0:00:03 – Frankly, in the beginning, they didn’t work at all.
0:00:05 That’s when the DOD first started to realize
0:00:07 that it was a totally new threat.
0:00:08 – You can learn them the hard way
0:00:10 by just start a company and go do it,
0:00:11 or maybe you can have some training wheels,
0:00:14 but ultimately there’s no way to learn how to do this job
0:00:15 other than to just do it.
0:00:19 – As taxpayers, as veterans, and as founders,
0:00:21 we should want that level of competition
0:00:22 and that cutthroat competition
0:00:25 to onboard things quickly and offboard things quickly.
0:00:28 – You can’t have a company where people are split
0:00:29 on what they care about
0:00:31 and what those foundational values are.
0:00:33 You have to be all on board.
0:00:35 – Do you want to do something meaningful?
0:00:37 Do you want to do meaningful work?
0:00:38 – It’s very clear what the stakes are
0:00:42 and it’s very clear why it’s important.
0:00:45 – In 2025, the battlefield is not just overseas.
0:00:48 It’s in the tech stacks, the logistics networks,
0:00:50 and airspace security systems
0:00:52 that power our national defense.
0:00:54 And who better to design solutions
0:00:57 than those who have faced these problems firsthand?
0:00:58 Those who have served.
0:01:01 In this episode, recorded live at our third annual
0:01:04 American Dynamism Summit in the heart of Washington, D.C.,
0:01:07 A16Z’s Matt Shortle, operating partner and chief of staff,
0:01:11 plus a veteran himself, sits down with three veterans
0:01:12 who have turned to the private sector
0:01:14 to build technology companies,
0:01:16 tackling secure communications,
0:01:19 military logistics, and drone defense.
0:01:21 Joining this conversation is John Doyle,
0:01:23 founder and CEO of Cape,
0:01:25 David Tuttle, co-founder and CEO of Rune,
0:01:29 and Grant Jordan, founder and CEO of SkySafe.
0:01:31 Today, you’ll get to hear on-the-ground stories
0:01:32 from these veterans,
0:01:35 like how their time in uniform has shaped the scope of problems
0:01:38 that they see and ultimately what they’ve chosen to build.
0:01:41 They also discuss whether a jump directly from military
0:01:43 to entrepreneurship is possible,
0:01:46 or whether a stint in the private sector is needed,
0:01:48 like the time that David spent at Anderil,
0:01:51 or that John spent at Palantir.
0:01:54 And finally, how does this unique group of individuals
0:01:57 who have spent time on the battlefield and in the boardroom
0:02:01 think our collective culture is changing around the national project?
0:02:05 Listen in to find out.
0:02:09 As a reminder, the content here is for informational purposes only,
0:02:12 should not be taken as legal, business, tax, or investment advice,
0:02:14 or be used to evaluate any investment or security,
0:02:19 and is not directed at any investors or potential investors in any A16Z fund.
0:02:22 Please note that A16Z and its affiliates may also maintain investments
0:02:25 in the companies discussed in this podcast.
0:02:27 For more details, including a link to our investments,
0:02:31 please see A16Z.com/disclosures.
0:02:38 I’d like to start with just hearing your initial story,
0:02:40 what you did in the service,
0:02:42 and then also what inspired you to join initially.
0:02:46 What inspired me to join initially was what I think inspires plenty of people,
0:02:48 which is paying for school.
0:02:51 So the Air Force was good enough to pay for me to go to MIT.
0:02:54 And for that, when I commissioned as an officer,
0:02:56 I went and worked in AFRL for four years,
0:02:57 working in the Air Force Research Lab,
0:03:01 and focusing on how do we actually innovate in the Air Force?
0:03:03 How do we bring new systems out to the warfighter?
0:03:07 And how do we rapidly get those systems out there?
0:03:09 I mean, similar family history of service to the nation.
0:03:12 I just service to our people, went to Cornell,
0:03:14 did ROTC there, commissioned into the Army.
0:03:16 I left active service for a period of time,
0:03:17 went to the private sector,
0:03:20 and then actually went back into active service.
0:03:22 Ended up down at the Joint Special Operations
0:03:24 Camp for a number of years and ended up in some technology orgs
0:03:27 at the end of my time there, and then came back out and said,
0:03:28 “Hey, I think we can do some great work
0:03:32 from the private sector technology side going into the defense world.”
0:03:34 I should say I’m still serving,
0:03:36 still serving Army National Guard officer up in New York.
0:03:38 So I do the one weekend a month driving back there,
0:03:41 and that’s been a number of years now at this point.
0:03:43 That’s great. John?
0:03:45 So I almost joined, like a lot of folks of my vintage,
0:03:48 I almost joined on 9/11 or on 9/12.
0:03:50 I’m obviously heavily affected by that.
0:03:52 By that day, I was a senior in college,
0:03:57 and I ultimately made the decision to finish my computer science degree.
0:03:59 But then when we invaded Iraq in 2003,
0:04:02 I felt that same call to service decided it wasn’t just a whim,
0:04:04 it’s something I shouldn’t and couldn’t ignore.
0:04:06 And so the day after the invasion, I went to the recruiter,
0:04:07 and signed up.
0:04:10 I think I’m unique on the panel in that I enlisted.
0:04:14 I was on a program called 18 X-ray, which I think still exists.
0:04:19 You can join the Army as an enlisted soldier and commit to five years up front.
0:04:24 And in exchange, the Army will give you a shot to try out to be a Green Beret,
0:04:27 to be Army Special Forces, after doing basic training in airborne school.
0:04:32 And so I signed that contract the day after the invasion and I was successful.
0:04:36 And so I spent two years doing really amazing training in the Special Forces training pipeline,
0:04:39 and three years on an SF team at the Special Forces Group.
0:04:43 Was entrepreneurship something you envisioned while you were serving?
0:04:44 Did that come after?
0:04:47 Tell me how all that works, because you’re all three founders now.
0:04:52 I don’t think it was something I thought about at the time in service, but I think part of my frustration
0:05:01 in the Air Force was seeing how some of the acquisitions programs were broken or were outdated for what the pace of current technology was.
0:05:09 And I think I saw a lot of opportunities to try to change that from the outside and to try to innovate and bring those technologies in from the outside.
0:05:12 To be honest, it was not something that was on my radar.
0:05:17 When I left the active service for the first time, I went to about the biggest companies you could imagine.
0:05:23 I ended up as an investment banker on Wall Street and did that, obviously massive international bank presence.
0:05:24 I think that was great for me.
0:05:33 I don’t think I had at the time, at my stage in career at that point, I don’t think I had what I thought I needed to go out and start my own company or do the thing that we’re all doing now.
0:05:39 So I think for me, it was like, how do I build that toolkit of knowledge and how do I do those sorts of things in a large organization?
0:05:46 And then now that the time was right later on, opportunity presented to us, hopefully, give back to the mission and help the mission that we can from a startup standpoint.
0:05:49 I think for me, I’ve always been a builder.
0:05:52 The thing I loved about computer science in college was building things.
0:05:54 I’m very proud of my service.
0:05:58 In some ways, that was a little bit of a detour on my inevitable path to starting a company.
0:06:00 In fact, when I left the service, I went to law school.
0:06:05 And the reason I never really practiced law was because I figured out pretty quickly that that was not a path to,
0:06:09 or not, it’s really not a direct path to entrepreneurship or the kind of work I wanted to be doing.
0:06:13 So I wound up at Palantir instead in 2013 and spent nine years at that company.
0:06:17 It was not among the first hundred employees, but it felt very early stage.
0:06:23 And I got a lot of exposure that I think helped me on the eventual journey to founding Cape three years ago.
0:06:30 As you look at when you’re in the service and your problems, you work in aerospace management, logistics, and then privacy first, mobile care.
0:06:35 Were those problems you saw while serving, or did that help influence what you founded later?
0:06:36 Absolutely.
0:06:45 So we at RUN are focused on how do we enable military logistics, cross-services, but really on the Army and the Marine Corps side right now, how do we do field logistics?
0:06:46 How do we enable that?
0:06:52 And I think from my career side, we saw that not to say that military logistics can get the job done in the GWAT era, in our era of serving.
0:07:05 But, you know, I saw very quickly that the same things we had in GWAT from a military logistics standpoint were not necessarily to enable the force to be successful in a peer adversary, whether that’s in competition phase or conflict phase.
0:07:10 When Peter was my co-founder at RUN, we were still at Anderil before we left and founded RUN.
0:07:14 We were thinking about this problem setting, saying like, yeah, this is something that actually needs help and needs attention.
0:07:17 No great technology companies necessarily were really focused on it.
0:07:25 And I said, hey, this is a need that I felt still serving when I was serving in the military, and then this is a need that we think we can help advance now from the private sector.
0:07:38 When I was in AFRL, it was very, very early in the era of DOD thinking about drones on the battlefield and thinking about especially small, inexpensive drones, which at that time, we’re talking like 07, 08 kind of era.
0:07:45 Expensive drones on the battlefield were like, oh, wow, a $100,000 drone, that’s so inexpensive, or $50,000.
0:07:51 Whereas now, what anybody can buy off the shelf for $1,000 or $500 is greater than any capabilities we had at that time.
0:08:02 But at that point, the DOD was first starting to think about what does it really mean when adversaries can bring low-cost capabilities to the battlefield, adversaries who may not have a traditional air force.
0:08:19 And what I saw early on in those initial tests and those initial developments was kind of a disconnect of the old-school, big, heavy military systems trying to be thrown at these small, light, fast threats that were kind of a totally new challenge.
0:08:25 And I saw how difficult it was for this traditional structure and the traditional defense contractors to actually do that.
0:08:36 We saw a lot of big systems, missiles and lasers and all of these things, which were big, expensive, bespoke systems built for a prior era of warfare, trying to be thrown at this small problem.
0:08:38 And frankly, in the beginning, they just didn’t work at all.
0:08:43 And I think that’s when the DOD first started to realize that it was a totally new threat.
0:08:43 It was a totally new thing.
0:08:52 And it’s something we’ve seen borne out since then in pretty much every conflict since then, you know, certainly in Ukraine, where we’ve got tens of thousands of drones flying around all the time.
0:08:59 And so I think that’s what really inspired me and a lot of the folks on our team to do something about that and to try to think about it in a new way.
0:09:03 As you brought up Ukraine, I think, obviously, that’s changed the whole paradigm in your space.
0:09:04 And then also you.
0:09:06 I think, frankly, I would say.
0:09:07 Oh, three, actually.
0:09:22 I don’t want to speak for John, but I think all of us, right, as we look at what has happened in the Russia-Ukraine conflict, right, the new technologies that have been effectively used or ineffectively used by both sides and how that changes things, right, from a logistics standpoint, certainly from a U.S. standpoint, from a communications standpoint.
0:09:23 Yeah.
0:09:31 I think it’s changed, like, that laboratory, for better or worse, has really certainly impacted the way I think about things and we think about things at Rune, and I’m sure it has for both of you.
0:09:38 I left Palantir in February 2022 to start CAPE, left Palantir on a Friday, checked into that we work on a Monday morning, and the invasion was happening.
0:09:52 The very first question that we asked ourselves at CAPE was, if you recall, commercial cellular was a huge factor, continues to be a huge factor in Ukraine, but was enormous in the initial days as a force multiplier, but also as a risk.
0:09:59 Both sides were using commercial cellular heavily and enjoying benefits of that, but also literally targeting missile strikes against each other based on that signature.
0:10:12 And so the first question we asked ourselves as a company was, what tech do we wish the Ukrainians were using right now, and how do we get that tech into Taiwan ahead of an eventual Chinese invasion that hopefully never happens, but we certainly need to prepare for?
0:10:15 And that’s really been a motivating question for the company from the beginning.
0:10:18 I think if you look at logistics, obviously they stopped outside of Kiev.
0:10:19 Yep.
0:10:21 So the Russians had a few issues there.
0:10:34 As we look at going from veteran to founder, can you jump straight from veteran and found a company, or do you need to stop somewhere in between an Anderol, a Palantir, and know what right looks like, know what culture you need to set?
0:10:36 I’d love to hear your thoughts on that.
0:10:39 I think you can become a founder at any point in your career.
0:10:44 If it’s something you’re going to do and it’s something that’s sort of in your blood, I think that it’s inevitable.
0:10:54 I will say personally, my stint, which turned out to be almost a decade, at Palantir in between service and starting a company, I learned a ton.
0:11:09 The culture at Palantir, I think, is borderline legendary at this point, and the reputation is warranted, but also the mechanics of how to operate in a company, what does an innovative company look like, fostering innovation and allowing a little bit of chaos and a little bit of things to bubble up within the stew, but without the wheels coming off.
0:11:12 Those are important lessons for a founder to learn.
0:11:18 You can learn them the hard way by just start a company and go do it, or maybe you can have some training wheels for a few years at a Palantir in Anderol.
0:11:29 I would say my experience at Palantir certainly was helpful and I think has helped set us up for success in some ways, but ultimately there’s no way to learn how to do this job other than to just do it.
0:11:30 Right.
0:11:34 Yeah, I would say I think it’s a very individual and a very personal question, like what are your life experiences, right?
0:11:39 I think we all come out of the military with a great set of leadership skills, a great set of communication skills.
0:11:42 We understand we have some domain expertise, right, in what we’re doing.
0:11:50 But I think it’s really boils down to, again, to use the toolkit analogy of like how strong do you as a veteran feel coming out of this and committed to it?
0:11:52 And do you think you have the skills to be able to do it?
0:11:58 To John’s point, I think for me personally, I wanted to get out in the time that I had in finance and then the time that I had at Anderol.
0:12:00 Very similar to like the Palantir ethos there.
0:12:03 How do you move fast and break things, but that’s okay to get towards a mission and do those things.
0:12:07 To me, that is super valuable experience as I now lead a team and lead the company.
0:12:11 But that’s not to say that we have different paths to get here.
0:12:12 I mean, all three of us have different paths.
0:12:17 That doesn’t mean you might not have had different experiences prior to service, right, before serving or coming out.
0:12:22 And I think that’s a very individual, I hate to be a cop out on it, but I think it’s a very individual choice to make.
0:12:24 I would agree with that.
0:12:28 I would say for me, when I left the Air Force, I went to grad school after that.
0:12:36 And I think having some distance from it is helpful, whatever that distance is, whether that’s a big corporation or doing something entirely different or grad school.
0:12:42 I think having a little bit of distance, seeing a little bit of a different set of culture or a different set of problem sets.
0:12:54 Also, you know, it helps you maybe in some ways distance yourself from some of the frustrations of the government bureaucracy and the military and all of that to then kind of refresh yourself to go face those challenges and jump back into it.
0:13:06 But I think also just trying to figure out how you should do things differently out in the private sector, out in the commercial sector, how you can take the good parts of the military, of government service and apply them.
0:13:13 Take the good parts, ignore the bad, drop the bureaucracy bits that are not helpful to anybody and figure out how you can benefit from it.
0:13:18 But I think there’s a lot of good things to take from it, especially a lot of the kind of mission-driven focus.
0:13:27 We’ve had lots of folks on our team over the years who have been prior military or prior intelligence, and there’s no replacement for that mission-driven focus.
0:13:30 There’s no replacement for that actual commitment to making a difference.
0:13:33 Matt, where do you come down on that question?
0:13:35 So that’s a good question, and we debate that internally.
0:13:41 Personally, I transitioned out, and I was part of Lehman Brothers’ 08 MBA class, which went really well.
0:13:42 I was there 22 years.
0:13:44 Probably not technically your fault, though.
0:13:45 That’s not your fault.
0:13:45 No, it wasn’t my fault.
0:13:47 Me and Dick Folder were really tight.
0:13:50 But I think it helps to go to somewhere and see what right looks like.
0:13:57 For me personally, especially Anderol, Palantir, SpaceX, and then Tesla, just some companies that are producing these founders.
0:13:58 I think it really helps.
0:14:01 But there’s obviously folks that can make that jump.
0:14:03 So I think the answer is either.
0:14:06 But for me personally, I would want to stop along the way.
0:14:08 You made a great point as well that I think is important.
0:14:12 I think you always have to be careful that you don’t want to just create the thing that you thought you knew.
0:14:14 needed when you were in the military, right?
0:14:16 And I think that is sometimes a trap, right?
0:14:17 We all need to use our experience to do that.
0:14:22 But when I talk to folks about that, if you try to build the thing that you thought you needed,
0:14:28 every month that goes by, every year that goes by, what you thought you needed then is now out of date and it’s stale, right?
0:14:34 So especially as you lead a company, and I’m sure Sean can talk about it too, is you need to be constantly thinking forward,
0:14:42 not just creating the thing that you thought you needed in the Argandab River Valley in 2011 or 12 or in Herat or wherever it happened to be.
0:14:42 Yeah, I agree.
0:14:44 And we went 4G to 5G.
0:14:44 Let’s go to 10G.
0:14:46 Why stop at 4?
0:14:50 Or we built the Hornet, which is a Category 4 fighter, and we built the Super Hornet with Category 4+.
0:14:52 Skip that like the Marine Corps did.
0:14:57 I think that’s the essential skill for a founder, is that mindset.
0:14:59 In my opinion, you have that or you don’t.
0:15:00 That’s part of who you are or it’s not.
0:15:07 There are other skills that support that overall mindset, that insistence on way forward thinking, ambitious goal setting.
0:15:10 And you can learn those skills, certainly in the military.
0:15:12 You can learn those skills at great companies like the ones you mentioned.
0:15:17 So they’re supporting skills you can learn, but the core, that core mindset, I think, is just innate.
0:15:22 I don’t know your founding stories necessarily, specifically, but finding the right co-founder was important.
0:15:23 For me, right?
0:15:26 So finding the right software engineer, technical co-founder, right?
0:15:29 So Peter is my co-founder and CTO.
0:15:32 Like, I don’t know how I would have done that straight coming out of service, right?
0:15:35 So I needed to go to a place, thankfully a great place like Anderil,
0:15:39 where we’re surrounded by those types of people to be able to find that partner.
0:15:43 And now he and I, our Venn diagrams don’t overlap that much, which is great in a co-founding pair.
0:15:46 Let’s pull in a thread that Grant brought up, and let’s go culture.
0:15:47 You might have some good responses here.
0:15:50 Culture in the military, culture in startup land.
0:15:54 And then you could even add finance, city, which is very different.
0:15:54 So that’s a third.
0:15:59 But let’s focus on military and then startup land differences and similarities.
0:16:06 I think I probably have a unique perspective from the military side by virtue of being an 18x-ray
0:16:10 and only ever experiencing the special operations community in my military service.
0:16:16 That is a naturally more meritocratic, flatter organization, I think, than the rest of the DoD.
0:16:19 Having said that, there’s still a rank structure.
0:16:20 There’s still centralized planning.
0:16:26 There’s still a very clear hierarchy and a very clear set of doctrine and a book that literally tells you how to do your job.
0:16:30 Even in the context of special operations where you’re granted a lot more creativity and a lot more autonomy,
0:16:32 that structure still exists.
0:16:36 What is amazing about a startup is none of that exists.
0:16:41 You know, Monday morning, you check into a WeWork, and now you’ve just got a company and a couple bucks in the bank, hopefully.
0:16:43 And there’s literally nothing else.
0:16:44 You don’t have an email address necessarily.
0:16:47 And so you get to write the entire thing yourself.
0:16:50 That can be really disorienting, especially in my experience.
0:16:52 We have a ton of veterans at the company, and they’re all amazing.
0:16:54 And I love to hire veterans.
0:16:56 But that can be disorienting coming out of service.
0:17:02 And so you have to create, in my opinion, a culture around, I think it’s a little bit trite to say celebrating failure,
0:17:06 but at least openly accepting failure when it happens.
0:17:11 If people are working hard and people are taking risks and pushing the envelope and their efforts fail,
0:17:16 at CAPE anyway, the only thing you need to do is be transparent about that and call it out when we fail
0:17:19 and adjust and maybe name a couple of lessons learned for next time.
0:17:22 And it doesn’t do any damage to your reputation at the company or your ability to operate.
0:17:27 I think that’s the most important cultural aspect at a startup because it encourages risk-taking.
0:17:29 It encourages people to lean forward and push hard.
0:17:33 I find that there’s like common threads across all of them.
0:17:38 So when I think of like out of broad buckets, military service, my finance experience, and now startup experience,
0:17:43 there are like broad threads that make somebody or make something successful there,
0:17:46 which is initiative, drive, taking chances and doing those things, right?
0:17:49 Whether that is part of a deal team in investment banking,
0:17:53 whether that was part of a team within the JSOC enterprise or the military writ large,
0:17:55 or now it’s part of a startup, right?
0:17:56 So it’s, yeah, how do you celebrate success?
0:17:58 How do you encourage risk-taking?
0:18:01 I think also I view it as like, how do you hire talent?
0:18:05 But you understand that when that talent comes in, you got to train, you got to mentor, you got to develop, right?
0:18:08 And it’s not necessarily always at a startup, at least from my standpoint, at Rune.
0:18:11 It’s not been hiring just because somebody has a skill.
0:18:17 It’s hiring somebody that I think can grow into a position or do these things or with the right coaching and development can take chances
0:18:20 and maybe fail, as John says, but like you move on from that.
0:18:23 So I think there are like commonalities across all of these.
0:18:28 Obviously, at the startup, you don’t have the structure in place and the support structure that’s in place.
0:18:31 It’s certainly at a large company, but also somewhat in the military side.
0:18:35 So there’s some differences there, but I think from a human standpoint and a culture standpoint,
0:18:39 you can have some threads of success that are common across those.
0:18:44 Probably for most of our companies, it’s not just about government or military.
0:18:45 Everything is dual use.
0:18:48 Technology crosses a lot of different industries.
0:18:52 For us, it’s not just the kind of like military and federal market.
0:18:57 It’s also critical infrastructure, oil and gas, power companies, all of these large organizations
0:19:02 that face a lot of the same challenges that military or federal government face.
0:19:07 I think for us, it’s about balancing that culture and trying to figure out how to take the really mission-driven
0:19:11 and what that really translates to is customer-driven, user-driven.
0:19:13 I think there’s commonality there.
0:19:18 Not an over-obsession with the kind of military portion of it, but taking the part about it
0:19:23 about what are we trying to accomplish and how are we trying to help these people, these organizations.
0:19:25 Yeah, you mentioned dual use.
0:19:28 And first of all, I’d love you to define, are you dual use?
0:19:30 Would you consider all three of you dual use or not?
0:19:36 And then any cultural issues with that, bringing folks on board on the national defense side?
0:19:40 Number one, I would say we’re definitely dual use because kind of threats and concerns
0:19:46 about drones in the national airspace, in critical areas, absolutely cut across all sorts of different areas.
0:19:52 Whether it’s an Air Force base or a power plant or border security, everyone is concerned.
0:19:56 And not just because they’re concerned about just malicious drones, it’s about being able
0:20:00 to use drones for good things and use drones for inspecting infrastructure and doing logistics
0:20:01 and all of these things.
0:20:06 But I think in that, you do need to make sure that you’re building a culture that understands
0:20:09 why you’re there and what your values are and what you care about.
0:20:12 In our world, right, we’re going to end up dealing with serious stuff.
0:20:17 And you can’t just have people who wanted to build iPhone games and so they just happen
0:20:17 to get a job.
0:20:20 They’re like, no, you have to understand we’re here to do serious stuff.
0:20:26 We had folks actually go over and assist the Ukrainians and provide support and services
0:20:28 and technology to them.
0:20:33 You can’t have a company where people are split on what they care about and what those foundational
0:20:33 values are.
0:20:35 You have to be all on board.
0:20:39 Yeah, there’s some great quotes out there by Palmer Luckey talking about taking technology
0:20:43 talent from the commercial sector and how do you convince them to come into the defense
0:20:43 world.
0:20:45 And I will say that, frankly, it’s not that hard.
0:20:46 Do you want to do something meaningful?
0:20:47 Do you want to do meaningful work?
0:20:51 And I think that’s the genius behind a lot of the American Dynamism companies, to include,
0:20:54 I would say, all three of ours, is like, how do you bring that amazing technical talent
0:20:58 and those amazing software engineers or hardware engineers, the case may be, and bring them
0:21:03 to something like, hey, do you want to do something meaningful for the nation, for the people and
0:21:04 do those types of things?
0:21:07 And how do you fuse that together with those of us who have come out of the military and
0:21:08 do those sorts of things?
0:21:12 I don’t want to say it’s easy, but it’s not as hard as people would think to get them motivated
0:21:16 about doing what’s right for the country, doing what’s right for our people and to do those
0:21:17 sorts of things.
0:21:21 And I’ll also add, I think what really sorts people out on whether or not they want to
0:21:26 join a company that does these sorts of work and these sorts of missions is you show them
0:21:27 what you’re doing, right?
0:21:30 Nothing sorts that out faster than real-world examples.
0:21:36 When we show drones in places that are being run by prison gangs to smuggle fentanyl into
0:21:40 prisons or in military environments, whatever, it’s very clear what the stakes are, and it’s
0:21:41 very clear why it’s important.
0:21:46 And for those people who it resonates, it’s instantaneous, and it’s, wow, I want to be working on this.
0:21:48 And for those, it doesn’t, great.
0:21:49 Like, that’s fine.
0:21:52 There’s plenty of iPhone game companies to go join, and that’s great.
0:21:57 I think one of the things I really love about Cape and the company we’ve built is we really
0:22:03 are truly dual-use, and we’re dual-use in some sense defense and consumer, much in the same
0:22:04 way that Signal is dual-use, right?
0:22:08 It’s like Signal is used by privacy-preferring people all around the world doing all kinds
0:22:08 of stuff.
0:22:13 And also everyone here at the Summit, everyone on Capitol Hill has Signal installed on their
0:22:13 phone.
0:22:15 It’s very valuable technology.
0:22:19 I’m personally very motivated, and the company is very motivated by building this tech to
0:22:24 keep folks doing special operations work safe and allowing them access to commercial cellular
0:22:26 without incurring all the risk that typically comes with that.
0:22:30 It’s the same set of issues, and frankly, everyone has the same use case for their phone.
0:22:32 We have customers who are journalists.
0:22:36 We have customers who are survivors of domestic violence.
0:22:41 We do work with the Electronic Frontiers Foundation on detecting cell-size simulators at protests
0:22:42 and at demonstrations.
0:22:46 All those people have the same set of issues and the same desire to leverage the value of
0:22:50 the commercial cellular network without making that typically inherent set of compromises.
0:22:54 And you ask a really good question, which is, does that change the kind of people you can
0:22:55 attract, or how does that affect recruiting?
0:23:00 I think at Cape anyway, first of all, I’m like, I’m incredibly proud of the team that we’ve built,
0:23:02 and so I’m biased here.
0:23:06 But you just have, you have to work hard to find people who embrace both of those missions
0:23:07 simultaneously.
0:23:11 But when you find them, they just tend to be awesome, awesome folks, and the kind of people
0:23:13 that I really like to work with, and the people I want to work with.
0:23:18 One thing I’ll just say, tactically, that we’ve done internally that I really believe in is
0:23:24 we have not split the business into commercial and defense divisions, for lack of a better
0:23:24 word, right?
0:23:29 There’s an engineering team, and the engineering team, we all agree on a set of priorities that
0:23:32 serve the entire customer base, and I think that’s worked out really well.
0:23:34 We’re building one product, and it really serves both purposes.
0:23:35 That’s great.
0:23:38 You’re all busy building your own companies.
0:23:43 When you served, did you see any gaps there that you wish people would start looking to
0:23:44 build companies?
0:23:45 Any requests for startups?
0:23:47 It’s a big question.
0:23:49 This is such an unfair question.
0:23:53 I realize now that I ask every government person I talk to, if you have the magic technical
0:23:55 wand, what would you wave it and create?
0:23:57 Who, height or weight, body armor, and kit?
0:24:01 Give me all that stuff that I need to carry around and make it weigh 30% of what it weighs.
0:24:02 80 pounds of gear in your back.
0:24:03 Yeah, yeah.
0:24:03 Right, right, right.
0:24:05 Make it 25 pounds and make it less cumbersome.
0:24:08 I mean, I was actually just having a conversation this morning.
0:24:13 As we think about logistics and how do you enable autonomous logistics systems, maritime,
0:24:14 assets, ground assets.
0:24:19 One of the things, though, that we’re interested in is heavy lift aerial resupply platforms,
0:24:19 right?
0:24:21 Which is a hard problem, right?
0:24:23 Autonomous air vehicles, obviously a thing.
0:24:27 UASs, how do you actually lift substantial cargo with that?
0:24:33 As we think about driving the automation of military logistics or through autonomous vehicles, right?
0:24:36 Getting an air system that actually can carry the weight for that is something that’s like
0:24:39 particularly interesting to me and is a very hard thing to do.
0:24:48 Part of the thing with both military and government now is the challenges they face are less kind of hardware centric than they used to be.
0:24:51 It’s not about owning a particular set of kit.
0:24:57 A lot of it is about data and data sharing and being able to have the correct information to make decisions.
0:25:03 A lot of what’s needed now is how does data get shared between organizations.
0:25:12 So for us, right, in the drone tracking world, we actually shifted business models a lot over the last few years from what was initially traditional hardware sales,
0:25:15 building the fancy piece of military hardware and selling it.
0:25:19 And pretty much as soon as it goes out the door, you kind of never see it again, mostly if things go well.
0:25:28 But what we saw was that the need really was for the information to make those decisions to take action and that it wasn’t about having a fancy piece of hardware.
0:25:36 But I think changing the way that the government looks at that and the way that they buy things and the way that they’re able to actually share information, that’s the hard part.
0:25:46 And part of dual use and part of the cross problems between government agencies and private companies is being able to share information between those two.
0:25:54 It doesn’t do the power plant any good if the federal law enforcement agency next door can’t share with them the threats that are happening.
0:26:04 So I think anything that encourages and enables data sharing between organizations with government is super, super valuable and definitely needed in a lot of these cases.
0:26:05 You touched on hardware, software.
0:26:09 As we look at the future of war, we don’t want to be fighting the last war.
0:26:10 Obviously, we want to fight the next war.
0:26:15 We look at Ukraine, perfect test case for all three of your companies there.
0:26:22 Do you think we have the right weapons, whether it’s hardware, software, or do we need to be looking out further and making a bigger leap?
0:26:26 Do we need different types of programs, vice trillion-dollar programs?
0:26:34 I think that maybe the common thread to pull out as we think about the next war is an emphasis on asymmetric capabilities.
0:26:39 A lot of attention is being paid, I know, rightfully, to the need for low-cost achievable systems, right?
0:26:44 That’s, I think, there’s a growing consensus that’s the future of warfare in a lot of ways.
0:26:52 I think, although it’s certainly no secret, people don’t always contextualize cyber warfare in the same way as an asymmetric capability.
0:26:56 Relatively low amounts of investment can cost huge amounts of damage in an adversary.
0:27:01 That’s sort of where CATE plays, is in the cyber vulnerabilities in the global cell network.
0:27:04 But in general, that’s the theme to pay attention to.
0:27:10 Rather than investing in trillion-dollar bets on absolute dominance in a relatively small number of exquisite systems,
0:27:15 how do we win the scrappy, messy, asymmetric war that’s sort of inevitable next time around?
0:27:18 Do we feel we have the things now?
0:27:19 I mean, no.
0:27:23 I mean, that’s why we started, or at least in the middle of the time, that’s why all of us started our companies, right?
0:27:28 Is we feel like there is something there that we can give back to the military that doesn’t exist right now,
0:27:30 or we can do it better, or we can help enable operations.
0:27:33 One of the things we need to look at is, how do we do these things faster, right?
0:27:35 At least as a software company, right?
0:27:42 The idea of a 20-month prototyping effort to potentially get to something that then maybe a year later is going to transition.
0:27:44 I’m using that as a very specific example right now.
0:27:46 It’s just, it’s absolutely insane.
0:27:49 Would be insane to the commercial sector, right?
0:27:51 Is insane when you think about software development cycles.
0:27:57 So I think the ability to rapidly prototype things, develop them, get them out to the force, test, iterate with them,
0:28:00 and then frankly for the government to say, yes, that worked, great, cool, move on.
0:28:02 No, it didn’t, and off-ramp.
0:28:07 I think that’s what we are totally on board with, is the ability for the government to force us to perform,
0:28:09 to compete, to do things for the warfighter.
0:28:11 And if we are doing great, then awesome.
0:28:15 And if not, then off-board us and bring somebody else in, right?
0:28:20 And I think how you do those things, I think, as taxpayers, as veterans, and as founders,
0:28:26 we should want that level of competition and that cutthroat competition to onboard things quickly and off-board things quickly.
0:28:30 And I think that’s finally, the administration’s obviously moving towards that way, that direction.
0:28:33 And I think that’s the goodness for the force and the goodness for the nation to move that way.
0:28:38 I think a lot of attention has rightfully been invested in, how do we onboard things quickly?
0:28:42 You know, the resurgence to OTAs is the most visible example of this, and it’s great.
0:28:44 DIU’s doing amazing work.
0:28:49 I think there’s sneaky work to be done on killing stuff that’s not working still.
0:28:51 And there are big, high-profile, very expensive examples.
0:28:55 But also within the innovation ecosystem, just call it when you see it.
0:28:58 And if something’s not working and something’s not going to pan out, just kill it.
0:29:03 You do everybody a favor, including the folks at that company, if you say, this isn’t working, and you cut it off.
0:29:08 Yeah, the worst thing, like you have a zombie program that just goes on with millions or tens of millions of dollars,
0:29:11 or sometimes even more of that, that’s not being used by warfighters.
0:29:14 I think to John’s point, like, you’re not doing the service any favors.
0:29:15 You’re not doing the warfighter any favors.
0:29:16 Definitely not the taxpayer.
0:29:22 And frankly, you’re not doing that company any favors because you’re putting them into a false sense of that solution is useful,
0:29:27 and you’re stifling potentially that company to, like, reinvent itself or go forward and do something new on that.
0:29:28 So I think you’re right.
0:29:30 I think off-boarding is just as important as onboarding.
0:29:35 Yeah, I think one of the gaps right now is, like you said, DIU’s done a lot of great stuff.
0:29:42 A lot of different efforts have been done to try to get early-stage startups involved in those first government contracts,
0:29:44 that kind of $1 million phase, $2 million phase.
0:29:50 But we still have that gap, that kind of valley of death, of how do we take any of those successful programs
0:29:53 and scale them up to that mid-size level?
0:29:57 Not everything is going to jump from a $1 million to a $200 million contract.
0:30:01 How do we scale them up, get them out to the forces, get them out to the services,
0:30:03 and see the potential once you actually scale it up?
0:30:11 Because I think that’s one of the challenges is, it’s easy to deploy a couple systems in a little POC and see,
0:30:12 okay, well, that’s pretty useful.
0:30:16 But a lot of the types of things we’re talking about, you don’t really see the real value of them
0:30:19 until you have them at some sort of scale.
0:30:22 If I’m talking about monitoring the airspace and looking at all the drones,
0:30:27 it’s great at the kind of tactical level to see, oh, we’ve got a drone crossing into this airbase here.
0:30:34 But as soon as you scale it up to the larger scale of saying, oh, well, now we can see patterns of activity.
0:30:39 We can see that, oh, that’s funny, this drone showed up at three different bases over time.
0:30:44 And we can do that only because we’ve started to build that out at a not large scale.
0:30:49 We’re not fully committing to everything, but we can see the potential of it at a kind of mid-size scale, larger scale.
0:30:51 And I think that’s a missing piece that we have.
0:30:55 How do you scale it up to actually try it in the real world at a scale that matters?
0:30:57 Yeah, you brought up programs.
0:31:02 CIA’s got In-Q-Tel, DIU’s for the DoD, not only programs, but how to select the best tech.
0:31:05 And obviously, we think you three are all the best.
0:31:06 You’re our portfolio companies.
0:31:14 But you need to build that market map, find out the category leader, because it’s going to go 90% leader, 10%, everybody else gets steak knives.
0:31:17 So we need to help them select the best tech also.
0:31:22 And I think one of the challenges on the government side with that, especially on the DoD side,
0:31:29 is because people and officers rotate out so frequently, you run into situations where somebody started out a really good program.
0:31:31 They started to see the benefits of it.
0:31:34 And then a brand new person walks in the door the next day and says, OK, what are we doing?
0:31:36 And you’re starting from scratch.
0:31:38 And they don’t really understand why were the decisions made?
0:31:39 What was the intent?
0:31:47 And so I think there needs to be a little bit of a better focus on continuity of ushering these programs across those eras and understanding why they’re doing them.
0:31:51 Military units are renowned for their trust and confidence, their cohesion.
0:31:53 We all saw that, especially you and the Special Forces.
0:31:57 How do you instill that in your companies?
0:32:00 How do you build that teamwork, that trust, confidence, cohesion?
0:32:05 We’re an in-person company, which I don’t think is the final answer, but really helps.
0:32:07 Proximity matters, in my opinion.
0:32:13 When people are in the office working together, you get a lot of that more easily than you do in a remote configuration.
0:32:20 The key ingredient, the thing you’re really trying to build that the military has in space, you really want at your startup,
0:32:27 you want to know when you need someone on your team that they’re there and they’re picking up on the first ring.
0:32:30 I think that’s the most important thing in life, certainly in company building.
0:32:37 And so my barometer for this within the company is when someone, it just happened this weekend,
0:32:42 we had a PZERO with someone who was traveling abroad in support of a customer and was having an issue,
0:32:46 and they spun up an internal thread, and immediately seven people were on the thread,
0:32:49 and it was the right seven people, and they were all helping to troubleshoot,
0:32:51 and within 20 minutes they had the guy turned around and good to go.
0:32:57 And that sort of mindset, no one complained about it, and we didn’t even mention it.
0:33:00 On Monday morning, it was just, that’s how we do business at Cape, right?
0:33:06 And that’s where you want to get, in my opinion, and the way you do that, to quote Ben Horowitz,
0:33:07 culture is what you do, it’s not what you say.
0:33:11 And so as the founder, your job is to be on every single one of those Slack threads,
0:33:12 especially in the early days, right?
0:33:15 And to be at the office every single day and to be working on those things.
0:33:17 And people will follow your lead.
0:33:21 And then if you do it enough, and you do it over and over and over again, it becomes part of the culture.
0:33:22 I’ll amplify that.
0:33:27 The collaboration that happens across engineering, product, growth, company leadership, all in one place.
0:33:31 I can’t imagine having not had that when we first opened our first initial hires,
0:33:34 because your first hires are really also building the culture with you.
0:33:38 Yes, it’s top-down from founders and from the executive level,
0:33:40 but like those initial hires are part of that culture for us.
0:33:42 And then I think it’s the normal things.
0:33:44 After work, during Sun-Fridays, we do a happy hour.
0:33:46 Every single Friday, we go to the same bar.
0:33:48 But it’s little things like that.
0:33:49 I mean, what does it cost?
0:33:50 It costs us an hour of time.
0:33:52 We cut out a little bit early on a Friday,
0:33:54 but I also know that everyone’s working generally right now.
0:33:56 We’re working Saturdays over the weekend.
0:33:59 You know, in one day, let’s presume success, and we have thousands of employees.
0:34:01 I don’t think I can still fit in the same bar.
0:34:03 But right now, that’s important, right?
0:34:05 It’s important to know that, especially as a software company,
0:34:07 where my capital is my people, right?
0:34:10 It is my engineers, and it is my growth professionals.
0:34:12 And that’s really what we need to grow and build.
0:34:17 I think the biggest thing for us has been actually recognizing and calling out
0:34:19 real-world impacts of the things we do.
0:34:24 I think, especially initially, we were actually very bad at celebrating our successes
0:34:25 and calling attention to it.
0:34:29 I think part of that is because so many of our folks are very mission-driven
0:34:31 and very like, okay, we got to do this thing.
0:34:35 And so you accomplish something, and you never even take the time to notice.
0:34:40 And I think that is one of the good things of having that mix in the culture
0:34:46 of kind of the mission-driven military and intelligence people versus the normal people
0:34:48 is sometimes the normal people kind of step back and they’re like,
0:34:50 holy shit, like, what did we just do?
0:34:51 That’s amazing.
0:34:52 That’s incredible.
0:34:54 And there’s plenty of other people who are just like,
0:34:55 oh, well, yeah, we do this all the time.
0:34:56 It’s no big deal.
0:35:00 But actually stepping back and recognizing that and celebrating those wins,
0:35:04 I think that’s part of what really builds that culture and that cohesion.
0:35:08 And then that builds a culture in which when things are going bad,
0:35:10 when things are going wrong, which are always going to happen,
0:35:12 there’s always going to be new things popping up.
0:35:16 Suddenly, that’s a culture where everyone understands why they’re like,
0:35:18 okay, cool, let’s pull together a team.
0:35:18 Let’s hop on a flight.
0:35:21 Let’s go fix this thing, do this thing right now.
0:35:25 And everybody’s on board because they understand why they’re doing it
0:35:25 and what the stakes are.
0:35:27 All right, big question here.
0:35:29 Take your time before you answer.
0:35:32 Would you ever go back to the military,
0:35:35 either in a leadership, advisory, or an innovation role?
0:35:38 I think innovation roles are really interesting.
0:35:40 There are so many of them across government.
0:35:43 And without picking on any in particular,
0:35:44 a lot of them never get anything done.
0:35:46 So I know internally,
0:35:49 someday if I’m lucky enough to be approached about that sort of a job,
0:35:51 I’m going to have a list of demands.
0:35:52 Here are the ways I need to be empowered.
0:35:56 Here’s the authorities that I need so that I can actually make a real impact
0:35:57 and be empowered.
0:35:59 But given the right set of conditions, I would love it.
0:36:02 That would be a defining career milestone just like starting a company.
0:36:05 This is a little bit different for me because I am still serving
0:36:07 in the Army National Guard in that way.
0:36:09 And I love leading soldiers and I love being around soldiers.
0:36:13 But I think going back into like full-time some kind of role or governmental role,
0:36:16 if I am successful to the point that I’m offered that opportunity at some point,
0:36:16 same as John,
0:36:18 yeah, I think that would be an honor of a lifetime to go back.
0:36:19 I mean, I think we were all,
0:36:23 yes, we are doing these to grow companies and to do those things.
0:36:25 And I’m not ashamed to say we are here to make some level of money.
0:36:28 I mean, that is what we do as companies.
0:36:30 But there is a selfless service aspect to that.
0:36:34 And if that selfless service could eventually be back in a governmental role
0:36:37 and helping the mission, if I believe I can make an impact and be helpful there,
0:36:39 then yeah, that would absolutely be something I would do.
0:36:41 Yeah, I totally agree with that.
0:36:43 I think being able to know that you can have an impact
0:36:46 and being able to know that you actually have the authority
0:36:50 and the ability to go and do the things that need to be done as you see them.
0:36:52 Coming from the Air Force acquisition side,
0:36:55 I have strong opinions about what’s needed in that world
0:36:57 and acquisition reform and whatnot.
0:37:00 But one request, one thing that I know I would have
0:37:02 that I didn’t realize until I knew it was an option.
0:37:05 A number of years ago, I was talking with the Secretary of the Air Force
0:37:10 and she said, wow, we need a lot more folks like you coming back into service.
0:37:12 And I said, well, I don’t really know.
0:37:14 And she said, well, I can get you a beard waiver
0:37:16 so you wouldn’t have to shave your beard.
0:37:17 I’m like, okay, now you’re talking.
0:37:20 That’s my, gotta have a beard waiver.
0:37:21 That’s nice beard.
0:37:22 There you go.
0:37:22 Done.
0:37:26 Now, if you made it this far,
0:37:28 a reminder that this was recorded live
0:37:30 at our third annual American Dynamism Summit
0:37:32 in the heart of Washington, D.C.
0:37:35 And if you’d like to see more exclusive content from the Summit,
0:37:38 head on over to a16z.com
0:37:41 slash American dash Dynamism dash Summit.
0:37:43 Or you can click the link in our description.

0:00:07 soon enough high schoolers will be donning those caps and gowns but what comes next is less of a
0:00:12 sure thing than it was a decade ago students are genuinely questioning if college is worth it and
0:00:17 if college is really the right thing for them knowing what they know about themselves this week
0:00:22 on explain it to me a look at the new range of alternatives to college and how some high schools
0:00:28 are setting up their graduates for success new episodes on sunday mornings wherever you get your
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0:01:53 welcome to office hours with prop g where we answer your questions on business big tech entrepreneurship and
0:01:58 whatever else is on your mind today we’ve got two great listener questions then after the break we’ll hit
0:02:04 the reddit hotline pulling questions straight from reddit want to be featured send a voice recording to
0:02:10 officehours of propgmedia.com or post on the scott galloway subreddit let’s get into it first question
0:02:17 hey scott ria here calling from new york city thanks so much for all you do really appreciate
0:02:22 the clear-eyed level-headed advice that you give my question to you is about the non-profit sector
0:02:27 which i’ve not heard you speak about i know you’re primarily focused on tech and for-profit businesses
0:02:33 but my question scott is that given the threats to federal funding eroding donor trust declining
0:02:40 philanthropy overall shifting foundation priorities ai disruption and the increased demand for services
0:02:45 to support the most vulnerable among us many of whom are being targeted by the trump administration as
0:02:51 you rightly point out what are your predictions for the non-profit sector and what strategic advice
0:02:58 do you have for non-profit leaders to survive and thrive thanks again whoa so i’m not sure i’m the person
0:03:04 here um religion and non-profits are two of my million achilles heel and that is i don’t know
0:03:10 that much about them so in january the trump administration enacted a funding freeze which
0:03:15 affected roughly three trillion dollars of federal grants and loans um including i don’t know if you’ve
0:03:18 seen but there’s been a lot of money that is not only going to non-profits but the educational
0:03:23 institutions although this freeze was blocked by a judge the white house has repeatedly threatened future
0:03:29 funding for non-profit working in the fields of lgbtq plus rights climate change immigration reproductive
0:03:37 rights dei and more jesus christ really additionally many of the philanthropic sector believe we’re going
0:03:42 through a generosity crisis as few americans less than ever are making donations less than half of
0:03:50 american households now give cash to charity 20 million fewer households donated in 2016 than in 2000
0:03:56 what is going on here because if you look globally people are globally spending more time helping other
0:04:01 people they won’t ever meet that’s a nice thing right but there’s something going on around a lack
0:04:06 of philanthropic mindset is it because we’re going to church less i don’t know what it is is it because
0:04:11 people’s prosperity is not what they’d hoped and their quality of life is going down because of
0:04:17 inflation and they just need to hold on to money is it because of social media algorithms pit
0:04:22 us against each other so we’re less inclined to trust institutions trust others and give money away
0:04:28 that we don’t have the same comity a man i don’t know uh where can non-profits look to fill these gaps
0:04:36 the ultra wealthy in 2024 billionaire wealth surged by two trillion dollars growing three times faster than
0:04:41 the prior year with nearly four new billionaires minted weekly there are now 10 times more billionaires
0:04:48 than in 1990 think about that there’s no demographic group not latinos not old people in the last 34
0:04:55 years the number of billionaires has increased tenfold so i work with a lot of universities and they have
0:05:01 very robust development departments and typically development departments are filled with very high
0:05:06 eq late 30s early 40s women who have kids at home and we’re working in the corporate sector and they need
0:05:13 more flexibility and they go to work in development for university and they just try and just a manicure
0:05:20 shepherd and develop and mature these relationships with really rich people or specifically really rich alum
0:05:24 and they’re very good at what they do but they invest long term in these relationships
0:05:30 and generally speaking they want to get the reason why they raise small amounts of money i just participated in the
0:05:37 one day giving thing at nyu stern once you give you’re invested and you’re more inclined to give
0:05:40 more and what they’re hoping the reason why they want to raise a hundred two hundred dollars a thousand
0:05:45 dollars from an alum it probably takes more time to raise the money it probably costs more money to
0:05:50 raise the money but once they get you to give you’re invested you’re comfortable giving you’re much more
0:05:57 likely to make a bigger cash donation sometime in the future and then where they make their money or how they
0:06:04 kind of butter their bread is that they occasionally find a whale who makes a huge gift and that is kind
0:06:09 of everything for non-profits and these institutions i’ll use scott harrison as an example scott who runs
0:06:17 charity water he is so good at establishing relationships with people one he’s just a people person who’s good
0:06:23 at it he was a club promoter and the guy i’ve known the guy for i think 25 years we shared office space
0:06:28 together i always gave him a little bit of money and he’s just always done a really good job curing or
0:06:34 manicuring evolving uh his personal relationship with me and i’ve given you know six figures seven
0:06:39 figures to scott i don’t know anyways and quite frankly i’m not passionate about bringing potable
0:06:43 water to africa sub-saharan africa it’s just not something i don’t say i don’t care about it i think
0:06:49 it’s wonderful the reason i do it is i i’m invested in scott i know that his non-profit is so well
0:06:54 run and so innovative that those dollars are being put to good use i don’t know if i have a lot of
0:07:00 inside here what i will say is that i appreciate the question and it’s one thing to cut funding to
0:07:04 non-profits because you think that maybe they’re not spending it as efficiently as they should be
0:07:09 what is most depraved about all of this is having a political bent on it and that is cutting money
0:07:15 to non-profits because of a certain political leaning i just find that i don’t know flag of a better term
0:07:23 gross thanks for the question question number two hi scott my name’s joe i’m 21 years old and i’m a
0:07:28 senior in college about to graduate this april with a bachelor’s in marketing i discovered your book the
0:07:33 algebra of happiness in 2023 and ever since then your content has really helped to change the trajectory
0:07:38 of my life your career advice helped me land a great internship that turned into a competitive
0:07:42 full-time job offer you helped motivate me to get in the best physical shape of my life
0:07:46 but most importantly your advice surrounding relationships with people greatly strengthened
0:07:51 my own relationships with my friends and family while i’m a big fan of your professional and political
0:07:56 works your advocacy for young men is what resonates with me the most i’m about to start the next chapter
0:08:01 of my life and i’m fortunate enough to be moving to a big city but my parents friends and girlfriend
0:08:06 will go from a quick walk or drive to an expensive plane ride i’m grateful for my situation but i’m also
0:08:11 slightly terrified how did you deal with experiencing true independence for the first time and do you have
0:08:15 any advice for maintaining a healthy mental state while dealing with the loneliness that will likely
0:08:20 accompany the independence please keep advocating for young men your message is working and if you’re
0:08:26 ever in boston i’d love to buy you a drink thanks uh what a thoughtful question so joe from boston first
0:08:31 off everyone wants to be you right now you’re 21 it sounds like you’re in good shape you have a girlfriend
0:08:38 good relationship with your parents and you’re starting your professional life so well done you should feel
0:08:45 really good about the you know where and when you are right now um so just just what popped into my mind is
0:08:53 it’ll be interesting it’ll be inspiring but but the real world is your first one or two years you’re gonna have
0:08:58 a moment where you go wow this fucking sucks or i didn’t realize that this would suck this much or that this is hard
0:09:03 and that’s exactly where you’re supposed to be and so just realize it’s temporary and that’s part
0:09:07 everyone’s first job or not everyone almost everyone’s first job sucks
0:09:12 my first job was at morgan stanley and fixed income and within about 48 hours i’m like jesus christ this is
0:09:18 awful and it ended up being good for me it was like serving the marines so i don’t know just sort of
0:09:23 expect i don’t want to say hate your first job but have it be not cracked up to what it’s supposed to
0:09:30 be in terms of maintaining a healthy outlook and being mentally fit it sounds like you got sort of
0:09:37 the keys figured out uh that is you’re exercising it sounds like you have good relationships you know
0:09:43 what call your parents every day um stay in close contact with your girlfriend find ways to have her
0:09:51 come visit uh or go visit her i don’t know how far away that is and then just be really open and
0:09:56 thoughtful about saying yes to things as much as you can in your new city such that you establish
0:10:02 relationships there the mistake i made when i first got to new york you know i was so immature i made the
0:10:08 i made the kind of the same mistake i made when i first got to college i couldn’t new york was too
0:10:13 much for me it was just a playground there was something to do every night i’d go out i’d get
0:10:21 fucked up i’d be totally hung over i’d wake up at 8 44 to get to work by like 9 15 try and do some work
0:10:27 and then try and go find a conference room no joke because i was so hung over and i would hide under the
0:10:34 conference room table and i’d sleep for an hour and then i would go get a greasy lunch and then that
0:10:39 and i think okay tonight i’m just going to go home call my mom get some sleep and you know go to bed
0:10:44 really early watch 30 something something like that and go to sleep early and then inevitably that mix
0:10:49 of grease and advil would make me feel just reasonable for an hour and a buddy would call me and say
0:10:54 we’re meeting some models downtown at obar and i’m like i’m in and i’d go do the same goddamn thing
0:11:01 again and i was smart enough to know okay i can’t handle the temptations of new york and i had an
0:11:08 opportunity to go to the la office of morgan stanley so i took it i moved back in with my mom and i kind
0:11:14 of stopped drinking and this is a long parable or way of saying try and tone down and i don’t know if
0:11:22 you’re doing this but i got better at making the requisite commitment to being good at my first job
0:11:28 which sucks and i expect a lot of you at a place like morgan stanley i got good i stopped drinking
0:11:35 and smoking pot for a while and i did work out i went to the ymca or the ywca ymca never was in
0:11:42 downtown los angeles and i worked out not every day but uh and i didn’t have a lot of relationships i had
0:11:47 some friends i used to go out with but my primary relationship was with my mom who i was living with
0:11:53 um and then i had some friends but i didn’t have any girlfriends i had a lot of girlfriends in college
0:11:59 i found that a young man right out of college is just not that attractive that all the women i wanted
0:12:03 to date were dating guys in their 30s who were hedge fund managers who could take them to st bart’s or
0:12:09 aspen or the hamptons and uh the women in college that i used to date were dating other college guys so
0:12:14 you’re sort of in no man’s land when you’re a young man or right out of college i found so the
0:12:19 fact that you have a girlfriend is really wonderful i would just say try and maintain your current
0:12:26 relationships be physically fit and recognize your first job is not going to be easy and also just
0:12:34 optimize your logistics what do i mean by that have a clean apartment near work try and cut commute town
0:12:38 even if it’s a small place just a clean place near work because you’re not gonna spend a lot of time
0:12:46 at home uh two if you can don’t buy a car have a uniform keep your clothes really simple really easy
0:12:52 um get a workout routine just try and starch out all the extra time and logistical constraints on
0:12:58 your life so you can focus on work and you can focus on relationships uh but my brother i don’t know
0:13:01 you’re so far ahead of the game the fact you’re even thinking this way means you’re going to be just
0:13:09 fine and let me finish where i started and that is um really good to be you congratulations on all
0:13:14 your success and again recognize that when you hit some rough spots and you think wow this isn’t
0:13:18 this isn’t what i thought it would be that’s exactly where you’re supposed to be most people’s
0:13:25 first job is is not fun and the fact that you’re you know just make sure your parents are there call
0:13:29 them every day it’s sort of a release valve or every week to tell them what’s going on in your life
0:13:35 um obviously uh other things will take over in terms of your desire or your ability
0:13:41 to ensure you maintain your relationship with your girlfriend uh but again i don’t know boss you
0:13:45 should be coaching me i wish i had you to tell me what to do when i was your age congrats on everything
0:13:51 and best of luck in your first job we have one quick break and when we’re back we’re diving into
0:13:53 the depths of reddit buckle up
0:14:02 support for the show comes from skims undergarments are the first thing you put on in the morning and
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0:15:04 the regular season is in the rearview and now it’s time for the games that matter the most
0:15:09 this is kenny beecham and playoff basketball is finally here on small ball we’re diving deep into
0:15:15 every series every crunch time finish every coaching adjustment that can make or break a championship run
0:15:21 who’s building for a 16 win marathon which superstar will submit their legacy and which role player is
0:15:26 about to become a household name with so many fascinating first round matchups will the west
0:15:31 be the bloodbath we anticipate will the east be as predictable as we think can the celtics defend
0:15:37 their title can steph curry lebron james koat leonard push the young teams at the top i’ll be bringing the
0:15:41 expertise to pass in the genuine opinion you need for the most exciting time of the nba calendar
0:15:47 small ball is your essential companion for the nba postseason join me kenny beecham for new episodes
0:15:52 of small ball throughout the playoffs don’t miss small ball kenny beecham new episodes dropping
0:15:56 through the playoffs available on youtube and wherever you get your podcasts
0:16:03 support for property comes from built rewards these days you can get reward points on basically
0:16:08 everything you get a couple of points for your morning bagel a few more for a pack of gum from
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0:16:18 your rent with built rewards you finally can there’s no cost to join and when you pay rent through built
0:16:22 you unlock two powerful benefits first just by paying rent you unlock flexible points that can be
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0:16:33 no matter where you live or who your landlord is your rent now works for you second you gain access to
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0:17:02 slash prop g make sure to use the url so they know we sent you join built.com slash prop g to sign up
0:17:03 for built today
0:17:15 welcome back let’s bust right into it today’s reddit question comes from mr roto
0:17:23 they say hey scott i’m a 35 year old who just got his first managerial position what advice would you
0:17:29 give to a first-time manager especially one whose team is remote that’s a good question so
0:17:36 people often make the mistake of thinking that okay i’m nice and i’m smart which means i’m going to be a
0:17:40 good manager no being nice and smart doesn’t make you a great tennis player it doesn’t make you a great
0:17:48 you know it doesn’t make you great at basketball management is its own sport and it takes a few
0:17:54 things when i find the great you know kind of the best managers have the following attributes in
0:18:02 common one they are willing to demonstrate excellence and share that excellence so um my partner in the
0:18:07 business uh katherine dylan has always been what i call a player coach and that is rather than managing
0:18:11 people you got to set up an incentive structure that works got to provide feedback that’s really
0:18:16 important but what she does is she helps people do their jobs instead of in addition to saying okay
0:18:20 this wasn’t great she will actually she can do that and she can do that almost as well as or better
0:18:25 almost everything in the company as well or better than anyone else and she she doesn’t provide just
0:18:29 feedback she provides learning she sits on i don’t have the patience for that if i say to someone
0:18:34 when i send feedback like this edit on this podcast sucked i don’t call them and say okay let’s edit it
0:18:39 together and let me teach you i just say it sucked that’s not that inspiring or that helpful they
0:18:43 demonstrate excellence and they’re willing to share that excellence with their team they take the time
0:18:48 to try and teach people and upskill them too they hold people accountable and that is they set out
0:18:53 definitive goals and benchmarks and they say okay we’re going to check in and they hold them accountable
0:18:57 all right you miss your target what’s up how can i be helpful but you missed your target or
0:19:02 you exceeded your target well done let’s celebrate that together they hold people accountable and the
0:19:08 third good managers demonstrate empathy and what do i mean by that i always assumed that everyone
0:19:12 just wanted to be like me rich and awesome that was my goal want to be super fucking rich and super
0:19:18 fucking awesome and so so that means you do as well well what you find out is that for some people
0:19:24 or most people they don’t share exactly your same aspirations some people want to manage other
0:19:29 people they get huge reward out of someone reporting to them and figuring out a man some people want to
0:19:34 see their name in lights okay fine you’re going to manage the the staff you’re going to manage this
0:19:39 group of people you’re going to take on this project you’re going to manage a kind of corporate
0:19:44 initiative oh you’re going to when the new york times calls and asks for a quote i’m going to turn them
0:19:49 over to you as an analyst and that they just light up right that’s just hugely important to them
0:19:54 other people need some flexibility and it’s like if you can provide that for them especially as a
0:20:00 i i was into remote work kind of a long time before other people and when i say that i think it’s terrible
0:20:05 for young people but i my secret kind of human capital weapon in the 90s was i hired quite a few
0:20:11 people who are brand managers out of really iconic companies who left because it wasn’t compatible
0:20:16 with having kids they weren’t nearly as generous with maternity leave back in the 90s and i would
0:20:20 say come in two days a week but you can be home three days a week and i found that mothers were
0:20:24 really efficient and disciplined because there was no like they had to leave at a certain time to get
0:20:30 home to pick up their kids or whatever and so i hired a lot of mothers and gave them remote work
0:20:35 flexibility which doesn’t sound that progressive now but it was back in the 90s why because i would learn
0:20:41 about their situation and think okay i’m going to cater my management of you to what is your specific
0:20:49 situation if you can demonstrate to people that you have a vested interest in their success that you
0:20:55 are fighting for them they will be loyal to you and that is a really powerful thing i didn’t figure that
0:21:01 out until later in my management career but understanding what’s important to people and then giving them the
0:21:08 sense that you’re fighting for them and that they can trust you and is really powerful first got to
0:21:12 demonstrate excellence got to be really good at what you do sit people down and not only demonstrate
0:21:17 excellence be willing to share with them to hold them accountable hold everyone accountable there’s a
0:21:22 reason you’re here you’re good at what you do and if other people don’t match you we hold them accountable
0:21:26 and we let people go i think that’s part of management and then finally you demonstrate empathy i’m
0:21:30 going to learn about you i’m going to learn about your priorities and i’m going to demonstrate to you
0:21:36 that i understand your priorities and i want you to be successful i i i have a vested interest sure you
0:21:43 my success is more important to me than anything but i also do spend a lot of time thinking what does this
0:21:48 person value how can i make them more successful how can i get them economic security how can i help
0:21:56 them participate in our success what specific soft things prestige management feedback do they really
0:22:02 value and they’ll see that i have listened and learn because again loyalty is a function of appreciation
0:22:07 and appreciation is a function of empathy and tailoring your specific empathy to that specific
0:22:14 person appreciate the question that’s all for this episode if you’d like to submit a question please
0:22:20 email a voice recording to office hours of property media.com again that’s office hours of property media.com or
0:22:26 if you prefer to ask on reddit post your question on the scott galloway subreddit and we just might
0:22:28 feature it in our next reddit hotline segment
0:22:43 this episode was produced by jennifer sanchez our intern is dan shalon drew burrows is our technical
0:22:48 director thank you for listening to the property pod from the box media podcast network we will catch you
0:22:55 on saturday for no mercy no malice as read by george han and please follow our property markets pod wherever
0:22:58 you get your pods for new episodes every monday and thursday

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0:00:43 There is no right to a court-appointed lawyer in immigration court.
0:00:47 So if the government puts you in deportation proceedings to try to deport you,
0:00:50 you have to be able to pay for a lawyer yourself.
0:00:52 And if you can’t, then you would have to represent yourself.
0:00:56 Meanwhile, the government is always represented by an attorney who’s trying to deport you.
0:01:00 Obviously, that’s very unfair for an adult, let alone for a child.
0:01:04 I would love to see all kids have a lawyer in immigration court.
0:01:07 Unfortunately, it looks like the administration is going to try to cut
0:01:10 legal services for unaccompanied kids this year.
0:01:11 So we will see what happens with that.
0:01:15 Good morning.
0:01:16 I’m Guy Kawasaki.
0:01:19 This is the Remarkable People podcast.
0:01:25 And I know Madison keeps telling me, you’re getting repetitious, Guy, but I have to say
0:01:27 we are on a mission to make you remarkable.
0:01:33 So what we do is look for remarkable people all over the world and bring them on our podcast
0:01:36 so that you can listen to them and be remarkable, too.
0:01:42 And today we have someone who is a CNN hero, which is hero with a capital H.
0:01:43 That’s a big deal.
0:01:46 It’s a big reward, a big award.
0:01:47 I wish I could be one.
0:01:49 But anyway, that’s an aside.
0:01:51 So this is Rachel Rudder.
0:01:58 She is executive director of Project Libertad, and she helps unaccompanied minors navigate
0:02:02 the immigration system and adjust to living in America.
0:02:06 So, Rachel Rudder, thank you very much for being on our podcast.
0:02:08 Thank you for having me.
0:02:13 Listen, let’s start off with just explaining what your organization does.
0:02:18 It’s fascinating that what you do, and it’s also kind of depressing that it’s necessary
0:02:20 that you do what you have to do.
0:02:22 So please explain what you do.
0:02:26 I think necessary and depressing is like the perfect way to describe my job.
0:02:28 Actually, no one’s ever said it like that before.
0:02:33 So our work is, as you said, primarily with unaccompanied minors, but generally with
0:02:34 newcomer immigrant youth.
0:02:40 And we have a whole host of services to try to help them adapt to being in the U.S. and
0:02:42 take a really holistic approach to meeting their needs.
0:02:46 So, of course, on the legal side, with my background as an immigration attorney, we help
0:02:48 them apply for different types of immigration status.
0:02:53 So that can be things like helping them apply for asylum, representing them in immigration
0:02:57 court while they’re going through that process, helping them apply for different types of visas
0:02:58 they may qualify for.
0:03:03 And then we have a whole bunch of social service programs as well to complement the legal services.
0:03:08 So we have a case management team that works with kids to connect them with resources that
0:03:13 they need in the community, whether that’s getting access to medical or dental care, mental
0:03:15 health support, whatever the case may be.
0:03:21 We offer mental health services, ESL classes, summer camp programs, in-school programming for
0:03:21 newcomers.
0:03:25 So just really trying to take that wraparound holistic approach.
0:03:31 This may sound like a dumb question, but why are these children unaccompanied when they’re
0:03:32 doing this?
0:03:37 So when we say that a child is unaccompanied, like in immigration world, that means that
0:03:43 they arrived in the U.S. by themselves as a minor under age 18 without a parent or legal
0:03:45 guardian and with no lawful immigration status.
0:03:48 So it’s like a very specific meaning in the immigration world.
0:03:52 And being designated as unaccompanied when they arrive in the U.S.
0:03:56 gives them certain special protections under the law as they go throughout their immigration
0:03:57 process.
0:04:02 And so what happens generally is that once they’re apprehended at the border, they’re
0:04:07 placed in a shelter through the Office of Refugee Resettlement, which is the branch of the government
0:04:08 that oversees unaccompanied children.
0:04:13 And then the goal is that there’s a caseworker through ORR who will help to match them with
0:04:15 a sponsor in the community.
0:04:20 So generally, that’s like a family member or family friend who’s already here in the U.S.,
0:04:23 maybe a parent or an aunt or an uncle or something like that who’s already here.
0:04:27 And then they would be released to go live with that person while at the same time they’re
0:04:30 being placed in deportation proceedings in immigration court.
0:04:35 So it’s not that they just get to come live here indefinitely with that person, that sponsor.
0:04:39 It’s just you’re going to live with that person while your immigration case is going on.
0:04:43 And that’s then how we would end up working with them in our programs here in the community.
0:04:47 And how long does this legal process take these days?
0:04:51 It really depends on what type of process they’re doing.
0:04:53 There’s a lot of different types of immigration status.
0:04:57 It also ebbs and flows depending on lots of different factors.
0:05:01 What country the child is from, what’s going on elsewhere in the government,
0:05:05 like what type of funding is available for officers to process the cases and all those
0:05:06 types of things.
0:05:13 But to give you an idea, I have kids who applied for asylum back in 2020, which is when we first
0:05:18 started doing legal work through the organization, because prior to that, we were offering some
0:05:20 other services, but not legal services yet.
0:05:24 And those cases have just been pending since 2020.
0:05:27 They have not been granted an interview, which would be their next step.
0:05:31 So that’s five years of just waiting for those kids with no resolution.
0:05:36 We also do another really type of common case called special immigrant juvenile status, which
0:05:41 is a type of status for kids who have been abused, abandoned or neglected by one of their parents.
0:05:44 And those cases can take many years as well.
0:05:49 There’s a part of that process that happens in family court without getting too into the weeds
0:05:50 that can take many months.
0:05:55 And then you apply for the special immigrant juvenile status after the family court portion.
0:06:00 That can take another, I would say, six to nine months to get a response.
0:06:04 If that gets approved, then they’re going to be waiting another three or four years to be
0:06:06 able to even apply for a green card.
0:06:10 And then after that, the green card can take a couple of years.
0:06:13 And then it’s five years of a green card before you can apply for citizenship.
0:06:17 So it varies depending on like the type of case and all these other factors.
0:06:22 But just to give you the two types of cases that I most often work on, you can see how long
0:06:22 that it can take.
0:06:23 Wow.
0:06:28 You mentioned the F word twice, F as in funding.
0:06:34 And you also said something kind of general about depends on what’s going on with the government.
0:06:41 So you just named some numbers of five years or so, but this was before all this drama.
0:06:44 So now what’s the conditions?
0:06:49 I mean, certainly you can’t make a case that things are going to go smoother and faster now,
0:06:49 right?
0:06:51 No, definitely not.
0:06:52 So there’s a lot going on.
0:06:53 That is a huge question.
0:06:59 We’ve definitely seen a big increase in ICE enforcement and the number of arrests and
0:07:00 people in detention and ICE detention.
0:07:02 So that is one thing.
0:07:07 In terms of just talking specifically about like case processing times, I think a lot of
0:07:11 the things that the Trump administration is doing is going to make case processing times
0:07:15 worse, which is interesting because they are always talking about wanting people to take
0:07:18 the legal pathways and things like that.
0:07:23 And this is a whole other tangent, but for the majority of people, there is no legal pathway.
0:07:26 It’s silly to ask people to take a pathway that doesn’t exist.
0:07:30 But they’re also doing a lot of things that are making the paths that do exist even less
0:07:32 efficient than they already were.
0:07:37 So we already have a huge immigration court backlog, millions of cases backlogged, and they’re
0:07:39 firing lots of immigration judges.
0:07:46 So that’s even fewer judges to hear those cases, which creates an even bigger backlog for people who are going through a legal process.
0:07:55 And then there’s also been lots of different funding cuts across the federal government, as we’ve seen, that are going to affect employees who would process those cases.
0:08:00 So they’re doing a lot of funding cuts that are actually making the system even less efficient.
0:08:08 At the same time, they’re doing a lot to try to chip away at due process and be able to deport people without hearings as quickly as possible.
0:08:19 So in that sense, the process can be sped up, but in a very unfair way where people aren’t getting a chance to defend themselves and demonstrate that they might qualify for legal status.
0:08:29 So there’s a lot going on in the world right now, if only they had $5 million each and they could just purchase their citizenship with the gold card.
0:08:35 I don’t want to be paranoid or I don’t want to sound like I’m trying to make something out of nothing.
0:08:41 But do you think all of this is like on purpose to discourage people from coming?
0:08:42 Oh, absolutely.
0:08:44 I don’t think that’s paranoid at all.
0:08:56 Well, I think the Trump administration and administrations on both sides of the political spectrum have always been about deterrence and border enforcement and any kind of like benefit that we give to people.
0:09:07 They want it to come at the expense of having more border enforcement or having stricter policies or pitting certain groups that they deem like the good immigrants against the bad ones in their eyes.
0:09:18 So like anytime there’s any talk of any type of reform, it’s always, OK, we’ll give you these couple of good things, but then we’re going to do these other things that make enforcement worse at the border.
0:09:23 And the reality is that long term, we’ve seen that deterrence just doesn’t actually work.
0:09:28 You may see a short term decrease in border crossings and things like that.
0:09:36 Like right now, what we’re seeing is that the border is totally shut down to asylum seekers, which I think is illegal under our own laws and under international laws and treaties.
0:09:49 And I did see that I believe the Florence Project and some other organizations actually just filed a lawsuit this week against the federal government to try to force them to allow people to seek asylum, which we’re supposed to be doing.
0:09:57 Deterrence doesn’t work. We try to shut down the border, maybe short term, we see a decrease in crossings and things like that, like with the border being shut down right now.
0:10:10 But in the long term, the root causes that are driving people to come here, and particularly with the population that I work with, a lot of Central American families who are fleeing gang violence and really horrible, life-threatening conditions.
0:10:17 And those conditions are, you know, for the most part, a result of U.S. foreign policy in that region over many years.
0:10:24 And unless those root causes that are driving them north to seek safety are addressed, they’re going to keep coming.
0:10:29 So that deterrence doesn’t really have a long term effect on stopping people from coming to the U.S.
0:10:32 But what it does do is make migrating even more dangerous.
0:10:40 Last year, the United Nations said that the U.S.-Mexico border is actually the most dangerous land route for migrants in the entire world.
0:10:56 There are entire organizations and programs at universities that are dedicated to trying to identify people’s remains who perish trying to cross the border because our enforcement and our border wall and all of that stuff forces people to go into more and more remote routes in the desert, and it’s super dangerous.
0:11:03 And the government knows that, and they choose to do it anyway, knowing that people will die in hopes of deterring people.
0:11:07 And again, it’s just silly because it doesn’t actually work, and we know that by now.
0:11:14 This is a dumb question, but I’m not assuming a world of perfect information.
0:11:19 So these poor people in Central America don’t know what they’re walking into.
0:11:21 I’m sure they know what they’re walking into.
0:11:29 So they know they’re walking into years and years of delays and prosecution and deportations and all that shit.
0:11:35 And it must be that it’s so much worse there that they’re willing to put up with all this here.
0:11:37 So what are they running from?
0:11:39 What are they trying to get away from?
0:11:40 Yeah, absolutely right.
0:11:51 And I always say that when we do trainings and presentations on this topic that you have to think about how bad things are that people are willing to come here and go through all of that and go through the dangerous journey to arrive here.
0:11:58 So some of the most common stories that we hear, particularly from Central America, which is kind of my niche, is the gang violence.
0:12:05 So there’s two primary gangs, that’s MS-13 and the 18th Street Gang, and they pretty much control large swaths of Central America.
0:12:10 In many places, they are synonymous with the government or the police are working with them.
0:12:14 So there’s really no kind of outside governmental authority people can go to for help.
0:12:28 In terms of the kids that we work with, really common fact patterns are boys, particularly teenage boys, being forcibly recruited into the gang, where they’re being threatened that either they’re going to be killed or their family members or loved ones are going to be killed if they don’t join the gang.
0:12:35 And they either have to leave because those threats are real, and they really do carry those threats out if they won’t comply.
0:12:42 And similarly, with girls, we see oftentimes girls trying to be forced to be like a girlfriend to an older man who’s a gang member.
0:12:49 And we’re talking young girls, 11, 12, 13 years old, who are being forced to be a quote-unquote girlfriend to a gang member.
0:12:53 And if they don’t want to, again, there’s threats against them and their family members if they’re not complying.
0:12:56 So we see a lot of girls coming because of that.
0:13:11 We also see a lot of, like, extortion cases where you have to, just to exist in these different gang territories, you have to pay, like, an extortion tax just to keep your business open or to ride the bus in that neighborhood or to pass through that territory or whatever.
0:13:18 And if you can’t pay it anymore, they start increasing it and you can’t pay it, then, again, there’s, like, death threats against you and your family.
0:13:26 We also have a lot of kids who deal with different types of abuse within their families of origin, whether that’s domestic violence, sexual abuse.
0:13:33 There’s also a lot of kids that we see who are being discriminated against or afraid for their lives because of their gender identity or orientation.
0:13:36 So those are some of the really common stories that we hear.
0:13:46 We also see a lot of kids who are out of school for a really long time in their home country or have very limited formal education, a lot of child labor trafficking.
0:13:52 And oftentimes, a bunch of these different kind of factors are at play in the same kid’s story.
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0:14:50 How do you wrap your mind around what this country is doing?
0:14:55 Because it seems to me that America being as wealthy and powerful as possible,
0:15:00 we should be trying to help other people who are less wealthy and less successful.
0:15:08 and less fortunate, but we seem to believe that we should just keep everybody out and just make things
0:15:09 good for us.
0:15:14 Is there another side to this story about why we should be making all these deterrents happen?
0:15:17 It’s really hard for me to understand.
0:15:23 And I think what I tend to find when I talk to people who are anti-immigration is that they really
0:15:25 just don’t actually know the facts of what they’re talking about.
0:15:30 There’s a lot of really common myths about immigration and immigrants that get repeated and repeated.
0:15:32 I mean, they’re simply not true.
0:15:39 So a lot of times when people will know what I do and try to argue with me or want to talk about it.
0:15:45 I grew up in a rural part of Pennsylvania where a lot of the people who live there are Trump supporters and things like that.
0:15:49 So whenever I come into touch with those people and they want to talk about immigration and things,
0:15:54 a lot of the talking points they repeat are just things that aren’t true, but they don’t know that.
0:15:58 So a lot of their belief system about it is just not based in real facts.
0:16:03 For example, we hear a lot of times, well, my great-great-grandparents came here the right way,
0:16:06 so people should just get in line and come here the right way.
0:16:12 The reality is that the laws were very different when a lot of our grandparents, great-grandparents came here,
0:16:18 and most of them would not actually be eligible to immigrate to the U.S. under the laws that we have today.
0:16:23 And for the vast majority of people, whether you’re someone who is in the U.S. and you’re undocumented,
0:16:27 or you’re somebody who is abroad and you want to try to immigrate to the U.S.,
0:16:30 for most people, there is literally no path to do that.
0:16:34 There’s no general application you can file to say,
0:16:36 hey, I want to become a green card holder.
0:16:37 I want to become a citizen.
0:16:39 I want to get a work permit.
0:16:40 That doesn’t exist.
0:16:43 You have to kind of fit into one of three primary buckets,
0:16:47 and those are humanitarian, which is things like asylum or refugee status,
0:16:50 employment-based visas or family petitions,
0:16:54 where you have a family member who is a close relative that can petition for you.
0:16:57 All of those different buckets have very strict requirements.
0:17:00 And if you don’t fit into one of them, there’s nothing else you can do.
0:17:02 You just don’t have a path.
0:17:04 So that’s one thing that gets repeated all the time.
0:17:07 We also hear a lot about immigrants bringing crime,
0:17:11 when really we know that many studies have shown that immigrants are less likely
0:17:16 to commit crimes, including violent crimes, than U.S.-born citizens.
0:17:19 We hear about immigrants stealing jobs or hurting the economy.
0:17:24 And again, many studies bear out that immigrants actually strengthen our economy
0:17:28 and create jobs and create businesses and pay lots of taxes that they don’t then benefit from.
0:17:34 So my feeling on that is a lot of people just keep citing these myths,
0:17:35 and they just actually don’t know the facts.
0:17:39 So part of what we do is try to put out a lot of educational information,
0:17:42 do a lot of stuff like this, do training sessions,
0:17:46 particularly with teachers and people like that who are coming into contact with these kids
0:17:48 so that they do have the facts.
0:17:51 And yeah, I think, as I said, immigrants make our community safer.
0:17:52 They help our economy.
0:17:56 They help us to have more culture, different types of food, different music,
0:18:00 all these different things that they bring to our communities to make them stronger.
0:18:06 So I really don’t see any valid argument for why we should be keeping people out.
0:18:07 You know, it’s really interesting.
0:18:11 I saw with Trump talking about trying to make Canada the 51st state,
0:18:15 he said something about how, like, it would be a great 51st state.
0:18:17 There’s just an arbitrary line there.
0:18:22 And I was like, it’s really interesting that he can say that the U.S.-Canada border is an arbitrary line,
0:18:26 but then make such a big deal about people coming across the southern border.
0:18:31 And I really think the lines are arbitrary and we’re all humans and we should all be supporting each other.
0:18:32 Wow.
0:18:33 That was a really long answer.
0:18:35 Yeah, that’s okay.
0:18:39 But this is why you’re a CNN hero.
0:18:41 In a sense, you’re doing God’s work, right?
0:18:45 I mean, God’s work is different these days.
0:18:48 It has a very different meaning today.
0:18:51 So how many kids are we talking about?
0:18:57 What’s the number you have to wrap your mind around of number of kids who are going through this experience every year?
0:19:01 Oh, I don’t know if I have that number off the top of my head.
0:19:06 In our area alone, I think I recently read that, like, as far as just unaccompanied children,
0:19:14 there have been over maybe nearly 1,500 to 2,000 released to our area from ORR in the last fiscal year.
0:19:20 When you say released to your area, what does that mean?
0:19:23 They came over the border in Texas or California or something.
0:19:25 And how did they get to Pennsylvania?
0:19:28 How did they get released in Pennsylvania?
0:19:31 Yeah, so that’s what I was talking about earlier with them having sponsors.
0:19:35 So they get sent to this Office of Refugee Resettlement.
0:19:37 That can be a group home shelter.
0:19:38 It can be, like, a foster home.
0:19:42 It depends on their age and, like, where there’s space and all those factors.
0:19:45 And then they have a caseworker at the shelter.
0:19:49 And that person will then ask them, like, do you have anybody here in the U.S.?
0:19:53 Maybe they have a parent who was already here or some other family member or whatever.
0:19:58 And then they’ll do background checks and have that child then released to the sponsor.
0:20:01 So that’s what I mean when I say released.
0:20:09 They’ll let them leave immigration detention and go to be with that sponsor while their court case is going through the immigration courts.
0:20:13 And what’s your caseload right now, your personal caseload?
0:20:15 I have about 90 right now.
0:20:16 90?
0:20:17 Yeah.
0:20:21 You are trying to take 90 kids through this system right now?
0:20:21 Yes.
0:20:22 Wow.
0:20:27 And are there legal firms who are doing pro bono work and helping you?
0:20:32 Or, I mean, are you, Rachel, I mean, are you just standing out there by yourself?
0:20:35 So for my cases, I’m representing them.
0:20:48 There are, you know, other nonprofits in our area and throughout the country that do different types of immigration work with families, with unaccompanied kids, with survivors of domestic violence, with people who have been exploited at their workplaces.
0:20:51 There’s all kinds of different, like, iterations and organizations and things doing this work.
0:20:54 There are definitely firms that also do pro bono work.
0:20:57 We don’t really have any partners in that at this moment.
0:21:05 We do work closely with some fellow attorneys who are family law attorneys to do those special immigrant juvenile status cases I was mentioning earlier.
0:21:08 But, yeah, we’re directly representing those 90 kids.
0:21:09 Wow.
0:21:13 That must be the third time I said wow in this conversation.
0:21:15 Let me ask you a question.
0:21:17 And you can say you don’t want to answer this question.
0:21:37 I, quite frankly, wouldn’t blame you maybe, but are you afraid at all that you’re on some kind of list at Doge or the FBI or Department of Justice because you’re this activist who’s trying to bring in these illegal immigrants and trying to help these people break the law and all that?
0:21:38 Do you think you’re on somebody’s list?
0:21:40 Yeah, I’ve definitely worried about that.
0:21:42 My husband and I have talked about it.
0:21:45 That list does exist, at least for government employees.
0:21:51 There’s a website that has, like, you know, radicals within USCIS or whatever.
0:21:53 And it’s just people who, like, support immigrant rights.
0:21:55 It’s, like, actually nothing that radical.
0:22:04 But a friend sent me that list because there was somebody that was a former co-worker of ours who was on it who now works for the government and ended up on that list.
0:22:09 And with all of the CNN publicity, most of it was very overwhelmingly positive.
0:22:12 But we definitely started getting a lot of, like, hate mail.
0:22:16 And it was interesting because I asked, like, the other honorees, hey, is this happening to you too?
0:22:19 And they were like, no, like, what are you talking about?
0:22:22 So immigration is a very heated, polarizing issue.
0:22:25 People get really mad about it, despite that they don’t really understand it that well.
0:22:35 We’ve gotten kind of scary emails and messages and things like that, which we did report to the police and keep a record of in case anything more were to come of it.
0:22:47 I don’t know of any, like, official lists, but I definitely think it’s a possibility, especially with the executive order that came down recently about student public service loan forgiveness for student loans, which I expect there to be, like, litigation about.
0:22:48 And I’m hoping that it will get blocked.
0:23:06 But basically, that order said that anybody who’s doing work under the public service loan forgiveness program at nonprofits that do things that Trump doesn’t like, basically, like doing immigration work was specifically mentioned in it, doing work with LGBT youth, things like that, trying to say those people wouldn’t then get their loans forgiven.
0:23:15 you’re supposed to get your loans forgiven after 10 years of public service, and they’re trying to undo that for people who are doing work that Trump doesn’t align with the mission.
0:23:19 So I could definitely see that spiraling into something more.
0:23:28 Pretty soon, that kind of work will be immigration work, it’ll be climate control work, it’ll be LGBTQ plus rights, it’ll be female.
0:23:30 What’s not going to be on that list?
0:23:33 Habitat for Humanity was like one of the latest ones they were going after.
0:23:33 Yeah.
0:23:38 Yeah, Jimmy Carter is literally turning over in his grave.
0:23:40 How can you do that?
0:23:45 Okay, so let me ask, what can people do to support you, people who are listening to this podcast?
0:23:48 Yeah, so we have a lot of volunteer opportunities.
0:23:53 If you’re not in our area, we also have a lot of remote opportunities that are available.
0:23:58 So definitely volunteering, there’s stuff for everyone, depending on your skill set and what you’re interested in.
0:24:01 There are opportunities to work directly with the community that we serve.
0:24:04 There are also opportunities to do behind the scenes stuff like fundraising.
0:24:11 We have remote ESL classes as well that are an option if you’re remote but want to work directly with the community.
0:24:13 So lots of options there.
0:24:16 If you go to our website, it’s projectlibertad.org slash volunteer.
0:24:18 You can learn more about that.
0:24:20 Donating, of course, is always welcome.
0:24:24 We have so many kids with so many needs that we’re trying to meet all the time.
0:24:28 Whatever people are able to give is appreciated and goes really far.
0:24:31 We’re a small organization trying to do a lot, so every dollar really does help.
0:24:42 And then if you can share the accurate information you’ve learned from listening to this podcast with somebody who might not have it, that’s also really helpful to help get correct facts out in the world about immigration.
0:24:59 AI’s impact on the environment is one of the most pressing issues facing the tech industry today.
0:25:04 People want to know, what’s the carbon footprint of a chat GPT query?
0:25:07 What does it mean to innovate sustainably?
0:25:10 And can AI actually be used to solve the climate crisis?
0:25:13 I’m Rana El-Khalyubi.
0:25:21 On my podcast, Pioneers of AI, we bring questions like this to some of the leading thinkers and builders working in AI.
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0:25:48 Let me ask you a very theoretical question.
0:25:58 Let’s say you had a magic wand or let’s say I had a magic wand and I waved it at you and I said, now you get to design the immigration system for the United States.
0:26:15 So Rachel, describe that system, the perfect system you would like to see implemented that I guess it has to represent both sides, the security and on one side, but also the empathy and the desire to make the world a better place on the other side.
0:26:21 And those things don’t necessarily have to conflict, but they are at least different.
0:26:25 So how would you design an immigration system?
0:26:30 I know this is probably a bridge too far for a lot of people, but I am all for open borders.
0:26:35 We already have open borders for wealthy white people who want to retire in another country.
0:26:44 So I think that people hear open borders and think it’s crazy, but from what I’ve read and studied about it, it would work like moving from state to state within the United States.
0:26:47 You still have identification and things like that.
0:26:50 So we still know who people are and where they are and all of that.
0:26:59 But without this like punitive aspect of ICE detention and all of this, barring that, I would love to see us just not put kids in immigration court proceedings, not deport children.
0:27:13 And short of that, if we’re like taking it back even further, having universal representation for immigrant kids or for all immigrants in immigration proceedings, because we didn’t really touch on this, but there is no right to a court appointed lawyer in immigration court.
0:27:20 So if the government puts you in deportation proceedings to try to deport you, you have to be able to pay for a lawyer yourself.
0:27:22 And if you can’t, then you would have to represent yourself.
0:27:26 Meanwhile, the government is always represented by an attorney who’s trying to deport you.
0:27:30 Obviously, that’s very unfair for an adult, let alone for a child.
0:27:38 If we’re dreaming a little smaller, something that’s maybe more feasible in the near future, I would love to see all kids have a lawyer in immigration court.
0:27:45 Unfortunately, it looks like the administration is going to try to cut legal services for unaccompanied kids this year.
0:27:46 So we will see what happens with that.
0:27:47 But yeah.
0:27:50 Just backing up a second here.
0:27:54 Could you just explain what open borders mean?
0:28:02 So open borders just means that you can travel freely anywhere and we’re not going to put you in jail or deport you.
0:28:04 It’s just the right to move freely.
0:28:06 So there’s no wall, obviously.
0:28:08 There’s no passports.
0:28:09 There’s no nothing.
0:28:11 It’s just welcome to America.
0:28:12 Come and go as you please.
0:28:19 I think we can still have passports and identify people, but you wouldn’t be thrown in jail for not having a visa.
0:28:22 And you have to remember, I know that it sounds crazy.
0:28:23 People think it sounds crazy.
0:28:32 And actually, if anybody’s interested in learning more about open borders from somebody that knows way more about it than I do, there’s a really great organization here in Philly called Free Migration Project.
0:28:36 And that’s one of their kind of core things is open borders.
0:28:39 And they have really good research on their website about it.
0:28:48 But if you think about if I decide that I want to go to Europe on vacation right now, I don’t need to do anything other than show my passport and go.
0:28:49 Right.
0:28:59 So a lot of people in the world basically exist under open borders, but we tend to put all these restrictions on black and brown people of color, people from poorer countries.
0:29:04 So if you’re like a wealthy white American or European, you already have open borders.
0:29:06 You just haven’t thought of it that way before.
0:29:12 Well, Rachel, I really appreciate you bringing attention to this issue.
0:29:19 And I have to say that it disgusts me that we treat unaccompanied minors this way.
0:29:22 There’s just something morally wrong with this.
0:29:24 And I don’t know what else to say.
0:29:25 I’ll give you as long as you want.
0:29:40 Just give your final message to my audience about the issue and what you can do and how you can do it and just promote the hell out of your organization and the concept so that we can get as much support for you as possible.
0:29:41 Thank you.
0:29:44 I thank you for having me and for sharing our work, too.
0:29:49 My final message is I think that we are heading into a really dark and scary time for immigrants in this country.
0:29:59 And if you are someone that has the privilege of citizenship, to be sure that you’re using your voice to stand up for immigrant communities and to draw attention to what is happening.
0:30:02 We have the Trump administration detaining green card holders.
0:30:03 It’s really frightening.
0:30:09 And I think it’s a test to see how far they can go, how far the public will let them go.
0:30:14 And I think it’s really, really important that all of us who do have that privilege of birthright citizenship.
0:30:25 And I say privilege in the sense of, like, we’re not at risk as much as others who don’t have that safety net to use our voices to stand up to what’s going on right now.
0:30:31 I mean, you just mentioned a very interesting topic, which is birthright citizenship.
0:30:35 That literally must make your head explode.
0:30:37 That could go away.
0:30:39 It is really crazy.
0:30:44 And I think, like, the people who are pushing for it don’t realize how easily that could come back on them as well.
0:30:45 Because, like, where do you draw the line?
0:30:51 If we don’t have birthright citizenship, then who is to say that our grandparents are citizens?
0:30:51 You know what I mean?
0:30:53 Like, how far back are we going to take this?
0:30:56 And I know that the executive order had specific parameters.
0:31:01 But if they are able to do away with birthright citizenship, they don’t have to stick to those parameters.
0:31:04 And nobody has the guarantee of citizenship then.
0:31:11 I am third-generation Japanese-American, and my great-grandparents came over to pick sugarcane in Hawaii.
0:31:18 It’s not like they had a red carpet welcome when they rolled out into Hawaii.
0:31:25 All right, Rachel, I really appreciate you coming on our show and explaining what you do and the problem that you’re facing.
0:31:33 And I hope you listeners will support her and other organizations to make this world a better place, a remarkable place.
0:31:38 And, Rachel, thank you for the work that you’re doing to make this world a better place.
0:31:39 So, I’m Guy Kawasaki.
0:31:49 If you’ve been listening to Rachel Rutter, she’s the executive director of Project Libertad, and she helps unaccompanied minors immigrate into the United States.
0:31:53 In the meantime, this has been the Remarkable People podcast.
0:32:00 The people I want to thank are Madison Neismar, producer and co-author, Tessa Neismar, researcher, Jeff C.
0:32:04 And Shannon Hernandez, who’s the sound design team.
0:32:06 And that’s the Remarkable People team.
0:32:15 And I hope that you enjoy our work and that you will be remarkable and help other people be remarkable, too.
0:32:19 Until next time, mahalo and aloha.
0:32:25 This is Remarkable People.

0:00:03 Technology companies aren’t entitled to their future success.
0:00:06 AI, I think, will change the landscape of software.
0:00:10 And I think it will help some companies and it will really hurt others.
0:00:15 And so when I think about what it means to build a company that’s enduring,
0:00:19 that is a really, really tall task in my mind right now.
0:00:24 Because it means not only making something that’s financially enduring over the next 10 years,
0:00:31 but setting up a culture where a company can actually evolve to meet the changing demands
0:00:36 of society and technology when it’s changing at a pace that is like unprecedented in history.
0:00:40 So I think it’s one of the most fun business challenges of all time.
0:00:45 I just get so much energy because it’s incredibly hard and it’s harder now than it’s ever been
0:00:49 to do something that lasts beyond you. But that, I think, is the ultimate measure of a company.
0:01:02 Welcome to the Knowledge Project Podcast. I’m your host, Shane Parish.
0:01:06 In a world where knowledge is power, this podcast is your toolkit for mastering the best
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0:01:36 Six months after Brett Taylor realized AI was about to change everything,
0:01:41 he walked away from his co-CEO job at Salesforce to start from scratch.
0:01:46 That’s how massive this shift really is. The mastermind behind Google Maps and the
0:01:51 former chief technology officer at Facebook, Brett reveals the brutal truths about leadership,
0:01:57 AI, and what it really takes to build something that endures long after you’ve reached the top.
0:02:03 Brett’s led some of the most influential companies in tech and seen exactly what makes businesses scale,
0:02:08 what kills them from within, and why most founders don’t survive their own success.
0:02:13 In this conversation, you’ll discover why so many companies are already on life support without
0:02:19 realizing it, how first principles thinking separates the next wave of winners from everyone else,
0:02:25 and the hidden reason most acquisitions fail. We’ll explore why AI is bigger than anyone suspects,
0:02:31 plus the mindset shift that turns great engineers into exceptional CEOs. Whether you’re a founder,
0:02:37 an operator, or simply someone who wants to think sharper, this episode will change how you see your
0:02:42 business, technology, and the future. It’s time to listen and learn.
0:02:53 What was your first real aha moment with AI where you realized, holy shit, this is going to be huge?
0:02:59 I had two separate aha moments. One that I don’t think I really appreciated how huge it would be,
0:03:07 but it kind of reset my expectation, which was the launch of Dolly in the summer of 22. Is that right?
0:03:15 I might be off by Europe. I think summer of 22. And the avocado chair that they generated. And I had
0:03:22 been, well, my background is in computer science and pretty technically deep. I hadn’t been paying
0:03:31 attention to large language models. I just didn’t follow the progress after the Transformers paper. And
0:03:38 I saw that and my reaction was, I had no idea computers could do that. And that particular launch,
0:03:43 you know, seeing a generated image of an avocado chair, I don’t think I extrapolated to what,
0:03:51 you know, where we are now. But it, for me, shook me and realized I need to pay more attention to this
0:03:57 space and open AI specifically than I had been. I think I had, that was the moment where I realized like,
0:04:01 I clearly have been not paying attention to something significant. And then it was,
0:04:08 you know, six months later, coincidentally, like the month after I left Salesforce, the chat GBD came out
0:04:14 and, uh, before it became a phenomenon, although it did so quickly, but I was already, you know,
0:04:20 plugged into it. And, and, uh, and I was, uh, from then on, you know, I could not stop thinking about it.
0:04:26 Um, but that avocado chair, I don’t know why I think it was the, there was a bit of an emotional
0:04:33 moment where you saw a computer doing something that wasn’t just rule-based, but, uh, creative and,
0:04:39 um, the idea of a computer doing something and creating something from scratch, uh, was, uh,
0:04:43 well, it doesn’t seem so novel. Uh, you know, a few years later just blew my mind at the time.
0:04:49 One of the unique things about you is that you’ve started companies. You’ve been acquired by Facebook
0:04:56 and Salesforce inside those companies. You rose up to be the CTO at Facebook, the co-CEO at Salesforce.
0:05:03 Talk to me about founders working for founders and founders working within a company.
0:05:10 Yeah. It’s, uh, it’s a very, um, challenging transition for a lot of founders to make. I think
0:05:17 there’s lots of examples of acquisitions that have been really transformative from a business standpoint.
0:05:23 Uh, I think YouTube, Instagram being two of the more prominent that have clearly changed the shape of,
0:05:30 of the acquiring company. But even in those cases, you know, the founders didn’t stay around that long.
0:05:33 Uh, and those guys, that’s maybe a little unfair, you know, stick around for a little bit.
0:05:39 I think the interesting thing about being a founder is it’s not just building a business,
0:05:43 but it’s very much your identity. Um, and I think it’s very hard for people aren’t founders to
0:05:48 experience it. If you take everything very personally, you know, from the product to the
0:05:55 customers, to the press, to, uh, your competitors, uh, the, the both in inner and outer measures of,
0:06:02 of, of success. And I think when you go to being acquired, there’s a business aspect to it. And,
0:06:06 you know, can you operate within a larger company, but that’s intertwined with a sense of identity.
0:06:12 You go from being a, the founder of a company and the CEO of a company or CTO of a company,
0:06:16 whatever your, your title happens to be as one of the co-founders to be in a part of a larger
0:06:22 organization. And to fully embrace that, you actually need to change your identity. Um, you need to go from
0:06:29 being, you know, the head of Instagram or my case, the head of quip to being an employee of Salesforce or
0:06:36 going from being the CEO of friend feed to being an employee of Facebook. And what I’ve observed is
0:06:42 it’s that identity shift is a prerequisite for most of the other things. It’s not simply your ability to
0:06:48 handle the politics and bureaucracy of a bigger company or to navigate a new structure. I actually
0:06:54 think most founders don’t make that leap where they actually identify, uh, with that new thing.
0:06:57 It’s even harder for some of the employees too, because most of the time in an acquisition,
0:07:02 an employee of an acquired company didn’t choose that path. And in fact, they chose to work for
0:07:07 a different company and they, you know, the, the acquisition determined a different outcome.
0:07:11 And that’s why integrating acquisitions is so nuanced. And I would say that, uh,
0:07:18 having the experience of having been acquired, uh, you know, before and having acquired some
0:07:23 companies before when I got to Salesforce, I really tried to be self-aware about that and really tried
0:07:29 to, you know, be a part of Salesforce, you know, and tried to shift my identity and, and not be a
0:07:35 a single issue voter around quip, you know, I’d really tried to embrace it. Um, and, uh, and I
0:07:38 think it’s really hard for some founders to do it. Some founders don’t want to, honestly, you know,
0:07:43 they, uh, maybe cash the check and, and, you know, that’s the, it’s more of a transactional relationship.
0:07:50 I, um, I, I really actually am so grateful for the experience of having been at Facebook and Salesforce.
0:07:53 I learned so much, but it really took a lot of effort on my part to just, um,
0:07:59 transform my perception of myself and who I am to get that value out of the company that acquired us.
0:08:04 How did you, how did it change how you did acquisitions at Salesforce? You guys did a
0:08:09 lot of acquisitions while you were there and you’re acquiring founders and sort of startups. And I think
0:08:15 Slack was while you were there too. How did that change how you went about integrating that company
0:08:20 into the Salesforce culture? I’ll talk abstractly about talking about some specific acquisitions too, but
0:08:27 first, I think I tried to approach it with more empathy, um, and more realism. You know, uh,
0:08:35 one of the nuanced parts about acquisitions is there’s the period of, um, doing the acquisition.
0:08:39 There’s sort of the period, uh, after you’ve decided to do it, of doing due diligence. And then there’s
0:08:44 a period when it’s done and you’re integrating the company and sort of the period after one of the
0:08:51 things that I have observed is that, uh, companies doing acquisitions, often the part of deciding to do
0:08:58 it is a bit of a mutual sales process. Um, uh, you’re trying to find a fair value for the company
0:09:04 and, and there’s some back and forth there, but at the end of the day, there’s usually some objective
0:09:09 measure of that, um, influenced by a lot of factors, but, but there’s some fair value of that.
0:09:15 But what you’re trying to do is what are, uh, and corporate speak could be synergies, but like,
0:09:19 why do this? Why is one plus one greater than two? You know, that’s, that’s why you do an acquisition
0:09:26 just from first principles. It’s often a exercise in storytelling. You know, uh, you, you know,
0:09:31 bring this product together with our product and customers will, you know, find the whole greater
0:09:38 than the sum of its parts. This team applied to our sales channel, or if you’re a Google acquisition,
0:09:44 you know, imagine the traffic we can drive to, to this product experience. Uh, you know, in the case of
0:09:49 something like an Instagram, imagine our ad sales team attached to your, you know, amazing product
0:09:55 and how quickly we can help you realize that value, whatever it might be. I find that people,
0:10:01 because there’s sort of a craft of storytelling to, uh, for both sides to come to the same conclusion
0:10:08 that they should, uh, do this acquisition sometimes, uh, either simplifies or sugarcoats,
0:10:14 like some of the realities of it. Um, you know, little things like, you know, how much control
0:10:21 will the founding team of the acquired company have over those decisions? Um, uh, will it be operated
0:10:27 as a standalone business unit or will your team be sort of broken up into functional groups within the
0:10:33 larger company? And it’s sort of those little, they’re not little, but those I’ll say boring,
0:10:38 but important things that often people don’t talk enough about. And you don’t need to figure
0:10:42 out every part of an acquisition to make it successful, but often you can end up running
0:10:47 into like true third rails that you didn’t find because you were having the storytelling discussions
0:10:51 rather than getting down to brass tacks about how things will work and what’s important.
0:10:55 The other thing that I think is really important is being really clear what success looks like.
0:11:02 Um, and you know, I think, uh, sometimes it’s a business outcome, sometimes it’s a product goal,
0:11:11 but I found that, um, if you went to most of the like larger acquisitions, uh, in the valley and you,
0:11:18 two weeks after it was closed, interviewed the management team of the acquiring company and the
0:11:21 acquired company and you asked them like, what does success look like two years from now?
0:11:28 My guess is like 80% of the time you get different answers. Um, and I think, uh, it goes back to this
0:11:31 sort of storytelling thing where you’re talking about the benefits of the acquisition. We’re talking
0:11:35 about like, what does success look like? So I really tried to approach it. I tried to, um,
0:11:40 pull forward some harder conversations when, when we’re, you know, when I’m doing acquisitions or
0:11:45 even when I’m being acquired since it’s happened to be not twice so that, you know, when you’re approaching
0:11:50 it, you’d not only get the, Hey, why is one plus one equal greater than two? Everything’s gonna be
0:11:57 awesome. You know, but no, for real, like what, you know, what does success look like here? And then,
0:12:02 you know, as a founder, your job of an acquiring acquired company is to tell your team that and align
0:12:07 your team to that. And I think founders don’t take on enough accountability towards making these
0:12:11 acquisitions successful as I think they should. And, um, and it goes back to again, a certain,
0:12:17 uh, naivete, you know, it’s like, you’re, you’re, you’re not your company anymore.
0:12:22 You’re a part of something larger. And I think, you know, successful ones work when everyone embraces,
0:12:27 um, embraces that. What point in the acquisition process is that conversation? Is that after we’ve
0:12:33 signed our, our binding, you know, sort of commitment or is it, we should have that conversation
0:12:39 before. So I know what I’m walking into. My personal take is it’s not something you have,
0:12:44 you have to get to the point where the two parties want to merge, you know, and that’s a,
0:12:49 obviously a financial decision, particularly if it’s like a public company, there’s a board and
0:12:56 shareholders. Most acquisitions in the Valley are a larger firm acquiring a private firm. That’s not
0:13:00 all of them, but I would say that’s the vast majority. And in those cases, there’s often a
0:13:04 qualitative threshold where someone’s like, yeah, let’s do this. We’ve kind of have the high level
0:13:09 terms, sometimes a term sheet, you know, formally, I think it’s right after that. Um,
0:13:16 so where people have really committed to the, the key things, how much value, why are we doing this,
0:13:23 the big stuff. And there’s usually, uh, you know, many, lots of lawyers being paid lots of money to
0:13:28 turn those term sheets into, you know, uh, uh, more complete set of documents, usually more complete
0:13:33 due diligence, stuff like that. That’s a, there’s an awkward waiting period there. And
0:13:38 that’s a time I think where like the strategic decision makers in those moments can get together
0:13:44 and say, let’s talk through what this really means. And, um, the nice part about having them for all
0:13:48 parties is you’ve kind of made the commitment to each other. So it’s, you’ve, I think you have more
0:13:54 social permission to have real conversations at that point. Um, but you also haven’t consummated the
0:14:01 relationship, you know? And so, uh, there’s a, the power imbalance isn’t totally there and, and you can
0:14:06 really talk through it. And it also, I think engenders trust just because by having harder
0:14:11 conversations in those moments, you’re learning how to have real conversations and learning how each
0:14:16 other works. So that’s my, my personal opinion when to have it. So you mentioned the board, you’ve been
0:14:22 on the board of Shopify, you’re on the board of open AI, you’re a founder. What’s the role of a board
0:14:26 and how is it different when you’re on the board of a founder led company?
0:14:34 I, um, really like being involved, um, in a board. Um, and I’ve been involved in multiple boards
0:14:40 because I think I am an operator through and through. I probably self-identify as an engineer first
0:14:48 more than anything else. And I love to build learning and how to be an advisor, um, is a very different
0:14:54 vantage point that I think, uh, you see how other companies operate and you also learn how to
0:15:00 have an impact and add value without doing it yourself. Um, and it’s a very, and I’ve really,
0:15:05 I think become a better leader, you know, having learned to do that. I have really only joined,
0:15:12 uh, boards that were led by founders because typically I think they, you can speak to them,
0:15:17 but I think they sought me out because I’m a founder and I like working with founder led companies. Um,
0:15:26 I, I think the, uh, founders, I’m sure there’s lots of studies on this, but I think founders drive better
0:15:34 outcomes for, uh, companies. Um, there’s a, I think founders tend to have permission to make bolder,
0:15:39 more disruptive decisions about their business than a professional manager. There’s exceptions
0:15:44 like Satya, I think is, you know, uh, one of the greatest and not the greatest CEO of, you know,
0:15:50 our generation and, uh, as a professional manager, but you know, you look at, uh, everyone from, uh,
0:15:57 Toby Lukey to Mark Benioff to Mark Zuckerberg to, uh, Sam at open AI. And I think when you have founded
0:16:04 a company, it’s all your stakeholders, employees in particular, uh, give you the benefit of the
0:16:09 doubt. You know, you created this thing. And if you say, Hey, we need to, um, do a major shift in
0:16:16 our strategy, even hard things like, uh, layoffs, founders tend to get a lot of latitude and are
0:16:21 judged, I think differently. And, and I think rightfully so in some ways, because of the interconnection
0:16:26 of their identity to the thing that they’ve created. And so I actually really believe in founder led
0:16:32 companies. Um, one of the real interesting challenges is going from a founder led company to
0:16:35 not, and you know, Amazon has gone through that transition. Microsoft has gone through that
0:16:42 transition, um, for that reason. Uh, but I love working with founders. Um, and I, I love working
0:16:49 with people like Toby and Sam because they’re so different than me yet. Um, uh, and I can see how
0:16:54 they operate their businesses and I am inspired by it. I learned from it and obviously working for, for
0:16:58 market Salesforce, you, you have like, wow, that’s really interesting. Like most like an
0:17:03 anthropologist. Like, why did you do that? You know, I want to learn more. And so I love working
0:17:07 with founders that inspire me because I just learned so much from them. It’s such an interesting front
0:17:11 row seat into what’s happening. Do you think founders go astray when they start listening to
0:17:16 too many outside voices? And this goes back to the, I’m sure you’re aware of the Brian Chesky,
0:17:20 founder mode, the founder mode. Do you think, talk to me about that.
0:17:27 I have such a nuanced point of view on this because it is decidedly not simple. Uh, so
0:17:34 broadly speaking, I really like the spirit of founder mode, which is just having
0:17:42 deep founder led accountability for every decision at your company. Um, I think that that’s how great
0:17:49 companies operate. Uh, and when you, you know, proverbially make decisions by committee or you’re
0:17:55 more focused on process than outcomes, um, that produces all the experiences we hate as employees,
0:17:59 as customers, you know, that’s the proverbial DMV, right? You know, it’s like process over outcomes.
0:18:06 Um, and then similarly, uh, you look at the disruption in all industries right now because
0:18:12 of AI, you know, the companies that will recognize where things are clearly going to change. Like
0:18:18 everyone can see it. It’s like a slow motion car wreck. Everyone knows how it ends. You need that
0:18:24 kind of decisive breakthrough boundaries, layers of management, um, to actually make change as fast
0:18:30 as required in business right now. The issue I have not with Brian’s statements, Brian’s amazing,
0:18:37 um, is how people can sort of interpret that and sort of execute it as a caricature of what I think
0:18:44 it means. Uh, you know, there was a, I remember after Steve jobs passed away and you know, um, I don’t
0:18:50 know, I’ve met Steve a couple of times. I haven’t never worked with him in any meaningful way, you know,
0:18:54 know, but he was sort of, uh, if you believe the story is like kind of, uh, pretty hard on his
0:18:59 employees and, and very exacting. And I think a lot of founders were like mimicking that, you know,
0:19:04 done to wearing a black turtleneck and yelling at their employees. I’m like, not sure that was the
0:19:10 cause, you know, uh, I think Steve jobs taste and judgment through, you know, executed through that,
0:19:15 you know, packaging was the cause of their success and somehow. And then similarly, I think founder
0:19:21 mode can be weaponized as an excuse for just like overt micromanagement. And that probably won’t
0:19:27 lead to great outcomes either. And most great companies are filled with extremely great individual
0:19:35 contributors who make good decisions and work really hard. And, uh, uh, companies that are like solely
0:19:40 executing through the judgment of individual probably aren’t going to be able to scale to be truly great
0:19:46 companies. So I have a very nuanced point because I actually believe in founders. I believe in actually
0:19:52 that accountability that comes from the top. I believe in cultures where, you know, founders have
0:19:58 license to go in and all the way to a small decision and fix it, the infamous question mark emails from
0:20:02 Jeff Bezos, you know, that type of thing. That’s, that’s the right way to run a company, but that doesn’t
0:20:08 mean that you don’t have a culture where individuals are accountable and empowered. And, uh, you don’t
0:20:12 want, uh, you know, people trying to decide, make business decisions because of what will please our
0:20:17 dual leader, you know, which is like the caricature of this. And so, you know, after that came out,
0:20:20 I could sort of see it all happening, which is like, some people will take that and be like, you know what,
0:20:24 you’re right. I need to go down and be in the details. And some people will do it and probably make
0:20:28 everyone who works for them miserable and probably both will happen as a consequence. So.
0:20:34 That’s totally. Thank you for the detail and nuance that I love that too. Do you think engineers make
0:20:39 good leaders? I do think engineers make good leaders, but one thing I’ve seen is that
0:20:50 I think that I really believe that great CEOs and great founders, um, start usually with one specialty,
0:20:57 but become, uh, more broadly specialists in our parts of their business. Um, you know,
0:21:04 I think the, uh, businesses are multifaceted and rarely is a business’s success due to one
0:21:08 thing, uh, like engineering or product, which is where a lot of founders come from.
0:21:14 Often your go to market model is important, uh, for consumer companies, how you engage with the
0:21:22 world and public policy becomes extremely important. And I think as you see, um, uh, founders, you know,
0:21:27 grow from doing one thing to growing, to be in a real meaningful company like Airbnb or Meta or
0:21:32 something, you can see those founders really transform from being one thing to many things.
0:21:38 Um, so I do think engineers make great leaders. I think the first principles thinking the system
0:21:46 design thinking, um, really benefits things like organization design strategy. Um, and, but I also
0:21:53 think that, you know, uh, when we were speaking earlier about identity, I think one of the main
0:21:58 transitions founders need to make, especially engineers, uh, is you’re not like the product
0:22:06 manager for the company or the CEO. And at any given day, do you spend time recruiting an executive
0:22:12 because you have a need? Do you spend time, uh, on sales because that will have the biggest impact?
0:22:19 Um, do you spend time on public policy or regulation? Because if you don’t, uh, it will happen to you and,
0:22:25 and could really impact your business in a negative way. And I think engineers who are
0:22:31 unwilling to elevate their identity from what they were to what it needs to be in the moment
0:22:36 often leads to sort of plateaus, uh, in companies growth. So a hundred percent, I think engineers
0:22:43 make great, um, leaders and it’s not a coincidence. I think that most of the Silicon Valley great Silicon
0:22:49 Valley CEOs came from engineering backgrounds. Um, but I also don’t think that’s sufficient either
0:22:54 as your company scales. And I think that making that transition as all the great ones have is incredibly
0:23:00 important. To what extent are all business problems, engineering problems? That’s a deeper philosophical
0:23:06 question that I think I have the capacity to answer. Um, what is engineering? What I like about
0:23:14 approaching problems, uh, as an engineer is, uh, first principles thinking and understanding,
0:23:21 uh, the root causes of issues rather than simply addressing the symptoms of the problem. And I do think
0:23:27 that coming from a background in engineering, that is, um, everything from process, like how engineers do a
0:23:32 root cause analysis of a outage on a server is a really great way to analyze why you lost a sales
0:23:39 deal. You know, like I love the systematic approach of engineering. One thing that I think going back to
0:23:45 good ideas that can become caricatures of themselves, like one thing I’ve seen though with engineers who
0:23:53 go into other disciplines is, um, sometimes you can overanalyze decisions in some domains. Let’s just take
0:24:00 modern communications, which is driven in social media and, and very fast paced. Um, having a
0:24:07 systematic first principles discussion about every, you know, tweet you do is probably not a great comms
0:24:15 strategy. Um, and so, uh, and then similarly, um, you know, there are some aspects of say enterprise
0:24:22 software sales that, you know, aren’t rational, but they’re human, you know, like forming personal
0:24:27 relationships, you know, and, and the importance of those to building trust with a partner. It’s not
0:24:33 all just, you know, product and technology. And so I would say, I think a lot of things, uh, coming
0:24:40 with an engineer mindset can really benefit, but I do think that, uh, taking that to its like logical
0:24:47 extreme can lead to analysis paralysis, can lead to, uh, over intellectualizing some things that are
0:24:52 fundamentally human problems. And so, yeah, I think a lot can benefit from engineering, but I wouldn’t say
0:24:57 everything’s an engineering problem in my experience. You brought up first principles a couple times. You’re
0:25:03 running your third startup now, Sierra. It’s going really well. How do you use first principles in terms
0:25:12 of how to use that at work? Yeah, it’s, it’s particularly important right now because the market of AI is
0:25:22 changing so rapidly. So if you rewind two years, you know, most people hadn’t used chat GPT yet. Uh,
0:25:31 most companies hadn’t heard the phrase large language models or generative AI yet. And in two years, you have chat GPT
0:25:38 becoming one of the most popular consumer services in history faster than his than any service in history.
0:25:46 And you have across so many domains in the enterprise, uh, really rapid transformation.
0:25:52 The law is being transfer transformed. Marketing is being transformed. Customer service, which is where my
0:26:00 company Sierra works is being transformed. Software engineering is being transformed. And the amount of
0:26:06 change in such a short period of time is, uh, I think unprecedented. Uh, and, uh, perhaps I lack the
0:26:12 historical context, but it feels faster than anything I’ve experienced in my career. And so as a consequence,
0:26:19 I think, uh, if you’re responding to the facts in front of you and not thinking from first principles about
0:26:25 why we’re at this point and where it will probably be 12 months from now, the likelihood that you’ll make the
0:26:33 right strategic decision is almost zero. Uh, so, uh, as an example, uh, it’s really interesting to me that
0:26:39 with modern large language models, one of the careers that is being most transformed is software engineering.
0:26:47 Uh, and, uh, you know, one of the things I think a lot about is how many software engineers will we have
0:26:54 our company three years from now? What will the role of a software engineer be as we go from being authors of code to
0:26:57 operators of code generating machines? Um,
0:27:01 What does that mean for the type of people we should recruit?
0:27:06 And if I look at the actual craft of software engineering that we’re doing right now, um,
0:27:11 I think it’s literally a fact that it’ll be completely different two years from now.
0:27:16 Yet I think a lot of people building companies hire for the problem in front of them rather than doing
0:27:22 that. But two years is not that long. Those people that you hire now will just be getting really
0:27:28 productive a couple of years from now. So we try to think about most of our long-term business from
0:27:34 first principles, everything from, I’ll say a couple examples in our business. Our pricing model is really
0:27:40 unique and comes from first principles thinking rather than having our customers pay a license for the
0:27:45 privilege of using our platform. We only charge our customers for the outcomes. Uh, meaning if the
0:27:50 AI agent they’ve built for their customers solves the problem, there’s like a usually a pre-negotiated
0:27:55 rate for that. And that comes from the principle that in the age of AI, software isn’t just helping
0:28:02 you be more productive, but actually completing a task. Uh, what is the right and logical business
0:28:06 model for something that completes a task? Well, charging for a job well done rather than charging for
0:28:12 the privileges using the software. Um, similarly, um, you know, we, with a lot of our customers,
0:28:18 you know, we help deliver them a fully working AI agent. We don’t hand them a bunch of, uh, software
0:28:25 and say, good luck, you know, configure it yourself. And the logic there is, you know, uh, in a world where,
0:28:32 uh, making software is easier than it ever is before. And you’re delivering outcomes for your customer.
0:28:37 Um, the delivery model of software probably should change as well. And we’ve really tried to
0:28:41 reimagine what like the software company of the future should look like and trying to,
0:28:46 you know, model that in everything that we do. That’s brilliant. How do you think software
0:28:50 engineering will change? Is it you’re going to have fewer people or the people are going to be
0:28:57 organized differently or how do you see that? How geeky can I get as geeky as you want?
0:29:04 I actually wrote a blog post, uh, right before Christmas about this. I think this is an area
0:29:09 that deserves a lot more research. Uh, I’ll describe where I think we are today and smart people may
0:29:18 disagree, but a lot of the modern large language models, both the traditional large language models
0:29:24 and sort of the new reasoning models are trained on a lot of source code. And it’s a, an important input
0:29:29 to all of the knowledge that they’re trained on. Um, as a consequence, even the early models were
0:29:37 very good at generating code. Um, so, you know, uh, every single engineer at, at CIRA uses a cursor,
0:29:43 which is a great product that basically integrates with the IDE visual studio code to help you
0:29:50 generate code more quickly. Um, it feels like a local maximum, uh, in a really obvious way to me,
0:29:57 which is you have a bunch of code written by people, um, written in programming languages that
0:30:04 were designed to make it easy for people to tell a computer what to do. Probably this funniest example,
0:30:10 this is Python. Um, it almost looks like natural language, but it’s notoriously not robust. Um,
0:30:16 you know, most Python bugs are found by running the program because there’s not static type checking.
0:30:25 Um, similarly, there’s most bugs, uh, while you could run a fancy static analysis, like most bugs show up
0:30:32 simply at runtime because, uh, it’s just not designed. Um, it’s designed to be ergonomic to write. Um,
0:30:39 yet we’re using AI to generate that. Uh, we, and so we’ve sort of designed most of our computer
0:30:47 programming systems to make it easy for the author of code to type it quickly. Um, and we’re in a world
0:30:53 where actually generating code is going to like the marginal cost of doing that is going to zero,
0:30:58 but we’re still generating code and programming languages that were designed for human authors.
0:31:07 And similarly, um, if you’ve ever like looked at someone else’s code, um, which a lot of people
0:31:13 do professionally, it’s called the code review. It’s actually quite hard to do a code review. Um,
0:31:18 you know, you’re end up interpreting, you’re trying to basically put the system in your head and simulate
0:31:25 it as you’re reading the code to find errors in it. So the irony now that I’ve taken things that are
0:31:30 code programming languages that were designed for authors and now having humans do the job of
0:31:36 essentially code reviewing code written by an AI. And, and yet all of the AI is being in the code
0:31:42 generation part of it. I’m like, I’m not sure it’s, it’s great, but we’re generating a lot of
0:31:47 code with similar flaws to that we’ve been generating before from security holes to just functional bugs
0:31:54 and in greater volumes. And I think we’re, uh, what I would like to see is if you start with the
0:32:01 premise that generating code is free or, or, or going towards free, what would be the programming
0:32:06 systems that we would design? So for example, uh, you know, Rust is an example of a programming
0:32:13 language that was designed for safety, not for programming convenience. Uh, you know, my understanding is
0:32:18 that the, you know, Mozilla project, you know, there were so many security holes in
0:32:24 Firefox. They said, let’s make a programming language that’s very fast. Uh, you know, uh,
0:32:28 but everything can be checked statically, including memory safety. Well, it’s a really interesting
0:32:34 direction where you weren’t operating, like optimizing for authorship convenience or optimizing for correctness.
0:32:40 Are there programming language designs that are designed so a human looking at it can very quickly
0:32:46 evaluate, does this do what I intended it to do? There’s an area of computer science I studied in
0:32:50 college called formal verification, which at the time was turning a lot of computer programs into
0:32:55 math proofs and finding inconsistencies. And it sort of worked well, not as well as you’d hope.
0:33:02 But, you know, in a world where AI is generating a lot of code, you know, should we be investing in
0:33:08 more informal verification so that the operator of that code generator machine can more easily verify
0:33:14 that it does in fact to do what they intended is to do? And could a combination of a programming
0:33:19 language that is more structurally correct and structurally safe and exposes more primitives for
0:33:26 verification plus a tool to verify? Could you make an operator of a code generated machine 20 times
0:33:30 more productive, but more importantly, make the robustness of their output 20 times greater?
0:33:36 And then similarly, you know, there’s themes, things go in and out of fashion, but like test driven
0:33:40 development, you know, where you write your unit test first or your integration test first and then
0:33:44 write code until it fulfills the test. Most programmers I know who are really good,
0:33:49 not despise it, but it’s just like a, it sounds better than it, than it is in practice. But
0:33:54 again, writing code is free, you know, so writing tests is free, you know, how can you create a
0:34:00 programming system where the combination of great programming language design, formal verification,
0:34:05 robust tests, because you didn’t have to do the tedious part of writing them all. Could you make
0:34:12 something that made it possible to write increasingly complex systems that were increasingly robust?
0:34:15 And then similarly, like the elephant in the room for me is the anchor tenant of most of
0:34:19 these code generating systems are an IDE right now, you know, and
0:34:26 that obviously doesn’t seem as important in this world. And even with coding agents,
0:34:30 which is sort of where the world is going, it doesn’t change the fact that like, you know,
0:34:33 who’s accountable for the quality of it, who’s fixing it. And I think
0:34:39 there is a world where we can make reasonable software by just automating what we as software
0:34:46 engineers do every day. But I have a strong suspicion that if we designed these systems with the role of a
0:34:53 software engineer in mind, being an operator of a machine rather than the author of the code,
0:34:58 we could make the process much more robust and much more productive. And it feels like a research
0:35:03 problem to me, it doesn’t feel. And I think a lot of people, and for good reason, including me,
0:35:08 are just excited about the efficiency of software development going up. And I want to see the new
0:35:11 thing, though. I’m constructively dissatisfied with where we are.
0:35:16 It’s so interesting that if software AI is good enough to write the code, should we get enough to
0:35:17 check the code?
0:35:23 That’s a great, great question. But actually, I’ll, you know, it’s still funny to me that we’d be
0:35:28 generating Python, you know, just because for anyone who’s listening right now has ever operated a web
0:35:34 service running Python, it’s CPU and intensive, really inefficient. You know, should we be taking most of
0:35:41 the unsafe C code that we’ve written and converting it to a safer system like Rust? You know, if authoring
0:35:46 these things and checking it are relatively free, shouldn’t all of our programs be incredibly
0:35:52 efficient? Should they all be formally verified? Should they all be analyzed by a great agent? I do
0:35:58 think it can be turtles all the way down. You can use AI to solve most problems in AI. The thing that I’m
0:36:04 trying to figure out is like, what is the system that a human operator is using to orchestrate all those
0:36:10 tasks. And, you know, I go back to the history of software development, and most of the really
0:36:15 interesting metaphors in software development came from breakthroughs in computing. So, you know, the C
0:36:20 programming language came from Unix. And when these time sharing systems were really, it went from sort
0:36:27 of punch cards to something that were a lot more agile. Small talk came out of the development of the
0:36:34 graphical user interface at Xerox PARC. And, you know, there was a sort of a confluence of message
0:36:39 passing as a metaphor and the graphical user interface. And then there was a lot of really
0:36:46 interesting principles that came out of networking, you know, and sort of distributed systems, distributed
0:36:52 locking, sequencing. I think we should recognize that we’re in this brand new era as significant
0:36:57 as the GUI. You know, it’s like a completely new era of software development. And if you were just to say,
0:37:03 I’m going to design a programming system for this new world from first principles, what would it be?
0:37:06 And I think when we develop it, I think it will be really exciting because rather than
0:37:13 automating and turning up the speed of just generating code and with the same processes we
0:37:21 have today, I think we’ll feel native to this system and give a lot more control to the people who are
0:37:24 orchestrating the system in a way that I think will really benefit software overall.
0:37:29 Let’s dive into AI a little bit. How would you define AGI to the layman?
0:37:39 I think a reasonable definition of AGI might be that any task that a person can do at a computer,
0:37:49 that system can do on par or better. I’m not sure it’s a precise definition, but I’ll tell you where that
0:37:54 comes from and it’s flaws, but there’s not a perfect definition of AGI in my opinion,
0:37:59 or there’s not a precise definition of AGI. I’m sure there’s good answers.
0:38:06 One of the things about the G and AGI is about generalization. So can you have a system that is
0:38:14 intelligent in domains that it wasn’t explicitly trained to be intelligent on? And so I think that’s
0:38:22 one of the most important things is like given a net new domain, can this system become more competent
0:38:31 and more intelligent than a person sort of trained in that domain? And I think that’s sort of the,
0:38:35 you know, at or better than a person is certainly a good standard there. And that’s sort of the
0:38:40 definition of super intelligence. The reason I mentioned at a computer is I do think that
0:38:51 it is a bar that means like if there’s a digital interface to that system, it affords the ability
0:38:59 for AI to interact with it, which is why that’s a bar that’s reasonable to hit. I say that because
0:39:08 one of the interesting questions around AGI is how quickly it does generalize. And there are domains in
0:39:19 the world that the progress in that domain isn’t necessarily limited by intelligence, but by other
0:39:24 social artifacts. So as an example, and I’m not an expert in this area, but if you think about
0:39:33 the pharmaceutical industry, my understanding is, you know, the one of the main bottlenecks
0:39:42 is clinical trials. So no matter how intelligent a system would be in discovering new therapies,
0:39:50 it may not materially change that. And so you may have something that’s discovering new insights in math,
0:39:55 and that would be delightful and amazing. But the existence of that
0:40:01 system that’s super intelligent in one domain may not translate to all domains equally.
0:40:06 I just heard at least a snippet of a talk by Tyler Cohen, the economist. And
0:40:12 it was really interesting to hear his framing on this about which parts of the economy
0:40:18 could sort of absorb intelligence more quickly than others. And so I choose that definition of AGI,
0:40:24 recognizing that there’s not a perfect definition, because it captures the ability of
0:40:31 this intelligence to generalize, while also recognizing that the domains of society might
0:40:36 not apply with equal velocity, even once we reach that point of a system being able to have that level
0:40:37 of intelligence.
0:40:44 When I think about what artificial intelligence is limited by, or the bottlenecks, if you will,
0:40:50 I keep coming back to a couple of things. There’s regulation, there’s compute, there’s energy,
0:40:54 there’s data, and there’s LLMs. Am I missing anything?
0:40:57 Uh, so you’re saying the ingredients to AGI?
0:41:03 Yeah, like, there’s limitations on each aspect of those things. And there seem to be the main
0:41:09 contributors to the, what’s limiting us from even accelerating at this point. Is that, how do you
0:41:10 think about that?
0:41:14 Yeah, what you said is roughly how I think about it. I’ll put it into my own words, though.
0:41:25 I think the three primary inputs are data, compute, and algorithms. And data is probably obvious,
0:41:30 but you know, one of the things after the Transformers model was introduced is it afforded
0:41:37 an architecture with just much greater parallelism, which meant models could be much bigger and train
0:41:44 more quickly on much more data, um, which just led to a lot of the breakthroughs with, that’s the LLM,
0:41:50 just they’re large. And, uh, the scaling laws, you know, a couple years ago, you know, indicated like the
0:41:58 larger you make the model, um, the more intelligent it would be, and at a degree of efficiency that was, uh, tolerable.
0:42:06 Uh, and there, we are, you know, there’s lots of stuff written about this, but you know, there’s
0:42:11 in terms of just like textual content to train on, you know, the availability of new content is
0:42:16 certainly waning. And some people would say, I think there’s like a data wall. Uh, I’m not an expert in
0:42:21 that domain, but it’s been talked about a lot and you can read a lot about it. There’s a lot of interesting
0:42:27 opportunities though to generate data too. Um, so, uh, there’s a lot of people working on simulation.
0:42:31 If you think about a domain, like self-driving cars, simulation is a really interesting way to
0:42:36 generate. Uh, is that synthetic data? Is that what? Yeah, I would say that synthetic data,
0:42:42 though synthetic data has a, uh, uh, simulation and synthetic data are a little different. So
0:42:49 you can generate synthetic data, like generate a novel. Um, simulation, I would put at least in my
0:42:56 head and I’m sure that like academics might critique what I’m saying, but, uh, I’ve used simulation is
0:43:00 based on a set of principles like the laws of physics. So if you were to build a real world
0:43:06 simulation for training, a self-driving car, um, you’re not just generating arbitrary data,
0:43:10 like the roads don’t turn into loop-de-loops, you know, because that’s not possible with physics.
0:43:18 So by constraining a simulation with a set of, uh, real world constraints, the data has more efficacy,
0:43:24 you know, and so, uh, and there’s, uh, sort of a, it constrains the different permutations
0:43:29 of data you can generate from it. So it’s, I think a little bit higher quality, but then along those
0:43:37 lines, you know, uh, a lot of people wonder if you generate, uh, synthetic data, um, how much value can
0:43:43 that add to a training process? Um, you know, is it sort of, uh, regurgitating information it already
0:43:49 had? What’s really interesting about, you know, reasoning and reasoning models is I think, uh,
0:43:54 I feel really optimistic these models are generating net new ideas. And so it really affords the opportunity
0:44:00 to break through, uh, some of these, the data wall as well. So data is one thing. And I think both
0:44:06 synthetic data and simulation are really interesting opportunities to, to grow there. Then you have compute.
0:44:15 And, uh, this is, um, something that, you know, it’s why, uh, there’s so many data center investments.
0:44:21 It’s why Nvidia as a company has, has grown so much. Um, the, probably the more interesting kind
0:44:27 of breakthroughs there are these reasoning models where, uh, there’s not quite such a formal separation
0:44:33 between the training process and the inference process where you can spend more compute at the
0:44:38 time of inference to generate more intelligence, um, which has really been a breakthrough in a variety
0:44:42 of ways, I think is really interesting, but it shows you how you can run up against walls and,
0:44:47 and find new opportunities to use it. And then finally algorithms. And the biggest breakthrough
0:44:52 is obviously the transformers model attention is all you need that paper from Google that sort of led to
0:44:56 where we are now. But there’s been a number of really important papers since then, from
0:45:02 the idea of chain of thought reasoning into, um, what, uh, at open AI, what we did with the O1 model,
0:45:08 which is to, um, do some reinforcement learning on those chains of thought to, uh, really reach new
0:45:14 levels of intelligence. Um, and so I do think that I mentioned some anecdotes about some breakthroughs
0:45:20 there because my view is that each one of them has their own problems. You know, compute,
0:45:27 it’s very capital intensive. Um, and a lot of these models, the half-life of their value is pretty
0:45:32 short because new ones come out so frequently. And so, you know, you’re, you wonder like, you know,
0:45:37 can we afford, uh, what’s the business case for investing this capex? And then you have a
0:45:44 breakthrough like, uh, you know, O1 and you’re like, gosh, you know, with a distilled model and
0:45:48 moving more to inference time, it changes the economics of it. You have data. You say, gosh,
0:45:53 we’re running out of textual data to train on. Well, now we can generate reasoning. We can do
0:45:56 simulations. Oh, that’s an interesting breakthrough. And then on the algorithm side, as I mentioned,
0:46:02 just the idea of these reasoning models is really novel itself. And each of these at any given point,
0:46:06 if you talk to an expert and in one of them, and I’m an expert in none of them,
0:46:12 they will tell you the sort of current plateau that they can see on the horizon. And there usually is
0:46:16 one, I mean, you’ll talk to different people about how long the scaling laws for something
0:46:19 will continue and you’ll get slightly different opinions, but no one thinks it’s looking to last
0:46:26 forever. Um, and at each one of those, because you have so many smart people working on them,
0:46:32 you often have people discovering a breakthrough, um, in each of them. And so as a consequence, I,
0:46:39 I really do feel optimistic about the progress towards HEI because one of those plateaus might
0:46:44 extend a while if we just don’t have the key idea that we need to break through the idea that we will
0:46:50 be stuck on all three of those domains feels very unlikely to me. And in fact, what we’ve seen
0:46:55 because of the potential economic benefits of AGI is we’re in fact seeing breakthroughs in all three of
0:47:02 them. And, um, as a consequence, um, you know, you’re just seeing just the blistering pace of progress
0:47:09 that we’ve seen over the past couple of years. At what point does AI start making AI better than
0:47:15 we can make it or making it better while we’re sleeping or we can’t be too far from that?
0:47:19 Well, it might reflect back to our software engineering discussion, but you know,
0:47:25 the broadly, this is the area of AGI around self-improvement, which is meaningful from a
0:47:31 improvement standpoint, but also obviously from a safety standpoint as well. So I, um,
0:47:36 I don’t know when that will happen, but I do think, you know, by some definition,
0:47:42 you could argue that it’s happening already in the sense that every engineer in Silicon Valley is already
0:47:49 using coding agents and, um, platforms like Cursor to help them code. So it’s contributing already.
0:47:54 Um, and I imagine as, uh, coding assistants go to coding agents in the future,
0:47:58 most engineers in Silicon Valley will show up in the morning and,
0:48:04 but this is sort of the difference between, uh, you know, the assisted driving and Tesla versus like
0:48:10 self-driving, right? Like at what point do we leap from, I’m a co-pilot in this to,
0:48:12 I’m, I don’t have to do anything.
0:48:17 I mean, it’s a question that’s, there’s so much nuance to the answer. I’m not sure to answer
0:48:21 because I’m not sure you’d want to necessarily, like, I think for some software applications,
0:48:25 that’s important. But when we brought up, you know, we were talking about the active software
0:48:32 development, people have to be accountable, um, for the software that they produce. Um, and that
0:48:38 means if you’re doing something simple, like a software as a service application, that it’s secure,
0:48:45 that it’s reliable, that it, the functionality works as intended for something as meaningful as,
0:48:51 uh, you know, uh, an agent that is, um, somewhat autonomous, does it have the appropriate guard
0:48:57 rails? Um, does it actually do what the operators intended? Is there appropriate safety measures?
0:49:02 So I’m not sure there’s really any system where you’d want to turn a switch and, and go get your
0:49:08 coffee. But I do think to the point on, you know, uh, these broader safety things is I think that when
0:49:15 you think about, uh, more advanced models, we need to be developing not only more and more advanced,
0:49:22 um, safety measures and safety harnesses, but also, um, using AI to supervise AI and things like that.
0:49:28 So it’s a part, uh, probably my colleague on the board, Zico Coulter is probably a better person to talk
0:49:32 through some of the technical things, but there’s a lot of prerequisites to get to that point. And I’m not
0:49:37 sure it’s simply like the availability of the technology. Um, just because it is, uh, that at
0:49:41 the end of the day, we are accountable for the safety of the systems we produce, not just opening
0:49:47 I like every, every engineer. Um, and, and, and that’s a principle that should not change.
0:49:53 What does that mean? Like when we say safety and AI, that seems so vague in general that everybody
0:49:58 interprets it quite differently. Like, how do you think about that? And how do you think about that
0:50:05 in the world where, uh, let’s say we regulate safety in the United States and another country
0:50:11 doesn’t regulate safety? How does that affect the dynamic of it? I’ll answer broadly and then
0:50:18 go into the regulatory question. So I really like opening eyes mission, uh, which is to ensure that
0:50:24 AGI benefits all of humanity. That isn’t only about safety. Um, and, and I, and I believe intentionally.
0:50:29 So though the, the, obviously the mission was created prior to my arrival because it’s both
0:50:34 about safety, kind of Hippocratic oath first to no harm. And I don’t think one could credibly achieve
0:50:39 that mission if we created something unsafe. So I would say that’s the most important part of the
0:50:45 mission, but there’s also a lot of other aspects of benefiting humanity. Um, is it universally accessible?
0:50:52 Is there a digital divide where some people have access to AGI and some don’t? Um, uh,
0:50:58 similarly, you could argue that does it, are we maximizing the benefits and minimizing the downsides?
0:51:04 Uh, clearly, uh, AI will disrupt some job, but it also could democratize access to healthcare,
0:51:11 education, expertise. Um, so as I think about the mission, it starts with safety, but I actually like
0:51:16 thinking about it more broadly because I think at the end of the day, benefiting humanity is the mission
0:51:21 and, um, uh, safety is a prerequisite, but it’s almost like going to my analogy of the Hippocratic
0:51:28 oath. A doctor’s job is, you know, uh, to cure you first do no harm, but then to cure you and a doctor
0:51:32 that did no harm, but didn’t cure you wouldn’t be great either. So I really like to think about the,
0:51:40 uh, holistically and, um, again, uh, Zika or Sam might have a more complete answer here, but broadly,
0:51:48 I think about does the system that represents AGI, um, align with the intentions of the people
0:51:53 created it and the intentions of the people operating it, um, so that it, it does what we want and it’s
0:52:00 a tool, um, uh, that benefits humanity that, um, a tool that we’re actively using, um, to affect the
0:52:05 outcomes that we’re looking for. And that’s kind of the way I think about safety. Um, and, uh,
0:52:10 it can be meaningful things like misalignment or, or more subtle things like unintended consequences.
0:52:16 Um, and I think that latter part is probably the areas, uh, really interesting from, uh, um,
0:52:23 intellectual and ethical standpoint as well. If I look at, um, uh, what was the bridge in Canada
0:52:28 that fell down where it motivated the ring that a lot of engineers. Oh yeah. I forget the name of it,
0:52:34 but just like the, whether it’s the Tacoma Narrows Bridge in Washington or, uh, three mile island or
0:52:43 these intersections where, um, uh, we’ve engineered these, um, you know, what, what at the time people
0:52:49 hope would be positively impact humanity, but something went horribly wrong. Um, sometimes it’s
0:52:54 engineering, sometimes it’s bureaucracy, sometimes it’s a lot of things. And so I don’t think when I think
0:53:00 about safety, I don’t just look at the technical measures of it, but how does this technology
0:53:05 manifest in society? How do we make decisions around it? And you could take, put another way,
0:53:10 technology is rarely innately good or bad. It’s sort of what we do with it. Um, and I think those
0:53:15 social constructs and, uh, matter a lot as well. Um, so I think it’s a little early to tell because we
0:53:21 don’t have this kind of super intelligence right now. Um, and I think it won’t just be a technology
0:53:28 company defining how it manifests in society. And you could imagine, uh, taking a very well aligned
0:53:35 AI system and a human operator directing it towards something, um, that would, uh, objectively hurt
0:53:40 society. And, and there’s a question of like, who gets to decide who’s accountable? And it’s a
0:53:47 perennial question. I mean, it’s whether you’re deciding, uh, you know, uh, uh, should you use
0:53:51 your smartphone in school? You know, who, who should decide that? And I, there’s parents
0:53:55 who will tell you, Hey, it’s my decision. It’s my kid. And then there’s principals who will tell
0:54:00 you it’s not benefiting the school. And I’m not sure that’s going to be my place or our place,
0:54:04 but there’ll be a number of those conversations that are much deeper than that question that I
0:54:12 think we’ll need to answer. Um, as it relates to regulation, uh, there’s two, uh, not conflicting
0:54:16 forces, but two forces that exist somewhat independently, but relate to each other. One
0:54:22 is the pace of progress in AI and ensuring that, you know, uh, the, the folks working on frontier
0:54:29 models are ensuring those models do benefit humanity. And, uh, and then there’s the, uh,
0:54:36 sort of geopolitical landscape, which is, you know, do you want, uh, AGI to be created by the freedom,
0:54:43 uh, sort of, uh, the West, um, by democracies, um, or do you want it to be created by more
0:54:50 totalitarian governments? And so I think the inherent tension for regulators will be, um,
0:54:57 a sense of obligation to ensure that, you know, uh, the technology organizations creating AGI
0:55:04 are in fact focusing enough on, um, that I’m fitting humanity, all the other, uh, uh, stakeholders
0:55:11 there that whose, uh, interests that they’re accountable for and ensuring that the West remains
0:55:16 competitive. Um, and, and, uh, I think that’s a really nuanced thing. And I think, uh, you know,
0:55:23 my, my view is it’s very important that the West is, uh, leads in AI and I’m very proud of the fact that,
0:55:28 um, you know, open AI is based here in the United States and we’re investing a lot in the United
0:55:32 States. And I think that’s very important. And I also, you know, having sort of seen the inside of,
0:55:37 I think we’re really focused on benefiting humanity. So I, I tend to think that, you know,
0:55:41 it needs to be a multi-stakeholder dialogue, but I think there’s a really big risk that some
0:55:47 regulations could have the unintended consequence of, of, uh, slowing down this larger conversation.
0:55:51 But I don’t say that to be dismissive of it either. It’s actually just a impossibly hard,
0:55:55 uh, problem. And I think you’re seeing it play out as you said, and in really different ways in
0:56:00 Canada, United States, Europe, China, elsewhere. I want to come back to compute and the dollars
0:56:07 involved. So, I mean, on one hand you have, um, if I just, I could start an AI company today by,
0:56:12 you know, going, putting my credit card down and using AWS and leveraging their infrastructure,
0:56:17 which they’ve, they’ve built, they’ve spent the hundreds of billions of dollars and I get to use
0:56:24 it on a time-based model. On the other hand, you have people like open AI, uh, and Microsoft investing
0:56:32 tons of money into it that may be more proprietary or, um, how do you think about the different models
0:56:38 competing? And then the one that really throws me for a bit of a loop is Facebook. So Facebook has
0:56:47 spent meta, I’m like aging myself here. So meta comes along and, you know, possibly for the good
0:56:53 of humanity, but like I tend to think Zuck is like incredibly smart. So I don’t think, I don’t think
0:56:59 he’s spending, you know, a hundred billion dollars to develop a free model and give it away to society.
0:57:05 How do you think about that in terms of return on capital and return on investment?
0:57:11 It’s a really complicated business to be in just given the capex required to build
0:57:15 a frontier model. But let me just start with a couple definitions of terms that I think are useful.
0:57:22 Um, I think most large language models I would call foundation models. And I like the word foundation
0:57:28 because I think it will be foundational to most intelligent systems going forward. And
0:57:37 most people building modern models, uh, particularly if they involve language image or, or audio shouldn’t
0:57:42 start from building a model from scratch. They should pick a foundation model, either use it off the shelf
0:57:47 or fine tune it. Um, and so it’s truly foundational in many ways. Uh, in the same way,
0:57:52 most people don’t build their own servers anymore. They lease them from one of the cloud infrastructure
0:57:58 providers. I think foundation models will be something trained by companies that have a lot
0:58:06 of capex and leased by a broad range of customers who have a broad range of use cases. Um, and I think
0:58:12 that leads in the same way that data center builders having a lot of data centers enabled you to have
0:58:17 the capital scale to build more data centers. I think the same will largely be true of, uh, you know, building
0:58:23 the huge clusters to, to do training and things like that. Foundation models, I think are somewhat distinct
0:58:30 from frontier models and frontier models. I think it’s a term credited to, to Reed Hoffman, but, uh, I may be mistaken
0:58:35 on that. That’s where I heard it from. And these are the models that are usually like the one or two that are
0:58:43 clearly the leading edge. Oh, three, as an example from open AI. And these frontier models are being built by labs
0:58:52 who are trying to build AGI that benefits humanity. And I think if you’re deciding whether you’re building a
0:59:00 foundation model and, uh, what your business models around it, it’s very different business than I’m going to go pursue AGI.
0:59:07 Uh, because if you’re pursuing AGI, really, there’s only one answer, which is to build and train and move to
0:59:13 the next front, you know, horizon, because if you can truly build something that is AGI, the economic value
0:59:20 is so great. Uh, I think there’s a really clear business case there. If you’re pre-training a foundation
0:59:28 model, that’s the fourth best, uh, that’s going to cost you a lot of money. And the return on that
0:59:36 investment is, is probably fairly questionable because why use your fourth best large language model versus a frontier
0:59:43 model or an open source one, uh, for meta. And as a consequence of that, I think we have probably have too many people
0:59:49 building models right now. There’s already been some consolidation actually of companies being folded into Amazon and
0:59:56 Microsoft and others. But I do think it will play out a bit like the cloud infrastructure business where a very small number of
1:00:04 companies with very large CapEx budgets, uh, are responsible for both building and operating these data centers.
1:00:10 And then developers and, and in consumers will use things like chat GPT as a consumer or as a developer,
1:00:16 you’ll license, uh, and rent, you know, one of these models, uh, in the cloud. Um,
1:00:21 how it will play out is a really great question. You know, I think the, uh, I heard one investor
1:00:28 talk about these as like the fastest depreciating assets of all time. Um, uh, on the other hand,
1:00:34 you know, I, uh, if you look at the revenue scale of something like an open AI and, and what I’ve read
1:00:41 about places like Anthropic, let alone Microsoft and Amazon, it’s pretty incredible as well. And so you
1:00:46 can’t really, if you’re one of those firms, you can’t afford to sit on the sidelines as, as the
1:00:51 world transforms. But I, I would have a hard time personally like funding, uh, a startup that says
1:00:56 I’m going to do pre-training. You know, it’s, it’s, uh, it’s, I don’t really know like what’s your,
1:01:00 what’s your, um, differentiation in this marketplace. And I think a lot of those companies,
1:01:05 you’re already seeing them consolidate because they have the cost structure of a pharmaceutical
1:01:09 company, but not the business model. But this is just it though, right? Like open AI has a revenue
1:01:16 model around a revenue model. Microsoft has a revenue model around their AI investments. They
1:01:24 just updated the price of teams with copilot. You know, uh, Amazon has a revenue model around
1:01:29 AI in a sense to getting other people to pay for it through AWS. And then they’re getting the advantages
1:01:35 of it, uh, at Amazon too, from a consumer point of view and all the millions of projects. Bezos
1:01:40 was doing an interview last week. He said, there’s every project at Amazon basically has an AI component
1:01:46 to it now. Facebook on the other hand has spent all of this money already. And with, you know, an endless
1:01:54 amount, presumably insight or like not insight and endless amount to go, but they don’t have a revenue
1:01:59 model specifically around AI where it would have been cheaper obviously for them to use a different
1:02:05 model, but that would have required presumably giving data away or like, you know, I’m just trying to work
1:02:12 through it from Zuck’s point of view. If you know, I actually will take Mark at his word and you know,
1:02:18 that post he wrote about open source, I think was very well written and encourage people to read it.
1:02:23 I think that’s a strategy. And you know, if you look at Facebook, um, you know, you’ve got me saying
1:02:28 Facebook too. So that was, that was what it was called. You know, the company has always really
1:02:34 embraced open source. And if I look at really popular things from react to, uh, you know, now
1:02:40 the llama models, it’s always been a big part of their strategy to court developers around sort of their
1:02:46 ecosystem. And Mark articulated some of the strategy there and I’m sure there’s elements of commoditizing
1:02:52 your compliment. But I also think that, you know, if you can attract developers towards models, there’s
1:02:58 a strength. Um, I, you know, I’m not really on the inside there, so I don’t really have a perspective
1:03:04 on it other than I actually think it’s really great that there’s different players with different
1:03:10 incentives, all investing so much. And I think it is really furthering the cause of like bringing these
1:03:18 amazing tools to society. And, um, but a lot changes. I mean, if you look at the price of GPT
1:03:24 4.0 mini, you know, it is, uh, so much higher quality than like the highest quality model two years
1:03:30 ago and much cheaper. Um, I, I haven’t done the math on it, but it’s probably cheaper to use that than
1:03:37 to host self host any of the open source models. So even, even the existence of the open source models,
1:03:43 it’s not free. I mean, inference costs money. And so there’s a lot of complexity here. And, and
1:03:48 actually I have the email even being relatively close to stuff. Like I have no idea where things
1:03:53 are going, but you know, it’s, um, you could talk to a smart engineer and they’ll tell you,
1:03:59 oh yeah, if you built your own servers, you’ll spend less than renting them from say Amazon Web
1:04:04 Services or Azure. That’s sort of true in absolute terms, but misses the fact, like, do you want someone
1:04:09 on your team building servers? Oh, and in fact, if you change the way your service works and you
1:04:14 need a different SKU, like you all of a sudden are doing training and you need Nvidia, uh, you know,
1:04:21 H 100s. Now all of a sudden you’re built servers like this, you know, asset that’s worthless. So
1:04:26 I think with a lot of these models, you know, the presence of open source is incredibly important and,
1:04:32 and, uh, and, uh, and I really appreciate it. I also think like the economics of AR pretty complex
1:04:39 because the hardware is very unique. The cost to serve is much higher. Um, techniques like
1:04:45 distillation have really changed the economics of models, whether or not it’s open source or, or,
1:04:52 uh, hosted and, and, and leased. Um, so it’s, I think broadly speaking for developers,
1:04:57 it’s kind of an amazing time right now because you have a, like a menu of options that’s incredibly
1:05:02 wide. And I actually think of it as, you know, just like in cloud computing, you’ll end up with
1:05:06 a price performance quality trade off. And for any given engineering talents, they’ll have a different
1:05:13 answer and that’s appropriate. And some people use, uh, open source Kafka. Some people work with
1:05:19 confluent. Um, great. You know, like that’s just the way these things work, you know, and, um,
1:05:22 so you don’t think AGI is going to be like a winner take all. You think there’s going to be multiple
1:05:30 options that have by definition, whatever the definition is of AGI. Well, first I think open AI,
1:05:36 I believe will play a huge part in it because, uh, there’s both the technology, which I think open AI
1:05:44 continues to lead on. Um, but also chat GPT, which has become synonymous with AI for most consumers.
1:05:50 But more than that, um, it is the way most people access AI, um, today. And so one of the interesting
1:05:57 things like what is AGI, we talked about, you know, opinions on what the definition might be. But the
1:06:02 other question is like, how do you use that? Like, what do you, what is, uh, what is the packaging? Um,
1:06:09 and some of, uh, intelligence will be simply the outcomes of it, like a discovery of a new drug,
1:06:15 which would be, you know, remarkable and hopefully we can cure some illnesses. Uh, but others will be
1:06:20 just how you as an individual access it. And, you know, I, most of the people I know, like if they’re
1:06:26 signing an apartment lease, we’ll put it into chat GPT, you get a legal opinion. Uh, if you get, you know,
1:06:34 lab results from your doctor, you can get a second opinion on, on chat GPT, um, Clay and I use, uh,
1:06:42 the O1 pro mode for like criticizing our strategy at Sierra all the time. And so for me, what’s so
1:06:48 remarkable about chat GPT, which was this, you know, quirkly named research preview that has come to be
1:06:56 synonymous with AI as I do think that it will be the delivery mechanism for AGI when it’s produced and, uh,
1:07:02 not just because of the many researchers at open AI, but because of the amazing like utility it’s become
1:07:06 from individuals. And I think that’s really neat because I don’t know if it would have been obvious
1:07:12 if we were having this conversation three years ago, um, you know, and you were talking about
1:07:17 artificial general intelligence. I’m not sure either of us would have envisioned something so simple as
1:07:24 a form factor, uh, to absorb it that you just talk to it. Um, so I think it’s great. And especially as I
1:07:30 think about the mission of open AI, which is to ensure that AGI benefits humanity, what a simple,
1:07:37 accessible form factor, there’s free tiers of it. Like what a kick ass way to benefit humanity. So I
1:07:42 really think that will be central to what we come as society to define, uh, as a AGI.
1:07:50 You mentioned using it at Sierra to critique your, your business strategy. What do you know about prompting
1:07:54 that other people miss? I mean, you must have the best prompts.
1:07:56 People think that, you know, cause I’m affiliated with it.
1:07:59 You’re not going like, here’s my strategy. What do you think? What are you putting in there?
1:08:07 Um, I often with the, uh, reasoning models, which are slower, we’ll use a faster model
1:08:17 first GPT four O to refine my prompts. Um, so, uh, over the holidays, um, partly because I was thinking
1:08:21 about the future of software engineering, I’ve, I’ve written a lot of compilers in my time. I’m like
1:08:27 written enough that I, you know, it’s like, uh, uh, uh, it’s, it’s easy for me. So I decided to see
1:08:36 if I could have a one pro mode, um, generate end to end, a compiler front end, parsing the grammar,
1:08:42 um, checking for semantic correctness, generating an intermediate representation, and then using,
1:08:49 uh, uh, LLVM, which is sort of a compiler collection, um, that’s very popular to actually do,
1:08:56 you know, run, run it all. And I would spend a lot of time iterating on four O to sort of like
1:09:02 refine and, and make more complete and specific what I was looking for. And then I would put it
1:09:07 into a one pro mode, go get my coffee and, you know, come back and get it. I’m not sure if that’s
1:09:11 a viable technique, but it’s really interesting because I do think in the spirit of AI being the
1:09:20 solution to more problems than AI, um, having a, uh, lower latency, simpler model help refine,
1:09:24 essentially. I like to think of it as like, you’re like a product manager and you’re asking,
1:09:29 you know, an engineer what to do is your, is your product requirements document complete and specific
1:09:35 enough. And, uh, waiting for it is sometimes slower than, and so I, I like doing it in stages like
1:09:38 that. So that’s my, my chip at some point. There’s probably someone from open AI listening. He’s gonna
1:09:43 like roll their eyes, but that’s just, uh, that’s, uh, who can I talk to at open AI? That’s like the prompt.
1:09:50 Yeah. I’m like so curious about this because I’ve, I’ve actually taken recently to, uh, getting
1:09:56 open AI or chat GBT, I guess, if you want to call it that I’ve been getting chat GBT to write the prompt
1:10:03 for me. So I’ll prompt it with, I’m prompting an AI. Yeah. Here are the key things similar to my
1:10:08 technique. I want to accomplish what would an excellent prompt look like. And then I’ll copy
1:10:15 paste that prompt that it gives me back into the system. Uh, but I’m like, I wonder what I’m missing
1:10:18 here. Right. Like it’s a good technique. I mean, there’s lots of engine techniques like that. Like
1:10:25 self-reflection is a technique where you have a model observe and critique, you know, a decision
1:10:30 like a chain of thought. Uh, so in general, you know, that mechanism of self-reflection is I think
1:10:39 a really effective technique. You know, at Sierra, we help companies build customer facing AI agents. So, um,
1:10:44 if you’re setting up a Sonos speaker, you’ll now chat with an AI. If you’re a SiriusXM subscriber,
1:10:50 you can chat with Harmony, who’s their AI to manage your account. Um, we use all these tricks,
1:10:56 you know, self-reflection to detect things like hallucination or decision-making, generating
1:11:02 chains of thought for more complex tasks to ensure that it’s, you know, you’re putting as much, uh,
1:11:10 compute and cognitive load into important tricks. So, uh, you know, we’re the, there’s a whole
1:11:15 industry around sort of figuring out how do you exact the, like robustness and, and, um,
1:11:18 precision out of these models. So it’s really fun, but changing rapidly.
1:11:25 Hypothetical question. You, you’ve been hired to lead or advise a country, uh, that wants to become
1:11:33 an AI superpower. What sort of, um, steps would you take? What sort of policies would you think
1:11:37 would help create that? How would you bring investment from all over the world into that country?
1:11:41 And researchers, right? Like, so now all of a sudden you’re competing. It’s not the United States.
1:11:46 Like, how do you, how do you sort of set up a country like from first principles all the way
1:11:48 back to like, what does that look like? What are the key variables?
1:11:54 Well, I mean, especially, uh, this is definitely outside of my domain of expertise, but I would say
1:12:04 one of the key ingredients to modern AI is compute, um, which is a noun that wasn’t a noun until recently,
1:12:11 but now compute is a noun. And, uh, you know, I do think that’s one area where policymakers can,
1:12:19 um, uh, because it involves a lot of things that, uh, touch, uh, federal and local governments like power,
1:12:28 land. Um, and then similarly attracting the capital, which is immense to finance, uh, to the real estate,
1:12:35 to purchase the, uh, you know, uh, compute itself, um, and then to sort of operate the data center. And again,
1:12:41 there’s really immense power requirements for these data centers as well. Um, and then, you know,
1:12:47 it’s attracting sort of the right researchers and research labs to, you know, leverage that. But in
1:12:52 general, where there is compute, the research labs will find you, you know, and so I think that’s it.
1:12:56 And then there’s a lot of national security implications too, just because, you know,
1:13:01 these models are very sensitive, at least the frontier models are. And so, um, you know,
1:13:08 how you, your place in the geopolitical landscape is quite important. Like will research labs and,
1:13:13 uh, will the U S government be comfortable with training happening there and, and export restrictions
1:13:20 and things like that. But I think a lot of it comes down to infrastructure, uh, as it relates to policy,
1:13:28 is my intuition. Uh, you know, I think right now so much of AI is constrained on, on infrastructure that,
1:13:35 that is the input to a lot of, uh, of this stuff. Um, uh, and then there’s a lot around,
1:13:39 you know, attracting talent and all that. But as I said, you know, you look at the research labs,
1:13:44 it’s not that many people, actually, it’s a lot, but the compute is a limited resource right now.
1:13:49 That’s a really good way to think about it. I think about this from the lens of Canada,
1:13:56 right? Which is like, we don’t have enough going on in, in AI. We, we tend to lose most of our great
1:14:02 people to the states, uh, who then go to set up infrastructure here for whatever reason and don’t
1:14:09 bring it back to Canada. And I, I wonder how Canada can compete better. So this is like sort of the lens
1:14:15 I like look at these questions through, how do you see that the next generation of education?
1:14:20 Like if you were setting up a school today from scratch and again, hypothetical, not your domain
1:14:27 of expertise, but like using your lens on AI, how do you think about this? So like what skills will kids
1:14:32 need in the future and what skills do we probably don’t need to teach them anymore that we have been
1:14:39 teaching them? Well, I’ll start with, uh, the benefits that I think are probably obvious, but I’m incredibly
1:14:48 excited about. I think education can become much more personalized. Oh, totally. Have you seen synthesis
1:14:53 tutor by the way? No, I have not. Oh, so they developed this, uh, synthesis, this AI company developed this
1:15:00 tutor, which actually teaches kids. And it’s so good that El Salvador, the country just recently adopted
1:15:06 it and replaced their teachers and, uh, like it’ll teach you, but it teaches you specific to what you’re missing.
1:15:11 So it’s not like every lesson’s the same. It’s like, well, you’re not understanding this foundational concept.
1:15:17 So it’s like K through five or six right now. That’s amazing. And you know, I actually, and the results are like off the charts.
1:15:22 Well, it doesn’t surprise me. And I, I don’t actually view it as like necessarily replacing a teacher,
1:15:26 but my view is if you have a teacher with 28 kids in his or her class,
1:15:33 the likelihood that they all learn the same way or learn at the same pace is very unlikely.
1:15:39 And, you know, I can really think of a, say an English teacher or history teacher orchestrating
1:15:45 their learning journeys through a topic, say AP European history in the United States,
1:15:50 there’s a curriculum, they need to learn it. Um, how someone will remember something or understand
1:15:57 the significance of Martin Luther, you know, is very different. And, um, you can, you know,
1:16:03 generate a audio podcast for someone who might be an audio auditory learner. Um,
1:16:08 you can create cue cards for someone who needs that kind of repetition, repetition. Um,
1:16:14 you can visualize, uh, key moments in history, um, for people who just maybe want to more viscerally
1:16:19 appreciate why this was a meaningful event rather than this dry piece of history. And all of that,
1:16:23 as you said, can be personalized to the way you learn and how you learn. And I think it’s just
1:16:30 incredibly powerful. And so one of the things I think is neat about AI is it’s, uh, democratizing
1:16:35 access to a lot of things that used to be fairly exclusive. A lot of wealthy people, if their child
1:16:40 was having trouble in school, would pay for a tutor, a math tutor, science tutor. Um, and, you know,
1:16:46 you know, if you look at, uh, kids who are trying to get into, you know, uh, big name colleges, you
1:16:51 know, if you have the means, you’ll have someone prep you for the SATs or help you with your college
1:16:58 essays, all of that should be democratized if we’re doing our jobs well. And it means that we’re not
1:17:05 limiting people’s opportunity from by their means. And I think that’s a, just the, the most, uh,
1:17:09 American thing ever, uh, Canadian as well. Um, it’s the most incredible thing.
1:17:14 It’s the most incredible thing, humanity. And, and so I, I just think education will change for
1:17:20 the positive in so many ways. Um, because, uh, I, I actually, with my kids walking around when they
1:17:24 ask, uh, you know, if you have little kids, they ask why, why, why? And, you know, there’s some point
1:17:28 a parent just starts making up the answer or being dismissive. And like, we have chat TVT out and it’s
1:17:34 like the best when you’re traveling and put on advanced voice mode and be like, ask a hundred percent.
1:17:39 And I’m listening to, you know, it’s like you’re, uh, you live through your children’s curiosity.
1:17:44 And, um, you know, my daughter went to high school and came home with Shakespeare for the first time.
1:17:49 And I was, she asked me a question. I was like, I, I felt this is like total inadequacy. I was like,
1:17:54 I was very bad at this the first time. And then we put it into chat GPT and it was the most thoughtful
1:17:59 answer. And she could ask follow-up questions. And I actually was, you know, with her because I was
1:18:02 like, Oh, I forgot about that. You know, didn’t even think about that. So I, I just think it’s
1:18:09 incredible. And I would like to, uh, in public school systems, uh, I think it’s really, I think
1:18:16 it would be a really great, uh, when public school systems formally adopt these things so that they lean
1:18:25 into, uh, tools like chat GPT, uh, as mechanisms to L like raise, uh, the performance level of their
1:18:30 classroom. And, and hopefully you’ll see it in things like test scores and other things because,
1:18:35 uh, kids can get the extra time, even if the school system can’t afford it for everyone.
1:18:40 Uh, and then most importantly, kids care getting explanations according to their style of learning,
1:18:44 which I think will be, um, quite, uh, important as well. As it relates to skills,
1:18:50 it’s really hard to predict right now. And I, I would say that I do think learning how to learn and
1:18:54 learning how to think will continue to be important. So I think most of, you know,
1:19:00 primary and secondary education shouldn’t and is not vocational necessarily. Um, some of it is,
1:19:06 uh, you know, I took auto shop and all of that and I’m glad I did, but I couldn’t fix my electric
1:19:10 car today with that knowledge, you know, things change and I don’t think it needs to be purely,
1:19:16 you know, um, non-vocational, but you know, the basics of learning how to think, uh, learning,
1:19:25 um, uh, uh, writing, reading, math, physics, uh, chemistry, biology, not because you need to memorize
1:19:32 it, but understand the mechanisms that, uh, uh, create the world that we live in is, is quite
1:19:42 important. Um, I do think that the, there’s a risk of people sort of, uh, becoming ossified in the tools
1:19:49 that they use. Um, so, you know, uh, let’s go back to our discussion of software engineering for a
1:19:54 second, but I’ll give other examples. You know, if you define your role as a software engineer is how
1:20:01 quickly you type into your IDE, the next few years might leave you behind, you know, because that, um,
1:20:06 that is no longer a differentiated, you know, part of the software engineering experience or will not
1:20:13 be, but your judgment as a software engineer will continue to be, uh, incredibly important in your
1:20:21 agency and making a decision about what to build, how to build it, um, how to architect it, uh, maybe
1:20:27 using AI models as a creative foil. And so I think that, uh, just in the same way, if you’re an accountant,
1:20:32 you know, using Excel doesn’t make you less of an accountant, uh, and, um, and just because you
1:20:38 didn’t, you know, handcraft that math equation, it doesn’t make the results any less valuable to your
1:20:44 clients. Um, and so I think we’re going to go through this transformation where I think the, um,
1:20:50 the tools that we use to create value in the world will change dramatically. And I think some people who
1:20:57 define their jobs by their ability to use the last generation’s tools really, really effectively,
1:21:06 um, will, will be disrupted. But I, I think we, if we can empower people and, and to reskill, um, and also
1:21:10 broaden the aperture by which they define the value they’re providing to the world, I think a lot of
1:21:16 people can make the transition. The thing that is sort of uncomfortable, not really in education,
1:21:23 or it’s just earlier in, in most people’s lives. It’s just, I think the pace of change, uh, exceeds
1:21:29 that of most technology transitions. And I think it’s unreasonable, um, to expect most people to
1:21:34 change the way they work that quickly. And so I think the, the next five years, I think will be,
1:21:40 you know, for some jobs really disruptive and tumultuous. But if you take the longer view and you
1:21:46 fast forward 25 or 50 years, I’m incredibly optimistic. I think it’s the, the change will
1:21:53 require, um, from society, from companies and from individuals, like an open-mindedness about
1:21:58 reskilling and, and re-imagining their job to the lens of this like dramatically different new technology.
1:22:04 At what point do we get to, I mean, we’re probably on the cusp of it now and it’s happening in pockets,
1:22:10 but what point do we start solving problems that humans haven’t been able to solve or eliminating
1:22:15 paths that we’re on maybe with medical research that it’s like, no, that, that the, this whole
1:22:22 thing you’ve spent $30 billion on, you know, based on this 1972 study that was fabricated. But that one
1:22:27 study had all these derivative studies and like, I’m telling you it’s false, you know, because I can look
1:22:32 at it through an objective lens and get rid of these 30 billion while you’re smiling.
1:22:40 So, Oh no, I just, I hope soon. I mean, I hope, I mean, I, uh, there was a, a lot of, there’s a,
1:22:44 one of the models I can’t remember which one introduced a very long context window. And there’s a lot of
1:22:50 people on X over the weekend putting in their, uh, thesis, you know, like grads, grad school thesis in
1:22:55 there. And, and it was actually critiquing them with like surprising levels of fidelity. Uh,
1:23:02 and, uh, I think we’re sort of there perhaps with the right, um, right tools, but certainly over the
1:23:06 the next few years, I, you know, we talked about how, what does it mean to generalize AI?
1:23:15 Certainly in the areas of, um, science that are, you know, largely represented through text and
1:23:20 digital technology, like math being probably the most, uh, most applicable, there’s not really
1:23:24 anything keeping AI from getting really good at math. There’s not really an interface to the real
1:23:30 world. You don’t need to do a clinical trial to verify something’s correct. So I feel a ton of optimism
1:23:36 there. Um, it’ll be really interesting in like, you know, areas of like theoretical physics. Um,
1:23:40 uh, you’ll tend to, you’ll continue to have the divide between the applied and the theoretical
1:23:45 people. But I think there could be like really interesting new ideas there and perhaps some,
1:23:50 uh, finding logical inconsistencies with some of the, you know, uh, fashionable theories,
1:23:56 which has happened many times over the past few decades. Um, I think, I think we’ll get there soon.
1:24:01 And I actually, I, um, what’s really neat about is most of the scientists, I know people
1:24:05 who are actually like doing science, like they’re the most excited about these technologies and I,
1:24:09 they’re using them already. And I think that’s really neat. And I think we’re hopefully going to be,
1:24:15 I really hope we see more breakthroughs in science. One of the things I am not an expert in, but I’ve
1:24:25 read a lot, like, uh, a lot as a amateur about is just the slowdown in scientific breakthroughs over
1:24:29 the past, you know, a few decades and, and some theories that it’s because of the degree of
1:24:34 specialization that we demand of grad students and things like that. And I hope with, you know,
1:24:43 in general with AI, um, democratizing access to expertise, um, I, I have a completely personal
1:24:50 theory that it will benefit deep generalists in a lot of ways too, because your ability to understand
1:24:57 a fair amount and a lot of domains and leveraging AI, um, knowing where to prompt the AI to,
1:25:04 to go explore and, and, um, bringing together those domains, it will start to shift sort of the
1:25:10 intellectual power from people who are extremely deep to people who actually can, uh, orchestrate,
1:25:15 uh, intelligence between lots of different domains for breakthroughs. I think that’ll be really good
1:25:20 for society because most scientific breakthroughs aren’t, they tend to be, you know, cross-pollinating
1:25:24 very important ideas from a lot of different domains, which I think will be really exciting.
1:25:26 How important is the context window?
1:25:33 I think it could be quite important. Um, especially it certainly simplifies working with an AI. If
1:25:39 you can just give it everything and instruct it to do something. Um, and so, and, and assuming it
1:25:46 works, you know, you can extend a context window and it can, um, uh, the, the tension can be spread
1:25:52 fairly thin and, and, and the robustness of the answer can be questionable. So, but assuming,
1:25:58 let’s just for argument’s sake, you know, perfect robustness, um, I think it can really simplify the
1:26:05 interface, uh, to AI. The, not all uses, I also think that we’re talking about open source models and
1:26:13 APIs. Um, I also think that, you know, most, what I’m excited about in the software industry is not
1:26:19 not necessarily a large language model with a prompt and a response being the product of AI,
1:26:25 but actually end in closed loose systems that use large language models as pieces of infrastructure.
1:26:30 And I actually think that a lot of the value in software will be that. And for many of those
1:26:35 applications, the context window size can matter, but often because you have contextual awareness of
1:26:40 the process that you’re executing, um, yeah, context window is a little bit less important.
1:26:45 So I think it matters a lot to intelligence. Um, you know, there’s a, I can’t remember someone,
1:26:49 one of the, some researcher said, you know, you put all of human knowledge in the context window,
1:26:54 and you ask it to invent the next thing. You know, it’s a, uh, obviously a reductive, uh, thought,
1:26:59 but, but interesting. Um, uh, but I actually, I’m equally excited about sort of the industrial
1:27:02 applications of large language models, sort of like my company, Sierra. And if you’re,
1:27:08 you’re, um, returning a pair of shoes at a retailer and it’s a process that’s fairly complicated and,
1:27:15 uh, you know, is it within the return window? Uh, you know, uh, do you want to return it in store?
1:27:19 Do you want to send it? Do you want to print to your QR code? Blah, blah, blah, blah. Um,
1:27:23 the orchestration of that is as significant as the models themselves. And I actually think as we,
1:27:28 um, just like, uh, uh, computers, you know, there’s going to be a lot of things where computers are a
1:27:32 part of the experience, but it’s not like manifesting itself as a computer. So I,
1:27:36 I’m actually equally excited about those. And I think context window is slightly less important
1:27:41 than those applications. Do you think that the output from AI should be copyrightable
1:27:46 or patentable or let, let me just take an example. If I go to the U S patent office,
1:27:53 I download a patent for, let’s say the AeroPress and I upload it to, uh, Oh one pro. And I say,
1:27:58 I can’t upload it yet. Cause you don’t let me do the PDS, but I upload it to four. And, uh,
1:28:05 so I say, Hey, what’s the next logical leap that I could patent office. It would give me back diagrams
1:28:10 and an output. And presumably if I look at that and I’m like, yeah, that’s legit. I want to file
1:28:14 that patent. Can I, I don’t know to answer that question. I’m not an expert in sort of intellectual
1:28:21 property, but I, uh, uh, I think there will be an interesting question of, was that your idea
1:28:27 because you used a tool to do it? I think the answer is probably yes, that you, you use the tool
1:28:37 to do it. But I also think that the, um, uh, uh, in general, like the sort of marginal cost of intelligence
1:28:43 will go down a lot. So a lot of the, you know, I think in general, like we’ll, we’ll be in this
1:28:50 renaissance of, of new ideas and intelligence being produced. And so, uh, I think that’s broadly a good
1:28:55 thing. And I think, you know, the marginal value of that insight that you had might be lower than it
1:29:00 was, you know, and years ago, what I was hoping you would say is that, you know, that’s going to become
1:29:05 less and less important because I feel like all the patent trolls and all of this stuff that slows down
1:29:10 innovation in some ways, uh, obviously like there’s legitimate patents that people infringe on and
1:29:15 there should be legal recourse. But if I could just go and patent like a hundred things a day,
1:29:19 because it seems like that should not be allowed. This is what I’m saying though.
1:29:24 Well, in general, I think that, you know, companies, you know, I think patents make sense
1:29:29 if it’s protecting something that’s an active use that you, you know, invented and you’re, you’re trying to
1:29:35 uh, you know, like the standard, you know, uh, legal rationale for patents, just generating a
1:29:40 bunch of ideas and patenting. It seems destructive to the value of it. So here’s the idea I had last
1:29:45 night to counter this because I was like, I don’t want somebody doing this. Uh, and I was thinking
1:29:50 like, what if prior art eliminates patents? Yeah. So I was like, what if I just set off like an
1:29:55 instance and just publish it on a website? Nobody has to read that website. Here’s a billion ideas.
1:30:00 Exactly. But it’s like basically patenting like anything, not patenting,
1:30:05 but it’s creating prior art for everything. So like, you can’t compete on that anymore.
1:30:10 I don’t know. I was like thinking about that. I thought it was fun. Um, tell me about the
1:30:16 Google maps story. This is like now legend and I want to hear it from you. Uh, this is my weekend
1:30:22 coding. Is that what you want to hear about? Yeah. Um, yeah. So, uh, I’ll start with just
1:30:28 like the story of Google maps, the abbreviated version. Uh, we had launched a product at Google
1:30:33 called Google local, which was sort of a yellow pages, uh, search engine. Uh, you probably,
1:30:37 probably most listeners don’t even know what yellow pages are, but it was a thing back then.
1:30:44 And, um, we had licensed maps from map quest, which was the dominant sort of mapping provider at the
1:30:49 time. And it was sort of an eyesore on the experience and also always felt like it could
1:30:54 be a more meaningful part of the kind of local search and navigation experience on Google. So
1:30:58 Larry Page in particular was really pushing us to really invest more in maps.
1:31:05 Um, we found this, uh, small company with a, like four people in it, if I’m remembering correctly,
1:31:10 started by Lars and Jens Rasmussen called where to technologies where, um, they had made a windows
1:31:16 application called expedition. Um, that was just a beautiful, uh, mapping product. Um,
1:31:20 it was running on windows long after it was sort of out of fashion to make windows apps, but they,
1:31:25 they were sort of where the technology they’re comfortable with, but they’re really, um,
1:31:32 their, their maps modeled the A to Z maps and in the UK were just beautiful. And they just had a lot
1:31:39 of passion for mapping. So we did a little aqua hire of them and took together the Google local team and
1:31:44 Lars and Jens’s team and, and said, okay, like, let’s take the good ideas from this windows app and
1:31:49 the good ideas from Google local. And like, let’s bring them together to make something completely
1:31:54 new. And that, and that’s what became Google maps. But there was a couple of idiosyncrasies
1:32:00 in the integration because it was a, um, Windows app. It really helped and hurt us in a number of
1:32:05 ways. Like one of the ways it helped us is the reason why Google maps, we were able to drag the map
1:32:11 and it like, uh, was so much more interactive than any web application that preceded it was
1:32:17 the standard that we needed to hit from interactivity was set by a native windows app, not set by the
1:32:25 legacy, uh, you know, websites that we had used at the time. And I think that by having the goalposts
1:32:30 so far down the field, because they had just started with this windows app, which was sort of a quirk of
1:32:36 Lars and Jens, just like technical choices. We made much bolder technical bets than we would
1:32:41 have. Otherwise, I think we would have ended up much less interactive had we, uh, not started with
1:32:47 that quirky technical sort of a decision. But the other thing was this windows app. There’s a lot of
1:32:52 like, it’s hard to describe the like early two thousands. We wouldn’t live it, but like XML was
1:32:58 like really in fashion. So like most things and windows and other places like XML and XSLT,
1:33:03 which was a way of transforming XML into different XML was the basis of everything. It was like all
1:33:09 of enterprise software was like XML this, XML that. So similarly, when we were taking some of these ideas
1:33:14 and putting them in a web browser, we kind of like went into autopilot and used like a ton of XML
1:33:23 and it made everything just like really, really tedious. And so Google maps launched with some
1:33:27 really great ideas like the draggable maps. And, and we did a bunch of stuff for the local search
1:33:32 technology. So you could, you know, overlay restaurant listings. It was really great. It was a really
1:33:38 successful launch. Uh, we were like the hot shots within Google afterwards. And, uh, but it really
1:33:41 started to show its craft. And we got to this point where we decided we wanted to support the Safari
1:33:45 web browser, which was relatively new at the time. This is before, you know, mobile phones.
1:33:52 And, uh, there was much less XML support in Safari than there was an internet explorer in Firefox. And
1:34:00 so one of the engineers implemented like a full XSLT transform engine in JavaScript to get it to work.
1:34:07 And it was just like shit on top of shit on top of shit. And so what was a really elegant fat,
1:34:12 like fast web application had sort of quickly become something, you know, there’s a lot of
1:34:17 dial up modems at the time and other things. So like you’d show up to maps and it just was slow.
1:34:21 And like, it just bothered me as like someone who takes a lot of pride in their craft. And so
1:34:29 I got really, uh, energized and like over a weekend and a lot of coffee, like rewrote it.
1:34:34 Um, but it rewrote the whole thing there rewrote. Yeah. More or less the whole thing. And it took
1:34:38 probably another week of like, you know, working through the bugs, but yeah, I sent it out to the,
1:34:43 you know, the team after that weekend. And it was, it was nice. The reason I was able to do it,
1:34:50 yeah, I’m like a decent programmer, but you know, you’d also like lived with every bad decision up to
1:34:56 that point too. So I knew exactly the output I was going to like, I had simulated in my head,
1:35:01 like if I could do it over again, this is the way I do it. So by the time I like put my hands on the
1:35:07 keyboard on like, you know, Friday night, I, it wasn’t like I was designing a product. Like I knew
1:35:11 I had been in every detail of that product since the beginning and including me, the bad decisions
1:35:16 too. They’re not all the bad decisions. And so it was just very clear. I knew what I wanted to
1:35:20 accomplish. And for any, you know, any engineers worked on a big system, you have the whole system
1:35:27 mapped out in your head. So I knew, knew everything. And I, and I, and, and I also knew that, you know,
1:35:33 there’s a lot of pride of authorship with engineering and code. So I sort of knew I really wanted to
1:35:38 finish it over the weekend so that people could use it and see how fast it was and kind of overcome
1:35:44 anyone who was like, you know, you know, protective of the code they had written a few months ago.
1:35:50 And so I really wanted the prototype to go out. And so I did it. And then I didn’t,
1:35:53 it’s funny, I never talked about it again, but I think Paul Buhite, who’s was a co-creator,
1:36:00 Gmail, and, and I worked and started friend feed with me. He was on an interview and mentioned this
1:36:03 story. So now all of a sudden, it’s like, everyone’s talking about it. And I was like,
1:36:07 well, thank you, Paul. It’s a little embarrassed that people know about it, but it was, it was,
1:36:10 it’s a true story. And, and, and XML is just the worst.
1:36:17 Did you get a lot of flack from the people who had built the system you effectively replaced? Like,
1:36:21 you were part of that team, but everybody else had so much invested in it, even though it was like,
1:36:23 shit on top of shit, on top of shit.
1:36:28 you know, um, I wrote a lot of it too. So yeah, I’m sure there was some around it, but actually,
1:36:33 I think good teams want to do great work. And so, uh, I think there was a lot of people constructively
1:36:41 dissatisfied with the state of things too. And, um, uh, you know, I think, uh, you know, the engineer
1:36:46 had written that XSLT transform, I think was like, you know, a little bit, it’s a lot of work. So you have
1:36:52 to throw out a lot of work, which feels bad, but particularly, you know, um, Lars and Jens and I,
1:36:56 like, we want to make great products. And so I don’t think there was a, you know, at the end of the day,
1:37:01 everyone’s like, wow, that’s great. You know, we went from a bundle size of 200 K to a bundle size
1:37:06 of 20 K and it was a lot faster and better. So, you know, broadly speaking, I think good engineering
1:37:12 cultures. You don’t want a culture of, um, you know, ready, fire, aim, but I also think you just
1:37:20 need to be really outcomes oriented. And I think people, if they become, they’d start to treat
1:37:26 their code is too precious. It can really, uh, impede forward progress. Um, and yeah, I’ll just take,
1:37:33 like, I, my understanding is like a lot of the early self-driving car software was a lot of hand-coded
1:37:38 heuristics and rules. And, you know, a lot of smart people think that eventually it’ll probably
1:37:43 be a more monolithic model that, uh, encodes many of the same rules. You have to throw out a lot of
1:37:47 code in that transition, but it doesn’t mean it’s not the right thing to do. And so I think in general,
1:37:51 um, yeah, there might’ve been some feathers ruffled, but at the end of the day, everyone’s like,
1:37:55 that’s faster and better. Like, let’s, let’s do it, you know, which is, I think the right decision.
1:37:59 That’s awesome. I’m going to give you another hypothetical. I want you to share your
1:38:05 inner monologue with me as you think through it. So if I, uh, told you, you have to put 100% of
1:38:11 your net worth into a public company today and you couldn’t, you couldn’t touch it for at least 20
1:38:15 years, what company would you invest in? And like, walk me through your thinking.
1:38:20 I literally don’t know how to answer that question. Um, how would you think about it without giving me
1:38:24 an answer? Like, what? Yeah, that’s a good question. I, first of all, I’ll give you how
1:38:29 I think about it, but I’m so, uh, having not been a public company CEO for a couple of years, I’m
1:38:34 blissfully don’t pay attention as much, um, to the public markets. And in particular right now,
1:38:40 it’s obviously valuations have gone up a lot. So there’s a, but because it’s a long-term question,
1:38:45 maybe that doesn’t matter. I think what I’d be thinking about right now is, um,
1:38:53 over the next 20 years, like what are the parts of the economy that will most benefit from this
1:38:57 current wave of AI? That’s not the only way to invest over a 20 year period, but certainly it’s
1:39:03 a domain that I understand. And in particular, you know, I mentioned that, uh, talk, I heard a snippet of
1:39:09 from Tyler Cohen, which is like, it will probably AI will probably benefit different parts of the economy.
1:39:13 Um, disproportionately, there will be some parts of the economy that can essentially,
1:39:20 um, where intelligence is a limiting factor to its growth and where you can absorb
1:39:26 almost arbitrary levels of intelligence and generate almost arbitrary levels of growth.
1:39:30 Obviously there’s limits to all of this just because you change one part of the economy,
1:39:36 it impacts other parts of the economy. And that was what, uh, uh, Tyler’s point was, uh, in his talk.
1:39:39 But I would probably think about that because I think that
1:39:44 over a 20 year period, there are certain parts of society that won’t be able to change extremely
1:39:49 rapidly. Um, but there will be some parts that probably will, and it’ll probably be
1:39:56 domains where intelligence is, is the scarce resource, uh, right now. And then I would probably
1:40:00 try to find companies that will disproportionately benefit from it. And I assume this is why like
1:40:06 Nvidia stock is so high right now, because if you want to sort of get downstream, you know,
1:40:11 Nvidia will probably benefit from all of the investments in AI. Um, I’m not sure I would
1:40:15 do that over a 20 year period, just assuming that the infrastructure will shift. So I don’t have
1:40:19 an intelligent answer, but that’s the way I would think about it if I were, if we’re doing that exercise.
1:40:26 I love that. Where do you think, like, what’s your intuition say about what areas of the economy are
1:40:33 limited by intelligence and not just economy? I mean, perhaps politicians, uh, might be limited by,
1:40:40 by this and, and aid and benefit from, in which case countries could benefit enormously from AI and unlock
1:40:45 growth and potential in their economy. But I think maybe just to scope the question, like what areas of
1:40:51 the economy do you think are limited by intelligence or workers, like smart workers, in which case, like,
1:40:59 that’s another limit of intelligence. Yeah. I mean, uh, two that are, I think probably going to benefit a
1:41:07 lot are technology and finance. Um, you know, where you’re, you know, if you can make better financial
1:41:12 decisions than competitors, you’ll generate outsized returns. And that’s why over the past, you know,
1:41:18 30 years, you know, of machine learning, um, you know, uh, hedge funds and financial service
1:41:24 institutions, everything from fraud prevention to true investment strategies, it’s already been an area
1:41:30 of domain, uh, domain of investment, um, software, similar, as we talked about, I think that, uh,
1:41:36 at some point we will be, um, we will no longer be supply constrained in software, but we’re not anywhere
1:41:42 close to it right now. And you’re taking something that has always been the scarce resource, which is
1:41:47 software engineers and you’re making it not scarce. And I think as a consequence,
1:41:53 you just think of like, how much can that industry grow? We don’t know. Um, but we’ve been so constrained
1:41:59 on software engineering as a resource, uh, who knows over the next 20 years, but we’ll find out, uh,
1:42:04 where, where the limits are. But to me, intellectually, there’s just a ton of growth there. Um,
1:42:09 and then broadly, I think areas of like processing information are areas that will
1:42:16 really benefit, um, quite a bit here. And so that, and I think the, the thing that I would think about
1:42:19 over a 20 year period is like second and third order effects, which is why I don’t have an intelligent
1:42:22 answer. And if you’re asking me to put all my money in something, I would think about it for a
1:42:28 while, um, probably use a one pro a little bit to help me. Um, but, uh, you know, because you can end
1:42:33 up, uh, generated a bunch of growth in the short term, but then, you know, if everyone does it,
1:42:39 it commoditizes the whole industry, you know, type of thing. So, you know, there used to be, you know,
1:42:44 before the introduction of the freezer ice was like a really expensive thing and now it’s free,
1:42:49 you know? And so I think it is really important to actually think through those. If you’re talking
1:42:52 in a timeframe of like 20 years. And that’s why having not thought about this question ahead of
1:42:57 time, I, um, you could be quite simplistic elsewhere, but I would say software and finance
1:43:02 are areas that I, I think stand to reason should benefit quite a bit. I love that response. How do
1:43:12 you balance, uh, having a young family with also running a startup again? I work a lot. Um, I don’t,
1:43:20 uh, I really care and love care about and love working. Um, so one thing is that I, um,
1:43:27 well, there’s always trade-offs in life. Um, if I didn’t love working and I wouldn’t do it as much
1:43:34 as I do, but I, I just love, uh, love to create things and love to have an impact. And so I like
1:43:39 jump out of bed in the morning and, um, work out, go to work and then spend time with my family
1:43:45 broadly, probably, you know, being honest first. I’m not perfect. I think for a second,
1:43:48 I don’t have a ton of hobbies. You know, I basically work and spend time with my family.
1:43:54 Um, the first time we talked, you saw a couple of guitars in my background. Uh, I haven’t picked
1:44:00 one of those up in a while. Um, uh, I, I mean, I literally pick it up occasionally, but I, you know,
1:44:05 do not devote any time into it. And I don’t regret that either. Like I am so passionate about what
1:44:10 we’re building at Sierra. I’m so passionate about opening. I am so love my family so much. I don’t
1:44:15 really have any regrets about it, but I basically just like life is all about where do you spend your
1:44:20 time and mine is at work and with family. And so that’s how I do it. I don’t know if I’m particularly
1:44:26 balanced, but I don’t strive to be either. I really take a lot of pride and I love, I love to work.
1:44:32 Having sold the companies you started twice, how does that influence what you think of Sierra? Like,
1:44:37 are you thinking like, Oh, I’m building this in order to sell it? Or do you think differently? Like,
1:44:41 this is my life’s work. I’m building this with, that’s not going to happen.
1:44:47 I absolutely, uh, intend Sierra to be an enduring company and an independent company,
1:44:54 but to be honest, every entrepreneur, every company starts that way. And so, um, you know,
1:45:00 uh, I’m really grateful for both Facebook and Salesforce for having acquired my previous companies
1:45:04 and hopefully I had an impact about those companies, but you don’t start off. Well,
1:45:11 at least I never started off saying, Hey, I want to make a company to sell it. Um, uh, and, uh,
1:45:15 but I actually think with Sierra, we have just a ton of traction in the marketplace. Uh,
1:45:19 I really do think Sierra is the leader in helping consumer brands build customer facing
1:45:25 AI agents. And I’m really proud of that. So I really see a path to that. And I joke with Clay,
1:45:30 I want to be, you know, an old man sitting on his porch, you know, complaining how
1:45:33 the next generation of leaders at Sierra don’t listen to us anymore. You know,
1:45:37 I want this to be something that not only is enduring, but outlives me. Um, and I think
1:45:43 just actually, I don’t think we’ve ever talked about this, but it was really interesting, um,
1:45:47 moment for me when Google went from its one building in mountain view to its first corporate
1:45:53 campus. It, uh, we moved into the Silicon graphics campus, which was right over near shoreline
1:46:00 Boulevard and in mountain view. And, uh, SGI had been a really successful company enough to build a
1:46:04 campus. And when we, it was actually quite awkward. We moved into like half the campus,
1:46:08 they were still in half and they’re like, we’re this up and coming company. They’re declining.
1:46:14 And then when Facebook, when we moved out of the second building, we were in Palo Alto,
1:46:19 it was slightly larger building. I think we leased it from HP, but when we finally got a campus,
1:46:24 it was from Sun Microsystems who had gone through an Oracle acquisition and had been sort of on the
1:46:32 decline. And it was interesting to me because both SGI and Sun, um, had been started and grown to
1:46:36 prominence in my lifetime. Uh, obviously I was maybe like a little younger, obviously,
1:46:41 but in my lifetime enough to build a whole corporate campus and then declined fast enough
1:46:48 to sell that corporate campus to a new software company. And for me, I, it was just so interesting
1:46:54 to have done that twice to move into like a, you know, a used campus, you know, for the previous,
1:47:00 uh, uh, uh, owners. It was a very stark reminder that technology companies aren’t entitled to their
1:47:07 future success. And I think we’ll see this actually now with AI, AI, I think will change the landscape
1:47:14 of software to be, um, tools of productivity that to agents that actually accomplish tasks. And I think it
1:47:20 will help some companies who, for whom that’s a, uh, uh, amplifies their existing value proposition
1:47:25 and it will really hurt others where it will essentially the seat based kind of model of
1:47:32 legacy software will wane, um, very quickly and then really harm them. And so when I think about the,
1:47:40 what it means to build a company that’s enduring, um, that is a really, really tall, um, task in my mind
1:47:44 right now, because it means not only making something that’s financially enduring over the
1:47:52 next 10 years, but setting up a culture where a company can actually evolve to meet the changing
1:47:58 demands of, uh, society and technology at a, when it’s changing at a pace that is like unprecedented
1:48:03 in history. So I think it’s one of the most fun business challenges of all time. And I think it has
1:48:10 as much to do with culture as it has to do with technology because every line of code in Sierra
1:48:15 today will be completely different, you know, probably five years from now, let alone 30 years
1:48:19 from now. Um, and, uh, I think that’s really exciting. So when I think about it, I just get
1:48:26 so much energy because, um, it’s incredibly hard and it’s harder now than it’s ever been, um, to do
1:48:30 something that lasts beyond you. Um, but that I think is the ultimate measure of a company.
1:48:34 You mentioned AI agents. How would you define that? What’s an agent?
1:48:39 I’ll define it more broadly and then I’ll tell you how we think about it at Sierra, which is a more
1:48:44 more narrow view of it. The word agent comes from agency. And I think it means affording
1:48:53 a software, the opportunity to reason and make decisions autonomously. Um, and I think that’s
1:48:59 really all it means to me. And I think there’s lots of different, uh, applications of it. The three
1:49:04 categories that I think are meaningful and I’ll end with the Sierra one just so I can talk about it a
1:49:10 a little more, but one is personal agents. So I do think that most people will have
1:49:18 probably one, but maybe a couple AI agents that they use on a daily basis that are, uh, essentially
1:49:25 amplifying themselves as an individual. Um, you can do the rote things like help you triage your email to
1:49:32 helping you schedule a vacation. You know, you’re flying back to, um, Edmonton and help you arrange
1:49:38 your travel. Um, two more complex things like, you know, I’m going to go ask my boss for promotion,
1:49:44 like help me role play. And, um, you know, uh, I’m setting up my resume for this job. Help me do that
1:49:49 too. I’m applying for a new job. Help me find companies I haven’t thought of that I should be applying to.
1:49:55 Uh, and I think these agents will be really powerful. I think it might be a really hard
1:50:00 product to build because when you think about all the different services and people you interact with
1:50:06 every day, it’s kind of everything. So it’s not, it has to generalize a lot to be useful to you. And
1:50:12 because of the personal privacy and things like that, it has to work really well for you to trust it.
1:50:15 So I think it’s going to take a while to go. I think it’ll be a lot of demos. I think it’ll take
1:50:22 a while to be robust. The second category of agent is I would say, um, really filling a persona, uh,
1:50:32 within a company. So a coding agent, a paralegal agent, um, a analyst agent. Um, I think these
1:50:37 already exist. I mentioned cursor. There’s a company called Harvey that makes a legal agent. I’m sure
1:50:44 there’s a bunch in the analyst space. Um, these do a job and they’re more narrow. Um, but they, uh,
1:50:49 they’re really commercially valuable because most companies hire people or consultants that do those
1:50:55 things already, like analyze the contracts of the supply chain, right? That’s a kind of a rote
1:51:01 kind of law, but it’s really important and AI can do it really well. So I think that’s why, uh,
1:51:06 this is the area of the economy that I think is really exciting. And, and as, uh,
1:51:11 I’m really excited about all the startups in this space because you’re essentially, um, taking what
1:51:16 used to be a combination of people and software and really making something that solves a problem.
1:51:24 Uh, and by narrowing the domain of, of autonomy, you can have more robust guard rails and even with
1:51:29 current models actually achieve something that’s effective enough to be commercially viable today.
1:51:35 Um, and, uh, and by the way, it changes the total addressable market of these models too. Like,
1:51:39 I don’t know what the total addressable market of legal software was three years ago, but it
1:51:43 couldn’t have been that big. I couldn’t tell you like a legal software company. I probably should,
1:51:48 I just can’t think of one, but if you think about the money we spend on lawyers, that’s a lot. And so
1:51:56 you end up where you’re broadening the, the addressable market quite a lot. The domain we’re
1:52:03 in, um, I think is somewhat special, which is, um, a company’s branded customer facing agent. And
1:52:08 the reason why I think it’s, one could argue we’re sort of, uh, helping with customer service,
1:52:14 which is a, a, a persona, a role, but I do think it’s broader than that. Because if you think about,
1:52:20 um, a website, you know, like your insurance company’s website, try to list all the things
1:52:24 you can do on it. You can look up the stock quote, you can look up the management team,
1:52:31 you can compare their insurance company to all their competitors. You can file a claim. You can,
1:52:39 you know, uh, buy, you can bundle your home and auto. You can, um, uh, um, add a member of your
1:52:44 family to your premium. There’s a million things you can do on it. Essentially over the past 30 years,
1:52:50 websites, a company’s website singular has come to be the universe of everything that you can do with
1:52:54 that company. I like to think it was like the digital instantiation of the company.
1:53:01 And that’s what we’re helping our customers do at Sears, help them build a conversational AI that does
1:53:05 all of that. So, you know, most of our customers start with customer service and it’s a great
1:53:10 application because no one likes to wait on hold and, and having something that has perfect access
1:53:16 to information is multilingual and empathetic is just amazing. But you know, when you put a conversational
1:53:22 AI as your digital, um, front door, people will say anything they want to it. And, um,
1:53:28 we’re now doing product discovery, consider purchases, going back to the insurance example.
1:53:34 Hey, you know, I’ve got a 15 year old daughter. I really am concerned about the cost of her premium
1:53:39 until she grows up. Tell me, um, which plan I should be on. Tell me why you’ll be better than
1:53:43 your competitors. That’s a really complex interaction, right? That’s not something that,
1:53:47 can you make a webpage that does that? No, that’s, but that’s a great conversation.
1:53:54 And so we really aspire that when you encounter a branded agent in the wild, we want Sierra to be
1:54:00 the platform that powers it. And it’s super important because there was a case, at least in Canada,
1:54:06 where an AI agent for Air Canada hallucinated a bereavement policy, right? But they were found liable
1:54:12 to hold themselves to what the agent said. Yeah. I mean, it turns out, and it was an AI
1:54:17 agent. There was no human involved in the whole thing. Well, look, it’s one thing if chat GPT
1:54:22 hallucinate something about your brand. It’s another if your AI agent hallucinate something about your
1:54:29 brand. So the bar just gets higher. So the robustness of these agents, the guardrails, everything is more
1:54:35 important when it’s yours and it has your brand on it. And so it’s harder, but I also, I’m just so
1:54:41 excited for it because this is a little overly intellectual, but I really like the framing.
1:54:49 If you think about a modern website or mobile app, it’s essentially you’ve created a directory of
1:54:56 functionality from which you can choose. But the main person with agency in that is the creator of
1:55:02 the website. Like what are the universe of options that you can do? When you have an AI agent represent
1:55:08 your brand, the agency goes to the customer. They can express their problem any way they want in a
1:55:12 multifaceted way. And so it means that like your customer experience goes from the
1:55:18 enumerated set of functionality you’ve decided to put on your website to whatever your customers ask.
1:55:23 And then, you know, you can decide how to fulfill those requests or whether you want to.
1:55:29 But I think it will really change the dynamic to be really empowering to consumers. As you said,
1:55:35 I mean, the reason that that Air Canada case is the reason we exist. You know, companies,
1:55:42 if they try to build this themselves, there’s a lot of ways you can shoot yourself in the foot.
1:55:48 But in particular, too, your customer experience should not be wedded to one model, let alone even
1:55:54 this current generation of models. So with Sierra, you can define your customer experience once in a
1:56:00 way that’s abstracted from all of the technology. And it can be a chat. It can be, you can call you on
1:56:05 the phone. It can be all of those things. And as new models and new technology comes out,
1:56:10 our platform just gets better. But you’re not like re-implementing your customer experience. And I
1:56:14 think that’s really important because, you know, we were talking about what’s happened over the past
1:56:19 two years. Can you imagine if you’re a consumer brand like ADT Home Security and thinking about,
1:56:23 like, how can you maintain your AI agent in the face of all of that, right? It’s just not even,
1:56:28 it’s not tenable. I mean, it’s not what you do as ADT. So they’ve worked with us to build their AI agent.
1:56:35 Like, how do you fend off complacency? Like, a lot of these companies, and maybe not in tech
1:56:44 specifically, but they get big, they get dominant, and then they take their foot off the gas. And that
1:56:50 opens the door to competitors. And there’s like a natural entropy almost to bureaucracy in some of
1:56:57 these companies that, and the bureaucracy sows the seeds of failure and competition. How do you,
1:56:58 how do you fend that off constantly?
1:57:05 It is a really challenging thing to do at a company. One of the, there’s two things that
1:57:15 I’ve observed that I think manifest as corporate complacency. One is bureaucracy. And I think the
1:57:22 root of bureaucracy is often when something goes wrong, companies introduce a process to fix it.
1:57:32 And over the sequence of 30 years, the layered sum of all of those processes that were all created for
1:57:42 good reason, with good intentions, end up being a bureaucratic sort of machine where the reasons for
1:57:48 many of the rules and processes are rarely even remembered by the organization. But it creates this
1:57:55 sort of natural inertia. Sometimes that inertia can be good. You know, it’s like, you know, if you end up
1:58:01 with you, there’s definitely been stories of executives coming in and ready, fire, aim, new strategies that
1:58:08 backfire massively. But often, it can mean in the face of a technology shift or a new competitor,
1:58:13 you just can’t move fast enough to address it. The second thing that I think is more subtle is as a
1:58:20 a company grows in size. Often its internal narrative can be stronger than the truth from customers.
1:58:28 I remember one time when this sort of peak of the smartphone wars, and I ended up visiting a friend
1:58:37 on Microsoft’s campus. And I got off the plane and, you know, Seattle Tacoma Airport, drove into Redmond,
1:58:43 went on to the campus. And all of a sudden, everyone I saw was using Windows phones.
1:58:50 I assume it must have been a requirement or formal or social, like you were definitely uncool if you’re
1:58:56 using anything else. And from my perspective at the time, like the war had already been lost.
1:58:57 Yeah.
1:59:04 Like, it was definitely a two-horse race between Apple and Google on iOS and Android.
1:59:10 And I remember sitting in the lobby waiting for my friend to get me from the security check-in.
1:59:15 And I made a comment, like it wasn’t a confrontation, but I made a comment to someone who’s at Microsoft.
1:59:22 I was like, you know, something along the lines of, are you required to use Windows phones?
1:59:27 How these are? And I just sort of like curious. And then I got a really bold answer, which is like,
1:59:31 yeah, we’re going to win. Like, we’re taking over the smartphone market. And I was like,
1:59:36 I didn’t say anything because it was like a little socially awkward. I was like, no, you’re not. Like,
1:59:38 you lost like four years ago.
1:59:44 But there’s something that’s happening that’s preventing you from getting reality.
1:59:48 Well, that’s the thing is, if you think about it, if anyone, if you’ve ever worked for like a large
1:59:55 company, you know, when you work at a small company, you care about your customers and your
2:00:02 competitors and you feel every bump in the road. When you’re a, you know, junior vice president of
2:00:10 whatever, and you’re, you know, eight levels below your, you know, CEO, and you have a set of
2:00:15 objectives and results, uh, your, you might be focused as I want to go from junior vice president
2:00:20 to senior vice president. That’s what success looks like for me. And you end up with this sort
2:00:28 of myopic focus on this internal world in the same way your kids will focus on, you know, the social
2:00:33 dynamics of their high school, not the world outside of it. And it’s probably rational by the way, because
2:00:38 like, you know, probably their social life is more determined by those, you know, 1,000 kids in their
2:00:42 high school than it is like all the things outside. But this is, that’s the life of a
2:00:48 person inside of these big places. And so you end up where, uh, you know, if you have a very senior
2:00:53 head of product, who’s like, are this competitor says they’re faster, but this next version we’re
2:00:58 so much better. And then everyone says, all of a sudden that’s like the windows phone is going to
2:01:04 win. That’s what everyone says. And, and you truly believe it because everyone you meet says the
2:01:10 same thing and you end up reflecting, you know, uh, customer anecdotes through that lens and you end up
2:01:16 with this sort of reality distortion field manifested from the sum of, of this sort of myopic
2:01:22 storytelling that, um, exists within, within companies. The, what’s interesting about that
2:01:27 is like, you know, the ability for a culture to believe in something is actually a great strength of
2:01:33 a culture, but it can lead to this as well. And so the combination of bureaucracy and inaccurate
2:01:39 storytelling, um, I think is the reason why companies sort of die. Uh, and, and it’s really
2:01:45 remarkable to look at, you know, the blackberries of the world or the TiVos or the, you know, there,
2:01:52 you can really, um, you know, as the plane is crashing, like tell the story that you’re not. And,
2:01:58 um, and, and, and then similar, as I said, like culturally, you can still have like the person
2:02:03 in the back of that crashing plane being like, when am I going to get promoted to SVP? And then you’re
2:02:08 like, you know, and, and that’s, I mean, this is like, I mean, I’ve seen it a hundred times. And so
2:02:14 I think it really comes down to leadership, you know, and I think that one of the things that most
2:02:19 great companies have is they are obsessed with their customers. Um, and I think the, the free market
2:02:24 doesn’t lie. And so I think the, one of the most important things I think for any like enduring
2:02:29 culture, particularly in an industry that changes as rapidly as software is how close are your
2:02:35 employees to customers and how much can customer like the direct voice of your customers be a part of,
2:02:42 uh, your decision-making. Um, and that is something that I think you need to constantly work out because
2:02:52 that, you know, person employee number 30,462, you know, how does he or she actually, actually
2:02:55 directly hear from customers is, it’s not actually a simple question to answer.
2:02:58 Is it direct? Is it filtered? How many filters are there?
2:03:04 That’s exactly right. And then, um, I think the other part on leadership is, you know,
2:03:09 we talked about bureaucracy is process is there to serve the needs of the business.
2:03:18 And, uh, uh, often, um, mid-level managers, uh, don’t get credit for removing process.
2:03:24 They often are held accountable for things going wrong. Um, and I think it really takes top-down
2:03:31 leadership to, uh, you know, remove bureaucracy. Um, and, uh, it is not always comfortable, you know,
2:03:40 when companies remove spans of control or, uh, all the people impacted will, it’s like antibodies.
2:03:44 Uh, and for good reason, I mean, it makes sense their lives are negatively impacted or whatever it is,
2:03:51 but it almost has to come from the top because you need to give, uh, air cover, uh, almost certainly
2:03:56 something will go wrong by the way. I mean, like processes usually exist for a reason. Um,
2:04:01 but when they accumulate, um, without end, you end up with bureaucracy. So those are the two things
2:04:06 that I always, uh, and you could smell it when you go into a really bureaucratic company, the,
2:04:13 the inaccurate storytelling, the process over outcomes. And it’s just, uh, it sort of sucks the
2:04:19 energy out of you when you feel it. That’s a great answer. We always end these interviews with the exact
2:04:24 same question, which is what is success for you? Success for me, we talked about how I spend my
2:04:31 time with my family at work is, you know, having a happy, healthy family and being able to work with
2:04:35 my co-founder Clay for the rest of my life, making Sierra into an enduring company. That would be success for me.
2:04:47 Thanks for listening and learning with us. The Farnham street blog is where you can learn more
2:04:54 about my new book clear thinking, turning ordinary moments into extraordinary results. It’s a transformative
2:05:00 guide that hands you the tools to master your fate, sharpen your decision-making and set yourself up for
2:05:14 unparalleled success. Learn more at fs.blog slash clear until next time.