everything’s backed by research, but focused on marketing plays you can use tomorrow.
    0:01:58 Ready to turn marketing hurdles into results?
    0:02:02 Go to click HubSpot.com slash marketing to download it for free.
    0:02:07 Yeah, so my name’s Amanda Saunders.
    0:02:11 I’m the director of enterprise AI software here at NVIDIA.
    0:02:16 So you know a lot of people think about NVIDIA and all the amazing GPUs, CPUs, and DPUs that
    0:02:18 we make that go into these powerful systems.
    0:02:21 Well, actually, we have a lot of software that runs on that.
    0:02:26 So me and my team, we focus on how do we tell that story and bring it to customers.
    0:02:31 I’ve been at NVIDIA 10 years now, and I’ve covered everything from, you know, the graphic
    0:02:34 side of our business into the data science.
    0:02:37 And now, of course, the hottest topic, generative AI.
    0:02:38 Very cool.
    0:02:41 Let’s talk about agentic AI, because that’s not a hot topic.
    0:02:45 It seems like in 2025, everybody’s saying this is the year of agentic AI.
    0:02:48 How would you define agentic AI?
    0:02:49 Absolutely.
    0:02:52 So agents are helpful software tools.
    0:02:56 They are digital employees that help us augment our work.
    0:03:01 And what’s really important about agents and agentic AI is that it can perceive.
    0:03:02 So it sees the world around it.
    0:03:04 It sees the data that it has access to.
    0:03:06 It takes that information.
    0:03:08 It reasons about that information.
    0:03:09 It thinks about it.
    0:03:13 And then it can actually make actions based on that data.
    0:03:16 So it can perceive, it can reason, and it can act.
    0:03:20 And that’s what really sort of sets it apart from generative AI that we had in the past,
    0:03:26 is being able to take those actions, whether that action is alerting a human or actually
    0:03:30 actively using a tool and making something happen.
    0:03:34 And so, yeah, I think they’re really exciting because they’re able to solve complex problems
    0:03:37 that we’ve never been able to do with software before.
    0:03:37 Very cool.
    0:03:41 So what do you think makes agentic AI powerful right now?
    0:03:42 Why not a year ago?
    0:03:43 Why not two years ago?
    0:03:46 Why is now the time everybody’s talking about it?
    0:03:48 You know, I think this has been a journey that we’ve been on.
    0:03:51 You know, it started with having to have the accelerated computing.
    0:03:53 You had to have the computing power to do that.
    0:03:56 And that’s a problem that NVIDIA has been working on for 30 years.
    0:03:59 And then finally, we’ve got to the place where we have the computing systems.
    0:04:01 We also then needed the software systems.
    0:04:05 And so it started with the open models that became available.
    0:04:09 I think LAMA was a huge advancement and step forward.
    0:04:12 And more and more models have come out recently.
    0:04:16 Reasoning models, in fact, are one of the big pieces that have sort of driven that
    0:04:18 and sort of added to that.
    0:04:22 And then just the full ecosystem of tools and things that are required.
    0:04:28 I think AI is one of those really interesting fields where the more people use it, the more
    0:04:31 they want to use more of it and they want to do more.
    0:04:35 And so it’s really spurring this incredibly fast growth that we’re seeing, which is, you
    0:04:36 know, breakneck speed.
    0:04:42 But that’s what’s really driving us from, you know, the early onsets of generative AI,
    0:04:46 where you’re just spitting out answers into these worlds now where they’re actually thinking
    0:04:50 things through and making really smart actions, which is just sort of cool.
    0:04:50 Yeah.
    0:04:55 Or in my case, I got into AI and now I just make content about it because I’m obsessed with it.
    0:04:56 It’s all I ever want to talk about.
    0:05:00 Because yeah, once you get into it, you really, that’s all you want to do is you figure out
    0:05:01 what it can do.
    0:05:05 You learn where it’s, you know, boundaries are, and then you want to do more and more and
    0:05:05 more.
    0:05:10 And as it continues to, you know, get better, you can do more and more things with it.
    0:05:10 So cool.
    0:05:13 Well, can we walk through like a agentic workflow?
    0:05:17 Like give some examples of like, this is the types of things an agent can do.
    0:05:18 Yeah, absolutely.
    0:05:23 I think, you know, some of the most basic things that we see from agents are really about
    0:05:24 being able to talk to data.
    0:05:28 I think that’s probably the first thing that we see most people try to do is they have their
    0:05:34 own personal data, they connect an LLM to that data, and then they’re able to ask questions.
    0:05:38 And I think, you know, more recently, they’re able to ask more and deeper questions.
    0:05:40 Deep research has been a big topic.
    0:05:42 It’s a big topic here at GTC.
    0:05:48 And the more information you can get out of that data and the more quickly you can query that,
    0:05:51 that’s a pretty standard agent that we would see out there.
    0:05:54 Now, then they start to get a lot more complex.
    0:05:59 I think there’s some really cool stuff going on in the telecom space where they’re actually
    0:06:05 using agents to improve the network so they can actually predict when there might be network
    0:06:12 outages coming and they can make recommendations to the human employees who, you know, maybe you
    0:06:17 want to make these changes because maybe there’s a large show in town like GTC and there’s going
    0:06:20 to be a lot of traffic on that network.
    0:06:25 Here are the recommended changes so that you can actually, you know, still provide the service.
    0:06:27 Is that kind of thing happening at GTC right now?
    0:06:28 Are they using that?
    0:06:29 I wish they would.
    0:06:33 I think, I think the agents are just starting to be developed.
    0:06:37 So maybe next year we’ll actually see that, but it’s definitely something that we’re going
    0:06:37 to see more of.
    0:06:38 Yeah.
    0:06:38 But yeah.
    0:06:41 So those I think are, you know, some varying levels of examples.
    0:06:45 And then there’s obviously hundreds more in the healthcare space, you know, helping nurses
    0:06:50 and doctors become more efficient because we know they need the help to, you know, everyday
    0:06:51 people like you and I.
    0:06:56 I mean, I use it for almost everything I do, whether it’s in my personal life or my
    0:06:56 work life.
    0:06:59 Are there any like agents that might surprise people?
    0:07:04 Anything that’s like really sort of like interesting that, oh, I wouldn’t have thought people would
    0:07:05 be using agents in that area.
    0:07:07 It’s an interesting question.
    0:07:12 I mean, the most interesting one for me on the agent side is really, as you started to
    0:07:16 get into video and other types of data sources, like text, I think people have gotten the handle
    0:07:21 on, but adding sort of video understanding and things coming through it.
    0:07:25 Actually, one of the really cool use cases that we recently did was Jensen got to throw
    0:07:27 out the first pitch at a baseball game.
    0:07:28 I saw the clip of that.
    0:07:36 And we used a video agent to be able to critique his performance, which I just think is really
    0:07:36 cool.
    0:07:41 So you can imagine for, you know, athletes, whether they’re amateurs or professionals, being
    0:07:43 able to use that is really helpful.
    0:07:43 Yeah.
    0:07:44 Yeah.
    0:07:46 I can see that like golfers and stuff like that.
    0:07:47 Analyze my swing.
    0:07:48 Tell me what I’m doing wrong.
    0:07:48 Stuff like that.
    0:07:53 And it’s probably too much for the AI on my swing, but you know, it still would be helpful
    0:07:54 to know.
    0:07:54 Yeah.
    0:07:54 Yeah.
    0:07:56 Well, let’s, let’s talk about the NVIDIA blueprints.
    0:08:00 Because when I was at CES, that was a big topic during CES was the NVIDIA blueprints.
    0:08:03 And to me, that’s sort of like the beginnings of agents, right?
    0:08:06 But there’s sort of this pre-built agent.
    0:08:07 You could probably explain it better than I can.
    0:08:09 Well, exactly.
    0:08:12 We tried to name them blueprints so that people would get the idea that these are reference
    0:08:13 architectures.
    0:08:18 And what they do is they take the different building blocks that NVIDIA is offering to
    0:08:22 make these agents, and it helps you with a recipe on how to put them together.
    0:08:25 So it starts out with our NIM microservices.
    0:08:29 And so these are packaged, containerized, optimized models.
    0:08:30 Right.
    0:08:34 So all the leading open models that are out there in the market, we take them, we containerize
    0:08:39 them, we add a standard API call to them so that people, you know, developers out there
    0:08:40 can start building.
    0:08:42 And we package those up.
    0:08:43 So now you’ve got the model.
    0:08:45 And now you need to connect that model.
    0:08:46 Right.
    0:08:46 It’s right.
    0:08:47 A model and it’s only, only does so much.
    0:08:48 So that’s…
    0:08:54 So like a NIM would be like, you’ve got a video NIM that this one NIM can produce AI-generated
    0:08:55 video.
    0:08:57 This NIM could produce text-to-speech.
    0:08:59 This NIM could do speech-to-text.
    0:08:59 Exactly.
    0:09:00 Right.
    0:09:00 Okay.
    0:09:00 Exactly.
    0:09:05 So we actually have a hundred NIM that are now available that you can download and use.
    0:09:10 And they do, there are 40 different sort of domains or modalities that these different
    0:09:11 NIM can do.
    0:09:12 So yeah.
    0:09:14 So you can take a bunch of them, piece them together.
    0:09:17 And so when we built those, everyone was like, this is great.
    0:09:18 We need the models.
    0:09:22 We need them to run really well on, you know, our NVIDIA GPUs.
    0:09:24 But then they said, well, but how do we build them into the next thing?
    0:09:26 And that’s where Blueprints came from.
    0:09:26 Right.
    0:09:31 So they are the recipe that allows you to take the NIM, start with this recipe, piece this
    0:09:32 together, and you’ll get to an agent.
    0:09:36 And then what’s even better is you can then customize them.
    0:09:38 So you can, you know, add your own data sources.
    0:09:40 You can add your own pieces in.
    0:09:42 You can combine Blueprints together.
    0:09:47 So maybe you started with a chatbot and you wanted to add a digital human on the front end.
    0:09:49 We have a Blueprint for both of those.
    0:09:52 You piece them together and all of a sudden you have a digital avatar.
    0:09:57 Who can, you know, talk to you with a full, you know, face and expressions and natural language.
    0:09:58 Probably even your voice if he wanted to.
    0:09:59 Yeah.
    0:09:59 Absolutely.
    0:10:04 And you can, you can start with pieces from NVIDIA or you can start with pieces from the
    0:10:04 ecosystem.
    0:10:07 We try to make it really easy to piece it all together.
    0:10:08 Super cool.
    0:10:10 Let’s talk a little bit about like security.
    0:10:15 I know when it comes to AI agents, they can be used for both good and bad.
    0:10:19 What kind of things can we do to sort of protect and secure and make sure that people aren’t
    0:10:23 going and using these agents for, you know, bad actor type stuff?
    0:10:23 Absolutely.
    0:10:30 I think, you know, what’s really cool about AI is where it raises concerns or security challenges
    0:10:32 and it can actually also help answer them.
    0:10:36 So there’s a Blueprint out there for container security that does a lot of those pieces.
    0:10:43 But we also have other applications that can actually track activities and make alerts based
    0:10:45 on detecting some anomaly, right?
    0:10:49 So that’s tends to be how you recognize that something’s doing something it maybe wasn’t
    0:10:50 supposed to.
    0:10:54 And AIs are just really good at that because they can, you know, consume a lot of information.
    0:11:00 So I think where, you know, AI potentially can raise some concerns, it’s also in some ways
    0:11:03 the answer to addressing some of those concerns, which I think is really helpful.
    0:11:03 Right.
    0:11:09 So like the sort of like sci-fi movie scenario of like the AI rising up against us.
    0:11:14 I mean, is this something that people should be concerned about or is this something that
    0:11:16 you feel there’s pretty good guardrails in place for?
    0:11:20 I think the builders of these apps need to make sure those guardrails are in place.
    0:11:27 But I think, yes, in general, I think the tools exist and then it’s just about us as the application
    0:11:29 builders being smart about how we deploy them.
    0:11:30 Right.
    0:11:33 You know, in general, I think agents are really powerful.
    0:11:38 They’re incredible tools that help humans, but on their own, they’re not about to sort
    0:11:39 of go off the rails.
    0:11:41 It really takes a human to take it there.
    0:11:43 So I think, again, it is a tool.
    0:11:46 It is something that can, you know, work alongside you.
    0:11:50 But I don’t see the rise of the machines quite yet.
    0:11:55 So along similar lines, I think one of the fears a lot of people have is like, is an agent
    0:11:56 going to take my job?
    0:12:00 So, you know, what are your thoughts in that sort of realm?
    0:12:06 I mean, for me personally, I see all of AI as like a tool, something that makes me a lot
    0:12:09 more efficient and makes it so I can accomplish more in a day.
    0:12:11 But I’m curious, like, what’s your take?
    0:12:13 Do you think that, you know, people need to be concerned about that?
    0:12:18 I think the best thing I’ve ever heard is an agent’s not going to take your job, but somebody
    0:12:19 using an agent might.
    0:12:21 And that’s always the thing that I think gets framed.
    0:12:23 No, I think exactly that.
    0:12:27 Like we as humans have the capacity to do so much more.
    0:12:29 There’s just often so many hours in the day.
    0:12:29 Right.
    0:12:35 And so we don’t, you know, accomplish, you know, with agents, the work that we were trying
    0:12:36 to do and then just stop.
    0:12:36 Right.
    0:12:39 No, it allows us to go and do more and try more.
    0:12:42 And so I think, you know, for me, it’s not about replacing your job.
    0:12:47 It’s about making you more effective at your job and being able to do more and, you know,
    0:12:48 be more powerful.
    0:12:53 I think, you know, healthcare for me is one of those industries that this is driven out there.
    0:12:58 There’s actually a great partner of ours in the mental health space and they’re using
    0:13:03 agents to free up therapist time from all the administrative work that they have to do.
    0:13:05 We know their jobs are hard enough as it is.
    0:13:11 If we can free that time up, it allows them to spend more time with their patients, actually
    0:13:17 deeply understanding and working with them as opposed to worrying about calendars and taking
    0:13:17 notes and scheduling.
    0:13:22 So I think those are great examples where it’s, no, it’s something there that’s going
    0:13:23 to help your job.
    0:13:24 It’s not going to take your job.
    0:13:24 Yeah.
    0:13:28 I bumped into somebody on the street while I was walking yesterday and they had a very similar
    0:13:29 concept for their company.
    0:13:34 They work with therapists and they create chatbots specifically for the therapist so that their
    0:13:38 customers can go and have the initial conversation with the chatbot.
    0:13:40 But then the conversations get passed along to the therapist.
    0:13:45 The therapist can kind of, you know, detect when some bigger issue that they need to address
    0:13:47 comes up, but it’s not replacing the therapist.
    0:13:50 It’s just, hey, I can now handle more patients, you know.
    0:13:53 And give them more of my dedicated time.
    0:13:53 Right.
    0:13:57 And I think there’s something actually interesting as you start to see chatbots and particularly
    0:14:03 those with digital avatars is humans will open up in some ways to one of these, you know,
    0:14:07 chatbots or these avatars in ways that they may not necessarily feel comfortable opening up.
    0:14:11 So I think it’s just giving us new avenues to do things that we were already doing anyway.
    0:14:13 And I think that’s pretty cool.
    0:14:13 Yeah.
    0:14:17 This is sort of switching gears a little bit, but do you think there’s like a compute bottleneck
    0:14:17 for agents?
    0:14:21 Are we going to be able to scale agents and get to agents as fast as people would want
    0:14:21 to?
    0:14:25 There was a narrative, I feel like the narrative sort of faded a little bit, but there was
    0:14:28 a narrative maybe six months ago that there’s a wall and AI is hitting a wall.
    0:14:29 Do you think there’s a wall?
    0:14:31 Do you think we’re going to hit some compute bottlenecks?
    0:14:36 I think, you know, this is our life’s work at NVIDIA is to make sure that we have the compute
    0:14:39 to, you know, drive the world’s agents.
    0:14:44 And so I think, you know, a lot of the announcements we talked about today, both on the hardware and
    0:14:49 on the software side are really focused on making these as efficient as possible.
    0:14:55 And I think what’s really cool is as you’re looking at this AI space, when it first comes
    0:14:58 out, it’s usually big, it’s boxy, it’s maybe not the most efficient.
    0:15:03 And then over time, that efficiency comes in and allows us to do the next big leap.
    0:15:06 And again, that starts out, you know, big and heavy.
    0:15:09 And again, gets more and more efficient over time.
    0:15:13 So I think I’m not seeing a bottleneck, but I do know it’s an area we need to continue
    0:15:18 to drive because I do think the compute requirements are going to continue to grow.
    0:15:18 Cool.
    0:15:21 Well, so let’s extrapolate out a little bit.
    0:15:24 So if you’re looking like five, 10 years down the road, what do you sort of envision with
    0:15:25 AI agents?
    0:15:26 Where do you think this is all headed?
    0:15:29 And I know that’s hard because it’s sort of exponential technology.
    0:15:31 It’s really hard for humans to grasp back credentials.
    0:15:32 It’s incredibly hard.
    0:15:36 I mean, I can barely keep six months out, you know, and new things keep popping up.
    0:15:41 I mean, I think certainly the biggest things that we’re going to see are teams of agents.
    0:15:44 And that’s going to be, you know, that we’re already seeing it today.
    0:15:46 It’s starting now and it’s going to continue to grow.
    0:15:52 And so, you know, the way that I see it is, you know, when we first got agents and we first
    0:15:57 got, you know, chatbots and things, you could ask a question and it would respond to a question.
    0:16:00 And then you could ask it to do a function and it could now do that function.
    0:16:02 And then you can ask it to do a bigger job.
    0:16:05 And these agents are just getting more and more sophisticated.
    0:16:10 So five to 10 years out, you know, we may be able to give something, you know, as simple
    0:16:14 as, you know, design me my, you know, retirement home.
    0:16:18 And it will come back with all of the pieces involved in that without a human ever having
    0:16:19 to do another prompt to follow it up.
    0:16:21 And I think that could be pretty cool.
    0:16:24 So that might even be just two or three years.
    0:16:27 But yes, I certainly see that.
    0:16:28 I think that’s where we’re going.
    0:16:29 So you mentioned teams of agents.
    0:16:30 That’s really fascinating to me.
    0:16:35 Like, do you have any examples of like what sort of agents would team up to work together
    0:16:36 and what sort of tasks will accomplish?
    0:16:37 Yeah, absolutely.
    0:16:38 So I work in marketing, right?
    0:16:44 And so a lot of my job is about doing messaging, writing blogs, getting imagery, building
    0:16:45 demo videos, things like that.
    0:16:50 And today, you know, you start with one, you know, app that can help you write.
    0:16:52 And then you go to another app that helps you build images.
    0:16:55 And then you go to another app that helps you write the code for the website.
    0:16:56 And then, you know, right.
    0:16:58 And they’re all separate.
    0:17:00 And each one of them returns to me.
    0:17:03 And I, you know, move to the next step.
    0:17:09 I think in the future, what we’re going to see is those agents will all be connected through
    0:17:09 function calls.
    0:17:14 And we actually, uh, at GTC announced a blueprint that’s going to help us do this.
    0:17:15 It’s called IQ.
    0:17:21 And so by connecting all these together, again, as a human, I’ll be able to start putting
    0:17:23 my overall requests.
    0:17:27 I need to build a new website for a new announcement that’s coming out and it will be able to do
    0:17:29 all those functions together.
    0:17:34 And I think what’s really cool about this is it’s going to allow us to design agents that
    0:17:40 solve specific tasks, but by combining them together in that sort of that composable way,
    0:17:43 they’ll be able to do bigger and bigger job functions.
    0:17:43 Very cool.
    0:17:49 So like what sort of bigger world plot problems do you see like AI and AI agents solving for
    0:17:49 us?
    0:17:54 I mean, I think networking is a really interesting one because anything that has that much data
    0:18:01 that humans can’t solve, I think is, is amazing, you know, digital twins and simulation of those
    0:18:06 types of environments, whether it’s, you know, the climate and weather, whether it’s, you know,
    0:18:08 the businesses and things that we run.
    0:18:13 I think all of these are areas where agents are just going to add to the ability to work with
    0:18:14 these.
    0:18:16 So, yeah, certainly I think those are big overall.
    0:18:22 And then I think businesses, of course, it’s going to be about, you know, providing those
    0:18:25 tools so that their employees are just that much more effective.
    0:18:25 Yeah.
    0:18:30 And that’s, that’s not a big world problem, but it’s a very common serious world problem.
    0:18:30 Yeah.
    0:18:34 One of the things that I think was really fascinating and I’ve heard, I’ve, I’ve made jokes about
    0:18:38 how I feel like I’m on like the Jensen tour because I’ve actually seen his last like five
    0:18:38 keynotes.
    0:18:41 But one of the things that I’ve, I’ve seen him talk about that’s really fascinating to
    0:18:47 me is the earth too, where it’s got the, they basically couldn’t map out the weather patterns
    0:18:49 and figure out weather events a lot earlier.
    0:18:54 So I’m really excited to see the sort of overlap of like the earth to concept and agents and solving
    0:18:57 some of the more like bigger climate type issues as well.
    0:18:58 A hundred percent.
    0:19:03 I mean, I think one of the things when, when we introduced earth to is this idea that we,
    0:19:08 we think a lot about these problems, we have conversations about them, but it’s really hard
    0:19:13 as humans to sort of visualize what, you know, some of the changes that we make in government
    0:19:16 and things like that are going to do in the next five to 10 years.
    0:19:18 We’re very immediate creatures, right?
    0:19:19 So that’s where we’re focused.
    0:19:23 But I think, you know, imagine being able to go to earth too and through an agent say, Hey,
    0:19:26 you know, if we made these changes, what would happen?
    0:19:32 Or if we, you know, took these steps, how could that affect the world and what more can we
    0:19:32 do?
    0:19:37 And maybe there are things that we’re not even recognizing that the agent could recommend.
    0:19:41 Because I think that’s, what’s really cool about these agents is they’re not humans.
    0:19:43 They don’t think the way we do.
    0:19:48 And so by giving them all that data and giving them an earth simulation, they might be able
    0:19:49 to uncover things that we’ve never thought of.
    0:19:50 Yeah.
    0:19:50 Yeah.
    0:19:51 I think that’s pretty cool.
    0:19:51 Yeah.
    0:19:52 I think that’s amazing.
    0:19:57 What are some of the things in the AI world, you know, agents or otherwise that have you personally
    0:19:58 really excited?
    0:19:59 Like what sort of stuff do you use?
    0:20:00 What do you play with?
    0:20:02 Like, what’s your AI stack that you use?
    0:20:05 I mean, I use as many as I can get my hands on.
    0:20:08 We have a lot of them in NVIDIA.
    0:20:12 I mean, one of my favorites, and I know Jensen talked about it a little bit on stage, is perplexity.
    0:20:13 Yeah.
    0:20:13 Oh, I love perplexity.
    0:20:19 I love perplexity because I think it changes the dynamic of humans and how we interact.
    0:20:25 Rather than searching for information and having to spend the thought process on that, it’s really
    0:20:27 about who can ask the best questions.
    0:20:33 And I think that’s a really powerful change and, you know, power dynamic that perplexity
    0:20:34 is given to the users.
    0:20:36 Now, if you can ask great questions, you can find great information.
    0:20:38 So I love that tool.
    0:20:44 We have AI agents in our company that help us with everything from, you know, our benefits
    0:20:48 and understanding how to make the right decisions, you know, for each employee, which I think is
    0:20:51 pretty cool, into, you know, how we do our jobs.
    0:20:56 Whether that’s video creation, image creation, certainly content, which you have to write
    0:20:57 a lot of.
    0:20:58 So, yeah.
    0:20:59 So all of those, I think, are great.
    0:21:04 And then, you know, I take them into my personal life in terms of organizing and planning.
    0:21:09 I’m very organized at work and I don’t always save a lot of that organization for my personal
    0:21:10 life.
    0:21:13 And so I can hand it off to, you know, any sorts of chatbots.
    0:21:16 I think ChatTPT is excellent for this.
    0:21:17 It’s just pretty cool.
    0:21:18 And lately, deep research.
    0:21:20 So I’ve been using a lot of that.
    0:21:25 And again, we just introduced a blueprint that’s going to allow deep research on your
    0:21:26 own personal things.
    0:21:28 And so I can imagine that’s going to be pretty powerful.
    0:21:33 So for people that want to sort of stay in the loop on AI that are curious, maybe they’re
    0:21:33 worried about it.
    0:21:37 Like, what sort of advice would you give them to sort of stay on top of things?
    0:21:41 My best advice on AI is use it.
    0:21:46 It sounds really obvious, but I think, one, I think it actually alleviates a lot of concerns
    0:21:48 when you understand how the technology works.
    0:21:54 And by using it, you can see what works today, where the limitations are, and how it kind
    0:21:55 of functions.
    0:22:02 And I think it lets people see it as that tool versus that, you know, something that might
    0:22:02 be scary.
    0:22:06 So that’s really the first piece of advice.
    0:22:07 I think that’s really important.
    0:22:12 And then I think it’s about, you know, trying to identify where many of those, you know,
    0:22:17 concerns might be coming from, whether that’s data or security or things like that, and understand
    0:22:19 how AI can also help with those.
    0:22:23 And so that tends to be, you know, one of my best pieces of advice.
    0:22:23 Cool.
    0:22:24 Yeah.
    0:22:26 Well, let’s talk about Llama 3 and Nemo Tron.
    0:22:27 Yeah.
    0:22:28 So Llama, Nemo Tron.
    0:22:29 Llama, Nemo Tron.
    0:22:30 Let’s talk about Llama, Nemo Tron.
    0:22:31 Absolutely.
    0:22:33 So Llama, Nemo Tron was a really cool announcement.
    0:22:38 So NVIDIA, we work with all of the leading model builders that are out there, including Meta,
    0:22:39 who released Llama.
    0:22:46 And what’s really cool about Llama is there are, I think it’s 85,000 derivatives of this model.
    0:22:51 And so, of course, NVIDIA, we’ve got a lot of really smart people who know how to optimize
    0:22:53 and make models more efficient.
    0:23:00 And when Reasoning came out and when DeepSeek introduced this reasoning wave into the open
    0:23:06 source, we sort of said, well, how can we bring this and make it really efficient for,
    0:23:08 you know, people who want to deploy this on NVIDIA?
    0:23:15 And so by starting with the Llama model, which is an incredible model, we brought in our expertise
    0:23:20 to train it so that a model that previously couldn’t do reasoning now could actually think
    0:23:21 through problems.
    0:23:26 And so we found, you know, the data set to go be able to train the model and how to do
    0:23:27 this new task.
    0:23:32 And we trained it and then we made the data source open, which I think is really cool.
    0:23:36 So if others want to do their own training or if they want to train a different model or
    0:23:38 anything like that, that data source is available.
    0:23:44 But it’s just teaching a model a new skill is, I think, a really powerful thing to see because
    0:23:47 it shows how quickly the space is evolving.
    0:23:51 So is there anything actually happening like underneath Llama or is it just you’ve got
    0:23:55 the Llama model, but now there’s a new layer on top of it that knows how to think?
    0:23:57 Is there did any like new training happen to the model?
    0:23:58 Yes.
    0:24:00 So, yeah, the model was absolutely.
    0:24:01 So we post train the model.
    0:24:05 So this is what a lot of companies out there are doing today is they take a base model and
    0:24:07 they actually train it with new data.
    0:24:11 And in this case, sometimes you’re training it so it has more information on a particular
    0:24:12 topic.
    0:24:17 This is particularly popular when you’ve got domains and industries that have specific languages
    0:24:20 that they speak, things like maybe the finance industry.
    0:24:24 But in this case, we actually were training it on a skill.
    0:24:27 And that skill is to think through problems.
    0:24:31 And I think this is what’s really interesting about reasoning is the way a reasoning model
    0:24:36 works is it starts by thinking through the question that you’re asking.
    0:24:40 And it breaks down that question into multiple steps and multiple parts.
    0:24:45 And then it actually goes through and comes up with answers for each of those parts.
    0:24:51 And then it checks to say, OK, now that I’ve got those answers, does this actually, you know,
    0:24:53 come back with the right question?
    0:24:58 And it continues to do that until it gets to this highly accurate response.
    0:25:05 And so not only are reasoning models really good for improving accuracy, which we all know
    0:25:07 that when an AI model is useful, it’s when it’s accurate.
    0:25:08 Right.
    0:25:11 But it also allows us to solve problems we can never solve.
    0:25:12 Right.
    0:25:15 So a great example for me on this is I love puzzles.
    0:25:20 I love all sorts of puzzles, but particularly Sudoku, because I hear it’s good for your brain.
    0:25:27 Sudoku is a problem that humans can solve, but actually traditional LLMs couldn’t.
    0:25:34 There are actually almost 80 different decisions that go into solving a Sudoku puzzle.
    0:25:39 And each one of those affects the other decision, because obviously based on the rules that you
    0:25:44 have to follow, reasoning models, and Lama Nibodron is a great example of this, can solve Sudoku.
    0:25:45 Ooh, interesting.
    0:25:48 Where a Lama model on its own couldn’t.
    0:25:48 Wow.
    0:25:50 And so again, that’s the type of thing.
    0:25:54 It doesn’t sound like, you know, solving Sudoku puzzles is going to change the world.
    0:25:59 But when you look at a Sudoku puzzle, there’s actually a lot of things that go on in the
    0:26:01 world that are related to that.
    0:26:02 Yeah.
    0:26:03 I have a supply chain.
    0:26:07 I’ve got to ship items and get them to different stores around the country.
    0:26:12 I happen to know there’s a snowstorm coming in and I need to make sure that my trucks are
    0:26:14 taking the most efficient routes.
    0:26:18 All of those are steps that impact the other decisions that are being made.
    0:26:23 And so it’s actually quite a complex problem that relates in some ways to Sudoku.
    0:26:24 Yeah.
    0:26:28 Well, it’s so interesting too, because you take a normal model and it can’t tell you
    0:26:32 how many R’s are in strawberry, give it a thinking model, and it will actually count and then
    0:26:33 double check.
    0:26:34 Did I do that right?
    0:26:34 And then answer.
    0:26:36 And get the right answer.
    0:26:36 Exactly.
    0:26:40 It’s so fascinating because it seems like such a simple problem to a human brain.
    0:26:44 But then that’s, I think, the thing is we look at models and we think of them like
    0:26:46 we personify them as humans and they’re not.
    0:26:47 Right.
    0:26:49 And so, yeah, reasoning models are really cool for that.
    0:26:53 And we’ve seen a lot of those great examples that come out of what these reasoning models
    0:26:53 can do.
    0:26:55 And it is.
    0:26:57 It’s just, I think it’s really interesting to watch.
    0:26:57 Yeah.
    0:27:01 It’s so cool to actually see the thought process because you’ll actually see the models think
    0:27:03 through something and then go, wait, that’s probably not right.
    0:27:05 Let me think that through again.
    0:27:08 And you actually see that text come out of it thinking through.
    0:27:09 And that to me is fascinating.
    0:27:10 It’s fascinating.
    0:27:15 And it’s a great example of the compute story we were talking about, which is, you know, we do
    0:27:17 need more compute so it can think.
    0:27:19 The more it thinks, the more compute it requires.
    0:27:24 So it’s this really interesting cycle that we’re watching these things go through.
    0:27:28 One of my favorite things to do with reasoning models is to ask them to describe things to
    0:27:29 me like a five-year-old.
    0:27:29 Yes.
    0:27:32 And it will come up with a description.
    0:27:35 It will check if a five-year-old would understand that description.
    0:27:36 It will then make changes.
    0:27:39 And it’s really interesting to see what models think five-year-olds understand.
    0:27:41 So at some point, I’ll have to go test it with a real question.
    0:27:43 Yeah, read this.
    0:27:44 Does this make sense to you?
    0:27:45 Does this actually make sense?
    0:27:46 Exactly, exactly.
    0:27:47 Very cool.
    0:27:50 Well, along the same line, real quick, let’s talk about hallucinations.
    0:27:53 Do you see like a path to zero hallucinations?
    0:27:55 Do we want to get rid of hallucinations completely?
    0:27:56 Like, what are your thoughts on that?
    0:28:01 I think, you know, there’s certainly things we can do to reduce hallucinations.
    0:28:07 And some of those are, you know, as simple as putting a guardrail in place that says, if you’re
    0:28:12 not 100% confident in the answer or 99% confident in the answer, don’t answer the question.
    0:28:12 Right.
    0:28:16 That sort of, it stops the model from doing things that it shouldn’t.
    0:28:22 And Nemo guardrails, which is one of the products that NVIDIA offers, helps with building those.
    0:28:26 But to your point about, do we want to stop hallucinations altogether?
    0:28:31 Of course, we want it to give the right answers, but in being creative, we’re asking it to come
    0:28:32 up with new things.
    0:28:32 Right.
    0:28:37 So, it’s, you have to be able to distinguish between a hallucination and a creative generative
    0:28:37 response.
    0:28:38 Right, right.
    0:28:41 So, I think that’s where this balance plays.
    0:28:47 So, there are absolutely steps that can be taken, and depending on how targeted and focused
    0:28:51 you want the model to be, you can put more and more of those sort of guardrails or those
    0:28:53 policies in place that will keep it from hallucinating.
    0:28:54 Right.
    0:28:57 Yeah, because I think in a lot of scenarios, hallucinations are a feature, not a bug, right?
    0:29:01 If you wanted to write a short story for you, you wanted to hallucinate that short
    0:29:01 story for you.
    0:29:02 Exactly.
    0:29:06 So, I think that’s where we have to understand what’s the hallucination versus what’s the model
    0:29:07 doing what it’s supposed to do.
    0:29:08 Right.
    0:29:10 And again, and that’s where it becomes, what’s the use case?
    0:29:12 What are you trying to have it do?
    0:29:17 And really think those through, and then find the right tools from NVIDIA or others to be
    0:29:19 able to actually, you know, go and do that.
    0:29:20 Very cool.
    0:29:24 Yeah, so if somebody wanted to get started with agentic AI, they want to start playing with
    0:29:26 agents and testing the waters, what do they do?
    0:29:27 What are our steps?
    0:29:31 Well, so, from NVIDIA, we have something called build.nvidia.com.
    0:29:33 We made the URL super easy.
    0:29:36 If you’re trying to build something, we have a one-stop shop for you.
    0:29:39 It’s got all the models on there so that you can test them out.
    0:29:43 Whether it’s the new Llama Nemetron model with reasoning, you can actually turn reasoning on
    0:29:43 and off.
    0:29:45 It also has all the blueprints.
    0:29:47 So, you can actually test them and experiment from them.
    0:29:52 And then from there, there are also steps to go deploy them, test them out, and build them
    0:29:53 yourself.
    0:29:55 So, I think that’s a great starting point.
    0:29:56 Very cool.
    0:30:00 And for the more, like, technical people that maybe are trying to develop something, like,
    0:30:02 is there a place they can go play with the APIs?
    0:30:04 Like, what do we do there?
    0:30:05 Build.nvidia.com.
    0:30:05 Same place.
    0:30:06 Same place.
    0:30:11 It’s literally, whether you’re, you know, an enthusiast, whether you’re a developer, whether
    0:30:16 you’re actually trying to, you know, build something to put in production, this is the one-stop
    0:30:18 shop because everything’s on there.
    0:30:20 You can take it as far as you want.
    0:30:21 You can play around with the UI.
    0:30:23 You can play around with the APIs.
    0:30:28 You can actually download and deploy these models on any, you know, NVIDIA hardware.
    0:30:30 It’s your one-stop shop for everything.
    0:30:30 Yeah.
    0:30:32 Actually, that brings me to another question.
    0:30:35 Does this stuff work on older NVIDIA hardware?
    0:30:40 If you have a, you know, a 3080 or a 4070, can I use this stuff on those as well?
    0:30:41 Absolutely.
    0:30:45 The only restriction is, does it fit within the memory of the GPU?
    0:30:47 But if it fits, it ships.
    0:30:52 So, yes, this will run on GPUs that are out there in the market today.
    0:30:53 Very cool.
    0:30:53 Awesome.
    0:30:55 Well, thank you very much.
    0:30:57 This has been amazing, fascinating.
    0:31:00 I love talking AI and nerd now, especially agents.
    0:31:01 Agents is the hottest topic.
    0:31:02 So, really appreciate you taking the time with me.
    0:31:03 Absolutely.
    0:31:05 I could also show you that in the future one day.
    0:31:06 So, yeah, have a good time.
    0:31:07 Thank you.
    0:31:11 We’ll be right back to the next wave.
    0:31:14 But first, I want to talk about another podcast I know you’re going to love.
    0:31:18 It’s called Marketing Against the Grain, hosted by Kip Bodnar and Kieran Flanagan.
    0:31:24 It’s brought to you by the HubSpot Podcast Network, the audio destination for business professionals.
    0:31:30 If you want to know what’s happening now in marketing, what’s coming, and how you can lead the way, this is the podcast you want to check out.
    0:31:34 They recently did a great episode where they show you how you can integrate AI into the workplace.
    0:31:37 Listen to Marketing Against the Grain wherever you get your podcasts.
    0:31:46 Hi, this is Bob Petty, Vice President and General Manager of Enterprise Platforms at NVIDIA.
    0:31:59 Our first effort with AI developers, right, whether they’re enthusiasts, prosumers, or professionals, was basically using our RTX Pro cards or GeForce cards, running Linux.
    0:32:05 And for many people to run Linux, we did Windows subsystem for Linux, WSL2.
    0:32:21 Worked with Microsoft to eliminate a lot of what was intended for just an emulator and really turn it in, eliminate the latency blocks so that you could use it to truly develop AI and evaluate AI, not just from an accuracy standpoint, but also from a latency standpoint.
    0:32:26 So historically, we’ve gone out with AI workstations or AI PCs for that.
    0:32:26 Right.
    0:32:30 If you’re running Intel or AMD CPU, put those in there.
    0:32:39 The new RTX 6000 Pro Blackwell is a 96 gigabyte frame buffer, so you can run 70B models, fine-tune, do a lot of it.
    0:32:47 A lot of the infrastructure that people are buying today in the cloud or through some of our server partners is Grace Blackwell.
    0:32:50 It was Grace Hopper, now Grace Blackwell.
    0:32:50 Right.
    0:32:52 Grace being the ARM CPU, right?
    0:32:52 Right.
    0:32:58 And that’s kind of the same technology that all the big, big, like OpenAI, those types of companies are using.
    0:32:58 Exactly.
    0:33:03 And the beauty of that is the ARM CPU uses up so much less power.
    0:33:15 So if the majority of the workload was in the GPU and the CPU is kind of a traffic manager, you wouldn’t be able to do what you needed to do for as little as power as possible so you can put more GPUs in there.
    0:33:17 So hence, Grace Blackwell.
    0:33:18 Right.
    0:33:24 Well, you can certainly develop AI on Windows workstations with RTX Pro or GeForce.
    0:33:24 That’s all great.
    0:33:33 But if you need an ARM port of your software or getting familiar, that’s where Spark fits the gap.
    0:33:36 And this is the same thing as the Project Digits that was announced at CES, right?
    0:33:37 This is Project Digits, yeah.
    0:33:39 We finally chose the name Spark.
    0:33:41 We had people send in a lot of comments.
    0:33:55 But as Jensen mentioned in the keynote, you know, what was a box that was like this several years ago, 20-core CPU, one petaflop is now in this little 5×5 by less than 2-inch box.
    0:34:00 It’s got the C2C memory between the Grace processor and the Blackwell GPU.
    0:34:02 It was like 256 gigabytes a second.
    0:34:08 You won’t have that on a traditional workstation because you’re going to go over the PCI bus, right?
    0:34:16 So high memory bandwidth between the CPU and the GPU, 128 gigs of memory available from us online.
    0:34:20 But that’s really for the enthusiast who want the gorgeous bezel.
    0:34:26 So what kind of things can I do with this now that I can’t do with my 5090 at home?
    0:34:27 Good question.
    0:34:32 So from a frame buffer size, there are so many more models that you can run on this.
    0:34:34 You can do fine-tuning on 70B models.
    0:34:37 You can put two of these together with this cable.
    0:34:39 This is a ConnectX Ethernet.
    0:34:42 Put two of these together, you can run a 400 billion parameter model.
    0:34:44 You can’t do that on the 5090.
    0:34:44 Right.
    0:34:48 You can’t do the 70B on a 5090, right?
    0:34:48 Right.
    0:34:51 And so the size of the model is very dependent.
    0:34:56 The other thing with a 5090, your memory bandwidth between your CPU and your GPU is throttled by the PCI bus.
    0:34:57 Okay, right.
    0:35:02 And this has, you know, cache-coherent high-speed memory bandwidth.
    0:35:11 So it really enables you to test what your code might look like running on one of the data center providers, OEMs out there.
    0:35:17 Because same C2C memory, cache-coherency, speed, you can do more than just say it works.
    0:35:18 Right.
    0:35:25 You can get it to the point where you can remove a lot of the bottlenecks, whether you’re doing a vision language model or, you know, multimodal model.
    0:35:27 That’s the biggest benefit.
    0:35:33 One is getting your code ready for what’s the predominant AI infrastructure out there.
    0:35:33 Right.
    0:35:40 But the other is testing in a way that simulates how it’s going to run, you know, when you run it on a node there.
    0:35:48 And then the idea is you’re not wasting data center time or cloud time just debugging, right, or illuminating bottlenecks.
    0:35:52 When you’re here, you deploy, and you scale from one GPU to end GPU.
    0:36:00 So the main purpose of this was really to help spread the Grace Blackwell ecosystem.
    0:36:00 Right.
    0:36:06 They’re going as fast as we can make them, but they’re not necessarily accessible to enthusiasts.
    0:36:07 Right.
    0:36:16 Who want to use FP4 features of Blackwell, which you can do on 5090, but want to use it with some of the more popular models that have higher parameter sizes.
    0:36:22 So EZBox, the one terabyte version of storage, 128 gigs, is $29.99.
    0:36:25 The four terabyte version is $39.99.
    0:36:29 You can reserve on NVIDIA.com.
    0:36:33 Our initial go-to-market partners are Dell, HP, ASUS.
    0:36:38 They’ve got their own branded boxes without the gold foil, and then we’ll expand that.
    0:36:45 Yeah, I remember Jensen said something to the effect of, like, imagine it’s a cloud computer just sitting on your desk.
    0:36:46 It’s not going to the cloud.
    0:36:49 You know, you don’t have to worry about internet connections, anything like that.
    0:36:54 It’s just a cloud computer sitting on your desk that can do all of the inference right there on the bigger models.
    0:36:57 It’s an AI supercomputer on your desk, and there’s a bigger one that we’ll walk to in a second.
    0:37:04 But the other thing about this is we’re not suggesting that everybody just replace their existing, you know, laptop or workstation.
    0:37:08 You’ve got a GeForce laptop or RTX Pro laptop.
    0:37:10 You plug this guy into it.
    0:37:15 So you might do everything you need to on a 5090, run your games and everything.
    0:37:20 You do an AI development or want to write an AI inferencing that helps you on your 5090.
    0:37:21 Plug that into it.
    0:37:34 And that’s why Jensen showed the MacBook and the Spark, because it’s really meant to be both a plug-in to Uber Assist, maybe less capable machines, whether they have a GPU in them or not.
    0:37:41 And it’s not using all the processing on your computer, so you can be running AI models on this while playing Cyberpunk on your computer.
    0:37:42 Exactly, yeah, exactly.
    0:37:49 And, you know, just format factor-wise, cost-wise, we think it would be easy for people to do it as an add-on.
    0:37:52 But certainly there’s, you know, there’s a great GPU in here.
    0:37:54 We’re running games on this.
    0:38:04 I wouldn’t want it as my GeForce laptop, but I’d probably want to connect it to my GeForce if I was, you know, doing AI tuning for game development or things like that.
    0:38:09 So that’s DGX Spark, who has walked this way.
    0:38:11 This is the RTX Pro line.
    0:38:18 Our 6000 line is the one that’s, you know, somewhat akin to the 5090 on the GeForce side.
    0:38:22 The reason we have this Pro line, the manufacturing of it is a very precise bomb.
    0:38:25 It’s not built by many different AICs.
    0:38:34 There are computing benefits on here that we perceive the gaming community doesn’t need, so some of the high-end compute performance here is going to be much better.
    0:38:37 The AI inferencing performance is about the same.
    0:38:38 The big difference is frame buffer.
    0:38:39 Right.
    0:38:42 And doesn’t this one have, like, 96 gigs of RAM?
    0:38:44 Yeah, 96 gigs of RAM.
    0:38:48 And if you want to get full power, we’ve got the 600-watt version.
    0:38:53 We’ve got a 300-watt version that most desktops can take today.
    0:38:58 And then you get the same technology in the server version that would go in a rack here.
    0:39:07 So we used to call these, in the past, we’ve had A40s or L40s based on Ada Lovelace, the Ampere Lovelace.
    0:39:17 We’re going to call that B40 based on Blackwell, but kind of aligned around RTX Pro, Workstation, Max-Q.
    0:39:22 Max-Q is that optimal PowerPoint, and the server edition.
    0:39:24 So, again, same infrastructure.
    0:39:29 You can code and develop and then deploy on your RTX server and a rack in the data center, meant to save time.
    0:39:32 So, these are our DJX stations.
    0:39:39 Initial partners, Dell, HP, Asus, Lambda, and Supermicro.
    0:39:42 We show these two here because these are their boxes.
    0:39:43 This is what it will look like.
    0:39:47 If you look in here, this is a GB300 board.
    0:39:50 So, a much more powerful Grace processor.
    0:39:54 And then an extremely powerful B300.
    0:39:58 B300 is the same GPU in the latest DJX.
    0:39:58 Okay.
    0:40:03 So, 784 gigabytes of memory, again.
    0:40:10 So, the benefit is you’re doing ARM development, a lot of memory, and very, very high-speed memory bandwidth like Spark.
    0:40:18 So, you’re not just seeing that code works, you’re seeing how well it works before you chew up time on your data center rack.
    0:40:22 Now, does this need like a separate CPU, like an Intel AMD kind of thing?
    0:40:26 No, it’s on both the Spark and the station, we’re providing the Grace CPU.
    0:40:27 Okay.
    0:40:30 So, great CPU and the Blackwell GPU there.
    0:40:31 Graphics out.
    0:40:34 We don’t put a big graphics card in here.
    0:40:35 So, this one has a 4,000.
    0:40:37 That’s a small form factor.
    0:40:41 The reason being, we want this to plug into a standard 15-amp wall outlet.
    0:40:42 Right.
    0:40:44 Might need to be a dedicated one.
    0:40:47 Because, you know, it’s at 1,650 watts, I guess.
    0:40:49 And we’re going to come pretty close to that.
    0:40:52 Which is why the manufacturers are liquid cooling this.
    0:40:55 And they started doing that in the gaming side.
    0:40:57 So, some of the Alienware chassis, you’ve seen the liquid cool.
    0:40:58 Right.
    0:41:00 So, they’re well adept at that.
    0:41:05 They will liquid cool this so we can stay, you know, thermally be good and not need to take
    0:41:07 up a lot more power with a lot of fans.
    0:41:14 So, again, if you’re an enthusiast, just getting started, even an enterprise where you know the
    0:41:17 size of your models and what you want to get done, easily connect that.
    0:41:24 If your full-time job is, like, prepping AI, developing AI to deploy to the data center,
    0:41:27 you have your choice of logging it to the data center.
    0:41:31 Maybe you get a virtual workstation delivered back to you and you do your job.
    0:41:33 Or putting this at your desk.
    0:41:33 Okay.
    0:41:38 And this is, literally, it’s like one of those B300 nodes.
    0:41:39 Right, right.
    0:41:46 It is a server node in a desktop with graphics out, right there from a data privacy standpoint,
    0:41:48 from an IP protection standpoint.
    0:41:50 I’m not sending anything anywhere.
    0:41:50 Right.
    0:41:51 It’s right there.
    0:41:56 And that’s more important than just privacy and IP protection.
    0:42:00 It’s just the time and the cost of transport of data.
    0:42:00 Right.
    0:42:06 You might run a, you know, a 600 billion parameter model, but the data that you’re running it on,
    0:42:11 whether let’s say it’s Cosmos, the BLM, all the videos that you’re going to be processing
    0:42:19 and the amount of data, you’d want to just sit here versus upload all that data or even
    0:42:22 if your own dedicated data center and run it there and then download that.
    0:42:27 So you’ve got ingress and egress costs of data transport, all happening right here.
    0:42:31 That guy will be available in the summertime frame.
    0:42:31 Okay.
    0:42:33 Reservable today.
    0:42:34 This guy will be late summer.
    0:42:35 Okay.
    0:42:38 We have a founder’s edition for that because it’s cool.
    0:42:38 Right.
    0:42:41 Enthusiasts are going to want something on their desk, right?
    0:42:43 This one is only available for the OEMs.
    0:42:44 Okay.
    0:42:46 The way for us to scale our enterprise businesses through the OEMs.
    0:42:52 And so if you were to go to the Dell booth today or the HPI booth or Asus, you’ll see
    0:42:53 their versions of these here.
    0:42:59 You’ll see their versions of the Spark with the Dell blue and the NVIDIA green LED and the
    0:42:59 HPE blue.
    0:43:06 And that’s the way to expand the ecosystem for Grace Arm development, Grace Blackwell
    0:43:11 development, expand the access to technology that normally is only available if you’ve got
    0:43:13 the capital expense to put one of these racks in.
    0:43:15 And now it’s at your desktop.
    0:43:16 Very cool.
    0:43:17 Yeah.
    0:43:17 Amazing.
    0:43:18 Well, thanks, Bob.
    0:43:21 This has been really informative and I appreciate it.
    0:43:21 Thank you.
    0:43:21 Yeah, no problem.
    0:43:22 Thank you.
    0:43:41 Thank you.

    Episode 53: What role will AI agents play in addressing global challenges? Join Matt Wolfe (https://x.com/mreflow) Amanda Saunders (https://x.com/amandamsaunders), Director of Enterprise Generative AI Product Marketing at Nvidia, then Bob Pette (https://x.com/RobertPette) Vice President and General Manager of Enterprise Platforms at Nvidia, as they delve into the transformative potential of agentic AI at the Nvidia GTC Conference.

    This episode explores the concept of AI agents as digital employees that perceive, reason, and act, reshaping industries like healthcare and telecom. Discover Nvidia’s approach to building powerful AI agents and the measures in place to ensure their secure and productive deployment. From optimizing workflows with agentic AI blueprints to fascinating agent applications in sports coaching, the discussion unpacks AI’s promising future.

    Check out The Next Wave YouTube Channel if you want to see Matt and Nathan on screen: https://lnk.to/thenextwavepd

    —

    Show Notes:

    • (00:00) Exploring Nvidia’s AI Revolution
    • (03:29) AI’s Breakneck Growth Spurs Innovation
    • (06:29) Video Agents Enhancing Athletic Performance
    • (09:46) AI: Problem Solver and Concern Raiser
    • (14:54) Rise of Sophisticated AI Agents
    • (18:21) Earth-2: Visualizing Future Changes
    • (21:53) Nvidia Optimizes Llama for Reasoning
    • (23:50) Reasoning Models Enhance Problem Solving
    • (27:20) Balancing AI Creativity and Accuracy
    • (30:31) Nvidia’s AI Development in Windows
    • (34:16) AI Development Acceleration Benefits
    • (37:32) High-Power Servers & Workstations Overview
    • (39:37) Liquid Cooling in AI Workstations

    —

    Mentions:

    —

    Check Out Matt’s Stuff:

    • Future Tools – https://futuretools.beehiiv.com/

    • Blog – https://www.mattwolfe.com/

    • YouTube- https://www.youtube.com/@mreflow

    —

    Check Out Nathan’s Stuff:

    The Next Wave is a HubSpot Original Podcast // Brought to you by Hubspot Media // Production by Darren Clarke // Editing by Ezra Bakker Trupiano

  • Infiltrating an International Ransomware Gang

    Infiltrating an International Ransomware Gang
    Infiltrating an International Ransomware Gang
    AI transcript
    0:00:06 Something unexpected happened after Jeremy Scott confessed to killing Michelle Schofield
    0:00:08 in Bone Valley Season 1.
    0:00:11 Every time I hear about my dad, it’s, oh, he’s a killer.
    0:00:13 He’s just straight evil.
    0:00:17 I was becoming the bridge between Jeremy Scott and the son he’d never known.
    0:00:20 At the end of the day, I’m literally a son of a killer.
    0:00:26 Listen to new episodes of Bone Valley Season 2 starting April 9th on the iHeartRadio app,
    0:00:29 Apple Podcasts, or wherever you get your podcasts.
    0:00:37 Pushkin.
    0:00:44 Just a quick note, this is a bonus episode of What’s Your Problem?
    0:00:46 and it’s sponsored by Microsoft.
    0:00:50 John DiMaggio studies cybercrime for a living.
    0:00:51 It’s his job.
    0:00:56 But when he wanted to understand an international cybercrime gang called Lockbit,
    0:01:00 he realized he couldn’t learn everything he wanted to know from the outside.
    0:01:03 So he started trying to figure out how to get people on the inside
    0:01:05 to tell him what he needed to know.
    0:01:10 So I spent a lot of time studying, going back to, you know, World War II
    0:01:15 when they started having all these documents about how to use the human trade craft
    0:01:21 to sort of recruit and convince people to do things that they don’t necessarily know
    0:01:23 that they’re doing to support your cause.
    0:01:28 So were you telling me you started studying sort of World War II era spycraft?
    0:01:29 Yes, that’s correct.
    0:01:35 What’s something you learned from World War II era spycraft that helped you weasel your way
    0:01:36 into a ransomware gang?
    0:01:42 Everything from their ego to understanding who their adversary is
    0:01:47 and making them feel that being friends with you will benefit them
    0:01:48 because you have a common enemy.
    0:01:55 Or even being adversarial towards them and saying certain things
    0:01:58 just to see what their reaction is to sometimes understand the truth.
    0:02:01 There’s also sort of the plan and prepare phase
    0:02:03 where you have to go and sort of stalk them
    0:02:05 and understand who their contacts are,
    0:02:06 who their friends are,
    0:02:07 who their enemies are,
    0:02:09 where they hang out online,
    0:02:10 all of that stuff.
    0:02:16 So you have this set of strategic ideas in your mind.
    0:02:18 What do you actually do?
    0:02:23 So what I did, the first thing I did is I needed to figure out
    0:02:25 sort of their digital fingerprint.
    0:02:26 So I profiled them.
    0:02:29 I began looking across the dark web.
    0:02:31 I obviously started with the easy one,
    0:02:32 their data leak site,
    0:02:33 their own infrastructure.
    0:02:35 And I went from there
    0:02:38 and I eventually found the forums that they live on.
    0:02:41 And there’s some very prominent Russian hacking forums
    0:02:43 that have been around for about 20 years.
    0:02:44 So it made sense to start there.
    0:02:46 And sure enough,
    0:02:48 they were very prevalent on that website.
    0:02:51 They were very involved with conversations.
    0:02:53 They have friends there, enemies,
    0:02:54 and they do their business.
    0:02:57 So they actually would go there just to talk
    0:02:59 and sort of hang out with their buddies.
    0:03:00 And the drama,
    0:03:02 it was like a soap opera,
    0:03:03 the drama.
    0:03:05 These guys would get in these big arguments
    0:03:06 over the stupidest things.
    0:03:08 I just started profiling
    0:03:09 and visually mapping out
    0:03:10 who was who,
    0:03:12 who they were talking to,
    0:03:14 what those other people’s roles were.
    0:03:15 Again,
    0:03:16 then I would find the ones
    0:03:16 who are their friends
    0:03:18 and I would try to approach them
    0:03:21 and the people who worked for them.
    0:03:22 And did it work?
    0:03:24 It did.
    0:03:24 Well,
    0:03:25 it sort of worked.
    0:03:32 I’m Jacob Goldstein,
    0:03:34 and this is What’s Your Problem?
    0:03:36 My guest today is John DiMaggio.
    0:03:39 John is the chief security strategist
    0:03:41 at a company called Analyst One.
    0:03:44 And I wanted to talk with John about Lockbit,
    0:03:45 this ransomware gang
    0:03:47 that was behind a tax
    0:03:49 that extorted over $100 million
    0:03:50 from companies around the world.
    0:03:52 John wrote this sort of
    0:03:55 book-length series of online posts
    0:03:56 about Lockbit.
    0:03:57 It was part of a thing
    0:03:59 John called the ransomware diaries.
    0:04:01 The story of Lockbit
    0:04:02 is a great window
    0:04:04 into the ransomware industry.
    0:04:05 And it is an industry
    0:04:08 with a lot of remarkable similarities
    0:04:10 to ordinary non-criminal industries.
    0:04:12 Lockbit tried to brand itself.
    0:04:13 It tried to attract talent
    0:04:15 and notch key wins
    0:04:17 just like any software company.
    0:04:18 But then there’s also
    0:04:20 the part that is not like
    0:04:21 any software company.
    0:04:23 There is the crime part.
    0:04:24 And it was the crime part
    0:04:26 where Lockbit went too far
    0:04:28 and wound up drawing the ire
    0:04:30 of international law enforcement agencies
    0:04:31 that, in fact,
    0:04:32 have their own set
    0:04:33 of innovative strategies.
    0:04:34 And John watched
    0:04:36 all this happen up close.
    0:04:37 He told me his key contact
    0:04:38 on the inside
    0:04:40 had the username LockbitSup,
    0:04:42 short for Lockbit Support.
    0:04:45 I didn’t know it at the time
    0:04:46 when I first started talking to them.
    0:04:47 But what I found out
    0:04:49 as I began to talk more
    0:04:50 is there were two personalities
    0:04:51 behind the account.
    0:04:52 One seemed to be
    0:04:54 much younger, friendlier,
    0:04:56 more in tune
    0:04:57 with sort of pop culture.
    0:04:58 And the other one
    0:05:00 who I gave a name,
    0:05:02 Mr. Grumpy Pants,
    0:05:03 because he was all business,
    0:05:04 always serious.
    0:05:05 And that was kind of
    0:05:06 how I differentiated.
    0:05:08 tell me about the sort of
    0:05:11 conversations you had
    0:05:11 with LockbitSup.
    0:05:13 Like, what was the nature
    0:05:14 of those exchanges?
    0:05:16 Well, so you have to understand
    0:05:18 that when I did the initial part
    0:05:19 that was sort of covert
    0:05:20 pretending to be somebody else,
    0:05:22 I only got so far with that.
    0:05:23 And after I wrote
    0:05:25 The Ransomware Diaries Volume 1,
    0:05:26 they knew who I was.
    0:05:28 The farthest I got
    0:05:29 is talking to them is myself.
    0:05:31 And they, you know,
    0:05:31 it was just,
    0:05:32 I started with,
    0:05:33 with, hey,
    0:05:34 do you guys know who I am?
    0:05:35 I want to have a conversation
    0:05:36 with you.
    0:05:37 And they were, you know,
    0:05:38 said to me, yeah,
    0:05:39 you’re our favorite researcher.
    0:05:40 We love you.
    0:05:41 Okay.
    0:05:42 And they were very willing
    0:05:43 to talk,
    0:05:44 which is why I got so much
    0:05:45 farther talking to them
    0:05:46 as myself as I did
    0:05:47 pretending to be a hacker.
    0:05:48 Uh-huh.
    0:05:50 What’s a thing you learned
    0:05:52 from LockbitSup?
    0:05:52 What’s a, what’s a,
    0:05:54 what’s one detail
    0:05:54 of your understanding
    0:05:55 that was improved
    0:05:56 by that relationship?
    0:05:59 Well, there were a lot of things,
    0:06:00 but one of the key things
    0:06:02 I’d learned was information
    0:06:04 about they probably,
    0:06:05 internal problems
    0:06:06 that they had
    0:06:06 with affiliates.
    0:06:07 For example,
    0:06:09 they complained that
    0:06:10 they’ve got really good hackers,
    0:06:11 but some of these hackers
    0:06:12 are younger kids
    0:06:14 and they’re good at hacking,
    0:06:15 but they’re really bad
    0:06:16 at negotiating.
    0:06:17 Uh, and he was,
    0:06:18 they were unhappy
    0:06:19 about the amount
    0:06:20 of money coming in.
    0:06:21 Uh, so they talked
    0:06:22 about that
    0:06:22 and coming up
    0:06:23 with a, with a model
    0:06:24 of how much
    0:06:25 they would accept
    0:06:26 and they created
    0:06:27 sort of a formula
    0:06:28 per company.
    0:06:29 And so it’s just
    0:06:30 things like that,
    0:06:31 things around attack resources.
    0:06:32 They asked me one time
    0:06:33 if I would buy them a,
    0:06:34 they couldn’t get a,
    0:06:35 they couldn’t get
    0:06:37 a domain tools account
    0:06:37 and they wanted to know
    0:06:38 because they couldn’t
    0:06:39 pay for it with crypto.
    0:06:39 They want to know
    0:06:40 if I would buy it for them,
    0:06:41 which of course
    0:06:42 they’re playing with me,
    0:06:42 you know,
    0:06:44 and it was sort of
    0:06:44 a cat and mouse
    0:06:45 fun relationship
    0:06:46 for a while
    0:06:47 of going back and forth.
    0:06:49 So it was, it was friendly
    0:06:51 for most of our relationship
    0:06:53 until it wasn’t.
    0:06:53 So, okay, so you’re
    0:06:54 in this world
    0:06:55 and I just want to
    0:06:57 step back for a minute
    0:06:59 to, to, to talk about
    0:06:59 what’s going on
    0:07:00 in a big way, right?
    0:07:01 There’s this phrase
    0:07:02 that’s sort of central here,
    0:07:04 which is ransomware
    0:07:05 as a service.
    0:07:07 Ransomware is like
    0:07:08 straightforward,
    0:07:10 something a lot of people
    0:07:10 are familiar with.
    0:07:11 It’s basically
    0:07:13 some bad actor,
    0:07:14 some hacker,
    0:07:15 hacks into some company’s
    0:07:17 computers, locks them up
    0:07:18 and says,
    0:07:20 we’re not going to unlock them
    0:07:21 unless you pay us a ransom.
    0:07:23 That’s ransomware.
    0:07:24 Exactly.
    0:07:25 What is ransomware
    0:07:26 as a service?
    0:07:27 What is, I mean,
    0:07:28 we know about software
    0:07:29 as a service, right?
    0:07:29 It’s basically
    0:07:31 you pay whatever amount
    0:07:31 a month and you get
    0:07:32 to use software.
    0:07:33 What’s ransomware
    0:07:33 as a service?
    0:07:35 So ransomware
    0:07:35 as a service,
    0:07:37 there’s more than,
    0:07:38 than just ransomware.
    0:07:39 So you have
    0:07:41 this two-part model
    0:07:41 where you have
    0:07:43 a service provider.
    0:07:44 That service provider
    0:07:45 provides the actual
    0:07:46 ransomware code.
    0:07:48 They also provide
    0:07:49 infrastructure.
    0:07:50 So the provider
    0:07:51 provides these services.
    0:07:52 The hacker goes
    0:07:53 and does the dirty work
    0:07:54 of the actual hacking.
    0:07:55 And together,
    0:07:56 when a victim
    0:07:57 pays the extortion,
    0:07:59 they share the profit
    0:07:59 from it.
    0:08:00 The benefit
    0:08:01 from using this model
    0:08:02 is you can have
    0:08:04 a lot higher volume
    0:08:05 than if it was just
    0:08:06 five guys in a group
    0:08:07 doing it themselves.
    0:08:08 By using this model,
    0:08:09 you can have
    0:08:10 many people
    0:08:11 doing attacks
    0:08:12 on your behalf,
    0:08:13 much higher volume
    0:08:14 of attacks,
    0:08:15 much higher revenue.
    0:08:17 So Lockbit
    0:08:18 is basically
    0:08:20 just a software company.
    0:08:20 They’re like,
    0:08:22 they’re like
    0:08:23 an enterprise software company.
    0:08:24 They write software
    0:08:25 and provide various
    0:08:26 tools for users.
    0:08:27 But in this case,
    0:08:28 the users
    0:08:29 are criminals,
    0:08:30 are people
    0:08:31 who want to hack
    0:08:32 into various
    0:08:33 computer systems
    0:08:34 and steal data
    0:08:34 and extort money.
    0:08:36 That’s correct.
    0:08:37 But the other piece
    0:08:38 to it
    0:08:39 is the service
    0:08:40 provider aspect.
    0:08:41 They’re the ones
    0:08:41 that are sort of
    0:08:42 in charge,
    0:08:43 that run the show,
    0:08:44 that give direction,
    0:08:45 that step in
    0:08:46 whenever there’s
    0:08:47 an issue.
    0:08:48 If there’s a victim
    0:08:49 not paying,
    0:08:50 sometimes they’ll come in
    0:08:51 and help with the negotiation
    0:08:52 or take over
    0:08:53 or give direction
    0:08:54 on how much
    0:08:55 you can accept
    0:08:56 as a payment
    0:08:57 or even say,
    0:08:58 this is,
    0:08:59 you can or cannot
    0:09:00 hack this company.
    0:09:02 So they’re definitely
    0:09:03 in the leadership chair.
    0:09:05 So I want to talk
    0:09:05 about how LockBit
    0:09:06 sort of grows
    0:09:08 and makes a name
    0:09:08 for itself.
    0:09:09 And one of the things
    0:09:10 that’s really interesting
    0:09:12 is kind of how
    0:09:13 uninteresting it is.
    0:09:13 It’s like,
    0:09:14 oh, it’s this
    0:09:15 international criminal gang
    0:09:16 and they’re acting
    0:09:17 like a boring
    0:09:18 software company.
    0:09:20 And it seems like
    0:09:22 a key early moment
    0:09:22 for them
    0:09:23 as they’re trying
    0:09:23 to grow
    0:09:25 and differentiate
    0:09:26 themselves in the market
    0:09:27 is this
    0:09:29 summer paper contest
    0:09:31 in 2020.
    0:09:32 Tell me about that.
    0:09:35 Yeah, it’s pretty crazy.
    0:09:36 So on this
    0:09:37 long-running forum
    0:09:38 that I mentioned earlier,
    0:09:39 this Russian hacking forum,
    0:09:41 LockBit really wanted
    0:09:43 to get their brand
    0:09:43 out there.
    0:09:45 So what they did
    0:09:46 is they sponsored
    0:09:48 this hacking paper
    0:09:50 contest,
    0:09:52 meaning hackers
    0:09:52 would submit
    0:09:53 these papers
    0:09:54 on different ways
    0:09:54 to hack
    0:09:55 and LockBit,
    0:09:57 they would take part
    0:09:57 in this
    0:09:58 and they would
    0:09:59 help review.
    0:10:00 And there was
    0:10:01 five winners
    0:10:02 and I think,
    0:10:02 I don’t remember
    0:10:03 what the,
    0:10:04 I think it was
    0:10:06 $5,000 maybe.
    0:10:08 You put a screenshot
    0:10:09 in your report
    0:10:12 and what’s amazing
    0:10:13 is how banal
    0:10:14 it looks.
    0:10:15 It looks totally
    0:10:17 like some college
    0:10:18 software contest
    0:10:19 or just some boring
    0:10:21 enterprise software company.
    0:10:21 Like there’s this
    0:10:22 little kind of
    0:10:23 clip art
    0:10:23 of just like
    0:10:24 a dude
    0:10:25 at a laptop
    0:10:26 with a little
    0:10:26 plant next to him.
    0:10:27 Although there is
    0:10:28 also a skull
    0:10:28 and crossbones
    0:10:29 next to him.
    0:10:29 It’s like,
    0:10:30 we’re just coders,
    0:10:31 but we’re bad.
    0:10:33 And as you said,
    0:10:34 first place is $5,000,
    0:10:35 which seems like
    0:10:36 not that much,
    0:10:36 right?
    0:10:37 They’re exploiting,
    0:10:39 they’re stealing
    0:10:40 tens of millions
    0:10:40 of dollars
    0:10:40 at this point,
    0:10:40 right?
    0:10:41 And then it says
    0:10:43 like accepted
    0:10:43 article topics,
    0:10:44 just like it would
    0:10:45 in a college contest.
    0:10:46 But under accepted
    0:10:48 article topics,
    0:10:48 it says,
    0:10:50 hacks,
    0:10:50 any,
    0:10:52 methods for pouring
    0:10:52 shells,
    0:10:53 fixing,
    0:10:53 elevating rights,
    0:10:55 your stories and tricks,
    0:10:56 interesting hack stories.
    0:10:58 It’s such a fantastic
    0:11:00 combination of,
    0:11:01 well,
    0:11:03 banality and evil.
    0:11:04 It is,
    0:11:05 but here’s what
    0:11:05 you have to think about.
    0:11:07 There’s two benefits
    0:11:07 for this.
    0:11:07 One,
    0:11:08 what I mentioned,
    0:11:09 sort of getting their name
    0:11:10 out and getting known
    0:11:11 with hackers,
    0:11:12 but two,
    0:11:14 they’re looking for those
    0:11:15 upcoming rising stars,
    0:11:16 if you will.
    0:11:17 It’s recruitment,
    0:11:19 it’s talent pipeline,
    0:11:19 yeah.
    0:11:20 That’s right.
    0:11:21 And that’s why
    0:11:22 Lockbit was different
    0:11:22 than most of these
    0:11:23 other ransomware groups
    0:11:24 because they approached
    0:11:25 it as a business
    0:11:26 and they thought
    0:11:27 out of the box
    0:11:28 and that’s kind of
    0:11:29 what set them ahead
    0:11:31 and apart at the time
    0:11:32 from other ransomware groups.
    0:11:33 So,
    0:11:34 so does it work,
    0:11:35 this strategy?
    0:11:38 It absolutely works.
    0:11:39 I mean,
    0:11:40 there’s a reason
    0:11:41 that people know
    0:11:41 their name
    0:11:42 and know who they are
    0:11:43 and there’s a reason
    0:11:44 that they have
    0:11:44 so many people
    0:11:46 that at the time
    0:11:46 in a way
    0:11:47 I really wanted
    0:11:47 to work for them
    0:11:49 over other groups.
    0:11:51 It was propaganda
    0:11:52 and it worked.
    0:11:53 And so,
    0:11:54 it seems like
    0:11:56 by around 2021
    0:11:58 they’ve hit the big time
    0:12:01 and there’s this one hack
    0:12:01 in particular
    0:12:03 that you write about
    0:12:04 in the summer of 21
    0:12:06 of Accenture,
    0:12:06 the big
    0:12:08 international consulting company.
    0:12:09 Tell me about
    0:12:10 the Accenture hack.
    0:12:11 So,
    0:12:12 in the Accenture hack,
    0:12:13 you know,
    0:12:15 the affiliate
    0:12:16 had gone in,
    0:12:17 compromised them,
    0:12:18 they locked down
    0:12:18 their data
    0:12:20 and Lockbit,
    0:12:21 you know,
    0:12:22 put it on their site
    0:12:22 that,
    0:12:23 you know,
    0:12:23 they were a victim,
    0:12:24 reporters started
    0:12:25 to report about it
    0:12:26 and you got a lot
    0:12:27 of buzz in the media.
    0:12:28 Now,
    0:12:28 the problem
    0:12:30 with the Accenture hack
    0:12:31 is that Accenture
    0:12:33 denied that the hack
    0:12:33 took place
    0:12:34 initially
    0:12:36 saying that
    0:12:37 it wasn’t real
    0:12:38 and it didn’t happen.
    0:12:39 the issue with that
    0:12:41 is their customer’s data
    0:12:43 was on their website
    0:12:44 and you could
    0:12:45 go see it
    0:12:46 and validate it
    0:12:47 and download samples of it.
    0:12:48 The customer’s data
    0:12:50 was on the Lockbit website.
    0:12:51 That’s correct.
    0:12:52 That’s correct.
    0:12:53 And it was just a sampling
    0:12:54 but you could see
    0:12:55 this information
    0:12:56 and it looked
    0:12:57 quite authentic.
    0:12:58 So,
    0:13:00 so does this
    0:13:01 Accenture hack
    0:13:02 sort of
    0:13:03 put Lockbit
    0:13:04 on the map
    0:13:05 in a bigger way?
    0:13:06 Oh,
    0:13:07 100%.
    0:13:07 I mean,
    0:13:08 the media
    0:13:09 surrounding that
    0:13:12 was very loud.
    0:13:12 I mean,
    0:13:13 it was across
    0:13:14 many organizations.
    0:13:16 Lots of
    0:13:17 well-known
    0:13:18 journalists
    0:13:19 and organizations
    0:13:20 reported on it.
    0:13:22 All this feeds
    0:13:23 into the propaganda.
    0:13:23 Not that journalists
    0:13:24 shouldn’t report on it.
    0:13:25 I’m just saying,
    0:13:25 you know,
    0:13:26 Lockbit plays that
    0:13:27 to benefit him
    0:13:29 as them as well.
    0:13:29 So,
    0:13:30 so basically
    0:13:31 the press coverage
    0:13:32 is good for Lockbit
    0:13:32 because
    0:13:34 hackers see it
    0:13:35 and go to Lockbit
    0:13:35 and say,
    0:13:35 hey,
    0:13:36 I want to be
    0:13:36 an affiliate
    0:13:37 and do some
    0:13:37 hacking,
    0:13:38 essentially.
    0:13:38 That’s right.
    0:13:39 And to be fair,
    0:13:40 the same thing
    0:13:40 for me
    0:13:41 from writing
    0:13:41 these reports.
    0:13:42 Yes,
    0:13:42 it helps
    0:13:43 researchers,
    0:13:43 law enforcement,
    0:13:44 but it also
    0:13:45 helps them.
    0:13:46 That’s the reason
    0:13:46 that they were
    0:13:47 friendly to me
    0:13:47 is because
    0:13:48 they were fans
    0:13:49 of a lot.
    0:13:49 I have probably
    0:13:50 just as many
    0:13:51 criminal hackers
    0:13:52 that are fans
    0:13:52 of the ransomware
    0:13:53 diaries
    0:13:53 as there are
    0:13:54 researchers
    0:13:54 and,
    0:13:55 you know,
    0:13:57 regular people
    0:13:57 that are not criminals.
    0:13:58 Well,
    0:13:58 I mean,
    0:13:58 there’s an
    0:14:00 ecosystem here,
    0:14:00 right?
    0:14:00 Like,
    0:14:04 the job,
    0:14:05 there’s a universe
    0:14:05 of people
    0:14:06 whose job
    0:14:06 is fighting
    0:14:07 criminals
    0:14:09 and a universe
    0:14:09 of people
    0:14:09 who are criminals
    0:14:10 who are trying
    0:14:10 to evade
    0:14:11 being caught,
    0:14:11 right?
    0:14:13 That’s right.
    0:14:14 The kind of
    0:14:15 intellectual universe
    0:14:15 has got to be
    0:14:16 almost entirely
    0:14:17 overlapping.
    0:14:18 Everybody’s trying
    0:14:18 to figure out
    0:14:19 what everybody
    0:14:19 else is doing.
    0:14:20 Everybody’s
    0:14:21 sort of using
    0:14:22 the same tricks
    0:14:22 on each other.
    0:14:23 It makes sense
    0:14:26 that the bad guys
    0:14:26 and the good guys
    0:14:27 would be reading
    0:14:27 the same stuff.
    0:14:29 It does.
    0:14:30 And,
    0:14:30 you know,
    0:14:31 that’s really
    0:14:33 where that human
    0:14:34 framework came in
    0:14:35 because his ego
    0:14:37 was the main thing
    0:14:37 I was able
    0:14:39 to play on
    0:14:40 in order
    0:14:41 to get information.
    0:14:42 And even when
    0:14:43 there were lies
    0:14:43 in that information,
    0:14:44 you know,
    0:14:44 I talked to the people
    0:14:45 who work for them.
    0:14:46 So I would take
    0:14:46 those lies
    0:14:47 and I would present
    0:14:48 them in a different
    0:14:49 way to those people
    0:14:50 to get a response
    0:14:52 and that would help
    0:14:52 me to validate
    0:14:53 what’s real
    0:14:53 and what’s not.
    0:14:54 Is there some
    0:14:55 specific example
    0:14:56 of playing on his ego?
    0:14:57 Something you said
    0:14:58 to flatter him
    0:14:58 or something?
    0:15:00 Well,
    0:15:01 yeah,
    0:15:01 you know,
    0:15:02 one of the things
    0:15:03 that was big
    0:15:04 for him was,
    0:15:05 you know,
    0:15:06 he wanted to be
    0:15:06 sort of the
    0:15:08 Darth Vader of ransomware,
    0:15:09 my words,
    0:15:09 not his.
    0:15:10 But,
    0:15:10 you know,
    0:15:12 he wanted to be
    0:15:13 this top person.
    0:15:13 So,
    0:15:14 you know,
    0:15:14 when you would talk
    0:15:15 about him changing
    0:15:17 the game of ransomware
    0:15:18 and telling him,
    0:15:18 you know,
    0:15:21 you guys are on top,
    0:15:21 you know,
    0:15:22 how did you get there?
    0:15:24 How did you get ahead
    0:15:25 of other groups
    0:15:26 like Revol
    0:15:27 and,
    0:15:29 at the time,
    0:15:30 Black Matter
    0:15:31 and groups like that?
    0:15:31 And,
    0:15:32 you know,
    0:15:33 he loved that.
    0:15:33 You know,
    0:15:34 it would just,
    0:15:35 that was a thing
    0:15:35 that would get
    0:15:37 Mr. Grumpy Pants talking
    0:15:38 was sort of playing
    0:15:39 on his ego,
    0:15:40 you know,
    0:15:40 asking him questions
    0:15:41 about how he got
    0:15:43 to be the top brand
    0:15:44 in ransomware
    0:15:45 and how he’s better
    0:15:46 than all the other ones.
    0:15:47 and he fed right
    0:15:48 into that.
    0:15:52 Coming up
    0:15:53 after the break,
    0:15:54 what happens
    0:15:55 when LockBit
    0:15:55 is used
    0:15:55 to hack
    0:15:56 a hospital
    0:15:57 for children
    0:15:58 with cancer?
    0:16:09 Something unexpected
    0:16:10 happened after
    0:16:11 Jeremy Scott
    0:16:12 confessed to killing
    0:16:13 Michelle Schofield
    0:16:14 in Bone Valley
    0:16:15 season one.
    0:16:16 I just knew him
    0:16:17 as a kid.
    0:16:18 Long,
    0:16:19 silent voices
    0:16:20 from his past
    0:16:21 came forward.
    0:16:22 And he was just
    0:16:23 staring at me.
    0:16:25 And they had secrets
    0:16:25 of their own
    0:16:26 to share.
    0:16:28 Gilbert King,
    0:16:34 I was no longer
    0:16:35 just telling the story.
    0:16:37 I was part of it.
    0:16:38 Every time I hear
    0:16:39 about my dad,
    0:16:39 it’s,
    0:16:40 oh,
    0:16:40 he’s a killer.
    0:16:41 He’s just straight evil.
    0:16:43 I was becoming
    0:16:43 the bridge
    0:16:44 between a killer
    0:16:45 and the son
    0:16:46 he’d never known.
    0:16:47 If the cops
    0:16:48 and everything
    0:16:48 would have done
    0:16:49 their job properly,
    0:16:49 my dad would have
    0:16:50 been in jail.
    0:16:50 I would have
    0:16:51 never existed.
    0:16:53 I never expected
    0:16:54 to find myself
    0:16:54 in this place.
    0:16:56 Now,
    0:16:57 I need to tell you
    0:16:58 how I got here.
    0:16:59 At the end of the day,
    0:17:00 I’m literally
    0:17:01 a son of a killer.
    0:17:02 Bone Valley,
    0:17:04 Season 2.
    0:17:05 Jeremy.
    0:17:06 Jeremy,
    0:17:07 I want to tell you
    0:17:07 something.
    0:17:09 Listen to new episodes
    0:17:09 of Bone Valley,
    0:17:10 Season 2,
    0:17:12 starting April 9th
    0:17:13 on the iHeartRadio app,
    0:17:14 Apple Podcasts,
    0:17:15 or wherever you
    0:17:15 get your podcasts.
    0:17:17 And to hear
    0:17:18 the entire new season
    0:17:19 ad-free
    0:17:20 with exclusive content
    0:17:21 starting April 9th,
    0:17:22 subscribe to
    0:17:23 Lava for Good Plus
    0:17:25 on Apple Podcasts.
    0:17:30 early 2020s,
    0:17:31 Lockbit is
    0:17:34 king of the ransomware world.
    0:17:35 And then it seems like
    0:17:37 in about 2023,
    0:17:39 they sort of start
    0:17:39 going too far,
    0:17:40 or their affiliates
    0:17:41 start going too far,
    0:17:42 right?
    0:17:43 They start to
    0:17:45 get into trouble.
    0:17:48 And it seems like
    0:17:49 the hack of
    0:17:50 a hospital
    0:17:51 that is actually
    0:17:52 called Sick Kids,
    0:17:53 which is
    0:17:55 a children’s
    0:17:56 cancer hospital
    0:17:57 in Canada
    0:17:59 is kind of
    0:17:59 a turning point.
    0:18:00 And, like,
    0:18:03 I do wonder,
    0:18:04 like,
    0:18:05 you could hack
    0:18:06 anybody.
    0:18:08 Why would you
    0:18:08 hack a
    0:18:10 cancer hospital
    0:18:10 for children?
    0:18:10 Like,
    0:18:11 is it because
    0:18:12 you want to be
    0:18:13 as evil as possible?
    0:18:15 Yeah,
    0:18:16 it’s because
    0:18:16 they see them
    0:18:18 as an easy target
    0:18:19 because a hospital
    0:18:20 has to be available
    0:18:21 and make their
    0:18:22 resources
    0:18:23 easily
    0:18:25 accessible
    0:18:26 by their
    0:18:26 patients,
    0:18:27 clients,
    0:18:28 medical organizations.
    0:18:29 And inherently,
    0:18:31 the more accessible
    0:18:31 something is,
    0:18:32 the less secure
    0:18:32 it is.
    0:18:33 so it makes
    0:18:34 them an easy
    0:18:34 target.
    0:18:35 They have a lot
    0:18:36 of money
    0:18:37 and they’re
    0:18:38 more likely
    0:18:38 to pay
    0:18:39 because the
    0:18:40 data is so
    0:18:40 sensitive
    0:18:41 and the systems
    0:18:42 that are encrypted
    0:18:43 are so critical
    0:18:44 that it makes
    0:18:45 them a ripe target.
    0:18:46 And that’s the
    0:18:46 reason that
    0:18:47 they’ll go after
    0:18:47 them.
    0:18:48 Initially,
    0:18:51 the hospital
    0:18:51 was hacked,
    0:18:52 the systems
    0:18:53 were encrypted,
    0:18:54 data was stolen,
    0:18:55 and they
    0:18:56 weren’t going
    0:18:56 to let them
    0:18:57 out of this.
    0:18:58 They were going
    0:18:59 to force
    0:19:00 them to pay
    0:19:01 or they weren’t
    0:19:01 going to give
    0:19:02 them the key
    0:19:02 to decrypt
    0:19:03 their systems
    0:19:03 and didn’t
    0:19:04 seem to care
    0:19:04 that these
    0:19:05 kids couldn’t
    0:19:06 get the care
    0:19:06 that they needed
    0:19:07 and the treatments
    0:19:08 that they needed.
    0:19:09 The only reason,
    0:19:10 so what ended
    0:19:11 up happening
    0:19:12 was with all
    0:19:12 the media
    0:19:13 around it,
    0:19:14 it was such
    0:19:15 a bad look
    0:19:16 for Lockbit
    0:19:17 that the leadership
    0:19:17 of the group
    0:19:19 decided after,
    0:19:19 you know,
    0:19:20 about two weeks
    0:19:20 they decided,
    0:19:21 okay,
    0:19:22 we’re going
    0:19:22 to go ahead
    0:19:23 and we’re going
    0:19:23 to give them
    0:19:24 the encryption key
    0:19:25 just because this
    0:19:26 was getting
    0:19:27 to be too hot.
    0:19:28 And if you
    0:19:28 remember like
    0:19:29 the whole
    0:19:30 Colonial Pipeline
    0:19:30 thing with
    0:19:31 the Darkside
    0:19:31 ransomware
    0:19:32 group,
    0:19:33 that got so
    0:19:34 much attention
    0:19:35 that government
    0:19:36 agencies got
    0:19:37 involved and went
    0:19:37 after them
    0:19:38 and when that
    0:19:38 happens,
    0:19:40 it’s very bad
    0:19:40 for ransomware
    0:19:41 groups.
    0:19:41 So they
    0:19:43 essentially saw
    0:19:43 things could
    0:19:44 possibly go that
    0:19:45 direction with
    0:19:46 the amount of
    0:19:46 bad publicity
    0:19:47 they were getting
    0:19:48 and decided it
    0:19:49 wasn’t worth the
    0:19:50 payment they were
    0:19:50 going to get
    0:19:51 and they went
    0:19:52 ahead and provided
    0:19:53 the hospital with
    0:19:55 the encryption key
    0:19:55 so they could get
    0:19:56 those systems
    0:19:57 back online.
    0:19:59 And in fact,
    0:20:00 their concern
    0:20:01 about a backlash
    0:20:02 was justified,
    0:20:02 right?
    0:20:03 It seems like
    0:20:05 international governments
    0:20:06 kind of led by
    0:20:08 the UK do start
    0:20:10 to go after
    0:20:11 Lockbit around
    0:20:12 this point,
    0:20:12 right?
    0:20:14 What do you do
    0:20:14 if you’re a
    0:20:15 government and you
    0:20:16 want to go after
    0:20:17 a Russian hacking
    0:20:17 gang?
    0:20:20 Well, it’s not
    0:20:20 easy.
    0:20:22 The things that
    0:20:23 you have to do
    0:20:24 is you have to
    0:20:25 use resources
    0:20:25 that people like
    0:20:26 me don’t have
    0:20:28 available to try
    0:20:29 to figure out
    0:20:30 their infrastructure,
    0:20:31 their hosting
    0:20:32 infrastructure,
    0:20:33 where their
    0:20:34 servers live,
    0:20:36 which is very
    0:20:37 difficult when
    0:20:38 they’re on the
    0:20:39 dark web.
    0:20:40 It’s hard to
    0:20:40 figure that out.
    0:20:41 Because this is
    0:20:42 the cat and
    0:20:42 mouse thing.
    0:20:42 They’re like
    0:20:43 complicated,
    0:20:45 smart systems
    0:20:45 these people use
    0:20:47 to hide their
    0:20:47 location,
    0:20:48 essentially.
    0:20:49 That’s right.
    0:20:51 And so that’s
    0:20:52 one aspect is
    0:20:53 trying to figure
    0:20:53 out that
    0:20:54 infrastructure.
    0:20:55 In some cases,
    0:20:56 you can use
    0:20:57 legal means to
    0:20:57 take it down,
    0:20:58 but with groups
    0:21:00 like Lockbit,
    0:21:00 often they will
    0:21:01 use service
    0:21:02 providers that
    0:21:02 are in
    0:21:03 countries that
    0:21:04 cater to
    0:21:05 criminal activity
    0:21:05 and won’t
    0:21:06 respond to
    0:21:06 subpoenas.
    0:21:07 The other thing
    0:21:08 though that
    0:21:09 these governments
    0:21:10 and law
    0:21:10 enforcements try
    0:21:11 to get into
    0:21:12 is the
    0:21:13 infrastructure that
    0:21:13 is public,
    0:21:15 the panel that
    0:21:17 the bad guys
    0:21:18 use to log
    0:21:18 into,
    0:21:19 with the
    0:21:19 graphical
    0:21:20 interface to
    0:21:21 control these
    0:21:22 attacks.
    0:21:23 And there’s
    0:21:23 technical ways
    0:21:24 to do that,
    0:21:25 and then there’s
    0:21:25 also the ways
    0:21:26 of infiltrating
    0:21:27 the people who
    0:21:27 work for the
    0:21:28 group to get
    0:21:29 their credentials
    0:21:30 to gain access.
    0:21:30 So they’re
    0:21:31 basically hacking
    0:21:33 the hackers.
    0:21:34 so in February
    0:21:37 of 2024,
    0:21:39 this international
    0:21:40 coalition of law
    0:21:41 enforcement agencies
    0:21:42 actually takes
    0:21:44 over Lockbit’s
    0:21:45 sort of publicly
    0:21:46 facing site,
    0:21:46 right?
    0:21:47 Lockbit’s dark
    0:21:47 website.
    0:21:48 Tell me about
    0:21:49 that.
    0:21:50 Yeah, so it
    0:21:51 was great.
    0:21:52 When you went
    0:21:53 to the website
    0:21:53 that day,
    0:21:55 it was no longer
    0:21:56 Lockbit’s data
    0:21:56 leak site.
    0:21:57 instead,
    0:21:59 it was a
    0:22:00 mock site,
    0:22:01 so it looks
    0:22:02 just like it,
    0:22:03 except instead
    0:22:04 of having
    0:22:06 real victims
    0:22:06 within the
    0:22:07 site,
    0:22:08 the NCA
    0:22:09 put the
    0:22:09 criminals
    0:22:10 as the
    0:22:10 victims,
    0:22:11 and they named
    0:22:12 affiliates as
    0:22:13 the victims,
    0:22:14 they had a
    0:22:15 countdown timer
    0:22:16 for Lockbit’s
    0:22:17 up saying they
    0:22:17 were going to
    0:22:18 release his
    0:22:18 identity.
    0:22:19 And the
    0:22:20 countdown timer
    0:22:21 is the kind
    0:22:21 of thing that
    0:22:22 the bad guys
    0:22:23 use when they
    0:22:23 hack a company
    0:22:24 saying we’re
    0:22:24 going to…
    0:22:25 That’s right.
    0:22:27 Yeah, that’s
    0:22:27 what they do.
    0:22:27 The countdown
    0:22:28 timer for
    0:22:29 traditional victims
    0:22:30 is how long
    0:22:31 they have to
    0:22:31 pay until the
    0:22:32 data is leaked.
    0:22:33 So in the
    0:22:34 same way that
    0:22:35 Lockbit was
    0:22:36 essentially
    0:22:37 marketing itself,
    0:22:39 now the cops,
    0:22:40 now the law
    0:22:40 enforcement officials
    0:22:41 are doing that
    0:22:42 same kind of
    0:22:44 marketing.
    0:22:45 They’re sort of
    0:22:45 doing this kind
    0:22:46 of propagandistic
    0:22:47 thing to attract
    0:22:48 attention,
    0:22:49 presumably what,
    0:22:50 to scare off
    0:22:51 all the affiliates?
    0:22:52 Like why would
    0:22:52 they be doing it
    0:22:53 in this showy way?
    0:22:54 Just for attention,
    0:22:55 to get good press?
    0:22:55 No.
    0:22:56 It was a
    0:22:57 psychological
    0:22:58 operation.
    0:22:59 So prior to this,
    0:23:00 they never did
    0:23:01 this.
    0:23:02 The way they
    0:23:03 took sites down
    0:23:03 were just to
    0:23:03 take it down
    0:23:04 and put a
    0:23:04 message up
    0:23:05 saying law
    0:23:05 enforcement
    0:23:06 took this
    0:23:06 down.
    0:23:07 This was
    0:23:07 psychological.
    0:23:08 It was meant
    0:23:10 to put stress
    0:23:11 on the people
    0:23:12 who worked for
    0:23:13 the organization
    0:23:14 and being
    0:23:14 concerned that
    0:23:15 they no longer
    0:23:16 had anonymity
    0:23:17 and that their
    0:23:18 names and
    0:23:19 information was
    0:23:20 now being
    0:23:20 reviewed and
    0:23:21 revealed by
    0:23:22 law enforcement.
    0:23:23 And the whole
    0:23:24 goal of this
    0:23:26 was to affect
    0:23:26 the Lockbit
    0:23:28 brand and to
    0:23:28 make people
    0:23:29 not trust
    0:23:30 Lockbit or
    0:23:30 want to work
    0:23:31 for the
    0:23:31 organization.
    0:23:32 So it was
    0:23:33 very planned
    0:23:34 and thought
    0:23:34 out and
    0:23:35 methodical.
    0:23:36 It wasn’t
    0:23:37 just to get
    0:23:37 attention.
    0:23:38 It was
    0:23:39 specifically to
    0:23:40 hurt that
    0:23:41 brand and make
    0:23:42 affiliates
    0:23:42 afraid to
    0:23:43 work for
    0:23:43 them.
    0:23:44 And in
    0:23:44 addition to
    0:23:45 that mock
    0:23:46 website on
    0:23:46 the back
    0:23:47 end, that
    0:23:47 panel that
    0:23:47 I was
    0:23:48 mentioning, that
    0:23:49 admin panel
    0:23:49 that they
    0:23:49 would use,
    0:23:50 now when
    0:23:51 that took
    0:23:52 place, when
    0:23:52 the takedown
    0:23:53 took place,
    0:23:53 when the
    0:23:54 affiliates
    0:23:54 logged into
    0:23:55 that panel,
    0:23:55 they had
    0:23:56 tailored messages
    0:23:57 with their
    0:23:58 username by
    0:23:59 law enforcement
    0:23:59 saying,
    0:24:00 hey, you’re
    0:24:01 logging into the
    0:24:01 panel, we
    0:24:02 know who you
    0:24:02 are, we’ve
    0:24:03 been monitoring
    0:24:04 the activity
    0:24:04 you’ve been
    0:24:05 doing, we’ve
    0:24:05 got your
    0:24:06 wallets, we’re
    0:24:07 going to be
    0:24:07 coming to talk
    0:24:08 to you soon.
    0:24:09 So it
    0:24:10 was very
    0:24:12 detrimental to
    0:24:13 criminals, that
    0:24:14 was a brilliant
    0:24:15 operation in my
    0:24:15 opinion.
    0:24:16 And you
    0:24:16 mentioned that
    0:24:17 they had a
    0:24:18 countdown timer
    0:24:18 for when they
    0:24:19 were going to
    0:24:20 reveal the name
    0:24:21 of Lockbit
    0:24:22 Sup, the
    0:24:23 person, although
    0:24:24 you said there’s
    0:24:24 people, but at
    0:24:25 least one of the
    0:24:26 people behind
    0:24:27 this, behind
    0:24:28 Lockbit, one of
    0:24:29 the key Lockbit
    0:24:29 players, did they
    0:24:30 in fact reveal the
    0:24:31 name of that
    0:24:31 person?
    0:24:33 They didn’t.
    0:24:33 When the
    0:24:34 countdown timer
    0:24:34 they did not.
    0:24:36 At that time
    0:24:37 they didn’t, but
    0:24:37 there’s a reason
    0:24:38 that they didn’t,
    0:24:39 but they did not
    0:24:40 do that in
    0:24:40 February.
    0:24:42 The reason that
    0:24:42 they didn’t is
    0:24:44 because Lockbit
    0:24:45 agreed to tell
    0:24:46 them information
    0:24:46 about some of
    0:24:47 his adversarial
    0:24:48 group.
    0:24:48 There was a
    0:24:48 group called
    0:24:49 Black Cat who
    0:24:50 he didn’t like,
    0:24:51 and he agreed to
    0:24:51 try and give
    0:24:52 them information.
    0:24:53 So they used
    0:24:54 the thread of
    0:24:55 naming him as
    0:24:56 leverage and
    0:24:57 getting him to
    0:24:58 flip, basically.
    0:24:59 That’s correct.
    0:25:02 Do we know
    0:25:03 who he is now?
    0:25:04 Was he ever
    0:25:04 named?
    0:25:06 Yeah, it was
    0:25:08 several months
    0:25:08 later.
    0:25:09 The site came
    0:25:10 back online,
    0:25:12 meaning the
    0:25:12 law enforcement
    0:25:13 version of the
    0:25:14 site came back
    0:25:14 online.
    0:25:15 There was a
    0:25:16 new timer, and
    0:25:17 once again, they
    0:25:18 said they were
    0:25:18 going to reveal
    0:25:19 Lockbit’s name,
    0:25:21 and the timer
    0:25:22 began again.
    0:25:22 And on May
    0:25:23 7th, when that
    0:25:24 timer expired,
    0:25:25 they did.
    0:25:25 They released
    0:25:26 his name and
    0:25:26 his picture,
    0:25:28 Dmitry
    0:25:28 Koshev.
    0:25:31 They put that
    0:25:31 out there,
    0:25:32 indicted him,
    0:25:33 wanted posters,
    0:25:34 the whole nine
    0:25:34 yards.
    0:25:35 Is that
    0:25:35 grumpy pants?
    0:25:37 That’s, well,
    0:25:40 my opinion, my
    0:25:41 opinion is that
    0:25:42 that was the
    0:25:43 younger person,
    0:25:44 and the other
    0:25:44 guy’s still out
    0:25:45 there, but I
    0:25:46 think law
    0:25:46 enforcement might
    0:25:47 tell you
    0:25:47 otherwise, though
    0:25:48 they do agree
    0:25:49 with me that
    0:25:49 there’s two
    0:25:49 people.
    0:25:50 So he’s been
    0:25:51 indicted but not
    0:25:52 arrested?
    0:25:53 Is that what
    0:25:53 you’re saying?
    0:25:54 That’s correct,
    0:25:55 because he’s in
    0:25:56 Russia, and
    0:25:56 there’s protections
    0:25:57 there.
    0:25:58 The law
    0:25:59 enforcement just
    0:26:00 can’t get their
    0:26:00 hands on him,
    0:26:01 unfortunately.
    0:26:02 The criminals are
    0:26:03 protected when
    0:26:03 they’re in
    0:26:03 Russia.
    0:26:06 So is that the
    0:26:07 end of Lock
    0:26:07 Bit?
    0:26:09 It’s not.
    0:26:09 You would think
    0:26:10 it is, but
    0:26:12 almost every other
    0:26:13 group that this
    0:26:14 has happened to,
    0:26:15 that’s the end of
    0:26:16 the story, or at
    0:26:16 least it causes
    0:26:18 them to take that
    0:26:19 operation down, and
    0:26:19 they have to start
    0:26:20 from scratch
    0:26:21 somewhere else with
    0:26:22 a new operation,
    0:26:23 with a new name,
    0:26:24 and a new
    0:26:24 brand.
    0:26:25 But Lock Bit
    0:26:26 worked so hard
    0:26:27 on that brand, I
    0:26:28 don’t think he’ll
    0:26:30 ever take it away
    0:26:31 until they actually
    0:26:32 arrest everybody.
    0:26:34 But no, they
    0:26:35 continued, but they
    0:26:37 continued at a
    0:26:38 much lower level.
    0:26:39 They didn’t have
    0:26:40 the quality of
    0:26:41 hackers still working
    0:26:42 for them.
    0:26:43 They started having
    0:26:45 to lie about
    0:26:45 attacks to try and
    0:26:46 stack the numbers
    0:26:47 and things of that
    0:26:48 nature.
    0:26:49 Do you think the
    0:26:49 law enforcement
    0:26:50 officials campaign,
    0:26:51 the whole thing of
    0:26:52 naming the people
    0:26:53 and doing all the
    0:26:53 stunts on the
    0:26:53 website, you know,
    0:26:54 you think that
    0:26:54 worked?
    0:26:55 You think it was
    0:26:56 sort of like Lock
    0:26:57 Bit rose on
    0:26:57 marketing and in
    0:26:58 a way fell on the
    0:26:59 marketing of the
    0:27:00 government?
    0:27:02 Yeah, well, was it
    0:27:03 100% effective?
    0:27:04 No, but it was
    0:27:05 about 80% effective.
    0:27:06 And prior to this,
    0:27:07 I would say that
    0:27:07 most of those
    0:27:08 operations were like
    0:27:09 40% effective.
    0:27:11 And what I mean by
    0:27:12 that is this
    0:27:13 actually affected
    0:27:14 the brand where
    0:27:15 people, the
    0:27:16 quality hackers,
    0:27:17 the quality
    0:27:19 affiliates, why
    0:27:19 would they work
    0:27:20 for this
    0:27:21 organization with
    0:27:21 all this heat
    0:27:22 where they can’t
    0:27:23 trust that they’re
    0:27:23 going to be
    0:27:24 protected when
    0:27:25 they can go
    0:27:25 work for some
    0:27:26 other criminal
    0:27:26 organization?
    0:27:27 Yeah, like any
    0:27:27 software company,
    0:27:28 their biggest
    0:27:29 problem is finding
    0:27:30 and keeping good
    0:27:31 people.
    0:27:32 That’s right.
    0:27:33 That’s exactly
    0:27:33 right.
    0:27:34 And by good
    0:27:35 people, I guess,
    0:27:36 in this case, it
    0:27:37 means bad people.
    0:27:38 Right.
    0:27:39 So, okay, so this
    0:27:40 is a year ago,
    0:27:41 basically.
    0:27:42 This is early
    0:27:42 2024.
    0:27:43 Lockbit gets
    0:27:44 mostly taken
    0:27:45 down, not
    0:27:46 knocked out,
    0:27:47 at least knocked
    0:27:47 down.
    0:27:49 Where are we
    0:27:49 today?
    0:27:50 Like, what is the
    0:27:51 state of the
    0:27:52 ransomware industry?
    0:27:52 industry?
    0:27:53 So, it’s
    0:27:54 changed a bit.
    0:27:56 You have, I
    0:27:56 would say you
    0:27:57 have more
    0:27:58 groups, but you
    0:27:59 don’t have sort
    0:28:00 of these, you
    0:28:00 don’t have as
    0:28:02 many big
    0:28:04 organizations that
    0:28:05 sort of hold
    0:28:07 the majority of
    0:28:07 attacks.
    0:28:10 You have smaller
    0:28:10 to medium-sized
    0:28:12 groups that work
    0:28:12 more under the
    0:28:13 radar, meaning
    0:28:14 they’re not doing
    0:28:15 the same volume
    0:28:16 of attacks.
    0:28:17 They’re also not
    0:28:17 getting the same
    0:28:19 amount of money
    0:28:20 and ransom
    0:28:21 extortions as they
    0:28:23 did before, but
    0:28:24 they’re still out
    0:28:24 there.
    0:28:25 They’re just
    0:28:26 doing it.
    0:28:27 The model just
    0:28:27 changed a little
    0:28:28 bit.
    0:28:28 And so, is
    0:28:29 part of the
    0:28:30 idea that, oh,
    0:28:31 maybe trying to
    0:28:32 have a big name
    0:28:33 and be, like, a
    0:28:34 famous criminal
    0:28:36 gang is not a
    0:28:36 good long-term
    0:28:37 strategy?
    0:28:39 That’s exactly
    0:28:39 correct.
    0:28:40 I think that this
    0:28:41 is what really
    0:28:42 made them realize
    0:28:43 that people are
    0:28:44 sort of lower on
    0:28:45 the radar, just
    0:28:46 trying to get
    0:28:46 money and
    0:28:47 extort, but not
    0:28:48 necessarily have
    0:28:49 this voice that’s
    0:28:50 heard across the
    0:28:50 world.
    0:28:53 What’s the big
    0:28:54 lesson to you from
    0:28:54 the LockBit story?
    0:28:57 The big lesson
    0:28:59 there is being
    0:29:00 boisterous, having
    0:29:01 this ego, is
    0:29:02 actually a
    0:29:03 downfall.
    0:29:04 Being loud,
    0:29:05 getting publicity,
    0:29:07 getting your name
    0:29:08 out there, while
    0:29:09 that might help
    0:29:09 attract people to
    0:29:10 come work for
    0:29:11 you, there’s the
    0:29:12 opposite side of
    0:29:13 that, where it
    0:29:14 also attracts a
    0:29:14 lot of attention
    0:29:15 from law
    0:29:15 enforcement.
    0:29:16 And if you’re a
    0:29:16 criminal group,
    0:29:18 that’s not a good
    0:29:18 thing, and I
    0:29:19 think bad guys
    0:29:21 have figured that
    0:29:22 out between, mainly
    0:29:24 from 2024, with
    0:29:25 both the Black
    0:29:26 Cat ransomware
    0:29:27 group and with
    0:29:27 LockBit, those
    0:29:28 were your prominent
    0:29:30 players, and those
    0:29:30 guys both got
    0:29:31 decimated by law
    0:29:32 enforcement, and
    0:29:33 that happened
    0:29:33 because of the
    0:29:34 attention that they
    0:29:35 drew to themselves.
    0:29:37 So I think that’s
    0:29:37 the lesson that
    0:29:38 adversaries have
    0:29:39 learned, is you
    0:29:41 have to be quieter
    0:29:41 about what you
    0:29:42 do.
    0:29:45 We’ll be back
    0:29:46 in a minute
    0:29:46 with the
    0:29:47 lightning round.
    0:29:56 Something
    0:29:56 unexpected
    0:29:57 happened after
    0:29:58 Jeremy Scott
    0:29:59 confessed to
    0:29:59 killing Michelle
    0:30:00 Schofield in
    0:30:01 Bone Valley Season
    0:30:02 One.
    0:30:03 I just knew him
    0:30:04 as a kid.
    0:30:05 Long, silent
    0:30:06 voices from his
    0:30:08 past came forward.
    0:30:09 And he was just
    0:30:10 staring at me.
    0:30:11 And they had
    0:30:12 secrets of their
    0:30:13 own to share.
    0:30:15 Gilbert King.
    0:30:17 I’m the son of
    0:30:19 Jeremy Lynn Scott.
    0:30:21 I was no longer
    0:30:22 just telling the
    0:30:22 story.
    0:30:24 I was part of it.
    0:30:25 Every time I hear
    0:30:26 about my dad, it’s
    0:30:27 oh, he’s a killer.
    0:30:28 He’s just straight
    0:30:28 evil.
    0:30:29 I was becoming
    0:30:30 the bridge between
    0:30:32 a killer and the
    0:30:32 son he’d never
    0:30:33 known.
    0:30:34 If the cops and
    0:30:35 everything would
    0:30:35 have done their
    0:30:36 job properly, my
    0:30:36 dad would have
    0:30:37 been in jail.
    0:30:37 I would have
    0:30:38 never existed.
    0:30:40 I never expected
    0:30:41 to find myself in
    0:30:41 this place.
    0:30:44 Now, I need to
    0:30:44 tell you how I
    0:30:45 got here.
    0:30:46 At the end of
    0:30:46 the day, I’m
    0:30:47 literally a son of
    0:30:47 a killer.
    0:30:50 Bone Valley Season
    0:30:50 2.
    0:30:52 Jeremy.
    0:30:53 Jeremy, I want to
    0:30:54 tell you something.
    0:30:55 Listen to new
    0:30:56 episodes of Bone
    0:30:57 Valley Season 2
    0:30:58 starting April 9th
    0:30:59 on the iHeartRadio
    0:31:01 app, Apple Podcasts,
    0:31:02 or wherever you
    0:31:02 get your podcasts.
    0:31:04 And to hear the
    0:31:05 entire new season
    0:31:06 ad-free with
    0:31:07 exclusive content
    0:31:08 starting April 9th,
    0:31:09 subscribe to
    0:31:09 Lava for Good
    0:31:10 Plus on Apple
    0:31:12 Podcasts.
    0:31:16 Let’s finish
    0:31:17 with the lightning
    0:31:17 round.
    0:31:18 It’s going to be a
    0:31:18 little more random
    0:31:19 and a little more
    0:31:20 about you.
    0:31:21 Okay.
    0:31:23 What’s one thing you
    0:31:24 learned when you
    0:31:25 hacked into the
    0:31:26 Pentagon as a
    0:31:27 15-year-old boy?
    0:31:30 Oh, man.
    0:31:31 That’s the reason
    0:31:32 that I talk to
    0:31:33 these criminals and
    0:31:34 I sometimes have
    0:31:35 empathy to want to
    0:31:36 help them change
    0:31:37 what they’re doing
    0:31:38 is because I got a
    0:31:39 second chance and I
    0:31:40 remember that fear.
    0:31:42 and I want to
    0:31:43 try to help some
    0:31:43 of these young
    0:31:44 kids to change
    0:31:45 what they’re doing
    0:31:46 and not continue
    0:31:47 down this road.
    0:31:48 What actually
    0:31:48 happened there?
    0:31:49 What was it that
    0:31:49 happened?
    0:31:50 Yeah, so my
    0:31:52 stepfather worked
    0:31:53 for Colin Powell
    0:31:54 during the Iraq
    0:31:54 War.
    0:31:55 He was at the
    0:31:56 Pentagon and he
    0:31:56 had a classified
    0:31:57 system in our
    0:31:59 basement and I
    0:32:00 had a friend over
    0:32:01 and I was really
    0:32:02 into computers and
    0:32:02 hacking and
    0:32:03 figuring things out
    0:32:04 and I didn’t do
    0:32:05 anything elaborate.
    0:32:06 I just figured out
    0:32:06 his credentials and
    0:32:07 I logged in and
    0:32:08 was poking around.
    0:32:09 Nothing elaborate,
    0:32:11 enough that it
    0:32:12 got attention and
    0:32:13 bad things happened
    0:32:15 and the FBI showed
    0:32:16 up and things.
    0:32:16 The FBI showed up
    0:32:17 at your house.
    0:32:19 Yeah, they did.
    0:32:20 It was not a good
    0:32:20 day for me.
    0:32:23 I’m glad it worked
    0:32:24 out in the end.
    0:32:25 It did.
    0:32:26 It did.
    0:32:27 It only worked out
    0:32:28 though because of who
    0:32:29 he worked for, my
    0:32:29 stepfather and the
    0:32:30 connections that he
    0:32:31 had and the fact that
    0:32:32 I had no prior
    0:32:32 record.
    0:32:33 That’s the reason
    0:32:34 that it worked and
    0:32:35 I had a summer where
    0:32:36 I had to go work at
    0:32:37 Fort Belvoir doing
    0:32:38 community service but
    0:32:39 I just did such a
    0:32:40 good job they wanted
    0:32:40 to hire me to work
    0:32:41 there.
    0:32:43 So it was definitely
    0:32:44 a life-changing
    0:32:44 experience and then I
    0:32:45 joined the army and
    0:32:46 became a military
    0:32:47 police officer.
    0:32:49 So that was my story
    0:32:50 but it worked out
    0:32:50 well for me.
    0:32:52 So I understand that
    0:32:53 when you were a
    0:32:54 military police officer
    0:32:56 you did undercover
    0:32:57 drug buys.
    0:32:58 I did.
    0:32:59 What’s something you
    0:33:00 learned doing
    0:33:01 undercover drug buys
    0:33:02 as a military police
    0:33:02 officer?
    0:33:04 What I learned is
    0:33:05 it’s not black and
    0:33:05 white.
    0:33:06 It’s not just you’re
    0:33:07 a bad guy or a good
    0:33:07 guy.
    0:33:09 They’re still human
    0:33:10 beings.
    0:33:11 What’s one thing you
    0:33:12 learned pushing carts at
    0:33:13 Home Depot?
    0:33:16 That you should never
    0:33:17 have an ego because I
    0:33:18 did all that crazy work
    0:33:19 and I got out and I
    0:33:21 could not get a job in
    0:33:22 law enforcement because
    0:33:23 of my tattoos.
    0:33:25 At the time you
    0:33:26 couldn’t have visible
    0:33:27 tattoos at least in
    0:33:27 Virginia.
    0:33:28 I tried to join the
    0:33:29 FBI because I smoked
    0:33:30 weed in high school.
    0:33:31 At the time they had a
    0:33:32 zero tolerance.
    0:33:32 I couldn’t get into
    0:33:33 that.
    0:33:34 I couldn’t get a
    0:33:35 job and I had to
    0:33:36 start at the very
    0:33:36 bottom.
    0:33:38 I’ve been working
    0:33:38 retail.
    0:33:39 I’m not even in the
    0:33:39 store.
    0:33:40 I’m in the parking
    0:33:40 lot.
    0:33:43 I was living out of
    0:33:44 my truck for a couple
    0:33:44 weeks and then I
    0:33:45 rented a room at a
    0:33:46 house.
    0:33:47 They were selling
    0:33:48 drugs out of the
    0:33:48 house.
    0:33:49 The cops raided it,
    0:33:50 arrested everybody
    0:33:51 but me but I
    0:33:51 couldn’t even get in
    0:33:52 the house to get my
    0:33:52 stuff.
    0:33:54 It was a tough time
    0:33:54 in my life.
    0:33:57 I’m going to change
    0:33:58 gears to talk about
    0:33:59 something much more
    0:34:00 pedestrian now.
    0:34:02 What’s your
    0:34:03 favorite depiction
    0:34:04 of hacking in a
    0:34:04 work of fiction?
    0:34:09 Corey, there’s
    0:34:10 an author, Corey
    0:34:13 Doctro, brilliant
    0:34:13 guy.
    0:34:14 He’s one of my
    0:34:16 favorite authors and
    0:34:18 he does hacker
    0:34:19 fiction if you will
    0:34:21 and he’s got
    0:34:22 probably 20 books
    0:34:24 now but they’re
    0:34:25 phenomenal, especially
    0:34:25 the Homeland
    0:34:26 series.
    0:34:26 That’s one of my
    0:34:27 favorites.
    0:34:27 Okay, Homeland
    0:34:28 series.
    0:34:29 Who’s your favorite
    0:34:31 cyber criminal in real
    0:34:32 life?
    0:34:35 Um, I would
    0:34:37 probably say the
    0:34:37 hacker known as
    0:34:38 USDOD.
    0:34:41 He is a, he is a
    0:34:42 hacker who’s not
    0:34:42 Russian.
    0:34:43 He lives in
    0:34:43 Brazil.
    0:34:45 I became very good
    0:34:46 friends with him.
    0:34:47 I’ve never written
    0:34:47 about him.
    0:34:50 He wasn’t a target
    0:34:51 of mine.
    0:34:52 He helped me
    0:34:53 actually when I was
    0:34:54 going after Ransom
    0:34:56 VC and he gave me a
    0:34:57 lot of good inside
    0:34:58 information and we
    0:34:59 just became friends
    0:35:00 for a long time
    0:35:02 and we talked
    0:35:02 and he was
    0:35:03 somebody who I
    0:35:03 really had wanted
    0:35:04 to help.
    0:35:05 He’s in jail now
    0:35:06 so you can figure
    0:35:06 out if I was able
    0:35:07 to help him or
    0:35:07 not.
    0:35:09 Why?
    0:35:10 Why him?
    0:35:11 What was, what
    0:35:12 was that relationship?
    0:35:14 Um, you know, he
    0:35:16 had issues like
    0:35:18 everybody but, you
    0:35:19 know, he was a, he
    0:35:21 had a good side to
    0:35:21 him.
    0:35:21 He, there was a
    0:35:22 side to him.
    0:35:23 He was a decent
    0:35:26 person and I really
    0:35:27 thought if he hadn’t
    0:35:28 become a criminal, he’s
    0:35:29 somebody that would
    0:35:29 have been in the
    0:35:30 cybersecurity field.
    0:35:32 Um, he, he, he did
    0:35:33 have empathy for
    0:35:34 people.
    0:35:35 He hated law
    0:35:36 enforcement and the
    0:35:37 government but he did
    0:35:38 have empathy for
    0:35:39 people um and he was
    0:35:40 somebody who I could
    0:35:41 talk to and, and, and
    0:35:42 actually feel like I
    0:35:43 could, I could make a
    0:35:44 difference with the
    0:35:45 conversations that we
    0:35:45 had.
    0:35:52 John DiMaggio is the
    0:35:53 chief security
    0:35:54 strategist at
    0:35:55 Analyst One.
    0:35:57 Today’s show was
    0:35:58 produced by Gabriel
    0:35:58 Hunter Chang.
    0:35:59 It was edited by
    0:36:01 Lydia Jean Cott and
    0:36:02 engineered by Sarah
    0:36:02 Bouguer.
    0:36:04 I’m Jacob Goldstein
    0:36:04 and we’ll be back
    0:36:06 later this week with
    0:36:06 another episode of
    0:36:07 What’s Your Problem?
    0:36:25 Something unexpected
    0:36:26 happened after Jeremy
    0:36:27 Scott confessed to
    0:36:28 killing Michelle
    0:36:29 Schofield in Bone
    0:36:30 Valley Season 1.
    0:36:31 Every time I hear
    0:36:33 about my dad is, oh
    0:36:34 he’s a killer, he’s
    0:36:35 just straight evil.
    0:36:36 I was becoming the
    0:36:37 bridge between Jeremy
    0:36:39 Scott and the son he’d
    0:36:40 never known.
    0:36:41 At the end of the day
    0:36:42 I’m literally a son of a
    0:36:42 killer.
    0:36:44 Listen to new episodes
    0:36:45 of Bone Valley Season 2
    0:36:47 starting April 9th on the
    0:36:48 iHeartRadio app, Apple
    0:36:50 Podcasts, or wherever you
    0:36:51 get your podcasts.

    A few years ago, a ransomware gang called LockBit rose from obscurity to extort over $100 million from organizations around the world. A security strategist named Jon DiMaggio wanted to understand how the organization worked. So he used the techniques of World War II-era spycraft to make contact with the hackers.

    On today’s show, Jon tells the story of LockBit – from the way it borrowed techniques from mainstream companies to market itself and attract talent, to the response from international governments that used the gang’s own tactics against it. And he talks about how he got the hackers to talk to him.

    Jon described the rise and fall of the company in a series of posts he called the Ransomware Diaries. You can read those here: https://analyst1.com/ransomware-diaries-volume-1/

    Note: This bonus episode of What’s Your Problem? is sponsored by Microsoft.

    See omnystudio.com/listener for privacy information.

  • “Ocean is the new space” – 7 Wild Ideas for the $3 Trillion Dollar Frontier

    AI transcript
    0:00:01 That episode was a whirlwind.
    0:00:04 Yeah, we just recorded with our buddy Will O’Brien.
    0:00:07 This episode was like my favorite conversations living in San Francisco,
    0:00:12 where you run into a weirdo who knows a lot about something you know very little about,
    0:00:13 and you get way smart.
    0:00:17 In like 45 minutes, your mind gets blown like five times, and you just get smarter.
    0:00:18 So this is a Get Smarter episode for me.
    0:00:22 And it wasn’t just about like the business and the ideas that he talked about,
    0:00:27 but the mindset and how he thought about just like the philosophy of life that I was inspired by.
    0:00:28 Yeah, exactly.
    0:00:29 So, okay, what are we talking about?
    0:00:35 We’re talking about how the ocean is the new space, how there’s companies like SpaceX and Blue Origin,
    0:00:37 all these companies that are doing cool shit in space.
    0:00:40 He knows a lot about companies that are doing cool things in the ocean,
    0:00:43 which is something I honestly didn’t know anything about going in.
    0:00:44 Now I’m pretty fascinated with.
    0:00:47 But then we talked about the conversation toward the end gets really fun.
    0:00:51 Conspiracy theories, why conspiracy theorists make for great founders,
    0:00:54 his summer living with monks in Nepal, and what he took out of that.
    0:00:56 It was the end is really good.
    0:00:57 So get there to the end.
    0:00:59 I promise you, you will enjoy this episode.
    0:01:01 I feel like I can rule the world.
    0:01:03 I know I could be what I want to.
    0:01:06 I put my all in it like no days off.
    0:01:09 All right, what’s up?
    0:01:11 We got our friend Will O’Brien here.
    0:01:15 And Will is an Irish guy who talks my ear off about the ocean.
    0:01:42 And I honestly wasn’t thinking about the ocean at all until I saw maybe a tweet of yours, which was basically saying the ocean is the new space and how there’s companies like SpaceX and others that have built huge hundred billion dollar plus companies about exploring space, about putting satellites in space, about reusable rockets, and that there’s an opportunity for a similar wave of disruption for startups in the ocean.
    0:01:43 And I love that idea.
    0:01:45 I honestly, I’m never going to do it.
    0:01:46 So I’ll just put that up front.
    0:01:48 I’m never going to do something like that.
    0:01:52 I think 99.9% of people listening to this will also never go do that thing.
    0:02:02 But just from a, I don’t know, just as a fan of the game, just as a founder, I kind of love the theory and the intellectual idea here of what is the opportunity.
    0:02:09 And then if you’re one of the rare few hardcore founders that can go do this, you know, this is going to be right up your alley.
    0:02:10 So that’s my interest in it.
    0:02:13 Sam, I’m curious from your perspective, are you the same as me?
    0:02:15 Dude, I won’t even go on a cruise ship.
    0:02:25 Like, like I was at a party the other day and the, the, the, uh, like one liner or the icebreaker was what something you’re deathly afraid of.
    0:02:27 To me, it’s being in the ocean to where I can’t see land.
    0:02:33 So like, I’m not even going to be out there, but yeah, I, I agree with your premise.
    0:02:44 And, uh, Will, did I kind of frame your argument right as to like the potential that you see as far as, you know, the business opportunity of, of building startups that are focused on, on the ocean?
    0:02:46 Yeah, yeah, absolutely.
    0:02:46 Yeah.
    0:02:48 The framing is like, you know, something like this.
    0:02:53 It’s like, you know, you know, everyone is like here standing on earth, like looking, looking towards, um, the stars.
    0:03:04 And, and absolutely we, we, you know, we should be doing that and we should be going like, you know, full pelt with like trying to go interplanetary, trying to put a base on the moon and take, you know, take the, take the, take the dark side of the moon.
    0:03:07 And then, you know, go, uh, from there and use that as a line going to go to Mars.
    0:03:09 And we should be trying to fly supersonic as well.
    0:03:16 But then look, if you’re, if you’re trying to build a startup, like you’re always asking yourself, like where, you know, what is everyone else looking to do?
    0:03:19 And like, what, where, where, where is everyone else going and where is like underrated?
    0:03:27 And I suppose, uh, you know, I grew up by, by the seaside and like the, in the, in the Southwest of Ireland, I’ve always been obsessed with the ocean.
    0:03:34 If I wasn’t like on it, in it or near it growing up, there was something, something wrong in the same way that you’re, you’re afraid some of it.
    0:03:39 Um, yeah, I’m, I’m kind of like when I’m, when I’m away from it, I, I feel something wrong with me.
    0:03:41 So I’ve always been, been thinking about it.
    0:03:48 And I mean, if you just like look at it in like, you know, fundamental terms, like the ocean economy right now is like already massive.
    0:03:50 It’s not like, you know, the future space economy is going to be massive.
    0:03:51 Like the, the ocean economy is massive.
    0:03:55 It’s like $3 trillion in like annual spend in different ways, right?
    0:04:04 It covers like 70% of the planet, uh, 3 billion people rely on it as their primary source of food, a billion as their, as their primary source of income.
    0:04:14 Um, and then, you know, while we have like, you know, robots and Mars and, you know, these like low cost drones going in our skies, the technology like in our oceans, like still pales in comparison.
    0:04:20 Like, you know, you look at like the ships that are out there today, like much of the technology is like very same and similar to like what we had years ago.
    0:04:25 The unmanned, you know, underwater drones are like, you know, pretty much like the same as well.
    0:04:33 They’re like the, the kind of key core technology stack supporting like the, the key pillars of the ocean, whether it be transport, fisheries, defense, energy.
    0:04:35 Energy, you know, biodiversity, all these areas.
    0:04:44 It’s just like, it’s the same old, like stagnant incumbents, large scale incumbents offering solutions that, you know, are running on like ancient software.
    0:04:46 And there’s just like very little innovation going on there.
    0:04:50 It’s like, you know, you, you, you, you ask someone like, what is like a sexy ocean startup?
    0:04:53 And it’s like, they’re kind of scratching their heads for a bit, you know, whereas you ask them about space.
    0:04:54 It’s like SpaceX straight away.
    0:04:55 It’s like, you know, it’s straight away.
    0:04:57 It’s like, you ask them about aerospace.
    0:04:58 It’s like, oh, boom.
    0:05:02 So yeah, this is like the kind of like the, the core of the thesis.
    0:05:05 Sean, you just wound up really easily.
    0:05:09 This is going to be one of the, this is going to be one of those podcasts that we’ve had.
    0:05:16 We’ve only had maybe five of them ever, where at the end of the hour, we are like, we’re no longer podcasting.
    0:05:17 We’re getting into the ocean business.
    0:05:24 Like, I, I, let’s go.
    0:05:26 So Sam, he’s just said a bunch of stats.
    0:05:29 So which of those surprised you?
    0:05:30 So I’m just going to rattle a couple back.
    0:05:32 He said, all right, this one probably doesn’t surprise you.
    0:05:35 70% of the earth is covered in water.
    0:05:38 I think only 25% has ever been explored.
    0:05:44 He said a billion people rely on the ocean for the primary source of income.
    0:05:46 Three billion as their diet.
    0:05:47 Explain that.
    0:05:49 What’s the diet and the jobs one of the income one?
    0:05:53 Oh, it’s just like people like, you know, most of them.
    0:05:55 I mean, the human societies generally settle along coastlines.
    0:05:57 Like this is like a very like common trend.
    0:06:00 Yeah, but I’m in New York, but I don’t eat fish every day.
    0:06:00 Yeah.
    0:06:07 But in developed countries, it’s not as you, we’ve developed logistics, which means you can go down the street and walk into some sushi bar.
    0:06:10 And get like, you know, bluefin tuna probably flew in last night from Japan.
    0:06:18 However, if you are in, you know, Mogadishu or like Somalia or something like that, this might be a bit more difficult because the systems are not set up.
    0:06:23 And it’s important to remember, most of the world does not live in, you know, developed countries.
    0:06:26 So yeah, most humans just live along a coastline naturally.
    0:06:30 Then easiest source of food for them to get is fish.
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    0:07:02 All right.
    0:07:03 So what were some other stats, Sean, that caught your eye?
    0:07:05 A billion people rely on it for their income.
    0:07:07 So what are the jobs that you’re talking about here?
    0:07:10 So are you talking about fisheries, shipping?
    0:07:12 Is it like defense?
    0:07:16 Are those the big three or am I missing something big and obvious?
    0:07:22 The framing for me, how I think about like the ocean economy is like you generally kind of break it up into like three categories, right?
    0:07:24 Like you have like the biosphere, right?
    0:07:26 Which is like your fisheries.
    0:07:27 It’s your ecosystem restoration.
    0:07:29 It’s like your environmental mapping.
    0:07:31 It’s science in the ocean.
    0:07:33 It’s like all around like biosphere management.
    0:07:36 Then you have like, you know, the kind of prosperity oriented part around it.
    0:07:37 This is like the kind of commercial.
    0:07:41 This is like your energies, your infrastructures, your oil and gas.
    0:07:43 It’s your like data infrastructure, you know, these sorts of things.
    0:07:45 It’s your like logistics, shipping.
    0:07:52 And then you have like, you know, keeping the seas safe, which is like defense, defense and security, border security.
    0:07:57 Critical infrastructure protection, deploying ships in the South China Sea, these sorts of things.
    0:08:04 And so give me an example of a startup today that’s doing really well that’s based on this kind of ocean economy that you’re talking about.
    0:08:04 Yeah.
    0:08:12 I mean, I think one player that’s like interesting in the on-man systems space that’s been around for a long time, I think over a decade now.
    0:08:17 And was really kind of one of the first players to start doing interesting new things in the ocean is SailDrone.
    0:08:18 What problem are they solving?
    0:08:19 What does SailDrone do?
    0:08:25 I suppose they are solving the kind of data gathering at scale in the ocean problem.
    0:08:29 They build these like autonomous sailboats, these huge vessels.
    0:08:31 They look amazing.
    0:08:32 Yeah.
    0:08:33 They look awesome.
    0:08:40 They build these like huge vessels at that, these massive sailboats that can basically stay at sea for many, many months at a time.
    0:08:53 You can put a load of, you know, fancy sensors on them, you know, that can take data from the water, that can, you know, gather video footage at the surface and these sorts of things and then relay them back to someone like in the United States.
    0:09:00 It might be like a state, a government agency like NOAA, who want to know how much fish is in the, in the, in the Alaskan, in the seas off Alaska.
    0:09:07 They could sell it to the U.S. Navy to know, you know, how deep is the waters in and around Guam or something like that.
    0:09:09 And then they say, yeah, they sell these things as a service.
    0:09:11 But, you know, they’re very interesting founder there.
    0:09:16 You know, it seems like a super sharp guy who’s been obsessed with sailing for decades.
    0:09:20 Again, like a lot of these ocean fenders that you see, they’re very, very obsessed with the ocean.
    0:09:25 It’s the very thing, I’ll be saying people very often get obsessed about and then try to make a business out of it.
    0:09:27 What’s your, what’s your business do, Will?
    0:09:29 What’s Ulysses, Ulysses or Ulysses?
    0:09:30 Ulysses, yeah, yeah.
    0:09:36 Ulysses, Ulysses, we’re building a general purpose autonomy platform for, for maritime operations.
    0:09:38 Say that like we’re stupid.
    0:09:41 Just like, just pretend, pretend that we’re stupid.
    0:09:45 Yeah, yeah, I know it’s hard to believe, but just go ahead and dump that down for me.
    0:09:48 Autonomous robots for the ocean to do, to do important things.
    0:09:52 Okay, and what’s one important thing that you would do?
    0:09:58 You would, like, for example, you would go to a pipe in the ocean and determine if it’s got a hole in it?
    0:10:00 That, that is something you could do.
    0:10:05 Our first business line has been working with this weird plant that you’ve probably never heard of.
    0:10:09 There’s this, there’s this plant in the ocean that’s probably about 10 times more abundant
    0:10:11 than coral reefs.
    0:10:15 It is 35 times better than rainforest at removing carbon.
    0:10:23 It supports about, it holds about 20% of the carbon in the ocean, supports about a quarter of the world’s most critically important fish stocks.
    0:10:24 And it’s called seagrass.
    0:10:27 It’s basically just grass in the ocean.
    0:10:32 And this plant is dying off at an insane rate all around the world.
    0:10:33 About 7% loss per annum.
    0:10:35 If you follow these trends, we lose all our-
    0:10:38 7% a year of this thing is going away.
    0:10:39 Okay.
    0:10:40 Why is it dying?
    0:10:42 Is it because pollution or what’s the cost?
    0:10:43 There’s a few things.
    0:10:46 I mean, water quality is, like, a very common, you know, cause for loss.
    0:10:48 Other things are just, like, construction.
    0:10:51 Construction around, like, coastlines.
    0:10:52 Digging it up.
    0:10:54 Dredging, changing ocean temperatures.
    0:10:56 Changing, like, ocean currents.
    0:10:58 These sorts of things impact it.
    0:11:04 And basically, you know, the kind of context there is there’s a lot of governments in and around all over the world, like, really, really panicking around this.
    0:11:08 Like, if they lose their seagrasses, they lose their fish stocks.
    0:11:15 If they lose their fish stocks, you have the 1 billion people who rely on it and 3 billion people who rely on it for food and the billion people for income.
    0:11:18 And, you know, they’re in a tough situation.
    0:11:25 And basically, restoring it, i.e. bringing it back, is currently a very manual process.
    0:11:27 And how are you guys doing that?
    0:11:29 We built autonomous robots to do it.
    0:11:31 And you’re actually building the robots yourself?
    0:11:37 Or when you said you’re building a platform, I thought that meant you’re allowing other people to build it and use your technology to, like, track them.
    0:11:43 Yeah, so for this first use case, we’ve built a kind of custom robotic payload.
    0:11:49 You know, like, when you’re starting and trying to do something new, it’s kind of important to kind of get, you know, initial traction in a weird place.
    0:12:00 And I think if we just built something and hoped that people would use it for something, if we just built the platform, which is, like, an underwater vehicle and a surface vehicle that dock together, you know, we might have trouble getting traction.
    0:12:03 But we started off with this initial use case.
    0:12:12 We basically built these, like, attachments that go on to our underwater vehicle that do collecting seeds, planting seeds, measuring their growth to kind of get this initial traction.
    0:12:20 So, you know, in our first year, we did a million dollars in revenue, just kind of like, you know, first year, five-person team based here in San Francisco.
    0:12:22 Why would someone pay you to do this?
    0:12:32 So the reason people pay us is because it’s, you know, it’s a critically important, like, ocean ecosystem that, you know, if lost, has these, like, very negative downstream impacts.
    0:12:34 Yeah, that’s, like, you know, one reason.
    0:12:41 Another reason is, like, lots of governments now around the world have implemented laws that restrict your ability to damage this plant.
    0:12:44 Or if you damage it, you have to pay someone to plant it.
    0:12:48 So they’re paying us to plant it.
    0:12:49 It’s compliance-driven restoration.
    0:12:52 So that’s the kind of contract we’ve contracted in Western Australia.
    0:12:53 We have a contract in Florida.
    0:12:54 We have a contract in Virginia.
    0:13:00 And they’re all kind of for, like, these general reasons, either compliance-driven restoration or voluntary-led restorations.
    0:13:05 And I put, how important is seagrass into ChatGPT?
    0:13:05 Here’s what I said.
    0:13:07 Seagrass is wildly important to the world.
    0:13:12 And it basically says it captures carbon 35 times faster than rainforest, which I think he said.
    0:13:15 And then it says it’s like a baby crib for the ocean.
    0:13:26 The seagrass basically is where small fish, crabs, seahorses, and even endangered species and turtles, they’re born and they live early on in their life.
    0:13:31 And if lost, then you would, it says, lose the seagrass and entire marine ecosystems collapse.
    0:13:39 Well, what’s crazy is you, okay, the mission check, like, on board, amazing.
    0:13:43 Do you kind of skip the headline, Sean?
    0:13:49 Did we, he built a robotics business that in the first year, you’ve only, I think you said you only raised $2 million or something like that.
    0:13:55 So with only $2 million in funding, in your first 18 months of business, you did a million in revenue.
    0:13:55 Is that right?
    0:13:57 Yeah, and just five people as well.
    0:14:04 Is there something new about building, like, a robotics company today that lets you do it way cheaper?
    0:14:05 Like, did something change?
    0:14:07 Like, oh, we all use whatever.
    0:14:09 You know, it’s like when the Raspberry Pi came out.
    0:14:13 Then it’s like, oh, we can now have this little computer for $35 or whatever.
    0:14:17 Is there something that does, that’s made it a lot cheaper or maybe just there’s more talent?
    0:14:18 What’s changed?
    0:14:20 3D printers has been huge.
    0:14:22 Like, that’s just like a game changer.
    0:14:25 It means just, like, the speed of iteration has gone up massively.
    0:14:31 You know, it’s easier now to get parts overnight as well and just, like, get, like, sheet metal, sheet metal, cut.
    0:14:36 And the cost of a lot of things has gone down, like, massively as well.
    0:14:40 Like, with, like, the advent of, like, electric vehicles, batteries kind of went down massively.
    0:14:47 And a lot of electronic components with drones, like, motors went down massively in cost.
    0:14:53 You know, for us as well, like, a critical enabler of what we do, right, is Starlink.
    0:14:58 Because, you know, the way our system works is, like, we have this, like, autonomous boat.
    0:14:59 It’s, like, a surface vehicle.
    0:15:01 This is, like, our mothership.
    0:15:10 And then we have a docking system that releases these daughter robots, these, like, autonomous underwater vehicles to do the actual critical activity in the ocean that you want to do.
    0:15:15 And, you know, we wouldn’t be able to communicate with these assets without something like Starlink.
    0:15:18 You had Iridium before, but, like, the bandwidth on that wasn’t that strong.
    0:15:23 And so you have, like, other, like, kind of wind-out features like that.
    0:15:27 And that company you were talking about, Sail Drone, they’ve raised, like, over $100 million.
    0:15:30 It looks like they’re valued $500 to $1 billion.
    0:15:31 That’s interesting.
    0:15:33 There’s another one called Saronic.
    0:15:34 Sam, do you know Saronic?
    0:15:34 No.
    0:15:35 How do you spell it?
    0:15:38 S-A-R-O-N-I-C.
    0:15:40 Well, you probably know a little bit more about this company than me.
    0:15:43 I think Joe Lonsdale seeded this company, right?
    0:15:44 Yes.
    0:15:44 Yeah.
    0:15:45 God, this looks sick as well.
    0:15:50 So, like, when we had Joe on the podcast and I was at his house, he was telling me about this company.
    0:15:52 Should have just invested on the spot.
    0:15:57 But he was basically like, we’re building drone, like, drones for the water.
    0:16:01 And, you know, drones for defense, just like Anduril’s doing it for the sky.
    0:16:06 And, you know, modern warfare has turned into drone-based.
    0:16:11 They’re building these unmanned surface vehicles, USVs, for the ocean.
    0:16:15 And they talked about how, did you know this?
    0:16:17 Like, the U.S. Navy, Sam, just take a guess.
    0:16:18 How many ships are in the U.S. Navy fleet?
    0:16:20 Just, what’s the number?
    0:16:21 Oh, I don’t know.
    0:16:22 500?
    0:16:23 What?
    0:16:24 It’s hard to even say.
    0:16:25 100?
    0:16:27 Okay, so you’re a lot closer than I thought.
    0:16:29 I would have guessed that we have thousands of ships.
    0:16:31 We have 300 ships in the Navy.
    0:16:34 Is a ship considered, like, an aircraft carrier?
    0:16:35 Because those are huge, right?
    0:16:36 Those are, like, cities.
    0:16:36 Sure.
    0:16:37 Oh, my God.
    0:16:40 But only 300.
    0:16:41 That’s just, like, a very small number to me.
    0:16:43 And we have 67 submarines.
    0:16:44 That’s it?
    0:16:45 67.
    0:16:48 Dude, I had more kids at my three-year-old’s birthday party.
    0:16:51 Like, that’s insane to me.
    0:16:53 So we got 300 ships or whatever.
    0:16:57 And basically, every ship is, like, I don’t know the exact cost of it.
    0:16:59 But let me pull this up.
    0:17:02 I think they’re, like, Will, correct me if I’m wrong.
    0:17:10 But, like, the average cost of these is something like, or maybe it’s the average cost of these contracts, like, $250 million every time you get a contract to do one of these.
    0:17:12 And so you’re a startup like Saronic.
    0:17:16 And all you have to do is basically say, all right, we’re going to come in.
    0:17:19 We’re going to build the most innovative, autonomous vehicles here.
    0:17:22 And we’re going to operate.
    0:17:23 You know, what Anderil did was remarkable.
    0:17:29 So what Anderil did was, in Silicon Valley, the smartest tech people, nobody was working on defense.
    0:17:32 Google had famously shut down its defense project.
    0:17:34 And defense was taboo.
    0:17:35 Like, you’re going to make weapons?
    0:17:37 That was not cool at the time.
    0:17:44 And it was, there was basically zero weapons startups in San Francisco.
    0:17:46 And what they did was they said, we’re going to do this.
    0:17:49 We’re going to use the Silicon Valley method and talent to do this.
    0:17:56 We’re going to change the cost structure so all the big defense primes were operating on what’s called cost-plus model.
    0:18:04 And so their incentive really was to have really high-cost operations because they were making 10% on top of whatever the cost was, right?
    0:18:06 So the incentive model is sort of screwed up.
    0:18:11 And that’s how you get, you know, a single airplane that’s like a billion dollars or something like that to get made.
    0:18:14 And so it was costing the government a lot.
    0:18:17 These guys had no incentive to innovate, no incentive to cut costs.
    0:18:23 And they were using talent that was not the smartest engineering talent in the world, which was all centered in Silicon Valley.
    0:18:27 And then Andrew comes out, Paul Maleky and Trey and others, they basically came out.
    0:18:29 And what they said was, we believe this is important.
    0:18:31 We believe that America needs this.
    0:18:34 And we believe we should put the best talent in the world on this problem.
    0:18:39 And they’ve built now a $20 to $30 billion company doing this.
    0:18:44 And the reason I find this exciting is that I love these huge opportunities that are hidden in plain sight.
    0:18:49 I talked to a friend recently who knew Elon and I said, what was Elon like?
    0:18:50 Were you impressed with Elon?
    0:18:53 He goes, I was impressed with Elon, but not because he was the smartest guy in the room.
    0:18:55 You know, we would be at a party.
    0:18:55 There’s 20 people.
    0:18:57 You couldn’t say, oh my God, that’s the guy.
    0:19:05 He goes, but the thing that Elon did better than everybody else was that Elon looked down at the ground and saw a trillion-dollar opportunity that was just sitting there.
    0:19:13 You know, before Elon, it’s not like there were a bunch of people trying to build, you know, rocket companies or electric car companies.
    0:19:16 It wasn’t like they were trying and failed and he succeeded.
    0:19:17 They weren’t even trying.
    0:19:23 And he goes, the beautiful part about Elon is that he saw those and he didn’t ignore it like the rest of us.
    0:19:25 And the idea of let’s go to Mars was there.
    0:19:27 It was available to all of us.
    0:19:28 And we were all blind to it.
    0:19:32 And so similarly, I think Anderil did that in the defense space.
    0:19:45 And now it looks like Saronic is basically doing that in the sort of ocean defense space where, you know, you have this combination of elite talent at robotics and AI and autonomy.
    0:19:47 And you pair it with this old industry.
    0:19:52 And I think you have a pretty unique window to build a very big company doing this.
    0:19:59 Yeah, like they’re building, I think of it like they’re building the Humvees and we’re building the Toyota Hiluxes, right?
    0:20:03 Like they’re building like these like ultra fast, like defense focused, like vehicles.
    0:20:15 And like they’re, you know, going to make the South China Sea a hellscape and make China now want to cross that ocean and keep Taiwan safe if they keep going on the path they’re doing.
    0:20:17 And they’re doing like incredible job at that.
    0:20:19 And then we have, we occupy like a different niche.
    0:20:26 Like we’re like, we just want like every single like, you know, day to day task that is like done at sea once we want to done like on our platform.
    0:20:30 And so like, we want like all the servicing done by like Ulysses platforms and these sorts of things.
    0:20:35 There’s like a lot of things that are making the ocean like very important in this century more than previous ones.
    0:20:37 Like warfare is a good example.
    0:20:43 Like every other single war we fought in the last like three decades until now has been like in a desert, right?
    0:20:51 Now we’re going to the ocean that requires a complete retooling of the military, you know, and just even how we think about warfare just needs fundamentally needs to change.
    0:20:55 Like the climate question is ultimately an ocean question.
    0:20:58 Like the ocean is like the world’s largest natural carbon sink.
    0:21:02 It is like where most of the life on earth lives.
    0:21:10 It is, you know, one of our biggest sources of like food in a world where like a population is growing and food scarcity is always a question.
    0:21:12 Even just like you look at AI, right?
    0:21:15 Like the data infrastructure built out for AI is going to be like enormous, right?
    0:21:19 And basically that’s going to require more data infrastructure, i.e.
    0:21:23 Like cables connecting different parts of the world, transmitting data.
    0:21:24 We’re going to need more data centers.
    0:21:26 We’re going to need more energy.
    0:21:30 These are all things like we’re already putting and testing, putting data centers in the ocean.
    0:21:31 The cooling costs go down massively.
    0:21:33 They become like more efficient.
    0:21:35 So, well, let’s go back.
    0:21:40 So there’s already pipes under the ocean that basically like internet pipes under the ocean, correct?
    0:21:41 Yeah, yeah, yeah.
    0:21:48 I mean, most of the information that our internet connection now is most of that is like traveling through, is traveling underground.
    0:21:50 Who built that?
    0:21:52 Is that the government built that or Google built that?
    0:21:54 Who put those pipes in the ocean to do that?
    0:22:01 So a lot of the initial infrastructure build out for IT in the ocean came from telecoms companies, actually.
    0:22:08 Yeah, like in the 80s, Sean, there’s a handful of telecom companies that were startups, and they’re some of the fastest growing companies in the world.
    0:22:14 So like imagine the AI companies today that are scaling to $100 million in revenue in a year.
    0:22:16 That was what they…
    0:22:18 Did they die or what happened to them?
    0:22:20 A lot of them are still running.
    0:22:25 And then there was some of the, you know, if you look at like one of the biggest frauds on earth, like it’s like Bernie Madoff.
    0:22:31 And then like the third one is actually one of these telecom companies that was laying pipes in the ocean.
    0:22:32 But a lot of them are still around.
    0:22:39 They’re just like small, they’re not small, but they’re B2B companies that you wouldn’t even know, but they can be like a $10 billion a year company.
    0:22:45 But in the 80s, right, Will, or maybe, I don’t know if you know this, but in the 80s, that was like the birth of a lot of this, wasn’t it?
    0:22:47 Yeah, yeah, yeah, massively.
    0:22:52 And now you’re seeing a transition to the build out being coming from FANG, from like big tech.
    0:22:55 And soon I think it’ll be like the AI companies.
    0:23:04 So what you’re saying, Will, now is that AI companies need these data centers, just, you know, huge amounts of GPUs in a data center.
    0:23:08 And those data centers need cooling, they need power, they need tons of things.
    0:23:12 And they need, ideally, they need to be close to places where people are using it.
    0:23:17 And what you’re saying is that somebody’s going to build a data center in the ocean, or people are already building data centers in the ocean.
    0:23:19 And who’s doing that?
    0:23:21 Or is this a future idea?
    0:23:22 And why are they doing that?
    0:23:23 Why is that a good idea?
    0:23:26 Yeah, so I think the first experiment of this was a Microsoft project.
    0:23:26 They did it.
    0:23:32 There’s a YC startup as well, run by friends of mine, Sam Mandel.
    0:23:37 He’s got a company called Network Ocean, and they’re building and operating, starting to building and operate these things.
    0:23:39 Are they actually underwater?
    0:23:42 Or are they on top of the water, just out in the ocean?
    0:23:43 The plan is for it to be subsea.
    0:23:48 And again, like these are the sorts of businesses that like Ulysses, we want to be the kind of servicing partner for in the future.
    0:23:56 When they need maintenance, when they need like inspections done, when they like, it’s like us, they’re coming to, and we’re selling them like a kind of in the box solution to.
    0:24:06 I think the biggest opportunity in this like, you know, paradigm in the future where more and more data cables are being laid subsea is actually in the protection of them, right?
    0:24:14 So I don’t know if you guys are familiar what’s going on in like the Baltic Sea and places, but like, I think in the last year, about 11 cables have been cut by foreign actors.
    0:24:19 And these cables, by the way, they’re like, it’s like a human sized tunnel, right?
    0:24:21 Are they on the ocean floor?
    0:24:23 Or are they like floating in the ocean?
    0:24:27 Like to say cable, we’re not talking like a rope that you’re pulling.
    0:24:29 It’s like a tunnel, right?
    0:24:39 And like, okay, like literally the Chinese are literally publicly advertising these cutters that they have, these cable cutters, right?
    0:24:44 They’re literally putting in the South China morning press, China unveils powerful deep sea cable cutter could reset the roller.
    0:24:47 They’re not even, they’re not even fucking hiding this.
    0:24:48 Like they’re, they’re cutting the cables.
    0:24:51 They’re like, like, yeah, we’re just, look how big our cable cutter is.
    0:24:53 Like, this is just like the new paradigm.
    0:25:01 And then like, you know, they send these like little like, you know, Taiwanese ships into the, or these Chinese ships into the Baltic Sea on like fishing missions, right?
    0:25:04 Like what the hell are they doing in the Baltic Sea on fishing missions?
    0:25:05 Like they’re clearly just cutting cables.
    0:25:07 And then like two days later, oh, cable cut.
    0:25:12 Dude, calling, calling this a cable cutter is like calling a robo a ship.
    0:25:12 You know what I mean?
    0:25:21 Like maybe technically it’s, it’s correct, but they need to rebrand this because what you’re showing us is basically like a huge submarine, you know?
    0:25:23 Like I’m thinking of like a clip.
    0:25:24 It’s not scissors.
    0:25:24 Yeah.
    0:25:27 This is insane.
    0:25:29 So they’re going down and they’re cutting this.
    0:25:30 And what does that do?
    0:25:34 Like, does a country lose internet or is it just like damage it?
    0:25:34 Okay.
    0:25:36 I’ll give you this vision, right?
    0:25:38 So these cables run between like military bases as well, right?
    0:25:43 And okay, let’s say there’s like a hot war breaks out in like the South China Sea, right?
    0:25:47 First target then is going to be like a military base in like the Pacific, somewhere like Guam, right?
    0:25:54 What if you, if you want to like completely scramble what, you know, their understanding of and situational awareness of what is going on,
    0:26:01 you are going to be laying, sending these subsea drones down there to go and cut the cables that is giving them like comms, that’s giving them energy.
    0:26:06 And you’re going to be like scrambling their airwaves with like, you know, electromagnetic interference.
    0:26:12 And that’s how you’re going to like just completely prevent like American military responses in the Pacific, right?
    0:26:16 But how many, how many cables, like you’re talking to two dummies.
    0:26:19 How many cables does America rely on?
    0:26:22 There’s, there’s like not actually that many, right?
    0:26:28 Like as in like there, there’s like an insane amount of data that goes over them, but there’s only about like 600 active.
    0:26:29 I’m so impressed that you knew that number.
    0:26:30 That’s insane to me.
    0:26:32 So there’s not a lot of redundancy you’re saying.
    0:26:33 No, no, not at all.
    0:26:36 Like they’re, they’re very difficult to lay, right?
    0:26:38 And you need to respond quickly.
    0:26:41 So yeah, like, I mean, there’s like many critical things that rely on them, but like, yeah.
    0:26:50 You’re, you’re way better off defending them with unmanned water drones than trying to lay backup pipes down there and leave them undefended.
    0:27:01 We need to be persistently out at sea, like century style in the same way that like Anderil, like have the, you know, started with these like border systems to like see what was coming in and over like the land border.
    0:27:06 Like we need the exact same type of systems out at sea, like permanently just sitting there on top of them.
    0:27:10 They need to be cheap so that you can deploy them massively at scale.
    0:27:12 The ocean is huge.
    0:27:13 So they need to be cheap to be scalable.
    0:27:18 You need to be able to see what’s going on at the surface and you need to be able to see what’s going on sub surface.
    0:27:21 And that’s fundamentally, that’s like the platform that we’ve developed.
    0:27:28 We have this like surface vehicle with a docking system that can drop a number of water vehicle and we’ve made it like all about 10 times cheaper than anyone else.
    0:27:32 So like that’s, you know, Seagrass is like a nice place where we started.
    0:27:35 How deep do your vehicles go?
    0:27:37 Do they go to like the bottom of the ocean where these pipes are?
    0:27:46 So for the Baltic Sea, I mean, it’s one of the shallower seas and this is like a major, the major kind of like, you know, activity, area of activity where this is going right now.
    0:27:50 So our vehicle works in that sea, you know, at all depth profiles in that sea.
    0:27:54 So for the Baltic Sea part of it, it is, it works.
    0:28:00 When you get into like narrowlier parts of the ocean, like some of the Pacific where you’re getting down to like 8,000 meters, right?
    0:28:03 Like Mount Everest, you know, levels of depth.
    0:28:04 We can’t go there yet.
    0:28:06 It just starts getting difficult.
    0:28:10 But like, yeah, we will be adding future vehicles to the fleet that can be there.
    0:28:11 Well, though, you will.
    0:28:12 I’m 27.
    0:28:18 Sean, so when you and I moved to San Francisco and well, I moved there in 12 and we’re about the same age.
    0:28:20 It was the sharing economy.
    0:28:21 That was the thing.
    0:28:23 So it was Airbnb and Uber and Lyft were the winners.
    0:28:27 And then there was a bunch of derivative things like Airbnb for garages or, you know, or for storage.
    0:28:31 A few years later, it was AI or crypto.
    0:28:34 So like Bitcoin and Coinbase were winners.
    0:28:35 And then there was a bunch of like silly things.
    0:28:38 Right now, this is so strange to me.
    0:28:43 It’s AI, but it’s also, well, it’s whatever category you guys would go in.
    0:28:47 You’re not quite defense tech, but it’s like wild to me that this shift has happened.
    0:28:52 Because 10 years ago, I would have told you, like, you know, that was when Boom Supersonic was starting and a few other things.
    0:28:54 I would have said, this is, this is foolish.
    0:28:54 What are you guys doing?
    0:28:55 We’re technology.
    0:28:56 This is a technology city.
    0:28:57 Why don’t you do like software?
    0:29:00 To hear you say this, it’s so foreign to me.
    0:29:02 It’s also so interesting.
    0:29:04 For me, it’s like a no-brainer.
    0:29:08 I mean, like, you know, the low-hanging fruit of software has been eaten.
    0:29:09 Right?
    0:29:10 You guys, like, you know, it’s like.
    0:29:11 Yeah.
    0:29:12 We ate it.
    0:29:14 Like, how many more CRMs are there?
    0:29:15 Yeah, exactly.
    0:29:17 The boomers got cheap real estate.
    0:29:19 You guys got like B2B staff, right?
    0:29:24 Like, it’s like, that’s like, and now it’s like on us to do like something where like the next frontier is, which is like fundamental hardware.
    0:29:27 And then like, also, it’s like, it’s like a no-brainer.
    0:29:31 You look at like the top 10 most valuable companies in the world right now.
    0:29:35 It’s like seven out of 10 of them have like a hardware, like an extreme like hardware component, right?
    0:29:38 Like the biggest companies being built today are hardware companies.
    0:29:46 And also, in a world where you can just like vibe code overnight, like a CRM or a sales for, or maybe not a sales for, but like a Calendly competitor.
    0:29:50 It’s like, okay, well, is there really a moat in like these sorts of things anymore?
    0:29:50 It’s like.
    0:29:51 Yeah, you’re right.
    0:29:52 You’re absolutely right.
    0:29:54 And I think it’s so fascinating because.
    0:30:00 So when Sean and I lived in San Francisco, if, if someone who looked like you, so you look, you’re wearing a Ford Bronco shirt.
    0:30:04 I bet you you’re wearing cowboy boots and you got a little bit of swag to you.
    0:30:10 And you like, if you were to, if you were to talk about what you’re talking about, it would have been like, you’re, you’re so out of touch.
    0:30:14 You’re out of touch for the, for the, for the YC group of out of touch people.
    0:30:16 Like, it’s just so interesting to me.
    0:30:17 And I think it’s great.
    0:30:23 So there’s a, I did a podcast with James career and he has this thing about technology windows.
    0:30:24 Sam, did you ever see this part?
    0:30:24 No.
    0:30:25 About technology windows.
    0:30:28 So he basically says, all right, there’s a reason.
    0:30:34 There’s a, there’s a, almost like a scientific reason why, why what you just described happens, happens.
    0:30:40 And so he basically says like, when a wave of startups comes out, it’s because of a technology change.
    0:30:42 So, you know, for example, an inflection.
    0:30:44 So when we, you were right.
    0:30:46 When we first moved to San Francisco, I moved in 2012.
    0:30:48 And the mobile window was open.
    0:31:01 And that’s when Instagram, Uber, Snapchat, like a bunch of companies got built that relied on you having a computer with you at all times that had internet connection, that had an accelerometer, that had a map, a GPS feature in it.
    0:31:03 And then all these companies could get built.
    0:31:07 But that window opens for a very, like a fixed amount of time.
    0:31:10 And basically, like he said, the low hanging fruit all gets eaten.
    0:31:16 And so he, he went back all the way to the railroads and he’s like, the railroad technology window was open for 40 years.
    0:31:21 And like, if you just look, there was not another successful railroad company after that 40 year period.
    0:31:24 And because all the opportunities basically got eaten.
    0:31:26 Automobiles was 25 years.
    0:31:33 And so in a 25 year window, you got Buick, Dodge, Ford, Cadillac, GM, Chevrolet, Lincoln, Chrysler, all of it within a very short window.
    0:31:37 And then you had nothing for another, about 80 years.
    0:31:41 And then the window reopened because of battery technology.
    0:31:43 And you got Tesla and Rivian.
    0:31:49 And so that was almost a new technology window around automobiles because the tech had changed again around batteries.
    0:31:56 And so he was basically saying like B2B SaaS has had a 20 year window and now AI software, AI starting in 2016.
    0:31:58 And that’s like the current window that we’re in.
    0:32:07 And I would say, you know, what Will is doing and what a lot of smart entrepreneurs are doing right now is they’re in the technology window of AI, robotics and 3D printing.
    0:32:15 And basically those three technologies have opened up the door to build new things that couldn’t have been built 10, 15, 20 years ago.
    0:32:17 So this is what a technology window looks like.
    0:32:18 So just check this out.
    0:32:22 If you’re on audio, you have to be on YouTube to see this, but I’m sharing my screen here.
    0:32:29 So it basically says like step one, the technology is invented and only the hobbyists are playing with it out of interest and creativity, right?
    0:32:31 And then two is the status moment.
    0:32:35 One of the hobbyists achieved status and wealth using the tech.
    0:32:46 So, you know, for example, so this is like, you know, Mark Andreessen on the, on the cover of time barefoot because the hobbyist internet guy became rich by building, you know, the browser.
    0:32:48 And then this happened again with social networking.
    0:33:05 This happened again with Elon and Palmer Lucky and all those guys right now who’ve, who’ve had their status moment where, you know, Palmer was like literally like living in a RV building VR headsets for like 90 bucks using spare parts.
    0:33:11 He was a hobbyist and then the hobbyist got the wealth, the status moment when he sold to Facebook for $3 billion.
    0:33:13 And then, you know, same thing with Elon.
    0:33:18 Elon was building in relative obscurity, both OpenAI, you know, OpenAI was a nonprofit.
    0:33:22 It was relatively obscure for the first five years that it was out, that they were doing their thing.
    0:33:28 But now Sam Altman and Elon and Palmer again with Andrew have had a new status moment.
    0:33:39 And then there’s what he calls knowledge diffusion, which is suddenly there’s conferences, there’s podcasts like this, there’s newsletters, there’s Twitter where people are sharing ideas about how to do this, what’s going on.
    0:33:45 And you get this explosion of stuff and then competition floods and then the new incumbents are born.
    0:33:56 And then the new incumbent regime takes over due to their, their defensibility, like they build something that is defensible, maybe because it’s hardware, maybe because it, it requires scale, maybe it has a network effect.
    0:34:01 And the technology window closed is 90% closed and you’ll only have a few exceptions from there on out.
    0:34:08 All right, let’s take a quick break because as you know, we are on the HubSpot podcast network, but we’re not the only ones.
    0:34:10 There’s other podcasts on this network too.
    0:34:12 And maybe you liked them, maybe you should check them out.
    0:34:15 One of them that I want to draw your attention to is called Nudge by Phil Agnew.
    0:34:23 And whether you’re a marketer or a salesperson and you’re looking for the small changes you could make, the new habits you could do, the small decisions you could make that will make a big difference.
    0:34:25 That’s what that podcast is all about.
    0:34:26 Check it out.
    0:34:29 It’s called Nudge and you can get it wherever you get your podcasts.
    0:34:37 It’s so funny to Sean and to meet Will, who’s like in the thick of actually what you’re describing.
    0:34:38 Yeah.
    0:34:39 Will, when did you start?
    0:34:41 Were you like, were you a hobbyist?
    0:34:44 When did you start with doing what, doing what you’re doing?
    0:34:46 Like when were you messing around with drones or ocean tech?
    0:34:55 So, yeah, I mean, like, I, like, as I said, I’ve been like, you know, in the ocean, on the ocean, near the ocean since I was a kid, diving, surfing.
    0:35:05 You know, whatever, weightboarding, all these sorts of things growing up, but never, never had built in it really before this kind of scooter sharing startup thing popped off.
    0:35:12 I was like, you know, working, you know, in that my co-founders all kind of had been tinkering and these sorts of things.
    0:35:19 But again, none of us had ever actually really done anything in the ocean, which I actually think is a massive benefit, right?
    0:35:24 Like, because none of us came in with these preconceived notions for how, like, subsea drones should work.
    0:35:30 You know, two of my co-founders were building aerial drones in a drone delivery startup before.
    0:35:32 So they took, like, a lot of the primitives from that.
    0:35:36 One of them had worked on self-driving cars, took some of the, like, ideas from that.
    0:35:44 But again, I think there’s, like, definitely this, like, idea that I agree with that, like, you know, to really actually shake up an industry, it’s probably good if you don’t come from it.
    0:35:51 Because we came to it and, like, you know, we thought initially that we were going to be maybe using someone else’s platform and repurposing it.
    0:35:54 But we looked at all of the subsea drones on the market and they were crap.
    0:36:02 They cost, like, you know, they were, like, one of the ones we were looking at, which, like, actually had the specs that would have met what we wanted to do, cost, like, 500 grand.
    0:36:05 That’s, like, a quarter of our pre-seat to do what we want.
    0:36:16 Like, it’s, like, and then, like, my, our CTO, Jamie, he just, like, went into a cave for a few days and just, like, came back with, like, a design for, like, a new type of, like, autonomous underwater vehicle.
    0:36:23 And then we, like, tested and we’re, like, oh, shit, this works. Oh, shit, it’s, like, 10, 20 times cheaper than, like, anything we could have bought.
    0:36:30 You know, so it’s, like, sometimes you just need, like, a new idea and, like, an artist to go into a cave and then you can, like, change things.
    0:36:35 That’s how all the great things, that’s how all the biggest problems have been solved.
    0:36:44 This is, like, I mean, all religions, like, Muhammad went into the cave, like, Jesus went into the desert, you know, like, all these, like, prophets, like, they go off into the old and they come back with, like, this, like, secret.
    0:36:47 And then, you know, someone else spreads the word for them, right?
    0:36:54 Like, it’s, like, St. Peter does it in, like, the Catholic Church and, like, well, there’s, so, yes, this is a common archetype that, and, yeah, that does work, yes.
    0:37:03 You said something earlier about how a billion people rely on the sea for their food.
    0:37:08 Has anybody done, you know, food or, like, tuna or salmon in a way?
    0:37:13 Are they doing anything interesting there with, like, whether it’s, like, lab-grown or something innovative?
    0:37:21 Yeah, yeah, yeah, my friend’s got a very, very interesting startup called Wild Type, which is, like, sustainable sushi-grade salmon.
    0:37:24 So, basically, that’s, like, cultivated seafood.
    0:37:27 So, their first product, like, they’re…
    0:37:28 What do you mean by cultivated?
    0:37:29 It’s grown.
    0:37:29 It’s grown.
    0:37:32 It’s not, like, farmed in or caught at sea.
    0:37:34 Like, grown in a lab or grown?
    0:37:37 Yeah, like, in a, yeah, exactly, in this, like, industrial process.
    0:37:43 Yeah, they can basically grow cells and then put them together in such a way that it tastes like sashimi-grade salmon.
    0:37:53 So, you know, in the same way that Elon started off with, like, a sports car, right, they’re starting off with, like, your sashimi-grade salmon, the highest-end salmon to get.
    0:37:54 And I’ve tried it.
    0:37:54 It’s great.
    0:37:56 This is in San Francisco?
    0:37:58 It looks like a brewery.
    0:37:59 Yes, exactly.
    0:38:00 It’s, like, similar, like this.
    0:38:16 I mean, look, breweries are, like, where so much of the, like, best, kind of, biotech innovation has, like, come, like, from people building, like, mass industrial processes for, you know, cultivating food for, like, a very long period of time, in fact.
    0:38:21 So, you’re telling me that someone is growing salmon that I can go and eat right now?
    0:38:23 Yeah, yeah.
    0:38:25 I mean, I got it through my friend.
    0:38:27 I don’t know if they’re in stores yet.
    0:38:30 They’re still undergoing FDA approval.
    0:38:41 But, like, yeah, none of these nasty heavy metals or microplastics in them, you know, it’s reducing pressure on fish stocks, you know, this is good stuff.
    0:38:46 It doesn’t have any of the nasty, like, parasites that you get in some of this, like, farmed salmon as well.
    0:38:52 So, yeah, definitely, I think things like this will be important.
    0:38:54 Holy shit.
    0:38:55 This is crazy to me.
    0:38:56 This is crazy.
    0:39:00 Is it like the lab-grown meats where it’s, like, $10,000 an ounce?
    0:39:09 It’s, like, we can, we made, you either pick, you either have the cheap thing, like, Beyond Meat or Impossible Foods, but it doesn’t taste great or it’s not good for you.
    0:39:15 It’s made with a bunch of chemicals or you have the real thing, but it’s super expensive and so nobody can afford it.
    0:39:23 Well, I think given that, like, my friend shared it with me that it’s not that expensive, but it’s…
    0:39:25 You’re not that good of friends.
    0:39:25 No, no, exactly.
    0:39:26 Yeah, are you?
    0:39:28 But I think this stuff is, like, sooner than we think.
    0:39:29 It’s around the corner.
    0:39:32 Wow, these guys did $100 million Series B in 2022.
    0:39:33 That’s pretty crazy.
    0:39:34 What?
    0:39:35 What else is cool?
    0:39:36 Will, tell me everything.
    0:39:38 What are the guys like you are into?
    0:39:40 Like, some more ocean shit.
    0:39:42 Some crazy ones.
    0:39:43 Yes.
    0:39:43 Yeah.
    0:39:43 Okay.
    0:39:44 All right.
    0:39:46 This one is wild, right?
    0:39:46 Buckle up.
    0:39:47 Okay.
    0:39:49 Ocean treasure hunting, right?
    0:39:59 So, there is actually, like, you know, hundreds of wrecks out there in the ocean today that potentially have, like, more than a billion dollars on them, right?
    0:40:06 Like, gold bullions that, like, the Spanish were bringing back from their conquests and then, you know, they got hit by a storm or these sorts of things, right?
    0:40:10 And there’s, like, probably thousands that have, like, millions of dollars of funds in them, right?
    0:40:16 So, governments, the source governments, so, like, the Spaniards, the Portuguese still have claim on these things.
    0:40:22 However, there is precedent in history for you to do these kind of, like, profit-sharing agreements, right?
    0:40:29 It’s like, if we find that and we restore, we give you back all your artifacts, we give you back everything, but, you know, we sell some of them, we get to, we get, can we keep some?
    0:40:31 You can do this, right?
    0:40:40 It’s like these models where it’s, like, these SaaS negotiation companies are like, hey, if we go and save you money on your vendors, we keep 20%.
    0:40:50 It’s that, except for you go to the Spanish royal government and you’re like, hey, there are, if we find any hidden treasures in the ocean, can we keep a couple bars for ourselves?
    0:40:50 Exactly.
    0:40:51 Ram for piracy.
    0:40:52 Who’s building this?
    0:40:54 So, what, my friend used to do this.
    0:40:55 His name’s Chip Forsythe.
    0:40:59 And he would be like, hey, I’m going off the coast of whatever to go.
    0:41:01 Bro, you did not have a friend that used to do this.
    0:41:02 That’s insane.
    0:41:03 Chip Forsythe.
    0:41:06 You know, he went, he went off the coast and just, what, scuba dive?
    0:41:10 Well, it was Chip and A.J. Forsythe, who I think you’ve met, A.J., he’s crazy.
    0:41:14 His brother, Chip, they, basically, the way it works now is, it’s kind of like a movie.
    0:41:22 Like, you have these crazy people and you get other people to finance it and you say, if we find this treasure, you know, here’s the agreement on how we split it.
    0:41:30 And they would go off the coast, they would somehow narrow in on where they think it is, they would spend a week trying to find it, and most of the time you don’t find it, but occasionally you hit the lottery.
    0:41:32 Is that, I mean, is that right, Will, how it works now?
    0:41:33 Pretty much, yeah, yeah.
    0:41:36 So there’s, like, fundamentally two parts of, like, a mission.
    0:41:38 It’s, like, or three parts.
    0:41:52 There’s, like, there’s, like, you know, the pre-mission, you know, negotiating, like, you know, looking to restore the records to see, like, where we think it could be, you know, kind of scoping it out and also getting permission so that when you do the recovery, you have, like, some chance of being able to hold on to it.
    0:42:01 Then there’s, like, this kind of scouting where you’re actually on site and you’re doing, like, scouting and you’re, like, basically using sonar to scan this e-bed and understand what’s there.
    0:42:12 And then there’s, like, recovery where you’re bringing out these, like, gnarly, like, JCB-style ROVs and remote-operated vehicles that go down and just, like, dig it all up and bring it back up and then you have your party.
    0:42:15 And is anybody doing this?
    0:42:19 Like, has anybody, do you know someone who’s, like, made, like, $10 million finding treasures in the ocean?
    0:42:23 I know some people working on this that haven’t, like, shared their plans publicly yet, so I won’t, like, share it.
    0:42:28 But there is, like, some exciting developments coming in this space that we may or may not be helping with.
    0:42:33 Did you say there’s 3 million shipwrecks at the bottom of the ocean?
    0:42:39 So, I’m not sure, speaking on, like, a total amount of shipwrecks, I wouldn’t be surprised if there’s, like, that many shipwrecks.
    0:42:42 But there is, like, there’s, like, hundreds that potentially have billions on them.
    0:42:44 Wow.
    0:42:44 Okay.
    0:42:45 That’s insane.
    0:42:46 What else?
    0:42:50 So, there’s, okay, I’ll give you, like, a banger quote, right?
    0:42:54 There’s this, like, Canadian billionaire called Ross who had this quote a few years ago.
    0:42:59 He said, give me a tanker of iron filings and I will give you an ice age, right?
    0:43:06 What he meant by that is, like, you can actually alter, like, the kind of weather of the earth by, like, dunking iron into the ocean, right?
    0:43:13 So, basically, like, many, many parts of the ocean are low in iron.
    0:43:15 They need more iron.
    0:43:20 And if you add iron to these parts of the ocean, you stimulate, like, algae growing at the surface.
    0:43:22 Algae then draws down carbon.
    0:43:24 And then the fish eat it.
    0:43:25 And then the fish die.
    0:43:26 And they fall to the bottom of the sea.
    0:43:29 And then so the carbon goes from the air into the bottom of the ocean, right?
    0:43:34 So, this is, like, generally good because we have too much carbon in the atmosphere.
    0:43:36 We also want more fish.
    0:43:41 But you need to balance it because you don’t want to put too much in and then just, like, there’s too much salmon.
    0:43:47 And then there’s, like, salmon take over, like, you know, a certain, like, ecosystem, which is maybe, like, not good or something.
    0:43:54 Or, you know, there’s a, basically, when you’re doing stuff in, like, with ecosystems, it’s very, like, difficult to predict how things are going to pan out.
    0:43:55 So, you need to be careful.
    0:43:59 So, this dude didn’t, wasn’t, didn’t do it that carefully.
    0:44:08 It went out, like, off the coast of, like, Vancouver, partnered up with these, like, Native Americans and just did, like, this experiment where he just, like, basically dumped off a load of iron filings.
    0:44:14 Removed, through his quantifications, like, thousands of tons of carbon.
    0:44:19 Off the coast there that year, they had, like, the biggest, like, take of salmon ever as a result.
    0:44:27 But the kind of desal of Hardee’s did not like his experimentation and, like, the Canadians, like, the CIA, like, busted his home.
    0:44:31 And he got, like, yeah, like, a warrant and, yeah, he got in a lot of trouble.
    0:44:37 So, people haven’t really done it since then because he kind of got, like, you know, he was the kind of first crazy, maybe, like, the first hobbyist to do something like this at scale.
    0:44:43 But I think there is going to be, like, a billion-dollar company built in, like, marine geoengineering of some description.
    0:44:46 There’s this, and so I’m, I’m, I’m Catholic.
    0:44:57 So, it’s, like, there’s this, like, a lot of my beliefs around, like, environmentalism and stuff like that comes from, like, this Christian notion of stewardship that, like, we should, like, look after our lands and our seas because it’s, like, our duty to.
    0:45:01 And I think this is, like, kind of, like, where we’re going with, like, how we, we manage the climate.
    0:45:05 Like, climate used to be this kind of, let’s, like, avoid the worst-case scenario.
    0:45:09 And it was just very, like, kind of, like, let’s, like, stop emitting carbon.
    0:45:17 But, like, I think there’s, like, a more interesting idea of, like, this, like, stewardship, I think, of environmentalism where we actually just, like, control, you know, we, we, we steward the planet, right?
    0:45:19 We, like, we take control, we get involved.
    0:45:24 We, you know, someone like Augustus, a rainmaker, can make it rain when we want it to rain.
    0:45:36 You know, if someone like UACs can come in and bring back the, the, the seagrasses when we need the seagrasses, you know, someone could, like, you know, when we want to draw down carbon can do, like, or increase fish stock somewhere, we could just, like, do a bit of this.
    0:45:38 I think it’s going to, we’re going to have to build these tools, right?
    0:45:44 Because these, we need these in tandem with growing the size of the economic pie if we want to keep doing that.
    0:45:47 You know, we don’t want to just, like, shut down the economy.
    0:45:50 We don’t want to, like, just stop doing, like, emissions altogether.
    0:45:53 It’s important for us to have these other kind of compensatory mechanisms.
    0:45:58 And, yeah, I think marine geoengineering is, like, an interesting and, like, underexplored space.
    0:46:05 I think the main things we need to get right there are science, better science on it, better technology, and governance.
    0:46:08 The governance about it, because the ocean is, like, a public space.
    0:46:10 It’s, like, you know, you just need to get the governance part right.
    0:46:16 Have you seen, Sean, have you seen this guy, Augustus, the founder of Rainmaker?
    0:46:18 Incredible mullet.
    0:46:31 Yeah, dude, so there’s this whole cohort of people of which Will appears to be one of the, like, you know, class presidents where there’s this, like, they’re very strange.
    0:46:33 They don’t fit this stereotype.
    0:46:42 When you think of a tech entrepreneur, they’re, like, kind of manly men or they’re, like, they’re not, they’re not, like, this engineer, like, typical thing that you and I grew up with, Sean.
    0:46:52 Like, there’s something about them that is different, and I can’t tell if you guys are going to take over the world and be billionaires or if you’re going to go broke, but it’s only going to be one of the two.
    0:46:56 Do you understand, Sean, like, this new genre that I’m trying to describe?
    0:46:59 I don’t know exactly what I’m saying, Will.
    0:47:03 Maybe you can, like, put words to it, but there’s this new, like, breed that I—
    0:47:05 Austin and San Francisco had a baby.
    0:47:14 You get the stash and the mullet of Austin, and then you get the insane ambition and tech chops of Silicon Valley, and that’s what’s happening here.
    0:47:25 For example, this guy, Augustus, I think his name is, he’s on the cover, I think, of Forbes or something, and he’s sitting on a bench press, like, working out.
    0:47:31 That is not something that Brian Chesky or, you know, Travis Kalanick would have done in 2012.
    0:47:36 I think it’s emblematic of, like, of kind of the evolution of the technology industry, though.
    0:47:42 I think, like, you know, we began, like, as this kind of, like, hippies that found computers with people like Steve Jobs.
    0:47:46 We were, like, actualizing on, like, the axis of, like, the spiritual, you know, realm.
    0:47:52 And then it was, like, you know, you had, like, people like Bill Gates and Zuckerberg who were just, like, nerds, like, actualizing on the sense of, like, mind.
    0:47:54 You know, they were, like, smart and nerdy.
    0:47:59 And now you have, like, people who are, like, openly flexing on, like, we’re actualizing on the sense of the body, right?
    0:48:03 Like, we’re, like, becoming strong and, like, you have, like, this, like, full integration of, like, mind, body and spirit.
    0:48:14 And it’s, like, no wonder that this, like, tech becoming, like, fully actualized on all of the axis that, like, that a human needs to develop on is happening at the exact same time where you have, like, Elon, who is, like, chief tech bro in the fucking White House, right?
    0:48:17 Like, these things, this is, like, no coincidence to me.
    0:48:19 It’s, like, tech has, like, found its voice.
    0:48:21 It’s, like, found itself.
    0:48:22 It’s, like, self-confident.
    0:48:28 And it’s, like, ready to, like, actually change the world now because it’s, like, it’s, you know, it’s, it’s, like, spiritually, like, aligned.
    0:48:30 It’s, like, mentally, they’re, you know, we’re smart.
    0:48:35 And, like, we’re, like, now, like, a strong group of people as well who are taking health and fitness seriously.
    0:48:40 And it’s, like, yes, like, this is why I think, like, we’re at, like, the most interesting time in technology right now.
    0:48:41 I like that.
    0:48:42 Poetic.
    0:48:46 You know, last night I watched a clip of the final scene of Ratatouille.
    0:48:47 You seen that, Sam?
    0:48:47 No.
    0:48:49 It’s a great movie.
    0:49:00 And the final scene of Ratatouille is the, the critic, the critic who’s, is the, the most fearsome critic in, in all of the town, writes the review about the restaurant where the rat has been cooking.
    0:49:04 And he just gives this beautiful monologue.
    0:49:09 Maybe the, maybe the most beautiful four minutes in all of film is the last four minutes of Ratatouille here, the monologue.
    0:49:16 Will, I think you’re up there with the last four minutes of Ratatouille there with your mind, body, and spirit analogy for tech.
    0:49:17 I think that’s kind of amazing.
    0:49:21 I’ve actually heard that before with just the technology part of it.
    0:49:25 So it’s, like, you had the initial, you know, the bicycle for the mind.
    0:49:28 So you had Steve Jobs talking about how computers will enable creativity.
    0:49:31 And then you had, you know, sort of AI.
    0:49:33 It’s like, oh, we gave computers a brain.
    0:49:34 And now they can think for themselves.
    0:49:41 And then with robotics and self-driving cars, it’s like, we gave the computers a body so they can move around and pick up things and do things.
    0:49:49 And I like how you, you extended that to, you know, the, the entrepreneurial will has, has, has grown in that way.
    0:49:50 Look at Bezos and Zuckerberg, they’re getting jacked.
    0:49:51 Like, they’re doing TRT.
    0:49:57 They look like, look, like, this is, like, I think that’s traumatic of, like, the spirit that is in technology now.
    0:50:04 It’s like, you have the, like, you know, one of my favorite podcasts besides yours, you know, the Tech Bros and, like, what Jordy and, and John are doing there.
    0:50:09 It’s like, they’re, like, they’re, you know, the, the technology brothers, they’re, like, leaning into the fact that they’re, like, tech bros.
    0:50:10 That used to be a slur, right?
    0:50:12 Now it’s, like, oh, I’m confident in it.
    0:50:13 I’m, like, I’m owning it.
    0:50:17 And they’re, like, doing these, like, hilarious promo videos of them, like, sipping Dom Perignon.
    0:50:23 Like, it’s, like, there’s, like, a confidence and an air of, like, okay, let’s do it now.
    0:50:28 You know, we’re not, like, we’re not going to be, like, at, like, the events and functions anymore, kind of lying about what we’re doing.
    0:50:29 You’re not neutered.
    0:50:30 Dude, listen to this.
    0:50:34 I got an email from this guy named Jamie at the Wall Street Journal.
    0:50:38 So Jamie is a reporter for Wall Street Journal’s style team.
    0:50:40 And he, listen to this.
    0:50:44 He goes, I’m writing a story about tech guys embracing Western wear.
    0:50:45 So basically cowboy clothes.
    0:50:46 In the past recent years.
    0:50:55 And I want to write about how the tech bro uniform has changed from quarter zips and all birds to denim shirts, cowboy boots.
    0:50:59 And, like, when I saw this and he said tech bro, I was, like.
    0:51:01 Dude, that’s amazing.
    0:51:02 I was, like, I don’t think I can talk.
    0:51:03 Like, this is not going to be.
    0:51:07 So life win that he thinks you’re the expert to go to, right?
    0:51:08 Yeah, life win.
    0:51:10 But I was, like, I’m not exactly a tech.
    0:51:15 But that’s amazing that you think that I, like, I am a fashion influencer officially.
    0:51:15 No.
    0:51:18 Reply.
    0:51:19 Mission accomplished.
    0:51:20 Dude, that’s amazing.
    0:51:21 And you’re right.
    0:51:23 Like, dude, is there any difference?
    0:51:27 You know, the first time you saw Zuck doing MMA.
    0:51:29 Do you remember when that video came out?
    0:51:39 Is there any difference between that video and the first time you saw, like, a Boston Robotics or Boston Dynamics, like, robot dog getting kicked and, like, jumping around and, like, doing backflips and shit?
    0:51:41 Like, there’s no difference between the two videos.
    0:51:42 It’s the same video to me.
    0:51:44 It’s one of those days that everyone remembers where they were when they saw it.
    0:51:47 It’s like, wow, I didn’t know the robots could do that.
    0:51:48 That’s how I felt watching Zuck.
    0:51:52 That’s how I felt watching Boston, the Boston Dynamics robot.
    0:51:55 Well, one of the last questions.
    0:51:56 Can I invest?
    0:51:57 Yeah.
    0:51:59 Yeah, great.
    0:51:59 Okay, cool.
    0:52:01 Because I think this is awesome.
    0:52:05 You guys are insane, man.
    0:52:07 This energy is so wild.
    0:52:09 I’m not convinced that it’s going to end one.
    0:52:13 Like, okay, so on one hand, it goes both ways.
    0:52:20 So on one hand, there’s the hubris where, you know, you’re like a, you know, in the case of Andrel, you’re Boeing or you’re one of these huge companies.
    0:52:24 And you’re like, you know, Parker or Palmer, you know nothing.
    0:52:25 You know, just go back to.
    0:52:27 It would be better if they called him Parker.
    0:52:29 Sorry, little Parker.
    0:52:30 Listen, Parker.
    0:52:32 They would be like, Palmer, you know, you know nothing.
    0:52:42 You know, you’re just go back to making Facebook apps, you know, and like probably eight out of ten times that idea is right.
    0:52:43 Right.
    0:52:50 Where like there’s an incumbent and like they fail because it’s really hard and there’s centuries of hard work to go against in competition.
    0:52:52 And so that’s the same with you.
    0:52:57 I would have to imagine where you have these young, really smart people who have no experience.
    0:53:02 And is this the 10% of the time where you guys are just going to take over the world?
    0:53:07 Or is this another time where someone’s going to be like, look, this is exactly what you told you.
    0:53:13 Listen, that guy, John, that guy who said, give me half a tanker of iron and I will give you an ice age.
    0:53:14 Here’s what I say.
    0:53:18 Give me 100 mullets and I’ll give you a 10x portfolio.
    0:53:24 I just need Will, I need Augustus, I need Palmer with a mullet, right?
    0:53:24 Three mullets.
    0:53:28 I need 97 more mullets and I’ll give you a 10x return.
    0:53:29 Okay.
    0:53:30 Give me the fund.
    0:53:31 I’ll find the mullet for you.
    0:53:33 You find the mullet.
    0:53:38 Like I can’t, I don’t know enough to know if this is, if this is achievable or not.
    0:53:40 Oh, I definitely understand that feeling.
    0:53:41 Yeah, for sure.
    0:53:44 I am not qualified to judge the feasibility of something.
    0:53:53 But I think in general, it’s not about any, you know, if nothing, if anybody, if anybody who’s doing a startup like this thinks it’s a sure thing or a sure bet, you’re nuts, right?
    0:53:56 Like you’re going to have to perform a miracle, right?
    0:53:57 And that’s okay.
    0:54:15 The important thing is, oh, wow, we took a portion of our brainpower that was otherwise going to be building X or working at Y company, you know, working at Facebook, optimizing, you know, ad clicks or starting a company that was going to be doing, you know, B2B, HR, whatever software.
    0:54:19 And instead, now we peeled off a portion of that talent.
    0:54:22 And now we sent, you know, 100 mullets at these problems.
    0:54:28 And I think that that’s the winning strategy is 100 or 1,000 shots on goal like this.
    0:54:30 And then the winners will obviously emerge.
    0:54:33 Well, I can assure you what we’re doing is very real.
    0:54:36 You wouldn’t have a million dollars in our bank account without it.
    0:54:38 We wouldn’t have done all the things we’ve done in the last five months.
    0:54:43 If you want to come here to San Francisco and see some real robots in Ocean, the door is always open, Sam.
    0:54:44 Same for you, Sean.
    0:54:45 I got to ask you two quick questions.
    0:54:48 Number one, Seagrass seems so random.
    0:54:52 And if you started this company, you might have thought, oh, I’ll do drones like for warfare.
    0:54:55 How did you arrive at the Seagrass thing?
    0:54:56 Was it instant?
    0:54:57 Was that the initial idea?
    0:54:59 Or did you do some discovery to figure that out?
    0:55:00 It was the initial idea.
    0:55:00 It was the initial idea.
    0:55:13 I came to one of my co-founders when he was on a surf trip and he kind of, the same one who went into the cave and designed our AUV, went, heard about Seagrass and went into cave and like went deep on Seagrass and came back to us and presented like, this is a very interesting space.
    0:55:16 He heard about Seagrass on a surf trip from who?
    0:55:19 A marine biologist, friend of his, who was working on a wave.
    0:55:20 A guy out in the wave.
    0:55:21 Yeah.
    0:55:26 Dude, this co-founder is absolutely carrying your company.
    0:55:26 Yeah.
    0:55:29 This guy who built the tech and figured out the go-to market.
    0:55:34 He’s the, well, yeah, he found, he found the kind of, he was the one who brought Seagrass to us.
    0:55:37 And then myself and my other co-founders kind of put it together.
    0:55:39 We’re like, this is what the business probably should look like.
    0:55:42 But then, yeah, we kind of went out from there into other areas.
    0:55:52 And like, you know, like I think any brilliant company finds a local monopoly to build in first somewhere where there’s nobody else doing stuff with technology where you’ve, you know, it’s a great time.
    0:55:55 And nobody’s ever heard of what you’re even doing initially.
    0:55:56 And you can, it’s a pretty big market.
    0:56:00 You can, you know, bring cash into your business as like a lifeblood.
    0:56:03 And so it’s been a great place for us to start.
    0:56:04 It’s like the best place for us to start.
    0:56:05 Nobody’s ever heard of it.
    0:56:07 So I think that’s always a good place to start off on.
    0:56:12 And then, yes, we’re going to use that as a kind of launching point to do other interesting things in the ocean.
    0:56:13 Who do you admire, Will?
    0:56:15 Who do you want to be like?
    0:56:18 Steve Irwin, probably.
    0:56:20 Dude, motherfucker.
    0:56:22 I was going to say this earlier on.
    0:56:23 I go, you are Steve Irwin.
    0:56:25 I do.
    0:56:26 You got Steve vibes.
    0:56:27 Hardcore, man.
    0:56:29 Do you got any khaki shorts on right now?
    0:56:30 No, right now.
    0:56:32 But we have a picture of him up on the wall here.
    0:56:35 Oh, you scream Steve Irwin.
    0:56:37 You have Steve Irwin vibes through and through.
    0:56:38 Yeah, yeah.
    0:56:45 I know he’s, yeah, I’m hopeful I can get the Irwin family on the Ulysses trend at some point.
    0:56:47 We got to holler at Bindi.
    0:56:48 Bindi Irwin.
    0:56:49 That would be great.
    0:56:50 Robert as well.
    0:56:52 I love those guys.
    0:56:53 Yeah, Robert as well.
    0:56:57 Yeah, you know, look, Steve, I think is like, and it’s so funny.
    0:57:00 People say Steve on a podcast and the tech, it’s like Steve Jobs.
    0:57:01 It’s like for me, it’s like Steve Irwin.
    0:57:07 You should have just said Steve at the beginning and then let us fall into your trap.
    0:57:09 Dude, Sean, Sean, there’s this famous video.
    0:57:10 I know you’ve seen this, Will.
    0:57:14 There’s this famous video of, it’s Steve Irwin and his wife.
    0:57:14 What’s her name?
    0:57:16 I forget her name.
    0:57:20 And anyway, there’s an interviewer who asked Steve, like, you know, you don’t seem like you care.
    0:57:22 Sam, Sam, Sam, Sam, Sam.
    0:57:23 Just look, just look.
    0:57:24 Just look what’s on my screen.
    0:57:26 Just look what’s on my screen right now.
    0:57:26 Oh, there it is.
    0:57:26 Thank you.
    0:57:27 Just look what I was pulling up.
    0:57:28 I love that clip.
    0:57:29 I am with you, brother.
    0:57:30 I love this clip.
    0:57:31 I love this clip.
    0:57:32 Play it.
    0:57:32 Play it.
    0:57:36 What good is a fast car, a flash house, and a gold plate, a dunny to me?
    0:57:37 Absolutely no good at all.
    0:57:43 I’ve been put on this planet to protect wildlife and wilderness areas, which in essence is going
    0:57:44 to help humanity.
    0:57:46 I want to have the purest oceans.
    0:57:48 I want to be able to drink water straight out of that creek.
    0:57:50 I want to stop the ozone layer.
    0:57:52 I want to save the world.
    0:57:52 And you know, money?
    0:57:53 Money’s great.
    0:57:55 I can’t get enough money.
    0:57:56 And you know what I’m going to do with it?
    0:57:58 I’m going to buy wilderness areas with it.
    0:58:01 Every single cent I get goes straight into conservation.
    0:58:03 And guess what, Charles?
    0:58:04 I don’t give a rip whose money it is, mate.
    0:58:07 I’ll use it and I’ll spend it on buying land.
    0:58:09 This is how every man should be, by the way.
    0:58:11 Like, you’re passionate about something that’s good for others.
    0:58:13 And like, his wife’s just like eyeing him.
    0:58:16 And that’s one of my favorite clips of all time.
    0:58:16 Yeah.
    0:58:21 So I think the traits in him that I admire is just like raw passion.
    0:58:23 It’s like this unbridled passion.
    0:58:24 It’s like this nonsensical passion.
    0:58:27 It’s like, you think I’m going to have a conversation without a microphone?
    0:58:29 No, I’m going to put a microphone there.
    0:58:30 I’m going to record a podcast.
    0:58:32 I’m going to record a podcast every day.
    0:58:33 And I don’t give a rip who’s listening.
    0:58:34 Because you know what?
    0:58:35 I’m a podcaster.
    0:58:37 And I’m going to podcast my ass off.
    0:58:41 It’s a whole lot more lame, but you’re not talking about like saving the earth.
    0:58:41 You know what I mean?
    0:58:42 I tried.
    0:58:43 I tried.
    0:58:46 Like when we’re talking about like conversion rate optimization
    0:58:47 or B2B.
    0:58:52 Dude, in fact, Will, we kind of, my generation and the generation before me,
    0:58:54 we, you know, what do they say?
    0:58:59 Hard times create, or no, like we need a hard man to create soft times.
    0:59:01 That’s what I did for you.
    0:59:04 You know, we went and did the B2B software stuff.
    0:59:07 So you guys could do this fun, amazing stuff.
    0:59:09 So really, you’re welcome.
    0:59:10 Thank you.
    0:59:11 Thank you.
    0:59:14 New York City founders.
    0:59:17 If you’ve listened to my first million before, you know, I’ve got this company called Hampton.
    0:59:20 And Hampton is a community for founders and CEOs.
    0:59:26 A lot of the stories and ideas that I get for this podcast, I actually got it from people who I met in Hampton.
    0:59:29 We have this big community of 1,000 plus people, and it’s amazing.
    0:59:36 But the main part is this eight-person core group that becomes your board of advisors for your life and for your business, and it’s life-changing.
    0:59:42 Now, to the folks in New York City, I’m building an in-real-life core group in New York City.
    0:59:54 And so if you meet one of the following criteria, your business either does $3 million in revenue, or you’ve raised $3 million in funding, or you’ve started and sold a company for at least $10 million, then you are eligible to apply.
    0:59:57 So go to joinhampton.com and apply.
    0:59:59 I’m going to be reviewing all of the applications myself.
    1:00:04 So put that you heard about this on MFM so I know to give you a little extra love.
    1:00:05 Now, back to the show.
    1:00:13 Can we do just a quick happy hour of two topics that you had on this list that, you know, Sam, if you got to run or whatever, feel free.
    1:00:14 But I just got to ask you about these.
    1:00:17 So I want to do the fun one and then the spiritual one.
    1:00:18 The fun one is conspiracy theories.
    1:00:22 You’re a big conspiracy – you’re a fan of conspiracy theories, I believe.
    1:00:25 And you like people who like conspiracy theories.
    1:00:30 So can you just give me a rant on why conspiracy theories are underrated here?
    1:00:37 I think – well, I think it’s like, you know, a lot of the traits of like conspiracy theorists are like those of like a great, like, founder.
    1:00:44 I think like someone that like believes in something that everyone else tells them is like not real or like, you know, that they shouldn’t believe in.
    1:00:48 Or like, you know, people that are like able to see patterns that others can’t see.
    1:00:51 And, you know, they just like go down these like rabbit holes.
    1:00:55 And I think like just like this contrarian spirit, I think it’s like very, very good.
    1:00:59 And I think it’s just like a very important, you know, the default is like doing things that other people do.
    1:01:05 And so I think it’s very important to cultivate an ability to see the world differently, I think.
    1:01:13 Isn’t it funny how contrarian is this like really positive description and conspiracy theorist is like this like negative description?
    1:01:13 You know what I mean?
    1:01:14 It’s the same thing.
    1:01:19 I just think it’s very important to, you know, have weird ideas and take them seriously, right?
    1:01:25 Like if we just had heard the seagrass idea and just like rubbished it, you know, we wouldn’t – I would like – I don’t know what the hell I’d be doing today.
    1:01:28 You know, it’s like you need to take something weird and go with it.
    1:01:36 And so like I don’t believe like to blindly believe every report of telepathy and nonverbal autistic children or every like late night UFO sighting.
    1:01:37 But like I refuse to dismiss them outright.
    1:01:46 And I think, you know, history shows us that breakthroughs often happen at the edges where people are curious enough or foolhardy enough to investigate the unexplainable.
    1:01:53 So it’s like whether it’s like Christian mystics, you know, who swear by miraculous healings or physics experiments that like challenge our understanding of space time.
    1:01:58 I think it’s very important to like lean into these weird things and ask what if.
    1:02:01 And yeah, I think conspiracy theories are just kind of like fun as well.
    1:02:02 They’re like kind of like horoscopes for dudes.
    1:02:09 So they’re like – if nothing else, like they’re just like – it’s like – it’s just like a fun thing to kind of like spend your time reading about.
    1:02:11 On here, you talk about aliens.
    1:02:16 We are with Joe Gebbia recently who’s like the 90th richest person in the world.
    1:02:18 And I was like, Joe, look, you’re worth like $10 billion.
    1:02:23 Like if there’s Illuminati, like you are either in it or you’re friends with the people in it.
    1:02:26 Tell me one thing that like you guys talk about.
    1:02:26 And he looked at me.
    1:02:28 He goes, aliens are real.
    1:02:38 And he went on a – he went on a big – he had a big diatribe on his passion for like, you know, UFOs and aliens and how he absolutely is on board with them.
    1:02:39 100%.
    1:02:40 He’s on board with them?
    1:02:46 He came off very passionately like it is absolutely a thing.
    1:02:49 And the funny thing is if you meet Joe, he’s a serious dude.
    1:02:52 Joe doesn’t just say wild shit for wild shit’s sake.
    1:02:55 You know, Joe’s not like, oh, he’s a kooky billionaire.
    1:02:58 No, no, no, Joe is like an extremely principled artist.
    1:03:01 He is a very serious individual.
    1:03:09 And so for him to say something like that, it’s not like – you don’t discount it with the same discount rate you would if John McAfee was the guy saying it.
    1:03:10 You know what I mean?
    1:03:18 If your reader is wanting to go down to this rabbit hole, the best website I recommend going is a website a friend of mine runs, uapevidence.com.
    1:03:21 Is there any other dope conspiracy that I should go look at you?
    1:03:24 A rabbit hole that would waste a nice five hours of my time?
    1:03:32 Yeah, I think less of a conspiracy, more like wacky, weird rabbit hole you need to go down is you need to listen to the Telepathy Tapes podcast.
    1:03:34 I have, and I love it.
    1:03:36 What is this?
    1:03:37 Is this like I can read your mind?
    1:03:44 So basically, there’s this group of people that people have been calling crazy for like the last like two decades, right?
    1:03:52 It’s basically the teachers and parents of children with nonverbal autism because they’ve been convinced that their kids have been able to like read their mind.
    1:04:04 And now for like the first time with teaching kids how to spell on like iPads and also with like getting researchers in to study them, they’re actually verifying these telepathic capabilities, right?
    1:04:12 So like a mother will like go into one room and she’ll be shown like a random number generator and her son, Akil, in the other room will hit the exact same three numbers.
    1:04:14 100% of the time, consistency in tests.
    1:04:15 That’s awesome.
    1:04:16 Yes.
    1:04:26 It’s like the serial podcast, but it’s this woman investigating these claims and she’s like, you know, like an NPR skeptical, let me call it, right?
    1:04:30 So she comes in, she’s like, this didn’t make a ton of sense, but I’m open-minded.
    1:04:31 And she turned?
    1:04:33 I didn’t finish the whole thing.
    1:04:38 I listened probably the first two or three, but I was listening to while I was going to sleep and I just had some like wild, wild nights there.
    1:04:42 So I decided, all right, I need to only listen to this, you know, not falling asleep if I’m going to do this right.
    1:04:47 By the way, Will, did you walk away from that, you know, half convinced, three-fourths convinced, totally convinced?
    1:04:48 What did you walk away?
    1:04:54 Well, I was going into it already with some sort of like priors that I thought that like consciousness isn’t local to the brain.
    1:05:00 Like we like to think that like our brain is this kind of like DVD player where like consciousness is playing and it’s like being played to us.
    1:05:01 And that’s how we experience things.
    1:05:06 I think we’re more like, I always kind of thought and for different reasons that we’re more like a radio antenna.
    1:05:11 You know, you have these stories of people like their son dies in an accident and they just know something’s wrong, right?
    1:05:12 They just like know, right?
    1:05:17 Like there’s like, you know, everyone, every family has these stories about death or like something bad happened and they just like knew.
    1:05:20 They woke up in the middle of the night and they’re like, I couldn’t sleep then after that.
    1:05:23 And then they wake up the next day, they hear about this awful accident or something like that.
    1:05:29 Or you have like, there’s like knowingness and these other things, like just like telepathy, twins, telepathy and stuff.
    1:05:33 And there’s like this world of parapsychology, which is like the study of these kind of psi phenomena.
    1:05:42 There’s like a few very reproducible experiments in it, like the Gansfeld experiment, which if you allow me to go on this like very short rabbit hole, but like the most reproducible experiment in this field,
    1:05:45 is basically you take two people, you put them in like two separate rooms.
    1:05:46 These could be twins.
    1:05:47 These could be a husband, wife.
    1:05:48 They could be two artists.
    1:05:51 They could be two people who don’t know each other, different settings.
    1:05:56 And basically you give me a picture and you give and you’re the receiver then in another room.
    1:06:00 And I’m in one room and I’m talking about this random picture I’ve been given.
    1:06:01 Let’s say it’s one in four different pictures.
    1:06:03 I get a picture of an element.
    1:06:04 For five minutes, I talk about elephants.
    1:06:08 I saturate my brain with Africa and wild animals and savannah.
    1:06:09 You’re in the other room.
    1:06:14 You’re listening to white noise and you’re talking basically about what you’re sensing, feeling that they could be about.
    1:06:20 And then at the end of the five minutes, I stop and you get replayed what you were saying to yourself for five minutes.
    1:06:24 And you get the four random images and you get to pick one of the four.
    1:06:29 And then you would assume if complete chance, you know, you would 25% chance of getting it right.
    1:06:33 But pretty consistently you get like 30% or above in this like experiment.
    1:06:40 And then when there’s like twins, husband and wives and or artists, they actually score like more consistently 35.
    1:06:43 In some instances, like 70% in some of these experiments.
    1:06:48 And so I’ve always kind of been like primed to think that like actually maybe we’re more like we’re touching into something.
    1:06:51 And like that explains a lot of the spiritual and woo woo stuff.
    1:06:55 And then I see this and it’s like very good experimental evidence and really well done.
    1:06:57 And I’m like, okay, nah, that’s 100% legit.
    1:07:02 Like our brain is not like this like AI chip that like runs and just like tells us what to do.
    1:07:11 It’s like an AI chip, but it’s like, it also has a radio antenna that can connect to other people, can maybe connect to God, spirits, other things we don’t really know.
    1:07:15 Dude, I’m so bummed that I grew up in the B2B era of startups.
    1:07:18 Yeah, so bummed.
    1:07:21 Well, I wish I was like, I wish I was 10 years younger.
    1:07:21 You were 10.
    1:07:22 I wish we could have hung out.
    1:07:24 Dude, let’s grab some beers.
    1:07:37 I went to a bachelor party this weekend and everybody on the, it was a bachelor party where the bachelors and the bachelorettes were both doing it together basically as a party together.
    1:07:40 And the bachelorette side was so cool.
    1:07:44 Like every single one of them, just, you know, those tattoos that aren’t like filled in.
    1:07:47 They’re just like, it almost looks like a pencil sketch.
    1:07:56 Just seven or eight of those, some piercings, sense of style off the charts, knowledge of beer and music way beyond my recognition.
    1:08:00 You know, sexuality was a total spectrum.
    1:08:02 You never knew who was, who’s dating who.
    1:08:03 Anybody could be dating anybody in the room.
    1:08:04 It was insane.
    1:08:13 I just felt like, I literally felt like I came from a, I was a caveman and I was like, or like, you know, like I was the gingerbread man, actually.
    1:08:14 I wasn’t even a real human being.
    1:08:18 I was a cookie cutter shape that was placed in this room.
    1:08:18 That’s awesome.
    1:08:19 That is so funny.
    1:08:24 I think one like universal law about technology is that like it breeds variants, right?
    1:08:26 Like it just like, it creates like skewed outcomes.
    1:08:29 And I think you probably like see this in like younger generations as well.
    1:08:37 Like you’ve got like weird, like kind of like schizo people like me that will like burn your ear off by conspiracy theories and like, you know, go down like these weird rabbit holes.
    1:08:45 But like, I think you also, it’s like on the, like maybe on the more negative end that could send you down like some pretty dark places that maybe you wouldn’t be a productive member of society.
    1:08:53 If you go down like those, like into those like very dark corners of the internet or similarly, like it’s, you know, you have people who are like doing like great things, but then you also like, you know,
    1:09:05 I think there is like a very interesting question that’s posed in technology right now is like, you know, where are the, you know, the, the kind of like less than kind of 25, you know, you know, billion dollar company founders.
    1:09:12 This is like an interesting question that I think is still not really like, there’s no satisfying answers around like previous generations had like the Collison’s pretty early.
    1:09:16 You know, we had like Alexander Wang, like he’s maybe like a few years older than me pretty early.
    1:09:19 Still doesn’t seem clear whether there isn’t one in this generation.
    1:09:23 Maybe we have to wait another year or two for companies like Ulysses or Rainmaker or others to like,
    1:09:24 to get there.
    1:09:29 But there is definitely like a, I think a bigger skew in both the ideas that young people are interested in today.
    1:09:33 I think that’s like broadly just like downstream of, yeah, technology.
    1:09:34 Are you going to become an American?
    1:09:39 Yeah, I think I’m going to get, I’m on the green carriage path.
    1:09:39 Yeah.
    1:09:41 My last question was the spiritual one.
    1:09:45 You said you lived with Buddhist monks in Nepal and for a summer.
    1:09:46 You learned a lot.
    1:09:49 And one thing I liked, you said, I couldn’t come around to their view,
    1:09:52 which states that zero desires leads to enlightenment.
    1:10:02 And so you, and then you said like, you know, I, you wanted to be, you wanted to be action oriented and do something with your life rather than sit and sort of renounce everything.
    1:10:07 And then you said something like, I came to, I came to explain my five desires or six desires.
    1:10:11 Can you just give it, give me the quick story on your summer with the monks and then what you landed at?
    1:10:11 Yeah.
    1:10:17 So yeah, I just, um, kind of want, I just had heard that you, you could do this, right.
    1:10:21 You can actually just like find a monastery to like basically put you up if you teach them English.
    1:10:25 So I did that, found a monastery in Nepal that would like put me up.
    1:10:27 It’s pretty rural, a few hours outside.
    1:10:34 And the, uh, Kathmandu went there, flew there, taught them, I taught myself to teach English before I came over, was teaching them English.
    1:10:39 And then like in the downtime was like able to speak to some of the older monks who had like good English and like ask them about their ideology.
    1:10:44 Cause there’s just a, there’s just five monks with like a thick Irish accent speaking English out there.
    1:10:46 They’re like, yeah, I learned from an expert.
    1:10:48 Wild actual segue.
    1:10:56 I was out running in the middle of Nepal one day and I bumped into a dude who was wearing a Galway Bay 5k t-shirt.
    1:11:03 And I was like, I was like, sorry now you might have like no English, but I was like, where did you get this like t-shirt?
    1:11:04 Like he, this is like, we’re near where I’m from.
    1:11:07 And he was like, he was like, he had like kind of like an Irish accent.
    1:11:10 He’s like, oh, well, you know, I actually work with an Irish guy.
    1:11:13 Um, he has like an orphanage and like a charity out here.
    1:11:16 And I was like, oh wait, like what’s this like Irish guy’s name?
    1:11:25 The Irish guy he named was like the one Irish guy that my, my neighbor, who’s like my mom’s friend, my mom’s friend was like, my mom was worried about me going to Nepal.
    1:11:28 She was like, well, you know, you need to have a contact in Nepal when you go over there.
    1:11:33 You know, I was like, and it’s in my neighbor, new guy who’s in Nepal, who had a charity out there.
    1:11:36 Anyway, like this random guy I met in this like tiny village worked with him.
    1:11:38 So this is like, you know, there’s like Irish people everywhere.
    1:11:43 Everyone just talking like kind of like all these monks are like, I boxed them.
    1:11:43 They’re like, you’ll do nothing.
    1:11:45 I boxed the bullocks off them.
    1:11:45 Yeah.
    1:11:47 You’ll do nothing.
    1:11:48 Literally.
    1:11:49 There’s Irish people everywhere.
    1:11:50 We have, we have, we have people everywhere.
    1:11:52 That’s like the kind of moral.
    1:11:53 Is that what the monks are saying?
    1:11:54 We didn’t hear to come.
    1:11:55 We didn’t come to take part.
    1:11:56 We came to take over.
    1:11:58 All right.
    1:11:59 So, so sorry.
    1:12:02 So you go there and you’re, uh, continue.
    1:12:02 Yeah.
    1:12:04 I’m curious about the origin.
    1:12:05 I’m asking them questions about it.
    1:12:09 But one thing I just couldn’t get over was like, you know, they don’t believe in desire.
    1:12:11 Like they believe desire is like what leads to suffering.
    1:12:16 If you desire for something, then you’re creating a contract with yourself to be unhappy until you have that thing.
    1:12:19 And I’m just like, dude, I’m very like American dream pill.
    1:12:21 I’m like, you know, I should want for things.
    1:12:22 I should want for things.
    1:12:24 But I can see how that can go wrong as well.
    1:12:24 Right.
    1:12:27 Cause that leads to like, you know, keeping up with the Joneses type lifestyle.
    1:12:36 Um, or maybe like, you know, kind of like, you know, the fatties on, on the chair at Walmart kind of like thing, you know, like that, that’s like probably like when it goes like, uh, maybe like too far.
    1:12:37 Hey, you better watch it, Will.
    1:12:40 That’s our demo.
    1:12:41 You better watch it.
    1:12:42 You’re saying it.
    1:12:45 You’re not that fat, sir.
    1:12:51 So anyway, so that’s why I can see where I can go wrong.
    1:12:51 Right.
    1:12:58 But I do think there was like an essence of truth in there where it’s like, maybe you should like actually try to trim down things as little as possible.
    1:13:03 And I had this like bizarre experience where I was, I was, I went and did ever space camp afterwards.
    1:13:05 And I was thinking a lot about like the things that they were saying to me.
    1:13:13 And again, I feel like I had like a download, like one of these experiences where like something just came into my brain that I don’t think I hadn’t been thinking about it before.
    1:13:26 And I genuinely think it was a download from, you know, something spiritual that like gave me like some guidance on how I, I was literally, it sounds crazy, but I was sitting on a rock, um, like just like on a break in the hike, this like 10 day hike up to a base camp.
    1:13:32 And, uh, I like, it was like thinking through, it’s like, Hey, well, if you have no desire, like what do you do?
    1:13:34 It’s like, Oh, maybe you should have desire, but the minimum amount of them.
    1:13:36 Then I was like, what is like important to me?
    1:13:41 And I was like, on my hand, I was like, Oh, my family, my friends, my health, my wealth, my craft.
    1:13:42 And I was like, Oh shit.
    1:13:44 Like, that’s like five things that’s like nice and clean.
    1:13:48 And then I like had this like idea at the same time of like a rose bush, rose bushes.
    1:13:55 If you like leave them go unkept, they basically just grow like briars and they go thorns and the flowers don’t really grow.
    1:13:58 You have to like cut them back to let the energy go back to like the rose.
    1:14:03 And I was just like, I had this like very clear vision of like roses and I was like, Oh, okay.
    1:14:04 Right.
    1:14:04 So this is it.
    1:14:04 Okay.
    1:14:13 So whenever I’m like down over something, it’s like, if it’s not one of these like five important things to me, then it’s like, okay, just like let it go.
    1:14:15 Like stop desiring for it.
    1:14:17 Um, and I found that to be helpful.
    1:14:18 You had a girlfriend?
    1:14:19 Uh, no.
    1:14:27 That was your reaction to his story about the Buddhist monks and like realizing the meaning of life.
    1:14:28 You want to, dude.
    1:14:33 You tell me an Irish guy with that in his Tinder profile, isn’t he just going to destroy the whole city?
    1:14:37 Give me a break.
    1:14:47 Saving the world, uh, saving the world seagrass, former monk, Sam’s five desires, family, health, wealth, fitness, and will.
    1:14:49 Those are Sam’s five desires.
    1:14:52 This is so good, man.
    1:14:53 You’re the best.
    1:14:54 Will, this is awesome.
    1:14:55 People should check you out.
    1:14:56 Where on Twitter?
    1:14:57 You’re Will O’Brien.
    1:14:57 What’s your, what’s your handle?
    1:14:58 At Will O’Brien.
    1:15:00 W-I-L-O-B-R-I.
    1:15:02 Okay, great.
    1:15:04 And, uh, good luck with the company, man.
    1:15:04 Thank you, dude.
    1:15:05 Thank you.
    1:15:05 All right.
    1:15:06 That’s it.
    1:15:06 That’s the pod.
    1:15:07 Thank you.
    1:15:09 I feel like I can rule the world.
    1:15:09 I feel like I can rule the world.
    1:15:11 I know I can be what I want to.
    1:15:14 I put my all in it like no days off.
    1:15:17 On the road, let’s travel, never looking back.

    💰 Get the Side Hustle Ideas Database [free]

    Episode 694: Sam Parr ( https://x.com/theSamParr ) and Shaan Puri ( https://x.com/ShaanVP ) talk to Will O’Brien ( https://x.com/willobri ) about how the ocean is the new hot girl. 

    —

    Show Notes: 

    (0:00) Ocean is the new space

    (8:50) Ocean surveying

    (10:25) Sea grass restoration

    (16:39) Defense drones

    (22:34) Underwater cable networks

    (31:51) Technology windows

    (38:16) Lab-grown seafood

    (41:03) Pirate treasure profit sharing

    (44:03) Marine geo-engineering

    (47:43) A new era for tech guys

    (57:52) Who Will admires

    (1:01:00) Underrated conspiracy theories

    (1:10:27) Becoming a monk

    —

    Links:

    • Ulysses – https://www.ulysses.eco/ 

    • Saildrone – https://www.saildrone.com/ 

    • Saronic – https://www.saronic.com/ 

    • Wildtype Foods – https://www.wildtypefoods.com/ 

    • Rainmaker – https://www.rainmaker.com/ 

    —

    Check Out Shaan’s Stuff:

    Need to hire? You should use the same service Shaan uses to hire developers, designers, & Virtual Assistants → it’s called Shepherd (tell ‘em Shaan sent you): https://bit.ly/SupportShepherd

    —

    Check Out Sam’s Stuff:

    • Hampton – https://www.joinhampton.com/

    • Ideation Bootcamp – https://www.ideationbootcamp.co/

    • Copy That – https://copythat.com

    • Hampton Wealth Survey – https://joinhampton.com/wealth

    • Sam’s List – http://samslist.co/

    My First Million is a HubSpot Original Podcast // Brought to you by HubSpot Media // Production by Arie Desormeaux // Editing by Ezra Bakker Trupiano