0:00:07 American health care is in crisis we have a path to preventing disease it
0:00:12 isn’t reversing aging it’s just preventing the age-related morbidities
0:00:16 of the big three if we can keep people healthier healthier people would be much
0:00:22 less expensive seven years more of healthspan free of the major three
0:00:27 diseases seven years who wouldn’t take seven years there’s just billions of
0:00:33 data points for each person there should be a reboot new standard of care based on
0:00:41 intelligent partitioning of risk we have to do better the human obsession with
0:00:46 living longer is as old as time but in the last 20 years we have learned so much
0:00:51 more about human health and biology so what do we know today about what makes
0:00:56 humans live longer and do we have real evidence that longevity is an attackable
0:01:01 target today you’ll get to hear a 16th general partner vj ponday in conversation
0:01:06 with eric topol who recently released his new book super agers an evidence-based
0:01:11 approach to longevity eric is among other things the founder and director of the
0:01:16 scripps research translational institute he’s also published over 1200 peer-reviewed
0:01:21 articles with more than 300,000 citations making him one of the 10 most cited
0:01:26 investigators in medicine that resume puts eric in a perfect position to write this book
0:01:31 teasing the signal out from all the noise around health in 2025 one of those inputs
0:01:37 was the welderly group that eric studied which was a study of 1400 people 80 plus who
0:01:42 had never developed a chronic illness for comparison according to eric’s book among
0:01:49 those 65 plus 80 percent have two or more chronic diseases and 23 percent have three or more well
0:01:55 about seven percent have five or more and again that was the 65 plus group versus the welderly
0:02:02 group of 80 plus so what do we know about these quote super agers people who not only have a
0:02:07 longer lifespan but a longer health span is it genetics or human agency and do technologies
0:02:13 like ai glp ones gene therapies or the ability to understand organ clocks meaningfully change that
0:02:20 equation for the masses if so what difficult decisions do we have to make to rewrite the system today let’s
0:02:28 find out as a reminder the content here is for informational purposes only should not be taken
0:02:33 as legal business tax or investment advice or be used to evaluate any investment or security and is
0:02:39 not directed at any investors or potential investors in any a16z fund please note that a16z and its affiliates
0:02:44 may also maintain investments in the companies discussed in this podcast for more details including a link
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0:03:00 my joy to welcome dr eric topol to the podcast eric thanks so much for joining us i’m glad to be here
0:03:08 so you’ve written this really exciting book super agers and evidence-based path to longevity and i think
0:03:13 it’s a very timely topic and i was curious for you to maybe set the stage for why you want to write it
0:03:18 and how you see it in the context of other books that have been coming out recently as well yeah there
0:03:25 were a few things that came together we had done a big study we called the welderly where we basically
0:03:32 found very little in the genomes of people who had gone to the age of 87 on average with never having
0:03:39 had an age-related disease so that was of course one thing that was part of it the second was i got inspired
0:03:47 by a patient i saw recently who was 98 and had never been sick and so never been sick yeah her name
0:03:55 is lee rissall and her relatives had died in their 50s and 60s that’s her parents her uncles and aunts
0:04:02 she was the outlier and say why and then there were the books that came out i had patients coming to me
0:04:07 you know they wanted me to write a prescription for apple my sin or order a total body mri said wait we
0:04:13 got to get the story straight so these three things together were the impetus that why don’t i really
0:04:20 get deep into this everything we know today and then see if i could lay out some blueprints for where we can
0:04:27 go it’s coming into a world where american health care is in crisis i was curious to get your take on
0:04:33 where we are now in health care in the us and where do you think we get to yeah so there is this
0:04:39 bifurcation as i see it you could call it like the grand slam where you get reversing of aging so you
0:04:47 keep people healthier body-wide and that’s where we see all this remarkable investments in companies like
0:04:54 altos and reprogramming senolytics and a long list but they’re really focused on a monumental task which
0:05:01 hasn’t been shown in people right but rather in rodents and some of the results are striking and
0:05:07 i hope at least one if not all these are successful the other side of this is we’ve made these big strides
0:05:14 in the science of aging with all these layers of data that are using the metrics of aging and why don’t we
0:05:21 use that to prevent the age-related diseases cancer cardiovascular neurodegenerative we’ve never done
0:05:29 that in medicine to any appreciable extent and this is the opportunity because we have a path to
0:05:35 preventing disease it isn’t reversing aging it’s just preventing the age-related morbidities of the
0:05:40 big three i think that’s something that a lot of people may not realize is that the big three that you
0:05:48 mentioned cancer heart disease and alzheimer’s and dementia that they’re greatly exacerbated by age and
0:05:52 and it’s interesting because if you ever wanted to have something that could be a cure for multiple
0:05:57 diseases which would be the one the holy grails of medicine it would be understanding the biology of
0:06:03 aging where are we now in terms of things that we can use today the first and perhaps the most extraordinary
0:06:11 thing is it takes 20 years to get these diseases with rare exception you know for heart disease
0:06:18 almost all cancers and neurodegenerative they are incubating for a very long time they all have a
0:06:28 common thread of a defective immune system and inflammation underpinning they are preventable
0:06:37 variably so cardiovascular 80 90 from lifestyle and related factors modifiable factors like your ldl
0:06:43 cholesterol that kind of thing and cancer and neurodegenerative just from what we know today
0:06:50 with lifestyle factors we’re about half that can be prevented so we have some knowledge about averting these
0:06:57 diseases but we have a lot more with all these clocks and new layers of data that are really changing the
0:07:04 face of all outgrowths of understanding the biology of aging so maybe let’s double click on that so you
0:07:09 in your book outline the five dimensions of health i was wondering maybe you could walk us through them
0:07:18 yeah yeah sure so the first most important one is ai because you need that to pull all this other
0:07:24 data we’re going to talk about together this moment that is so exciting is because we have multimodal ai
0:07:29 not only large language but large reasoning models now well especially i think when you’re talking about
0:07:34 ai it’s all the things people have seen with generative ai and so on but also just the ability to
0:07:39 understand all this data yes that you’re measuring from people yeah because the other four are such big
0:07:47 domains and dimensions so the omics it includes not just gene sequence or arrays but it has all the
0:07:54 proteins all the proteomic panels that we can get which we never could get before inexpensively it
0:08:02 includes the gut microbiome metabolome and certainly epigenome or epigenetics so the omics are rich
0:08:10 we are now seeing moving in towards things like the virtual cell then there is of course cells that
0:08:16 have become a live drug where we can reset the immune system and cure autoimmune diseases like we’ve never
0:08:23 done before could you give examples of that yeah so in the last couple years we’ve seen unprecedented
0:08:31 cures i mean never had anything lupus progressive systemic sclerosis even cases of multiple sclerosis
0:08:39 dermatomyositis so basically it’s a depletion of all the b cells and when they come back
0:08:44 they have forgotten what they were attacking it’s amazing yes it’s really amazing that leads to the
0:08:49 autoimmune reaction but the bigger lesson is we have learned how to control our immune system
0:08:56 like a rheostat and we’re going to keep getting better and better as we measure our immunom but when you can do
0:09:05 that when you can quash an autoimmune disease or when you’re trying to cure a cancer by just whatever it
0:09:12 takes to keep bringing up that immune system specific to the tumor so the immune system is fundamental and
0:09:21 that also now is involving cells and vaccines so vaccines now are capable of cures of pancreatic
0:09:28 cancer kidney cancer with these personalized vaccines using the proteins of the person’s tumor yes and
0:09:34 these are in clinical trials right now yeah i mean they’re stuff like we’ve never seen and that’s just a
0:09:41 front runner of what vaccines that’s to treat cancer we’re going to be using vaccines to prevent cancer
0:09:48 again as we get older some of us especially our immune system is getting senescent and weak and a
0:09:54 vaccine before there’s any cancer before there’s anything else could prop it up we also have drugs to
0:10:01 modulate our immune system well beyond checkpoint inhibitors and so whether it’s antibody drug
0:10:08 conjugates tumor infiltrating lymphocytes and all these different ways it’s hard to imagine that in the
0:10:13 future we’re going to lose people with cancer because of being able to bring their immune system to the
0:10:20 highest level when we need it but more importantly preventing the cancer we can do that now that’s what’s exciting
0:10:26 well and so if we put all this together what does this mean for the individual like how would their
0:10:33 life change what should people be doing yeah so i call it lifestyle plus it’s a lot bigger than diet
0:10:40 sleep and exercise it’s involving you know all the environmental burdens air pollution the plastics
0:10:46 microplastics nanoplastics and forever chemicals and then there’s other things like time in nature
0:10:53 so if each of us pulled out all the stops for the lifestyle factors which is a long list that will
0:11:01 help but it’s not going to be only lifestyle factors that are the ways to prevent the big three age-related
0:11:07 diseases you know you described a large range of things from the sort of most almost sci-fi like
0:11:12 drugs that are in trials for preventing cancer to lifestyle when people think about lifestyle it’s
0:11:17 maybe a little vague in their mind for what to do how do you make that into a science or how do you
0:11:23 help people take that to the next step to bring evidence into that i go into perhaps great pains high
0:11:32 density to cite all the studies that link like for example when you have really good sleep health and deep
0:11:38 sleep what does that do to slow your brain aging or you know if you drink sugar sweetened beverages
0:11:45 what does that do to specific not just risk of type 2 diabetes but you know all-cause mortality so
0:11:55 there are very compelling sets of data about lifestyles and these key outcomes and they’re linked to healthy
0:12:00 aging i was amazed how much data is out there that can help us it’s not just like in the year when we had
0:12:07 polygenic risk scores and we just say oh your risk for alzheimer’s but we don’t know if when you’re age 56 or 96
0:12:14 so what good is that yes now we’re saying we know it’s within a couple of years between 77 and 79
0:12:20 that you’re going to have mild cognitive impairment if we don’t do these things which includes the
0:12:27 lifestyle factors and it’s much harder to get people to do all their stuff they have no specificity that’s
0:12:35 that it’s about them yeah that they can change the arc of a condition especially when it isn’t our genes the healthy
0:12:42 story about a genetic underpinning it’s just not there we studied that it’s minimal i mean maybe it’s ten percent of what
0:12:51 accounts for healthy aging most of it is in the lifestyle factors and related matters such as the immune system
0:12:57 not functioning properly too much too little well it’s generally believed that just telling someone to
0:13:03 eat better and exercise doesn’t work but what i’m hearing you say is that you have a way to do that by
0:13:09 making it very personalized yes i mean there was a finnish study that was on just polygenic risk
0:13:16 score which is rudimentary and they gave that to a large cohort and they studied whether that affected
0:13:23 their lifestyle and the results were remarkable the people who got the data stopped smoking changed
0:13:28 their diet changed their physical activity really amped it up so we know when people get data that’s
0:13:36 specific to them a large proportion much more likely to make changes now i’m not claiming that lifestyle is
0:13:43 going to be the only part of the prevention story but once you define the high risk and it’s
0:13:48 particularized to a person that’s a big part of how we’re going to succeed i could also imagine ai coming
0:13:53 into this because one of the things ai is very good at is to take a set of data and maybe you can mask
0:13:59 out the last bit so you can maybe have someone’s health records over 30 years and train on that except for
0:14:05 the last five years and see if you can predict the last five from the first 25 and once it gets really
0:14:10 good at that you can take my records and say hey look vj if you don’t do anything this is where you’re
0:14:17 going to be and we have 99 confidence on this that would be pretty chilling yeah well you’re exactly
0:14:23 right because the pinpointing here about the timing yeah is so extraordinary for example with alzheimer’s
0:14:31 since we were talking about that you get a p tau 217 it’s modifiable by lifestyle you check it again in
0:14:37 six months or a year now you have two data points and you could say with all the other data that’s
0:14:44 available when you’re going to see 18 years from now 12 years four years mild cognitive impairment
0:14:51 unless these steps are taken this was fully dependent on ai on models that can just take all
0:14:59 this data if we didn’t have the science of aging and the ai we’d be nowhere we wouldn’t be talking about
0:15:03 this today i wouldn’t have written a book yeah well it’s important for people not familiar with the term
0:15:08 of health span that’s basically not just lifespan but how long you can be healthy yeah i don’t think we
0:15:14 really want to get to some age and be demented or compromised what we’re talking about is if you
0:15:20 don’t have heart disease cancer or neurodegenerative you’re pretty darn intact you may have some achy
0:15:27 joints and other matters but those are the things that really interrupt our health span now we’re
0:15:31 talking about health care meaning something different to be preventative and we’ll talk
0:15:38 about chronic in a second how do we help make that mind shift this is perhaps the biggest point so far
0:15:44 that we’ve been discussing because in medicine and i’ve been in it for almost 40 years we don’t do
0:15:50 primary prevention the person has a heart attack and then we get all over it but for the most part
0:15:57 we don’t prevent cancer we don’t prevent alzheimer’s and neurodegenerative diseases it’s been a desire i
0:16:04 would say a fantasy for millennia yes but we are at a very different point right now we have a path to
0:16:12 prevention primary prevention not after somebody has one of these diseases and that is what is extraordinary
0:16:20 and it was all these recent advances that led to this capability and we’ve got to jump on it because
0:16:25 it’s exciting that we could actually do this well also the thing about prevention is that i’ve talked
0:16:32 to doctors who very boldly assert that prevention doesn’t work yeah and i look at them a bit confused
0:16:37 because i say well there’s been numerous examples and they’re like well name one i was like how about
0:16:42 smoking that’s the prototype we have this huge incidence of lung cancer which has just disappeared
0:16:48 now because we don’t smoke in restaurants or airplanes and so on but one of the things that i think about
0:16:53 about that movement is that while doctors played a significant role in that that was also very much
0:16:59 a cultural movement yes and so we talked about lifestyle changing people’s behaviors i think some of
0:17:04 this or much of this has to be as much cultural as medical there’s a definite cultural component and you
0:17:10 know tobacco is one of the most impressive but there’s so many others yes i think what we’ve learned
0:17:17 like for example with sleep i didn’t pay enough attention to that but with sleep when you promote your own
0:17:27 deep sleep which we tend to lose a lot as we age then you see much less dementia alzheimer’s even less
0:17:36 cardiovascular and cancer related illnesses cases and mortality sleep regularity we need to be more ritualistic
0:17:42 about it and there are many things just on sleep itself no less about physical activity about for example
0:17:50 not just even resistance training but balance posture things like that so the more you go deep nutrition
0:17:56 especially we’ve learned a lot about that convincing compelling evidence i would say that you say these
0:18:03 effects we’re talking about just with that seven years more wow of health span free of the major three diseases
0:18:09 wow seven years who wouldn’t take seven years that’s just with what we know today once we can define high
0:18:16 risk which is one of the things we turn to with ai that changes everything because then you focus on that
0:18:21 maybe let’s turn to another aspect of it which is the chronic disease aspect yeah when we’re talking
0:18:27 about chronic disease we’re talking typically about diabetes heart disease cancer how do we start to
0:18:31 make an impact in that i don’t know if you want to pick one if you want to start with cancer i think we
0:18:36 can make a huge impact in cancer because we have just simple polygenic risk scores for all the common
0:18:43 cancers that’s like one layer of data to say you’re at higher risk and we have multi-cancer early detection
0:18:50 tests that can pick up microscopic cancer why people would get a total body mri when you could find
0:18:57 microscopic cancer not a mass on a mri which may or may not be cancer so we have some tools for cancer but
0:19:08 the one thing that i think is unanticipated is the glip one drugs the ozempic zep bound world yes it’s
0:19:15 the most momentous drug class in medical history and we’ve only seen part of the story so far in the
0:19:21 book i write about how it took 20 years to figure out that it wasn’t just about diabetes which is amazing
0:19:30 what if we had ai today and said should we test this for obesity because the developers nova nordisk
0:19:37 and later lily of these drugs they only saw three or four pounds that people with type 2 diabetes would
0:19:45 lose with these drugs and this woman in norway scientist lata nutzen she kept pushing we got to try it in
0:19:50 obesity and they wouldn’t listen to her because well she said diabetics are not losing weight
0:19:57 they finally did it and everyone knows the story 20 30 50 80 pounds of weight loss now when you lose that
0:20:05 much weight for people who are obese you reduce the risk of cancer you reduce the risk of heart disease
0:20:13 and neurodegenerative disease it wouldn’t be surprising to me that now with pills that are remarkably effective
0:20:20 to substitute for injections that can be much less expensively that a large proportion of the
0:20:26 population would be taking one of these drugs or even their successors that is those that are even
0:20:33 more potent and potentially with less side effects so we have a drug class now added to lifestyle factors
0:20:39 we didn’t have before right as you know they are in big trials for preventing alzheimer’s in people
0:20:46 who are not overweight yes okay we’re going to be doing a long covet trial in people who are not
0:20:53 overweight the effects are really quite extraordinary the ability to crack obesity yes we would have been
0:20:58 happy just to do that but all the other things that are coming from it who would have thought that you
0:21:05 could treat prevent addiction yeah that’s remarkable yeah the ability to stop reduce alcohol intake from
0:21:12 heavy intake gambling i mean the list just goes on because we’re learning about the brain circuitry
0:21:20 on how these drugs so some of the secrets of the gut brain axis which is tied into the immune system
0:21:25 and it’s tied into the science of aging this is what’s given us this newfound potential to change
0:21:31 we don’t have to only rely on drugs but there’s this as we discussed this kind of interdependence well and
0:21:37 i think having lifestyle infrastructure with these drugs that combination is particularly interesting
0:21:43 because you can make sure that you can lose weight while keeping muscle and also hopefully patients can
0:21:48 go off the drugs at least for some periods of time and not rebound we don’t have encouraging data at the
0:21:54 moment because at least half of people gain weight back when they stop yeah and that’s not good but i
0:22:02 do think that we’ll come up with a ways to hopefully not rely on such a long-term commitment the results on
0:22:09 muscle mass we’ve been very worried about that and i think when people combine taking the drugs with
0:22:15 strength training and we do know there’s muscle mass loss just with weight alone but that looks encouraging
0:22:21 even though the companies have been acquiring muscle making drugs yes that may not prove to be
0:22:26 particularly necessary well and i think one thing that’s interesting is that another knock on lifestyle
0:22:33 is if you’re extremely obese telling someone to exercise it’s a hard road oh to just get started
0:22:39 absolutely and so this could jump start a better lifestyle that then could get locked in that could be
0:22:45 really i’ve seen it in many patients just what you said couldn’t get them to really increase their
0:22:51 activity but when they were thinner everything changed when you think about if we can make a
0:22:57 huge dent there’s nothing more economically favorable for us at the public population health
0:23:03 level if we can achieve this and so what else would you put into the chronic bucket i think
0:23:08 one of the things that you’ve written about is ai plus all the things you can track i think the ability to
0:23:17 look at the organ clocks which was initially reported here at stanford by tony wiss corey and his colleagues
0:23:25 are now validated and replicated by multiple groups the fact that we can do that and have the brain the
0:23:33 heart the immune system and other vital organs and we can say this one organ of yours is five years
0:23:40 at a pace with your real age then we can integrate that with these other layers of data oh if that’s the
0:23:47 case what about your polygenic risk score is there anything pointing to that disease or organ we can
0:23:55 look at your whole body aging epigenetic horvath clock we can also look at specific proteins like for
0:24:03 example for the brain p tau 217 and what’s amazing about that protein which we can get now and it’s not
0:24:10 that expensive but that in itself gives us over a 20-year warning about mild cognitive impairment
0:24:19 it’s modifiable by exercise and lifestyle we’ve seen people in studies that drop more than 50 percent
0:24:23 even up to 80 percent it’s intriguing that it’s not binary too so you can track the gradient
0:24:28 exactly and that would get particularly scary if it’s increasing so we’re talking about in people
0:24:34 without symptoms but are at high risk having this assist i don’t recommend any of these things that
0:24:41 we’re talking about until you know you have an increased risk but once you do then you say hmm i can do
0:24:47 something about it and change the course of what otherwise would be that person’s natural history but
0:24:56 the molecular clocks this collection of proteins this is something else that’s striking the olink and somalogic
0:25:02 they’re between six and eleven thousand plasma proteins what we’ve learned from them the fact
0:25:08 that there’s three bursts of aging during our life is not just a linear story and the fact that we’re
0:25:14 learning about the underpinnings of diseases but most importantly we have these organ clocks that are
0:25:22 inexpensive to get the uk biobank is only paying fifty dollars per participant wow and they’ve done fifty
0:25:28 thousand and amazing data coming from it but another five hundred thousand is in process so it’s not that
0:25:34 expensive to get such rich data and when you start having genes and proteins and these other layers of
0:25:43 data that’s when you find out what is making us unique and what we are at risk for during our extended time and
0:25:47 and therefore what we should do to change it and improve yeah well let’s take a step back because
0:25:53 i think you’ve been laying out a very appealing picture for what we as individuals could do to
0:25:59 improve our health span get at least seven more years easy maybe more and more and more as the science
0:26:05 improves but you can also think about this from a societal level that the cost of health care is immense
0:26:09 yes just the cost of health care to the u.s government through medicare and medicaid is
0:26:14 approaching two trillion dollars and we live in a time where the united states is in massive debt
0:26:19 there’s a great desire to reduce the deficit or make the deficit negative would be ideal and you look at
0:26:25 health care and people are scared that health care could be cut or something like that and i think no
0:26:30 one wants to remove services but there is this alternative that is very natural from everything you’re
0:26:35 talking about which is that if we can keep people healthier yeah healthier people would be much less
0:26:41 expensive right and we could have a win-win how do we shift the system whether we’re talking about cms
0:26:47 or we’re talking about insurers or providers how do we shift the sick care system to be thinking about
0:26:55 preventative and chronic we have a barrier here because of the malincentives people could change their
0:26:59 insurance companies at any time so the insurance company doesn’t have a long view
0:27:06 whereas other countries like when i did the review of the nhs for the government there they’re well
0:27:13 positioned in the uk and in many countries except for the u.s have a better positioning for this if we
0:27:23 could make prevention now that it is emerging as a reality the priority and say every insurer whether it’s
0:27:29 medicare medicaid private insurers if they don’t pull out all the stops and
0:27:36 if they don’t make this a priority then you know we have to make some pretty drastic policy changes
0:27:43 we’ve not actually accepted yet that we have this newfound capability which completely changes the
0:27:51 economics beyond making a case for healthspan for a population possible and as the people who need
0:28:00 this the most are currently the least likely to get it to access and so this is another issue which if
0:28:08 this only is for the affluent if we don’t take care of everyone we’re not going to achieve that goal so it
0:28:15 can’t just be for people who can have the assets to get this it has to be broadly universally distributed
0:28:22 how can we translate all the existing programs to something that could be let’s say rolled out to
0:28:30 medicare yeah i mean i think that if we negotiated the ai is software it could be cheap whether it’s
0:28:36 some proteins a specific protein polygenic risk score these things can be done twenty dollars fifty
0:28:42 dollars cheaper than most any lab tests that we do right now if we could develop a package negotiated
0:28:49 at a very low rate one way that’s really great vj about this we don’t have to wait 10 years to see the
0:28:55 benefit if we see the clocks all changing in the right direction great idea we have an intermediate surrogate
0:29:03 endpoint so like for example we use ldl cholesterol to know if we have a person’s arteries in check we’re going to have
0:29:12 these proteins like p tau 217 say oh well all these preventative approaches are really kicking in this
0:29:19 should change the likelihood of or if ever developing a neurodegenerative alzheimer’s condition so we have
0:29:26 the metrics again to get a short quick assessment are we making a difference if we did that through cms
0:29:33 that would be phenomenal but maybe we can get one of the big insurers to pilot this to make it possible
0:29:40 if maymad oz is listening maybe he’ll get interested i don’t know yeah i think cms is interested in what
0:29:45 it can do to keep people healthy and reduce cost that’s the canonical win-win i think also as you’ve
0:29:50 written about ai could really have a huge role here too because prevention is expensive if you have to roll
0:29:57 roll this out with gps or nps but to roll out with ai could be very very scalable yeah and i think you made
0:30:04 a point earlier about the ai is that as we do this and we do this at scale it just keeps getting better
0:30:12 so that the ability to predict pinpoint temporally when a person is likely to develop one of these three
0:30:19 three conditions with 20 years runway if we can’t do this for these three diseases we’re not too smart
0:30:26 if ai was before just a few years ago the capabilities wouldn’t be there and neither would
0:30:33 these metrics of aging and all the sciences done to catapult that that’s what’s presented a unique
0:30:40 opportunity and if we don’t do this we’re just stupid well actually let’s double click on that because
0:30:45 there are a lot of enemies of the future you know and maybe a nicer way to put it is that
0:30:50 people could be skeptical yeah and they’re used to operating a certain way they have a certain belief
0:30:56 that this isn’t going to work or for whatever reason what would you tell them like to your fellow
0:31:01 clinical colleagues to try to change their mindset from a sick care mindset to a preventative mindset
0:31:06 yeah i mean it’s to me it’s all about compelling data yeah so for example the alzheimer’s drugs which
0:31:14 don’t really work and they’re very risky but the reason they were bought into by the fda ultimately
0:31:20 was because the amyloid came out on the scans right and there was a little bit of cognitive score
0:31:28 improvement but here we have metrics that are extraordinary to help us as a bridge for compelling
0:31:34 evidence ultimately you want to say we prevented these diseases in people that had definition of their risk
0:31:43 and then active surveillance preventive pull out all the stops right for example speaking about waste we
0:31:52 do mass screening for cancer we treat everyone as the same based on their age and that’s the only criterion
0:31:59 for the screening age we only pick up 14 percent of cancers from that mass screening which costs over
0:32:06 hundreds of hundreds of hundreds of billions of dollars a year now what about 88 of women will
0:32:12 never have breast cancer why do a hundred percent of women have to go through this and especially with
0:32:18 bayes rule you could actually use those priors that you could measure and we don’t do it yeah and this is a
0:32:24 corollary of what we’re talking about why don’t we take the risk profile and say you know what to a
0:32:29 for a woman or for a person having colonoscopy you don’t really ever have to have it or you can have
0:32:36 this once in your lifetime or twice whatever we don’t treat people as human beings with particular
0:32:42 aspects that we can define today and why do you think that is we’re ingrained in stupidity
0:32:49 maybe when these mass screening programs started that was the best we could do yeah but we’ve known about
0:32:55 polygenic risk scores and we learn now about all these other ways to assess risk and then was added
0:33:03 on the ai part of it we have to do better but just having the screening part cleaned up would save a
0:33:09 tremendous amount of money how much is that concerns about liability or other non-medical reasons right
0:33:15 you’re bringing up another good point here because it’s the standard of care so that’s the foundation
0:33:21 for malpractice it shouldn’t be the standard of care there should be a reboot new standard of care
0:33:28 based on intelligent partitioning of risk so each of the cancers there’s a way forward to do this
0:33:35 we have to come up with new ways to screen that is based on risk assessment and we don’t do it but
0:33:41 that could be changed in a flash based on the data that exists today which i review in the book well
0:33:45 that’s all very rational so i just want to double click like what needs to change then what’s the
0:33:50 process is this guidelines have to be done differently and what’s the process and what’s the body that
0:33:56 should be doing this and why aren’t they doing it well i mean we’re seeing how we can have sleeping
0:34:04 changes without data right now yeah so new policies can be made if people want to have more proof points
0:34:10 that can be quickly easily garnered but we have to have the will yeah the problem we have now is the
0:34:17 amount of money that’s being made by doing these screenings is humongous so what is the incentive for
0:34:24 the people that are for example doing the scans and the scopes and all this stuff do they want to change
0:34:31 their practice i don’t know i mean does the american hospital association want to have people in their own home
0:34:36 so they don’t have to go to the hospital i don’t think so we have some things here that need a little
0:34:42 adjustment yeah in any change there’s always new winners and losers and the potential new losers will
0:34:49 fight the change yeah we have a new way forward if we are willing to get it validated and i hope
0:34:55 we’ll seize this opportunity because we may never get another one like this for a long time and what’s
0:35:00 different now is it ai or is it the confluence of all these yeah i think it’s not one without the
0:35:09 other once you have these new ways to assess risk and the ways to i would not just call it intervene
0:35:16 you’re really going after prevention the way you can aggressively put someone in surveillance so with
0:35:23 imaging now for example we can use ai to tell if there’s inflammation in the heart arteries even without
0:35:30 a significant narrowing we didn’t have that before and we can also if we need to do brain imaging it’s
0:35:38 exquisitely sensitive so we have different ways we didn’t have before and the ai part of it is this is
0:35:45 beyond human capability there’s just billions of data points for each person but with the ways that the
0:35:52 models have progressed there’s a new day using ai to promote health and health span so let’s shift
0:35:58 gears talking about the future let’s assume things work out well yeah what is the best case scenario that
0:36:03 you think is plausible what’s the science that’s coming on the horizon let’s say we all decide to make
0:36:09 this shift towards prevention and chronic what do you think we will get for it in our next five to ten years
0:36:15 well i think we’ll start to see that people are eventually getting to much older ages than we are
0:36:22 now without these three major diseases i think that’s a gradual thing it’s not like we’re going to see a
0:36:28 light switch here but that’s what would be the trend we will see countries that will implement it because
0:36:34 they don’t have the obstacles that we have we’ll see much less of that and the shift the bending this
0:36:41 curve to the people that are older and healthier gradually we’re not talking about curing we’re
0:36:46 talking about preventing it’s a lot better than curing but it takes time to see the benefit that’s
0:36:51 a really deep line that prevention is better than curing yeah i think maybe for professionals involved
0:36:57 curing is really cool curing is cool but you don’t want to go there yeah because it’s much harder yeah
0:37:03 prevention is where it’s at well some of that is then even just changing doctor incentives yeah if we can
0:37:10 get them to get them to be rewarded prevention is maybe less connected to their actions it may seem
0:37:14 even though it could have such a great societal benefit yeah but you know and there are health
0:37:20 systems that really do emphasize prevention but they’re rudimentary did you get your pneumococcal
0:37:28 vaccine your drinking and your other social behavioral stuff that’s all types things they haven’t worked
0:37:35 we’re talking about a whole revamping of what we mean by going into prevent mode yeah one question i love
0:37:40 to ask our guests i think i’ve asked this before so it’d be fun to get an update is what do you do for your own
0:37:47 health yeah i’ve gone through some pretty major changes from the work that did to put the book together because
0:37:55 i’m a cardiologist i never really acknowledged that strength training resistant stuff was so important
0:38:02 no less balance and posture so i’ve totally changed that for me i’ve never been this strong in my life
0:38:09 awesome yeah and how does it feel it feels great i mean yeah i just i never paid attention to it i used
0:38:15 to even with patients that came in i’d say well gee you’re really doing a lot of weight lifting here but i was
0:38:20 thinking to myself well they should be spending more time aerobic we need both sleep was a big problem
0:38:27 with me not sleeping and particularly not getting enough deep sleep so i got both a smart watch and
0:38:34 an aura ring to track that i wear both every night and whichever one has the highest number of minutes of
0:38:41 deep sleep i’m going with that but they’re usually concordant after you measure how do you improve yeah i had
0:38:49 to go through a lot of changes okay so i needed to get like a ritual when i go to bed wake up which i was
0:38:58 erratic about and i also learned about when to exercise what to eat not to eat all these interactions when
0:39:06 should you exercise well early if i can not too late in the afternoon but not in the evening and for me the
0:39:13 morning had a negative interaction with sleep really exercising the morning yeah yeah i mean i dragged all
0:39:19 day because i do an hour hour and a half if i can but the morning just wasn’t working for me but late
0:39:25 afternoon no later than that but also learning about whether it’s alcohol other beverages how they affected me
0:39:32 caffeine probably yeah so i basically i’ve gone from a deep sleep i’ve doubled it pushing i’m working on
0:39:37 getting i don’t know if i’ll get to triple it but you know it’s been a steady trend and it’s been really
0:39:45 great and giving me more energy more readiness and all that now the other one besides those two i’ve really
0:39:52 gone after the nutrition so i didn’t realize how much ultra processed food i took in yeah it’s so easy
0:39:58 reading the labels now i don’t even want to have a label to read just stay away from it if it has a
0:40:03 label and it has anything more than two ingredients anything i don’t know that would be that’s a really
0:40:08 interesting point broccoli doesn’t have a label yeah and the steak doesn’t have no no i just i
0:40:15 completely bought in now because these three age-related diseases inflammation all of them have been
0:40:21 associated with ultra processed foods a dose response even and i have really cut that out i mean i
0:40:27 couldn’t relieve how much stuff i was eating that had this junk in there i’m also really attentive to
0:40:33 things like plastics i don’t like to see anything being stored in plastic i don’t even like to use
0:40:40 microwave but putting something in plastic in a microwave that is a triple wham yeah but we are taking
0:40:47 in these plastics in the artery with people have a four-fold or five-fold risk of heart attacks and
0:40:54 strokes once you see that study it just is indelible so that’s another big change i’m much more focused on
0:41:01 these environmental burdens but the other thing is much more inclined now to take hikes in nature
0:41:07 to go out you see the benefit of that yeah i mean i think that when i’m out in nature and of course the
0:41:13 data i presented in the book i always appreciate it but now i can see its effects even more impact with
0:41:20 respect to for example the best sleep surprisingly so what i’ve learned i’ve tried to share i don’t
0:41:26 really speak too much or write too much about myself in the book but all these things i’m doing i mean
0:41:30 i believe in them if i didn’t believe them i wouldn’t have written about them and it was after calling
0:41:37 through there’s about 1800 references in there so people can look at themselves and see what they think but
0:41:43 it’s data that i’ve really been impressed it’s a body of evidence that ought to push us into this
0:41:48 prevent mode and i hope that eventually it will yeah but that’s maybe a great place to end i think
0:41:53 we could follow your example we could all be super agents thank you vj it’s been a real pleasure
0:42:01 thanks for listening to the a16z podcast if you enjoyed the episode let us know by leaving a review at
0:42:15 rate this podcast dot com slash a16z we’ve got more great conversations coming your way see you next time

0:00:01 Hi, I’m Frances Frey.
0:00:02 And I’m Anne Morris.
0:00:06 And we are the hosts of a new TED podcast called Fixable.
0:00:09 We’ve helped leaders at some of the world’s most competitive companies
0:00:11 solve all kinds of problems.
0:00:15 On our show, we’ll pull back the curtain and give you the type of honest,
0:00:18 unfiltered advice we usually reserve for top executives.
0:00:21 Maybe you have a co-worker with boundary issues,
0:00:24 or you want to know how to inspire and motivate your team.
0:00:26 No problem is too big or too small.
0:00:29 Give us a call and we’ll help you solve the problems you’re stuck on.
0:00:32 Find Fixable wherever you listen to podcasts.
0:00:38 Welcome back to Office Hours with Prop G.
0:00:40 Today we’re kicking off a special three-part series,
0:00:43 Prop G on Marketing, where we answer questions from business leaders
0:00:47 about the biggest marketing challenges and opportunities companies face today.
0:00:48 What a thrill!
0:00:50 I’m a little bit self-conscious.
0:00:52 My whole career, not my whole career,
0:00:56 most of my career was about brand strategy and working with CMOs and CEOs,
0:00:58 but I am so out of shape.
0:01:00 I haven’t taught in over a year,
0:01:03 and my kind of brand strategy muscles are atrophying.
0:01:05 I’m worried about the next class I teach,
0:01:08 I’m going to be one of those guys that should have been put on an ice flow about 15 years ago,
0:01:11 i.e. most of the faculty at elite institutions.
0:01:14 Anyways, a little self-conscious, but I’m going to try and get over that.
0:01:15 Let’s bust right into it.
0:01:16 Let’s get into it.
0:01:18 He’s an imposter, but he’s your imposter.
0:01:24 How do you market to a world that doesn’t want to be bothered?
0:01:27 Nobody answers phone calls, texts, etc.
0:01:29 What medium drives engagement?
0:01:31 That’s a good question.
0:01:36 By the way, that question comes from teleheaddogfan on Reddit.
0:01:38 My subreddit is very entertaining.
0:01:39 Entertaining and upsetting.
0:01:41 I sometimes go on there and I think,
0:01:42 I’m not like that.
0:01:43 I’m a nice guy.
0:01:44 Say hi.
0:01:45 I’m a nice guy.
0:01:47 Anyways, okay, teleheaddogfan.
0:01:51 The mediums that drive engagement, there’s just no getting around it.
0:01:59 If you want to build a personal brand, if you want to build an aspirational brand, you have to allocate more money to social.
0:02:02 I think about just the amount of time.
0:02:04 I mean, you are where you spend your time.
0:02:09 One of the reasons I got off X is I found that I was speaking in 140 characters and I was becoming terse
0:02:16 and constantly looking for the weak point in people’s arguments such that I could weigh in and press on the soft tissue
0:02:20 and make a character or a cartoon of their comments such that I could feel good about myself.
0:02:22 In other words, I was becoming an asshole.
0:02:24 I mean, that’s literally what X is.
0:02:28 It’s like an asshole turns into a social media platform.
0:02:29 And I thought, you know what?
0:02:31 I already have too much tendency to be an asshole.
0:02:35 I don’t need an environment that turns me into an even bigger a-hole.
0:02:38 So you want to go where people are spending their time.
0:02:41 And the bottom line is social media is where everyone is spending their time.
0:02:47 In addition, the people who kind of set the trend for most aspirational brands are youth, right?
0:02:50 Once your dad starts wearing Nikes, the young people stop wearing them.
0:02:55 So everybody wants to kind of follow the lead of an 18 to 30-year-old aspirational male or female.
0:02:59 And those people are spending way too much time on social media.
0:03:02 So I would say that social is engagement.
0:03:04 I think events create a lot of engagement.
0:03:06 Content marketing, if you’re B2B.
0:03:12 At L2, we used to put out these weekly videos that went on one of the fastest growing social media platforms in the world, YouTube.
0:03:15 And we built essentially our own mic.
0:03:19 Instead of paying some PR agency $10,000 a month to get me on Bloomberg or whatever it was,
0:03:22 we went straight to consumer.
0:03:24 We went direct to consumer with our own media channels.
0:03:30 And we would put out thoughtful research and interesting data that a ton of consumer brands was focused on CMOs.
0:03:32 We’d watch the video.
0:03:35 And we were constantly in the selection set.
0:03:43 So when they thought, you know, I’d really like to benchmark my digital footprint relative to Clorox or Unilever or whoever’s in the competitive set of P&G.
0:03:46 P&G would think, well, call that crazy dude and his firm L2.
0:03:55 And within about seven years of launch, we were working with a third of the global 100 or the 100 biggest companies in the consumer world.
0:04:01 So B2C, I think you’ve got to be a master of social and find a voice and create two-way engagement.
0:04:05 B2B, I think it’s content marketing or thought leadership.
0:04:07 That’s my kind of quick and dirty answer.
0:04:09 Thank you so much, Telehead dog fan.
0:04:10 Question number two.
0:04:14 Our next question also comes from Reddit, user mxt240.
0:04:19 I work for a giant software company.
0:04:23 I do nerd work, not face work or management, and I am damn good at it.
0:04:27 Every so often, I get emails telling me to build my personal brand.
0:04:29 What the fuck does that actually mean?
0:04:31 Should I always wear cardigans?
0:04:33 Do I need a catchphrase?
0:04:35 Inspirational bullshit in my email signature?
0:04:38 I’m well-respected and well-liked by my peers.
0:04:41 And I take time to unofficially mentor those less experienced.
0:04:45 Isn’t there value in hyper-exclusive brands that don’t advertise?
0:04:48 Mike C240, so thanks for the question.
0:04:51 I teach an entire class on building a personal brand.
0:04:56 A lot of people think a lot about the brand of the company they’re working for, but they don’t actually take the time to think about their own brand.
0:04:59 And they might think, well, I’m not interested in building a brand.
0:05:01 You have a brand whether you want one or not.
0:05:11 A brand is essentially the promise or the associations that are linked to you and linked to your name, linked to your visual identity, linked to you when you show up.
0:05:16 Everybody has a certain preset set of expectations, the promise you present, if you will.
0:05:19 And then you have to deliver, hopefully, against that performance.
0:05:28 And ideally, you want to differentiate a brand such that when there’s an opportunity for a promotion or an assignment and they have five different cereal boxes, i.e. people to pick from, they pick you.
0:05:30 So how do you go about that?
0:05:35 The first thing is I think it’s helpful to think of what are your core associations?
0:05:37 What do you want to be known for professionally?
0:05:45 And that is the two or three kind of adjectives, descriptors that sort of identify, do you want to be known as especially empathetic?
0:05:46 That’s important.
0:05:47 Those people make great managers.
0:05:49 Do you want to be known as especially strategic?
0:05:52 And that is there’s a role for those people, all right?
0:05:55 Put them on figuring out our six- or 12-month plan.
0:05:56 Are you kind of no-nonsense?
0:06:07 All right, send that person, and kind of harsh, quite frankly, and good with numbers, send that person to the branch in Houston and have them do the analysis and come back and give it to me straight on what’s going on with that business.
0:06:22 There’s all sorts of qualities, features, attributes that are positive or differentiate someone in the work world, and I think it’s helpful to kind of identify what those three things are, those two or three things are, such that they can serve as sort of a guiding light or a religion.
0:06:23 Think about religion.
0:06:33 It’s a set of rules that you try and shape your actions and your life around, such that you behave in a way that reinforces the teaching of Jesus Christo, right?
0:06:34 What would Jesus do?
0:06:36 I remember that question in Sunday school.
0:06:38 By the way, rest in peace, El Papa.
0:06:39 Rest in peace.
0:06:42 Anyways, so think of some core associations.
0:06:50 If you want to be really formal about it, find some people in your life you trust and say, what do you think of when you hear me in a professional context, tell me you’re going through this process?
0:06:55 And not only think about the positive things, but also find out if there’s anything negative.
0:06:59 And here’s how you know if that criticism is valid.
0:07:04 If you feel as if you’ve been punched in the gut, that means it’s true.
0:07:07 If they say something stupid and it’s mean or whatever, you can write it off.
0:07:12 But if it’s like I remember in some of my student reviews, they said that use profanity too much.
0:07:13 And as a result, it reduces your credibility.
0:07:15 And it really upset me.
0:07:16 Why did it really upset me?
0:07:17 Because it’s probably true.
0:07:19 And I kind of deep down know that it’s true.
0:07:21 Now, have I done anything about it?
0:07:21 Fuck no.
0:07:23 Well, a little bit.
0:07:24 In class, I try to tone it down.
0:07:28 But anyways, we’re going to think about if there’s any negatives that get in the way of us.
0:07:30 Then we’re going to think about visual metaphors.
0:07:32 We are a very visual species.
0:07:35 You need to lean into your visual metaphors.
0:07:35 Are you losing your hair?
0:07:38 Then shape your fucking hair like the dog when he was 30 years old.
0:07:45 And all of a sudden back then, in whatever it was, 2004, 1994, it was seen as aggressive and different, right?
0:07:46 Are you in good shape?
0:07:48 Then get in fucking crazy shape.
0:07:50 Do you have really wonderful frizzy hair?
0:07:52 Then have out of control frizzy hair.
0:07:53 Do you like glasses?
0:07:57 Then wear big Sally, Jesse, Raphael glasses.
0:07:59 Visual metaphors are so powerful.
0:08:00 What’s the most powerful thing about Nike?
0:08:06 Some people would argue it’s the advertising or, you know, landmark endorsements from Tiger Woods or Michael Jordan.
0:08:07 I don’t think so.
0:08:08 I think it’s the goddamn swoosh.
0:08:10 I can recognize the swoosh in my peripheral vision.
0:08:14 Can you recognize the Reebok logo in your peripheral vision or Puma?
0:08:15 No, swoosh, yes.
0:08:16 What does that mean?
0:08:24 It means billions of times they’re getting unearned media from people on the street who recognize that swoosh without even thinking about it consciously.
0:08:25 What is your visual metaphor?
0:08:28 What is your medium?
0:08:30 Are you really good at giving text?
0:08:32 Are you a great writer?
0:08:33 Are you fantastic on TikTok?
0:08:35 Can you put out PowerPoint?
0:08:37 Are you fantastic speaking in front of people?
0:08:47 Whatever your medium is, you need to identify it and then find every opportunity to display your expertise in that meeting and develop a following.
0:08:49 I am really good in front of a lot of people.
0:08:50 I’m not great one-on-one.
0:08:54 I’m very good on video and decent on social media.
0:08:55 I’m not that good on the phone.
0:09:03 So I try and shape my interactions and my contact with others around those mediums and specifically avoid the ones I’m not good at.
0:09:06 What is the one thing, the product you’re going to own?
0:09:09 You have to be known as the go-to person on one thing.
0:09:14 When it comes to pivot tables, looking at forecasting, our customer acquisition strategy.
0:09:15 Oh, we got to go to Lisa.
0:09:16 Okay.
0:09:27 When it comes to recruiting, sending someone to Carnegie Mellon to do recruiting and talk about the firm, you know, Bob is just so good, so young, so handsome, so excited about the firm.
0:09:29 That’s the person we want in front of the people.
0:09:32 Well, what about someone who knows how to manage people?
0:09:33 They’re just very good.
0:09:34 They’re a player or coach.
0:09:35 Okay, that’s Catherine.
0:09:38 We got to put Catherine in charge of this group and we’ll get better work out of them.
0:09:43 What is the one thing you are going to own?
0:09:45 Core associations, positives.
0:09:51 A negative association that might be getting in the way of people getting to the core associations, the positive ones that you need to dial down.
0:09:53 What is your medium?
0:09:55 What is your visual metaphor?
0:09:57 And what are you going to own?
0:09:58 Right?
0:10:00 And then we’re going to apply three hurdles.
0:10:01 First, is it differentiated?
0:10:04 Is that being able to do pivot tables and being known as empathetic?
0:10:06 Is that really differentiated in this firm?
0:10:07 You want to be different.
0:10:09 Two, does anyone care?
0:10:11 Is it relevant to what you do every day?
0:10:13 And then third, what are you going to do to make it sustainable?
0:10:23 What are you going to do to invest in it such that you pull away from the rest of the pack and consistently get better and go deeper and deeper and deeper in those chosen kind of domains which you want to own?
0:10:24 All right, that’s it.
0:10:25 Boom, your brand is done.
0:10:27 And yeah, it’s absolutely worth it, boss.
0:10:29 And be clear, you have a brand.
0:10:32 It’s just a question of whether you want to manage it or not.
0:10:33 Thanks for the question.
0:10:36 We’ll be right back after a quick break.
0:10:49 In most ways, Google and Apple are ruthless competitors.
0:10:59 But then a high-powered Apple executive gets up on the stand at the trial that might break up Google and argues that actually Google’s fine and the best thing you can do is leave it alone.
0:11:01 Why?
0:11:06 Because Google being left alone means $20 billion a year for Apple.
0:11:15 On the VergeCast this week, we talk all about what’s going on at the Google trial, plus the latest from the efforts to break up Meta, what’s going on with Netflix, and lots more.
0:11:17 All that on the VergeCast, wherever you get podcasts.
0:11:24 Question three.
0:11:25 Welcome back.
0:11:28 Our final question comes from Proof of Profits on Threads.
0:11:34 American companies are known for their great marketing.
0:11:38 They’ve been so effective that we’ve seen genericized trademarks.
0:11:42 How much of a role has this played into the monopolization of America?
0:11:45 And are America’s duopolies bad for consumers?
0:11:48 If so, what can we do to change it?
0:11:58 I think what you’re asking is, has great marketing led to the concentration of industries where we have monopolies or duopolies that extract unfair rents from other businesses or consumers?
0:12:00 I don’t think it’s a great marketing.
0:12:07 I think it’s mostly regulatory capture and a lack of FTC or DOJ who’s been essentially in a slumber for about 30 or 40 years.
0:12:27 The bottom line is, when you have one company that controls two-thirds of all social media, Meta, one company that controls 92% of search, Google, one company that controls 50% of all e-commerce, Amazon, and one company that controls, I don’t know, 50% of all smartphones and 90% of the revenues from smartphones, Apple.
0:12:34 It probably means we’d be better off if we broke these companies up, such that there’d be more entities trying to rent people’s labor.
0:12:35 Wages would go up.
0:12:41 They’d be more focused and more risk-taking because they wouldn’t be coordinating and cooperating with each other.
0:12:43 How did Google let OpenAI ever exist?
0:12:43 Why?
0:12:52 Because despite the fact that the majority of AI IP resided within Alphabet, they didn’t want to risk the existential threat.
0:12:54 They didn’t want to disrupt their own search business.
0:13:01 So they weren’t excited to kind of put out an AI product that might disrupt or cannibalize their Google search.
0:13:02 So what happened?
0:13:05 They left the door open or they left the garage door open.
0:13:09 And Sam Altman walked right in and took their AI.
0:13:12 In some, you’ve got to eat your own young or someone else will.
0:13:16 So effectively, what’s happened in breakups is that they’ve been good for shareholders.
0:13:37 I would argue the biggest increase in rents from Meta have been the rents on parents who have to put up with social media that is making their kids feel shittier and shittier about themselves.
0:13:38 There’s no real options.
0:13:42 And when you say, well, it’s bad parenting, just get them off of Instagram or just get them off of Snap.
0:13:44 Well, here’s the problem.
0:13:47 They end up more depressed because everybody is on Snap or Instagram.
0:13:49 And if they’re not on it, they feel ostracized.
0:13:54 So we’re in sort of this prisoner’s dilemma where we don’t know what to do as parents.
0:14:01 But we see a consolidation across majority of industries where they weaponize government and say, let us be regulated monopolies.
0:14:05 They buy off senators and congresspeople from both sides of the aisle.
0:14:10 And the DOJ and the FTC have been neutered and you have a concentration of industry.
0:14:12 I would want to do that.
0:14:12 I wouldn’t.
0:14:13 Why?
0:14:14 Peter Thiel said it.
0:14:16 Competition is for idiots.
0:14:17 You don’t want competition.
0:14:19 You want to figure out a way to have access to cheap capital.
0:14:25 Establish yourself as a leader, which gives you more cheap capital such that you can just pull away from the competition.
0:14:40 And then you start investing in D.C. where they basically say, despite the fact you have 93% share of search, despite the fact that you’re radicalizing young men on Google, we’re not going to move in and we’re not going to break you up because there are so many really charming, highly paid people.
0:14:48 Do you realize there are more full-time lobbyists in Washington, D.C., living in Washington, D.C., who work for Amazon than there are sitting U.S. senators?
0:14:50 Let me repeat that.
0:14:54 There are more full-time lobbyists, really well-paid.
0:15:08 They make a lot more than any senator whose entire job is to be likable and take senators to lunch and take them golfing and give their money for the campaign and have really thoughtful conversations about the future of e-commerce and why they should not break up Amazon.
0:15:09 And here’s the thing.
0:15:13 When you’re paid not to understand something, you’re never going to understand it.
0:15:19 So when you’re paid not to understand why that concentration of power is bad, you’re never going to understand it.
0:15:20 So what do we have?
0:15:21 Wireless.
0:15:24 Verizon and AT&T control 70% of the U.S. market.
0:15:28 Soft drinks, Coke and Pepsi, 70% of the U.S. soda market.
0:15:29 E-commerce.
0:15:30 Amazon.
0:15:33 Again, somewhere between 40% and 50%, depending on how you account for it.
0:15:39 According to the Brookings Institution, 75% of U.S. industries have seen an increase in concentration over the past two decades.
0:15:43 It’s everything from home improvement to big chicken to big pharma.
0:15:47 And what do they do when they have a lack of competition or they kind of wink at each other and cooperate?
0:15:48 They raise prices.
0:15:58 In some, there’s been a transfer of wealth from lower and middle-income households that don’t have a lot of stocks to these companies in the form of higher prices.
0:16:00 Why do you think inflation is so fucking out of control?
0:16:04 Every year, companies get more productive, which means they should be able to pass on the savings to the consumer.
0:16:06 But instead, they increase their profits.
0:16:09 And by the way, there’s some ancillary benefit to that.
0:16:10 It’s great to be a shareholder.
0:16:11 But what’s happened to wages?
0:16:12 Boom.
0:16:13 They haven’t moved at all.
0:16:14 Right?
0:16:15 What’s happened to household wealth?
0:16:19 Well, the average has gone way up because the top 10%, the shareholders are killing it.
0:16:26 So essentially, there’s been a transfer of wealth from the lower and middle-class households to the households that own shares.
0:16:31 So if you’ve made the jump from widespread from being an earner to an owner, you’ve done really well.
0:16:36 But the earners now have higher costs and can never make that jump to being an owner.
0:16:37 What’s the result?
0:16:39 Dynastic caste system.
0:16:41 Alphabet should be three companies.
0:16:45 It should be YouTube, it should be their advertising, and then it’s search and other companies.
0:16:47 It might even spin off Waymo.
0:16:50 Apple Services should be a different company from Apple.
0:16:55 Instagram should be an independent company, as should WhatsApp, for God’s sakes.
0:17:04 AWS should be an independent company, not part of Amazon where they cooperate and coordinate and get cheap capital to put every online retailer out of business.
0:17:05 What would we have?
0:17:06 Who wins?
0:17:06 Shareholders.
0:17:08 Who wins?
0:17:08 Employees.
0:17:10 Who wins?
0:17:12 Society, lower rents.
0:17:13 Who wins?
0:17:14 The tax base.
0:17:14 Who loses?
0:17:21 The one person who has dual-class supervoting shares who wants to sit on the iron throne of all seven realms, not just Westeros.
0:17:30 We absolutely need an absolute breakup palooza to bring costs down, to bring rents down on consumers.
0:17:36 The concentration of industry in the United States is the culprit around inflation.
0:17:38 Thanks for the question.
0:17:40 That’s all for this episode.
0:17:45 If you’d like to submit a question, please email a voice recording to officehours at propertymedia.com.
0:17:47 That’s officehours at propertymedia.com.
0:17:54 Or, if you prefer to ask on Reddit, just post your question on the Scott Galloway subreddit and we just might feature it in an upcoming episode.
0:18:05 This episode was produced by Jennifer Sanchez.
0:18:07 Our intern is Dan Shallon.
0:18:09 Drew Burroughs is our technical director.
0:18:12 Thank you for listening to the Prop G pod from the Vox Media Podcast Network.
0:18:17 We will catch you on Saturday for No Mercy, No Malice, as read by George Hahn.
0:18:23 And please follow our Prop G Markets pod wherever you get your pods for new episodes every Monday and Thursday.
0:18:23 Thank you.

0:00:04 In my years of entrepreneurship, I’ve seen countless startups.
0:00:06 And here’s the truth.
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0:00:43 AI is a tool for spiritual enlightenment.
0:00:48 It can’t get you enlightened, but it can show you the maps.
0:00:51 And there are many maps on spirituality,
0:00:53 just like there are many maps in any terrain,
0:00:56 but they all lead to the same destination,
0:00:59 which is spiritual realization.
0:01:05 I’m Guy Kawasaki.
0:01:07 This is the Remarkable People podcast.
0:01:10 And I know I say this every episode,
0:01:13 that we found some remarkable person to inspire you.
0:01:16 But today, truly, we have a remarkable person.
0:01:18 His name is Deepak Chopra.
0:01:21 And I bet every one of you have heard of him.
0:01:24 He’s world renowned for his integrative medicine
0:01:27 and personal transformation work.
0:01:29 He’s the founder of the Chopra Foundation.
0:01:32 And I mean, how much do I have to introduce you?
0:01:35 And he has touched millions of people’s lives
0:01:38 with his writing, his speaking, his podcasting.
0:01:43 And I met him in Hawaii at an EO conference,
0:01:46 which was a very special moment for me.
0:01:51 And Deepak, you were wearing like a really cool jacket.
0:01:54 That made a very big impression on me.
0:01:57 And so I think we discussed it.
0:01:59 Was it an Issey Miyake jacket?
0:02:01 It was Issey Miyake.
0:02:03 Yeah, those are, they’re cool,
0:02:05 but also easy to travel in, right?
0:02:06 Yeah.
0:02:09 So I came right home and I told my wife,
0:02:12 I met Deepak Chopra and he was in an Issey Miyake jacket.
0:02:15 She was also impressed.
0:02:20 So I want to dive right into your latest book, okay?
0:02:22 You’ve written 90 books and you have podcasts
0:02:25 and YouTube videos all over the place.
0:02:27 So people will understand the basis.
0:02:30 But I have to tell you that I read your latest book
0:02:36 and I was just, I guess the right word is astounded, Deepak,
0:02:38 because of all the people in the world
0:02:39 who have embraced AI,
0:02:42 I would not have thought it would have been you.
0:02:45 And so that was particularly enlightening to me.
0:02:47 So I’m going to start off with a quote, okay?
0:02:49 The quote from the book, quote,
0:02:52 I believe that no technology in decades
0:02:57 can equal AI for expanding your awareness in every area,
0:03:00 including spiritual and personal growth.
0:03:03 Can you just explain to me
0:03:06 how you came to have so much faith in AI?
0:03:07 When I read that, I thought,
0:03:08 next thing you know,
0:03:11 Warren Buffett is going to tell me he’s buying crypto
0:03:14 and Greta Thunberg is driving an SUV
0:03:17 and Jane Goodall loves ribeye steaks.
0:03:19 Deepak Chopra has embraced AI.
0:03:22 So can you just explain this for me?
0:03:27 I have my own definition of what is called reality.
0:03:34 So what we call the divine or God doesn’t have a form.
0:03:36 And not having a form,
0:03:40 that every spiritual tradition says God doesn’t have a form.
0:03:42 The divine doesn’t have a form.
0:03:43 Then people say, well,
0:03:46 what all these pictures of God did in the Vatican
0:03:47 and this and that.
0:03:52 the Hindus have hundreds of deities as do the Buddhists.
0:03:57 And those are symbolic representations of what we call divine.
0:03:59 Divine is infinite.
0:04:02 And being infinite doesn’t have a border,
0:04:08 is outside of space-time and has no cause.
0:04:10 In the world of space-time and causality,
0:04:12 everything has a cause.
0:04:16 But God transcends all causes
0:04:19 and all concepts and all definitions.
0:04:22 So I came up with a formula.
0:04:26 God has a digital workshop outside of space-time.
0:04:32 And the formula is zero is equal to infinity is equal to one.
0:04:36 So think of this workshop outside space-time,
0:04:38 which is divine.
0:04:43 And it’s spilling out zeros and ones in infinite combinations.
0:04:47 And the only difference between you and a mountain
0:04:53 or the earth and a star on this iPhone and AI
0:04:56 is a different combination of zeros and ones.
0:04:57 That’s it.
0:04:58 And it comes from one source.
0:05:01 So that is God’s language.
0:05:04 It’s not English with an Indian accent.
0:05:06 I would have liked to believe that.
0:05:11 But God’s language is digital language.
0:05:14 And once you get that understanding,
0:05:19 then you see, how did we create the human experience?
0:05:21 You and I create the human experience.
0:05:25 And it all began 40,000 years ago
0:05:29 when there were eight different kinds of human species.
0:05:31 So we call ourselves Homo sapiens,
0:05:34 but then we gave ourselves that name.
0:05:36 It means the wise ones.
0:05:38 We were humble enough to do that.
0:05:40 But we gave names to other humans.
0:05:45 Homo habilis, Homo erectus, fluences.
0:05:47 We gave names to other species.
0:05:50 Giraffes, elephants, this, that, the other.
0:05:54 So all started with naming experience.
0:05:57 And that created a language for stories.
0:06:02 And that is how the human evolution began.
0:06:02 Stories.
0:06:05 To be human is to have a story.
0:06:07 Right now, we’re sharing a story.
0:06:14 And then that way of telling stories evolved into what we call models.
0:06:20 So models means giving reality a stamp through the human mind.
0:06:25 Latitude, longitude, drainage, main time, North Pole, South Pole.
0:06:29 We can’t live without these concepts, even though we made them up.
0:06:31 But then we created more languages.
0:06:40 We created language of philosophy, science, anthropology, history, astronomy, biology, mathematics.
0:06:43 These are all human languages.
0:06:57 And we call AI a large language model because it has access to all these languages that humanity has created to look at what we call the human experience.
0:07:03 And now there’s no single human being that can compete with this kind of database.
0:07:21 So, in fact, we can’t compete with it, but we have access to the entire database of knowledge and wisdom from Jesus Christ to the Buddha, to Plato, to Socrates, to Einstein, to Tagore, to the prophets of the Old Testament.
0:07:25 AI is a tool for spiritual enlightenment.
0:07:30 It can’t get you enlightened, but it can show you the maps.
0:07:35 And there are many maps on spirituality, just like there are many maps in any terrain.
0:07:52 If I want to go to Boston from New York, I can use an aerial route, I can use a contour map, a road map, go by the ship, take a helicopter, but they all lead to the same destination, which is spiritual realization.
0:07:54 So I’m using AI as a tool.
0:07:55 It’s not just a book.
0:07:57 I have my own AI.
0:07:59 It’s called DeepakShopra.ai.
0:08:00 Try it out.
0:08:01 DeepakShopra.ai.
0:08:13 Ask any spiritual question or any dilemma that you have, spiritual, or about health, or about longevity, and you’ll get information from 96 of my books,
0:08:21 from every conversation I’ve had from every conversation I’ve had, from every discussion I’ve had, from my meetings with spiritual luminaries.
0:08:31 So, yes, AI is a tool for enhancing spiritual well-being, but also emotional well-being and physical well-being.
0:08:34 And my AI, DeepakShopra.ai, is the coach.
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0:09:43 So, the irony is that this quote-unquote technology is really democratizing spirituality, right?
0:09:53 It represents all the knowledge as opposed to just whatever narrow slice you had access to before, depending on what book or what person you knew.
0:09:55 Now you get everything.
0:09:58 Yeah, I’ll send you three short videos.
0:10:03 Feel free to show them on your program, my conversations with the Buddha on AI.
0:10:13 So, this is better than the conversation in 1930 with Albert Einstein and, God, I can’t remember the other fellow’s name.
0:10:17 The Buddha not to go to the Indian sage, yeah.
0:10:24 Here’s my next quote from the book, because I found this also stunning in a sense.
0:10:39 So, this is the quote, “The function of the guru needs to be overhauled in modern times, getting rid of the cult of personality, stepping away from superstitious beliefs in the magical attributes of enlightened beings.
0:10:45 AI can step in to renovate a time-honored role almost immediately.”
0:10:59 Now, Deepak, when I read that, I said, “Dipak is the mother of all gurus and he’s telling us that the function of a guru is being overhauled by AI.
0:11:02 Isn’t that, in a sense, putting you out of business?”
0:11:03 See?
0:11:05 Spell the word guru for me slowly.
0:11:08 G-U-R-U.
0:11:09 G-U-R-U.
0:11:20 So, the ultimate guru is you, and AI is helping you to discover your own guru, which is the only real guru.
0:11:29 others are deep fakes like me and so the concept of guru means that you’re like removing darkness
0:11:37 right so now you can remove darkness yourself with ai correct and so what does that mean for
0:11:43 all the other people who hold themselves out as gurus guru is a big industry i know it’s going to
0:11:49 slowly fade out but you know there are human beings who like to look up to other human beings
0:11:57 and they will never get enlightened if if jesus or the buddha are pointing their finger at the moon
0:12:03 i shouldn’t be worshipping the finger i should be looking at the moon and saying how can i get there
0:12:14 so a true guru is not into self-adulation a true guru allows you to become your own guru and that
0:12:21 happens only once in a few thousand years the rest are all deep fakes wow you earlier mentioned the
0:12:28 fact that there is i forget the name you use but let’s just for my purposes just let’s just call it
0:12:39 chopra gpt chopra.ai the data in that is only your stuff it doesn’t go outside so it cannot hallucinate
0:12:45 it’s only your data what you put into it or have you opened it up to the whole internet it’s only my
0:12:53 data and it does not hallucinate although there are advantages to hallucinations because anytime you have a
0:13:01 hallucination data it gives you creative ideas so i think hallucinations also have a role
0:13:11 but my ai doesn’t hallucinate its databases all my 96 books every conversation i’ve had publicly my youtube
0:13:19 videos my discussions my talks with luminaries etc yes can i interrupt really quick it sounds like there may be
0:13:26 some construction going on i can’t tell if it’s on your end or deepox end nothing happening on my side but
0:13:32 let’s just do it and then whatever happens we leave it up to the divine matrix i love it all right
0:13:38 there’s no construction on my side it’s a hallucination
0:13:44 madison is making her own reality
0:13:54 i have to mention that maybe i’m flattering myself but great minds think alike because
0:14:02 i also with madison’s help we created kawasaki gpt and kawasaki gpt has all my writings my podcasting
0:14:10 all that kind of stuff too and i swear deepak kawasaki gpt is better at being me than i am and
0:14:19 i often use it to draft newsletters to draft blurbs to figure out what to do on my podcast it’s better
0:14:26 at being me than me do you think your gpt is better at being you than you it is because it’s also
0:14:34 something called a rag model retrieval augmentation in a generation which means anything that’s obsolete
0:14:40 it automatically deletes it automatically deletes it and upgrades it yes it’s more effective than i am
0:14:46 you could have easily done this interview with my deepak chopra dot yeah yeah it could have been kawasaki
0:14:55 gpt talking to your gpt and it would have been interesting so you know have you thought that because you
0:15:02 created this you you are in a sense now immortal that for the rest of time people can ask you questions
0:15:09 yeah not only model it can keep updating as the years go by whatever i’ve said can be upgraded to
0:15:15 a new level of understanding and are lots of people asking and stuff and interacting with it a lot
0:15:27 yeah yeah now it’s available in four languages english hindi spanish and arabic and soon we’re introducing it in
0:15:38 china as well wow wow okay the next mind-blowing quote from the book is this to me ai is a mirror to the user’s
0:15:48 consciousness so can you please explain what that means and you know how in a sense what you ask ai reflects
0:15:56 what you are yeah because if you’re going to ask what kind of shoes i should buy or candidate do i prefer
0:16:07 democratic or or republican then my ai will not participate in that conversation my will only participate in
0:16:16 conversations about health longevity health span emotional and spiritual well-being so the way you
0:16:27 ask the question obviously reflects your own issues obviously so then ai becomes a mirror and depending on
0:16:36 how much experience it has from your asking it questions it actually knows more about you than you
0:16:46 know about yourself i agree so from a technical standpoint what you or your team has done is it
0:16:55 has constricted the answers of your gpt so that it only answers stuff that you care about or that you
0:17:01 feel you’re relevant to it won’t answer a question about how do i become a better surfer it will say
0:17:10 i cannot answer that question it will say yeah go you can consult chat gpt for that i only want to offer
0:17:20 to the world what i think i’ve spent my life doing otherwise i would be a hypocrite and getting outside of your
0:17:35 and so you can’t worry about that once a child is born it can’t return to the womb
0:17:44 so this child is born it’s not going to return to the womb and so we have to decide now whether we use it to
0:17:51 risk our extinction or we use it to create a more peaceful just sustainable healthier and joyful world
0:17:58 and that was the goal every technology can be used for harmful purposes a knife can be used to kill a
0:18:06 person but in the hands of a surgeon it heals a person and so too with every other technology ai can be used for
0:18:14 poisoning the food chain cyber hacking interfering with democracy causing a nuclear plan i don’t want
0:18:20 to give too many ideas somebody is listening but it can also be used for good purposes but it’s here you
0:18:25 can’t stop it it also seems to me deepak that you know when you read these doomsday articles about ai
0:18:35 they are comparing a worst case of ai against the best case of humans and to me that is an unfair
0:18:43 comparison if you compare best case ai best case human or worst case ai worst case human but you know
0:18:50 in this doomsday scenario that what if two ais get angry with each other and launch a nuclear war i would
0:18:56 say it’s much higher probability that some fascist dictators will do that than an ai will do it yeah
0:19:04 correct correct yeah it doesn’t have emotions yeah it doesn’t have subjective experience you can program
0:19:14 it to simulate that but it inherently does not have emotional experience therefore it cannot act out of
0:19:22 emotions now you can as a human being program it in a way that it simulates that and that’s a danger
0:19:29 because there are enough people who are crazy in the world i noticed in your book that sometimes you’re
0:19:37 citing chat and sometimes you are citing other llm so you know how do you pick when do you use which
0:19:45 one which is your favorite how do you decide which one to use right now my favorite is my own which is
0:19:54 deepak chopra dot air but perplexity is a good one because it gives you references and data and now this
0:20:01 deep seek that has come from china which came much after i wrote the book is actually far superior to anything
0:20:09 i’ve seen and as we move into the future we’re going to have all these different ai companies competing
0:20:14 with each other and that’s a good thing because you’re going to see something much more creative and
0:20:22 leapfrogging us into a new future so when i see in your book that sometimes you use one llm and sometimes you
0:20:30 use another in the writing of the book did you ask the same prompt of several llms and then pick the answer you
0:20:38 like the best or did you just ask one i asked you several llms and then i would also see how i could
0:20:46 corroborate the information with research and that’s how it happened okay and i never in a million years
0:20:55 thought i would be asking deepak chopra this question but how do you create great prompts what’s the art of a
0:21:05 deepak chopra prompt you act as if you’re speaking to a personal friend number one to a coach number two
0:21:17 to a research assistant number three and number four to someone or an instrument that can access the minds of
0:21:25 the greatest luminaries that humanity has so you assume those things and then you go back and forth back
0:21:35 and forth and actually you can train your ai ultimately even chat gpt or perplexity to actually have a
0:21:46 reasonably good debate or even argument without any contentiousness without any emotional engagement
0:21:53 then you get to the right answers but it’s called generative ai for a reason it generates new
0:22:00 information based on the context and the art of the prompt so in my book you had there’s a whole
0:22:06 chapter called the art of the prompt and basically if i figure out this prompts and i embrace this is going
0:22:13 you’re going to put me on my path to dharma yes for people not familiar with the term can you just
0:22:19 quickly define dharma dharma means purpose in life so there are many stages of dharma
0:22:30 first is survival and safety second is material success third is maximizing the delight of the senses
0:22:38 fourth fourth is love and belongingness fifth is creative expression sixth is intuition and higher
0:22:46 consciousness and the seventh is self-discovery or self-realization so these are stages of dharma
0:22:57 not purpose and how do i use ai to get myself down this path ask my ai this question deepakshopra.ai say
0:23:05 how do i get on the path to dharma see what it comes up with but ultimately it will resonate with you
0:23:13 what’s my unique talent how does it help the world and how can use my unique talents to be of service
0:23:23 and be in a state of gratitude then you’re in dharma
0:23:42 if you’re listening to remarkable people it’s a good bet you want to be more remarkable yourself
0:23:49 one way to do that spend three days in a room full of the sharpest minds in business i’m jeff berman
0:23:54 co-host of masters of scale inviting you to join me at this year’s masters of scale summit where you’ll
0:24:01 see bold leaders like reed hoffman fawn weaver andrew ross sorkin kara swisher dara treceder
0:24:09 asa raskin and more take the stage apply to attend at mastersofscale.com slash remarkable again that’s
0:24:19 mastersofscale.com slash remarkable deepak i took your spiritual intelligence tests in your book okay
0:24:27 yeah and maybe with what i’m going to tell you is going to show that i haven’t reached my dharma
0:24:33 but i have to say that i answered every question often or always
0:24:44 so does that mean that i’m doing pretty well spiritually it means you’re on the right track yes
0:24:51 that’s good to know and then i i asked madison if i answered all these this way am i deluding myself and
0:24:58 she said i wasn’t but then i asked her if i was deluding myself would you dare tell me that i wasn’t
0:25:02 and she said she would tell me so right madison that’s correct
0:25:10 i have a thought for you on the name of one of the chapters in the book and
0:25:18 let me be so bold as to offer this thought okay i realize i’m talking to deepak chopper but you know
0:25:29 so you have a chapter called trust the process and as i read that chapter i think that it would be more
0:25:36 accurate to call that chapter trust the processing as opposed to the process
0:25:43 because to me a process is like a sequence of steps and i think the point of that chapter is
0:25:52 not so much to to trust the documented steps but to trust the processing of the steps to going through the
0:26:00 processing not the process steps itself yeah no that’s good the process though is about self-reflection
0:26:08 and contemplative inquiry that’s the process but processing is good oh so i can say that i made a
0:26:11 good suggestion to the next edition
0:26:21 okay my life is complete my life is complete um so now next question for you because a lot of people
0:26:28 listen to my podcast including people like mark benioff they’re really into meditation and can you
0:26:35 just explain to people how ai could possibly help with meditation because most people’s initial reaction is
0:26:44 ai is the opposite of meditation it’s technical it’s staring at a screen it’s all this so how can ai help
0:26:51 meditation so there are many kinds of meditation there is meditation that is called contemplation
0:26:58 creative inquiry there’s awareness of the body there’s awareness of the mind there’s awareness of the
0:27:05 ego there’s awareness of the intellect there’s awareness of what’s happening inside your body
0:27:12 there’s awareness of relationship there’s awareness with the divine and there’s awareness
0:27:20 awareness with their own self so those are all the different disciplines of meditation ai can help tailor
0:27:29 meditation for you very precisely so you might go to my ai and say deepak i have a lot of stress
0:27:38 i’m in a relationship that is getting toxic can you help me with the meditation and my ai will give you a guided
0:27:46 meditation you don’t have to stare at the screen you just have to listen to me guiding you through the
0:27:57 meditation so that’s how it works okay do you think that science and spirituality are opposing forces
0:28:08 no science always asks what’s happening out there and spirituality asks who is asking and why science is
0:28:14 about the objective world and spirituality is about the subjective world and they go together you can’t
0:28:20 have an object without a subject and you can’t have a subject without an object they go together so they’re
0:28:28 complementary to each other so then you know how does one find spirituality are you just going to say use
0:28:35 ai ai but people are searching for spirituality how do they do it you start with four questions who am i
0:28:42 what do i want what is my purpose and what am i grateful for and then you sit in silence
0:28:52 and listen to the answers who am i what do i want what is my purpose what am i grateful for and that’s the first step
0:29:04 do you have any people that you would say this person really has integrated spirituality and leadership and
0:29:12 are there some shining examples that people should not necessarily worship they should be inspired by what
0:29:19 people have accomplished or who are people you hold up as they have their act together in recent times i would say
0:29:29 people like martin luther king jr nelson mandela mahatma gandhi mother theresa bishop tutu these were people
0:29:36 who had integrated their lives in a very spiritual way and made a big impact on the world and is there
0:29:45 anybody alive who would you put in that category i would have to think about that i would be interested based on my
0:29:50 limited knowledge of your work i would say the only person who qualifies is jane goodall
0:29:58 she does good i’m glad you mentioned yeah i would just like to know for you at this point in your life
0:30:04 how do you define success success is the progressive realization of worthy goals
0:30:12 it’s the ability to love and have compassion and it’s the ability to get in touch with your soul the
0:30:20 creative center from where everything happens by that the division of success there are many people who are
0:30:26 very rich and very famous and are failures yeah some people are so poor all they have is money
0:30:36 all right there has been some skepticism about you know your work and from quote unquote science in
0:30:43 medicine and stuff so how do you approach when you hear skepticism about your work and your alternative
0:30:48 medicine and things like that what what goes through your brain when people can flick you this way used
0:30:58 to get very defensive but now i ignore my critics and they can’t stand it and do you think are they flawed
0:31:04 or they’re ignorant like what’s going on with them they come from a different world view that’s all we all
0:31:11 express our world views how we were conditioned as children and then the schools we went to the education
0:31:18 we got and right now the world view in science is very physicalist and so anything that’s
0:31:25 non-physical is denigrated but that’s okay you need all kinds of people because it makes for maximum
0:31:33 diversity of opinion leads to creativity and how do you figure out sometimes you ignore people but sometimes
0:31:40 they have valid feedback so how do you separate the two you can’t ignore everybody who’s you don’t get
0:31:47 personally offended and you have always are open to feedback don’t take it personally emotionally
0:31:57 okay and i have one last question for you okay yeah and that last question is do you ever have moments of
0:32:07 personal doubt too i live in the wisdom of uncertainty at all times and without uncertainty there is no creativity
0:32:17 so yes doubt is a very important part of our creative process the more doubt you have about your habitual
0:32:26 certainties the more room there is to grow spiritually and how do you keep pushing through that uncertainty
0:32:32 i always ask what’s the creative opportunity here so you have these moments of uncertainty
0:32:42 you ask what the moment of not knowing not knowing is the highest knowing because if you know everything
0:32:54 then there’s nothing to know wow okay that is the way to end this podcast so the highest knowing is no no i’ll let you say it again deepak will you say that again that was a very
0:33:04 inspiring not knowing is the highest knowing is the window to infinite creativity
0:33:09 i can’t ask for a better end to the podcast than this thank you very much deepak
0:33:18 great pleasure to speak to you i hope we can speak at another event again soon maybe someday we can be on
0:33:26 stage together that would be great thank you guys i’m guy kawasaki this has been the remarkable people
0:33:33 podcast and truly we have had a remarkable episode today with the one and only deepak chopra and so i want to
0:33:40 thank you thank you again thank you madison for making this happen and tessa neismer her sister
0:33:48 and ace researcher jeff c and shannon hernandez our great sound design team and above all thank you deepak
0:33:54 chopra it’s been a very special moment for us thank you very much and i hope to see you again and
0:34:03 i hope you’re wearing that isse meyaki jacket because i just love that jacket thank you god bless oh god bless you too
0:34:09 this is remarkable people

0:00:06 Hey there, Stephen Dubner.
0:00:11 We are replaying a series we made in 2023 called How to Succeed at Failing.
0:00:13 This is the second episode.
0:00:17 We have updated all facts and figures as necessary.
0:00:19 As always, thanks for listening.
0:00:34 In early 2007, Carol Hemmelgarn’s life was forever changed by a failure, a tragic medical failure.
0:00:39 At the time, she was working for Pfizer, the huge U.S. pharmaceutical firm.
0:00:42 So she was familiar with the health care system.
0:00:46 But what changed her life wasn’t a professional failure.
0:00:47 This was personal.
0:00:59 My nine-year-old daughter, Alyssa, was diagnosed with leukemia, ALL, on a Monday afternoon.
0:01:01 And she died 10 days later.
0:01:07 In this day and age of health care, children don’t die of leukemia in nine days.
0:01:10 She died from multiple medical errors.
0:01:18 She got a hospital-acquired infection, which we know today can be prevented.
0:01:22 She was labeled.
0:01:27 And when you attach labels to patients, a bias is formed.
0:01:31 And it’s often difficult to look beyond that bias.
0:01:38 So one of the failures in my daughter’s care is that she was labeled with anxiety.
0:01:44 The young resident treating her never asked myself or her father if she was an anxious child.
0:01:45 And she wasn’t.
0:01:53 What happens is we treat anxiety, but we don’t treat scared, afraid, and frightened.
0:01:54 And that’s what my daughter was.
0:01:58 Hospitals are frightening places to children.
0:02:05 So my daughter, with her hospital-acquired infection, became septic.
0:02:11 But they were not treating her for the sepsis because all they could focus on is they thought she was anxious.
0:02:15 And they kept giving her drugs for anxiety.
0:02:23 Even though the signs, the symptoms, and me as her mother kept telling them something was wrong, something wasn’t right,
0:02:25 they wouldn’t listen to me.
0:02:35 So by the time she was failing so poorly and rushed to surgery and brought back out, there was nothing they could do for her.
0:02:43 The first harm was unintentional that they did to our daughter.
0:02:49 It was all the intentional harms after that, where we were lied to.
0:02:52 The medical records were hidden from us.
0:02:54 People were told not to talk to us.
0:03:03 And the fact that it took the organization three years, seven months, and 28 days to have the first honest conversation with us,
0:03:07 Those were all intentional harms.
0:03:13 And that’s why in health care, we have to have transparency.
0:03:20 Because how many other children suffered because of the learning that didn’t take place?
0:03:31 Hemelgarn says she filed a claim against the hospital, but she didn’t move forward with a lawsuit because of the emotional toll.
0:03:34 She ultimately took a different path.
0:03:40 In 2021, she co-founded an advocacy group called Patients for Patient Safety U.S.
0:03:43 It is aligned with the World Health Organization.
0:03:51 She also runs a master’s program at Georgetown University called Clinical Quality, Safety, and Leadership.
0:03:59 When harm does reach the patient or family, that is the time to really analyze what happened.
0:04:09 And while you never want to harm a patient or family, one of the things you’ll hear from patients and families after they have been harmed
0:04:15 is they want to make sure that what happened to them or their loved one never happens again.
0:04:22 The example I can give for myself personally is I did go back to the very organization where my daughter died.
0:04:25 And I have done work there.
0:04:32 Today on Freakonomics Radio, we continue with our series on failure.
0:04:36 In the first episode, we acknowledge that some failure is inevitable.
0:04:41 We are, by definition, fallible human beings, each and every one of us.
0:04:43 And that failure can be painful.
0:04:45 I don’t think we should enjoy failure.
0:04:48 I think failure needs to burn on us.
0:04:54 This week, we focus on the healthcare system, where failure is literally a matter of life or death.
0:04:59 Some organizations felt like they had already achieved the patient safety mission.
0:05:03 Others, it wasn’t even part of their strategic plan.
0:05:08 And we will learn where on a spectrum to place every failure.
0:05:10 From inexcusable.
0:05:14 There’s lots of examples of huge public sector failures.
0:05:16 But this was one of the biggest.
0:05:18 To life-saving.
0:05:22 I really believe that if we could do this, it would make a big difference in medicine.
0:05:26 How to Succeed at Failing, Part 2, Beginning Now.
0:05:42 This is Freakonomics Radio.
0:05:45 The podcast that explores the hidden side of everything.
0:05:48 With your host, Stephen Dubner.
0:05:58 The story of Carol Hemmelgarn’s daughter is tragic.
0:06:03 A hospital death caused by something other than the reason the patient was in the hospital.
0:06:07 Unfortunately, that type of death is not as rare as you might think.
0:06:12 Consider the case of Redonda Vaught, a nurse at Vanderbilt University’s Medical Center.
0:06:20 In 2019, she was prosecuted for having administered the wrong medication to a patient who subsequently died.
0:06:23 The patient was a 75-year-old woman who had been admitted to the hospital
0:06:27 for a subdural hematoma or bleeding in the brain.
0:06:31 Here is Redonda Vaught testifying at her trial.
0:06:32 I was pulling this medication.
0:06:40 I didn’t think to double-check what I thought I had pulled from the machine.
0:06:42 I used the override function.
0:06:47 I don’t recall ever seeing any warnings that showed up on the monitor.
0:06:54 The medication that Vaught meant to pull from the AccuDose machine was a sedative called Versed.
0:06:58 What she mistakenly pulled was a paralytic called Vecuronium.
0:07:01 Vecuronium instead of Versed.
0:07:05 I won’t ever be the same person.
0:07:24 When I started being a nurse, I told myself that I wanted to take care of people the way that I would want my grandmother to be taken care of.
0:07:30 Redonda Vaught was convicted of negligent homicide and gross neglect of an impaired adult.
0:07:32 Her sentence was three years probation.
0:07:41 You might expect a patient safety advocate like Carol Hemmelgarn to celebrate Vaught’s prosecution, but she doesn’t.
0:07:43 This doesn’t solve problems.
0:07:46 All this does is it creates silence and barriers.
0:07:54 When errors happen so often, the frontline workers, your nurses, allied health physicians were blamed.
0:07:59 But what we’ve come to realize is it’s really a systemic problem.
0:08:06 They happen to be at the frontline, but it’s underlying issues that are at the root of these problems.
0:08:10 It can be policies that aren’t the right policies.
0:08:13 It could be shortages of staff.
0:08:23 It can be equipment failures that are known at device companies but haven’t been shared with those using the devices.
0:08:31 It can be medication errors because of labels that look similar or drug names that are similar.
0:08:41 To get at the systemic problem in the Vanderbilt case, Hemmelgarn’s advocacy group filed a complaint with the Office of Inspector General in the Department of Health and Human Services.
0:08:46 What we found most frustrating was the lack of leadership from Vanderbilt.
0:08:50 Leadership never came out and took any responsibility.
0:08:52 They never said anything.
0:08:54 They never talked to the community.
0:08:58 It was essentially silence from leadership.
0:09:04 I think one of the other big failures we have in health care is fear.
0:09:09 Health care is rooted in fear because of the fear of litigation.
0:09:15 When there’s a fear of litigation, silence happens.
0:09:22 And until we flip that model, we’re going to continue down this road.
0:09:28 I absolutely share that worry.
0:09:33 And that case was, in my mind, a classic case of a complex failure.
0:09:34 Yes, there was a human error.
0:09:45 We also had faulty medication labeling and storing practices with alphabetical organization of drugs, which is not how you do it.
0:09:46 That’s Amy Edmondson.
0:09:46 That’s Amy Edmondson.
0:09:49 We heard from her in our last episode.
0:09:52 She is an organizational psychologist at the Harvard Business School.
0:09:57 She recently published a book called Right Kind of Wrong, The Science of Failing Well.
0:10:01 The Vanderbilt case was not an example of failing well.
0:10:06 Redonda Vaught, you will remember, dispensed Vecuronium instead of Versed.
0:10:14 You know, you don’t have a dangerous, potentially fatal drug next to one that’s routinely used in a particular procedure.
0:10:17 It’s what we might call an accident waiting to happen.
0:10:25 With that perspective in mind, Redonda is as much a victim of a system failure as a perpetuator of the failure, right?
0:10:29 So, this reaction, human error is almost never criminal.
0:10:37 To criminalize this, I think, reflects an erroneous belief that by doing so, we’ll preclude human error.
0:10:41 No, what we will do is preclude speaking up about human error.
0:10:44 And to her credit, she spoke up.
0:10:54 And that, one could argue, ultimately led her to the conviction she would have been better off somehow trying to hide it, which I wouldn’t advocate, obviously.
0:11:05 But when we recognize, deeply recognize, that errors will happen, then that means that what excellence looks like is catching and correcting errors.
0:11:10 And then being forever on the lookout for vulnerabilities in our systems.
0:11:15 How often do these kinds of deaths happen?
0:11:18 Researchers have a hard time answering that question.
0:11:24 In 1999, the Institute of Medicine, known today as the National Academy of Medicine, found that medical error,
0:11:28 medical error causes between 44,000 and 98,000 deaths per year.
0:11:38 A 2013 study in the Journal of Patient Safety estimated the number of preventable deaths at U.S. hospitals at 200,000 a year.
0:11:45 But in 2020, a meta-analysis done by researchers at the Yale School of Medicine re-evaluated those past estimates.
0:11:53 They put the number of preventable deaths at 22,000 preventable deaths a year is way too many.
0:11:57 This issue has gotten a lot of attention within the medical community.
0:12:01 But Carol Hemmelgarn says the attention hasn’t produced enough change.
0:12:06 Some organizations felt like they had already achieved the patient safety mission.
0:12:10 Others, it wasn’t even part of their strategic plan.
0:12:19 There’s areas where improvement has definitely escalated since the report came out over 20 years ago.
0:12:22 But it hasn’t been fast enough.
0:12:27 What we see is that not everything is implemented in the system.
0:12:32 That you can oftentimes have champions that are doing this work.
0:12:36 And if they leave, the work isn’t embedded and sustainable.
0:12:46 Amy Edmondson at Harvard has been doing research on medical failure for a long time.
0:12:50 But she didn’t set out to be a failure researcher.
0:12:55 As an undergraduate, I studied engineering sciences and design.
0:13:00 Tell me about the first phase of your professional life, including with Buckminster Fuller.
0:13:04 Yeah, so I’m answering that question with a huge smile on my face.
0:13:13 I worked three years for Buckminster Fuller, who was an octogenarian, creative person, an inventor, a genius, a writer, a teacher.
0:13:16 Best known for the geodesic dome, which he invented.
0:13:23 But single-mindedly about how do we use design to make a better world.
0:13:25 You can’t sort of get people to change.
0:13:29 You have to change the environment, and then they’ll change with it, was a kind of notion that he had.
0:13:38 My part was just doing engineering drawings and building models and doing the mathematics behind new, simpler geodesic configurations.
0:13:39 And it was so much fun.
0:13:42 And what was his view on failure generally?
0:13:49 Oh, he was a very enthusiastic proponent of using failure to learn.
0:13:53 He said often, the only mistake we make is thinking we shouldn’t make mistakes.
0:14:01 He would give the example of the very first time he got a group of students together to build a geodesic dome that he had, you know, he’d done the math.
0:14:05 He’d come up with this idea, and he got, you know, 20 students together.
0:14:06 They’re outside.
0:14:09 They built the thing, and it immediately collapsed.
0:14:11 Okay.
0:14:14 And he enthusiastically said, okay, that didn’t work.
0:14:16 Now, what went wrong?
0:14:22 And it was really the materials they were using, which were, I think, the best way to describe them is Venetian blind materials.
0:14:27 They had the tensile strength, but they certainly didn’t have the compressive strength to do their job.
0:14:27 Okay.
0:14:35 And what are the steps you take to turn that failure into a useful learning, I guess is the noun we use these days?
0:15:06 It was several years into her engineering career that Edmondson decided to get a PhD in organizational behavior.
0:15:17 I was interested in learning in organizations, and I got invited to be a member of a large team studying medication errors in hospitals.
0:15:21 And the reason I said yes was, first of all, I was a first-year graduate student.
0:15:23 I needed to do something.
0:15:27 And second of all, I saw a very obvious link between mistakes and learning.
0:15:39 And so I thought, here we’ve got these really smart people who will be identifying mistakes, and then I can look at how do people learn from them and how easy is it and how hard is it.
0:15:41 So that’s how I got in there.
0:15:43 And then one thing led to another.
0:15:45 After doing that study, people kept inviting me back.
0:15:46 I see.
0:15:48 She loves failure, they say.
0:15:49 That’s right.
0:16:00 Edmondson focused her research on what are called preventable adverse drug events, like the one from the Redonda Vaught case.
0:16:11 Now, you can divide adverse drug events into two categories, one which is related to some kind of human error or system breakdown, and the other which is a previously unknown allergy.
0:16:13 So it literally couldn’t have been predicted.
0:16:17 And those are still adverse drug events, but they’re not called preventable adverse drug events.
0:16:22 But within the first category, there’s probably 10 subcategories at least, right?
0:16:26 There’s bad data entry, bad handwriting, wrong eyeglasses.
0:16:27 On and on it goes.
0:16:28 Yep.
0:16:33 Or, you know, using language badly so that people didn’t understand what you said and they didn’t feel safe asking.
0:16:43 My wife had a knee surgery, easy knee surgery, and the painkiller that they prescribed on the spot, the doc actually stood there and wrote it, was for 100x the dosage.
0:16:44 Oh, no.
0:16:45 No.
0:16:45 Yeah.
0:16:46 Yeah.
0:16:49 See, that’s an error-driven, preventable adverse drug event.
0:16:50 Yes, I agree.
0:16:57 You know, there will always be things that go wrong or at least not the way we wanted them to.
0:17:10 And my observation in studying teams in a variety of industries and settings was that responses to failure were rather uniform, inappropriately uniform.
0:17:23 The natural response and even the formal response was to find the culprit as if there was a culprit and either discipline or retrain or, you know, shame and blame the culprit.
0:17:37 And it wasn’t a very effective solution because the only way to prevent those kinds of system breakdowns is to be highly vigilant to how little things can line up and produce failures.
0:17:51 Based on what she was learning from medical mistakes, Edmondson wanted to come up with a more general theory of failure, or if not a theory, at least a way to think about it more systematically.
0:17:58 To remove some of the blame, to remove some of the blame, to make the responses to failure less uniform.
0:18:03 Over time, she produced what she calls, well, here, let’s have Edmondson say it.
0:18:06 My spectrum of causes of failures.
0:18:11 After the break, we will hear about that spectrum of causes of failures.
0:18:15 It can clarify some things, but not everything.
0:18:17 Uncertainty is everywhere.
0:18:20 I’m Stephen Dubner, and you are listening to Freakonomics Radio.
0:18:24 We will be right back with how to succeed at failing.
0:18:41 How did Amy Edmondson become so driven to study failure?
0:18:44 Well, here’s one path to it.
0:18:47 Her whole life, she had been a straight-A student.
0:18:48 Right, I never had an A-.
0:18:50 Well, you know, I once had one in 10th grade.
0:18:53 It just was so devastating, I resolved not to have one again.
0:18:55 And I’m only partly joking.
0:18:57 But then she went to college.
0:19:02 I got an F on my first semester multivariable calculus exam.
0:19:03 An F.
0:19:04 Like, I failed the exam.
0:19:05 I mean, that’s unheard of.
0:19:06 What’d that feel like?
0:19:11 I didn’t see it coming, but I wasn’t baffled after the fact.
0:19:15 After the fact, it was very clear to me that I hadn’t studied enough.
0:19:22 In the years since then, Edmondson has been refining what she calls a spectrum of causes of failure.
0:19:30 The spectrum ranges from blameworthy to praiseworthy, and it contains six distinct categories of failure.
0:19:32 Let’s take two extremes.
0:19:34 Let’s say something goes wrong.
0:19:36 We achieve an undesired result.
0:19:40 On one end of the spectrum, it’s sabotage.
0:19:42 Someone literally tanked the process.
0:19:44 They threw a wrench into the works.
0:19:54 On the other end of the spectrum, we have a scientist or an engineer hypothesizing some new tweak that might solve a really important problem.
0:19:58 And they try it, and it fails.
0:20:12 And, of course, we praise the scientist, but the gradations in between often lull us into a false sense that it’s blameworthy all the way.
0:20:13 Okay.
0:20:17 So let’s start at the blameworthy end of the spectrum and move our way along.
0:20:20 Number one of the six.
0:20:25 My spectrum of causes of failures starts with sabotage or deviance.
0:20:30 I soak a rag in lighter fluid, set it on fire, throw it into a building, right?
0:20:37 Or I’m a physician in a hospital, I’m a surgeon, and I come to work drunk and do an operation.
0:20:43 You describe this as the individual chooses to violate a prescribed process or practice.
0:20:50 Now, I could imagine there are some cases where people violate because they think that the process is wrong.
0:20:51 That’s right.
0:20:52 There has to be intent here.
0:20:59 To label something a true sabotage, it has to be my intent is to break something.
0:21:02 It’s not a mistake, and it’s not a thoughtful experiment.
0:21:14 There certainly are protocols in hospitals, for example, where thoughtful physicians will deliberately depart from the protocol because their clinical judgment suggests that would be better.
0:21:20 They may be right, they may be wrong, but that would not qualify as a blameworthy act.
0:21:25 After sabotage on the spectrum comes inattention.
0:21:29 Inattention is when something goes wrong because you just were mailing it in.
0:21:30 You spaced out.
0:21:34 You didn’t hear what someone said, and you didn’t ask, and then you just tried to wing it.
0:21:43 Or you maybe are driving, you’re a trucker, and you’re driving, and you look away or fiddle with the radio and have a car crash.
0:21:50 Now, it sounds like those are mostly blameworthy, but what about inattention caused by external factors?
0:21:52 Well, that’s exactly right.
0:22:02 Once we leave sabotage and move to the right in the spectrum, it will never be immediately obvious whether something’s blameworthy or not.
0:22:05 It’s always going to need further analysis.
0:22:11 So, when we say the failure was caused by someone not paying attention, that just brings up more questions.
0:22:12 Okay, why weren’t they paying attention?
0:22:21 Now, it could be that this poor nurse was on a double shift, and that is not necessarily the nurse’s fault.
0:22:35 It might be the nurse manager who assigned that double shift, or it might be the fact that someone else didn’t show up, and so they have to just do it, and they’re quite literally too tired to pay attention fully, right?
0:22:40 So, we always want to say, well, wait, let’s see, what are the other contributing factors to this inattention?
0:22:48 Can you think of a large-scale failure, a corporate or institutional failure that was caused largely by inattention?
0:22:49 Yes.
0:23:00 One that comes to mind was a devastating collapse with the loss of many lives when a Hyatt Regency atrium collapsed in Kansas City in the early 80s.
0:23:18 And the inattention there was the engineer on record’s failure to pay close attention when the builder decided, out loud, not hidden, to swap one long beam for two smaller connected steel beams.
0:23:24 It would have been a five-minute calculation to show that won’t work with the loads that were expected.
0:23:34 It was a change that didn’t obtain the attention it needed to have avoided this catastrophic failure.
0:23:38 And was that change done to save money, or was it even more benign than that?
0:23:41 I think it was a combination of speed and money.
0:23:42 Speed is money.
0:23:44 Wow, wow, wow, wow.
0:23:45 That’s a great example.
0:23:46 Okay, let’s go to the next one.
0:23:47 Inability.
0:23:58 I’m reading one version of your spectrum here, which describes this as the individual lacks the knowledge, attitudes, skills, or perceptions required to execute a task.
0:24:01 That’s quite a portfolio of potential failure.
0:24:02 That’s right.
0:24:07 And that spans from a young child who doesn’t yet know how to ride a bicycle.
0:24:20 So as soon as they hop on that bicycle, they’re going to fall off because they don’t have the ability yet to, you know, multivariable calculus, which at least when you’re not studying hard enough, you don’t have the ability.
0:24:28 So it’s something that you just don’t have the ability to do to success, but usually could develop.
0:24:40 This reminds me of the Peter Principle, where people get promoted to a position higher than they’re capable based on their past experience, but their past experience may not have been so relevant to this.
0:24:42 That’s a great connection.
0:24:54 Yeah, the Peter Principle, where the failure gets caused by the fact that you don’t have the ability to do the new role, but no one really paused to reflect on that.
0:24:56 I sometimes think about this in the political realm, too.
0:25:04 The ability to get elected and the ability to govern effectively seem to be almost uncorrelated to me.
0:25:10 I’m sorry to say, do you think that’s the case and do you apply this spectrum sometimes to the political realm?
0:25:16 I don’t think it was always the case, but I think it might be increasingly the case.
0:25:28 There’s no theoretical reason why the two abilities to be compelling and win people over to your point of view should be at odds with the capability to do it.
0:25:35 But the way it is increasingly set up in our society might be putting them at odds.
0:25:40 After inability comes what Edmondson calls task challenge.
0:25:46 Yes, the task is too challenging for reliable failure-free performance.
0:25:47 Example?
0:26:01 A great example is an Olympic gymnast who is training all the time and is able to do some of the most challenging maneuvers, but will not do them 100% of the time.
0:26:12 And so when that person experiences a failure, they trip during their routine, then we would call that a failure that was largely caused by the inherent challenge of the task.
0:26:17 Can you give an example in either the corporate or maybe academic realm?
0:26:19 Let’s go to NASA, for example.
0:26:22 The shuttle program is very, very challenging.
0:26:24 I think we can all agree to that.
0:26:27 And over time, they started to think of it as not challenging.
0:26:35 But really, it’s a remarkably challenging thing to send a rocket into space and bring it back safely.
0:26:38 Kind of paradoxical, then, that the thing was actually called Challenger.
0:26:40 That’s a good point.
0:26:50 Actually, I love Richard Feynman looking back on the Challenger accident, his sort of simple willingness to just put the piece of O-ring in the ice water and see what happens.
0:27:00 That’s something that in a better run, more psychologically safe, more creative, generative work environment, someone else would have done in real time.
0:27:08 But, you know, if I recall correctly, even though he was on that commission to investigate, they tried to essentially shut him up.
0:27:11 They didn’t want that news coming out at the hearing.
0:27:14 They wanted, you know, they didn’t want the failure to be so explicit.
0:27:15 That’s right.
0:27:18 But that’s, I mean, that’s not a good thing.
0:27:19 That’s not a good thing.
0:27:21 You’ve got to learn from it so that it doesn’t happen again.
0:27:30 By the way, if you don’t remember the story of Richard Feynman and the Challenger investigation and the O-rings, don’t worry.
0:27:34 Last year, we made a three-part series about Feynman.
0:27:41 The story of his role in the Challenger investigation is covered in part one of that series called The Curious Mr. Feynman.
0:27:43 Okay, back to failure.
0:27:49 The fifth cause of failure on Amy Edmondson’s spectrum is uncertainty.
0:27:52 So uncertainty is everywhere.
0:27:58 There’s probably, you know, an infinite number of examples here, but let me pick a silly one.
0:28:03 A friend sets you up on a blind date, and you like the friend, and you think, okay, sure.
0:28:09 And then you go out on the date, and it’s a terrible bore or worse, right?
0:28:10 It’s a failure.
0:28:13 But you couldn’t have known in advance.
0:28:14 It was uncertain.
0:28:17 How about a less silly example?
0:28:18 You’re in a company setting.
0:28:29 You have an idea for a strategic shift or a product that you could launch, and there’s very good reasons to believe this could work, but it’s not 100%.
0:28:39 The final cause of failure we have by now moved all the way from the blameworthy end of the spectrum to the praiseworthy is simply called experimentation.
0:28:43 I’m being fairly formal when I say experimentation, right?
0:28:52 The most obvious example is a scientist in a lab and probably really believes it will work and puts the chemicals in, and lo and behold, it fails.
0:29:01 Or in much smaller scale, I’m going to experiment with being more assertive in my next meeting and doesn’t quite work out the way I’d hoped.
0:29:05 It’s the Edison, quote, you know, 10,000 ways that didn’t work.
0:29:12 He’s perfectly, perfectly willing to share that because he’s proud of each and every one of those 10,000 experiments.
0:29:21 So that is Amy Edmondson’s entire spectrum of the causes of failure.
0:29:27 Sabotage, inattention, inability, task challenge, uncertainty, and experimentation.
0:29:36 If you’re like me, as you hear each of the categories, you automatically try to match them up with specific failures of your own.
0:29:47 If nothing else, you may find that thinking about failure on a spectrum from blameworthy to praiseworthy is more useful than the standard blaming and shaming.
0:29:50 It may even make you less afraid of failure.
0:29:56 That said, not everyone is a fan of Edmondson’s ethos of embracing failure.
0:30:05 A research article by Jeffrey Ray at the University of Maryland, Baltimore County, is called Dispelling the Myth that Organizations Learn from Failure.
0:30:09 He writes, failure shouldn’t even be in a firm’s vocabulary.
0:30:15 To learn from failure or otherwise, a firm must have an organizational learning capability.
0:30:26 If the firm has the learning capability in the first instance, why not apply it at the beginning of a project to prevent a failure, rather than waiting for a failure to occur and then reacting to it?
0:30:38 But Amy Edmondson’s failure spectrum has been winning admirers, including Gary Klein, the research psychologist best known as the pioneer of naturalistic decision making.
0:30:40 I’m very impressed by it.
0:30:43 I’m impressed because it’s sophisticated.
0:30:44 It’s not simplistic.
0:30:48 There’s a variety of levels and a variety of reasons.
0:31:04 And before we start making policies about what to do about failure, we need to, you know, look at things like her spectrum and identify what kind of a failure is it so that we can formulate a more effective strategy.
0:31:08 Okay, let’s do that.
0:31:14 After the break, two case studies of failure, one of them toward the blameworthy end of the spectrum.
0:31:19 It was very much driven by, you know, the Prime Minister, Tony Blair.
0:31:21 The other, quite praiseworthy.
0:31:25 I failed over 200 times before I finally got something to work.
0:31:26 I’m Stephen Dubner.
0:31:28 This is Freakonomics Radio.
0:31:29 We’ll be right back.
0:31:42 John van Rienen is a professor at the London School of Economics.
0:31:43 He studies innovation.
0:31:47 But years ago, he did some time in the British Civil Service.
0:31:57 I spent a year of my life working in the Department of Health when there was a big expansion in the UK National Health Service of resources and various attempts at reforms.
0:32:17 One of the key things that was thought could really be a game changer was to have electronic patient records so you can see the history of patients, you know, their conditions, what they’ve been treated with.
0:32:41 And having that information, I mean, instead of having all this, you know, pieces of paper written illegibly by different physicians, you could actually have this in a single record would not only make it much easier to find what was going on with patients, but could also be used as a data source to try and help think about how patients have more joined up care and could even maybe predict what kind of conditions they might have in the future.
0:32:47 The project was called Connecting for Health, and there was substantial enthusiasm for it.
0:32:49 At least the ad campaign was enthusiastic.
0:32:53 All this is a key element in the future of the NHS.
0:32:58 One day, not too far away, you’ll wonder how you live without it.
0:33:03 It was very much driven by the Prime Minister, Tony Blair.
0:33:11 This was a centralised, top-down approach in order to have a single IT system where you could access information.
0:33:19 Instead of having all these different IT systems, these different siloed, you know, pieces of paper, to have it in one consistent national system.
0:33:25 The NHS is a big operation, one of the biggest employers in the world.
0:33:29 But then, if you drill down into it, it is pretty fragmented.
0:33:33 Each local general practitioner unit is self-employed.
0:33:35 Each trust has a lot of autonomy.
0:33:44 And that’s part of the issue, is that, you know, this was a centralised, top-down programme in a system where there’s a lot of different fiefdoms,
0:33:49 a lot of different pockets of power, who are quite capable of resisting this,
0:33:54 and disliked very strongly being told, this is what you’re going to have, this is what you’re going to do,
0:33:57 without really being engaged and consulted properly.
0:34:04 But the train rolled on, despite these potential problems.
0:34:10 Connecting for health required a massive overhaul of hardware systems as well as software systems.
0:34:19 And the delivery of those was, there was a guy called Richard Granger, who was brought in, I think he was the highest paid public servant in the country.
0:34:24 He was at Deloitte’s before he came, and then after he left, he went to work for Accenture.
0:34:29 He was brought in to do this, and he designed these contracts, very tough contracts,
0:34:35 which loaded the risk of things going wrong very strongly onto the private sector providers.
0:34:43 I think just about every single quote-unquote winner, eventually either went bankrupt or walked away from the contract.
0:34:49 The estimates vary of the cost of this, but, you know, estimates are up to $20 billion lost on this project.
0:34:53 It was the biggest civilian IT project in the Western world.
0:34:59 I mean, there’s lots of examples of huge, you know, public sector failures and private sector failures as well,
0:35:01 but this was one of the biggest.
0:35:06 British Parliament ultimately called this attempted reform, quote,
0:35:10 one of the worst and most expensive contracting fiascos ever.
0:35:14 So, what kind of lessons can be learned from this failure?
0:35:17 I think it’s a failure of many, many different causes on many different levels.
0:35:24 That top-down-ness, not really understanding what was going on at a grassroots level,
0:35:26 and the haste, it was attempted to them very quickly.
0:35:31 I’ve read that the haste, especially the haste of awarding contracts at the time,
0:35:35 was considered a great thing because it was so atypical of how government worked,
0:35:40 and it was hailed as, you know, a new way of the government doing business.
0:35:43 In the end, that haste turned out to be a problem, though, correct?
0:35:48 Correct. I mean, it seemed at the time when these contracts were formed,
0:35:50 the government was getting a good deal, and they were doing it quickly,
0:35:52 they were loading the risks onto the suppliers.
0:35:59 So, it wasn’t obvious from the get-go that this was going to be as bad as it turned out to be.
0:36:05 Looking back, trying to do things quickly in such a complicated system,
0:36:10 there was so much complexity that a lot of these contracts effectively had to be rewritten afterwards.
0:36:15 And I think, you know, another general lesson is that when you’re doing a long-term,
0:36:19 important, big contract, you can’t get everything written down quickly.
0:36:21 There has to be a lot of give and take.
0:36:24 It’s a kind of relationship that you have to adjust as things go.
0:36:26 You know, contracts are very fuzzy.
0:36:27 They’re very incomplete.
0:36:32 You just have to accept that, that you’re going to have to not get things right,
0:36:36 but not try to do everything really, really, really quickly.
0:36:39 An IT project’s never just about IT.
0:36:42 It’s also about the way you change a whole organization.
0:36:45 And to do it, it’s not just about spending money.
0:36:48 You also have to get players in that system on board,
0:36:52 because it’s very difficult to just get them to do things,
0:36:58 especially, you know, in a public system where you can’t just fire people if you want to fire them.
0:37:01 You really have to have a culture of kind of bringing people on board
0:37:04 if you want to make these type of changes, and that just didn’t happen.
0:37:06 So I don’t think it’s just one thing you could think of.
0:37:10 There’s the haste, there’s the design which worked out badly,
0:37:13 and there’s the cultural aspects that we’ve talked about.
0:37:19 When you’re trying to innovate, you want to have a way of allowing people to take risks
0:37:23 and do things wrong, but then you also have to have feedback mechanisms
0:37:26 to figure out, well, you know, what has gone wrong.
0:37:31 So creating an attitude of saying, well, we actually don’t know what the right thing to do is,
0:37:35 so we’re prepared to do experimentations and learn from that.
0:37:46 If you are the kind of person who likes to understand and analyze failure in order to mitigate future failures,
0:37:52 what might be useful here is to overlay the National Health Service’s IT fiasco
0:37:57 onto Amy Edmondson’s spectrum of causes of failure.
0:38:03 Reconfiguring a huge IT system certainly qualifies as a task challenge,
0:38:08 but there were shades of inability and inattention at work here as well.
0:38:13 All of those causes reside toward the blameworthy end of the scale.
0:38:19 As for the praiseworthy end of the spectrum, that’s where experimentation can be found.
0:38:23 The NHS project didn’t incorporate much experimentation.
0:38:29 It was more command and control, top-down, with little room for adjustment
0:38:35 and little opportunity to learn from the small failures that experimentation can produce
0:38:37 and which can prevent big failures.
0:38:40 Experimentation, if you think about it,
0:38:44 is the foundation of just about all the learning we do as humans.
0:38:48 And yet, we seem to constantly forget this.
0:38:54 Maybe that’s because experimentation will inevitably produce a lot of failure.
0:38:56 I mean, that’s the point.
0:38:59 And most of us just don’t want to fail at all,
0:39:02 even if it’s in the service of long-term success.
0:39:07 So let’s see if we can’t adjust our focus here.
0:39:10 Let’s talk about real experimentation.
0:39:17 And for that, we will need not another social scientist like John Van Rienen or Amy Edmondson,
0:39:22 as capable as they are, but an actual science scientist.
0:39:27 Here is one of the most acclaimed scientists of the modern era.
0:39:31 My name’s Bob Langer, and I’m an institute professor at MIT.
0:39:35 I do research, but I’ve also been involved in helping get companies started,
0:39:41 and I’ve done various advising to the government, FDA, and places like that.
0:39:45 And if I say to you, Bob, what kind of scientist are you exactly?
0:39:46 How do you answer that question?
0:39:50 Well, I would say I’m a chemical engineer or a biomedical engineer,
0:39:53 but people have called me all kinds of things.
0:39:55 You know, they’ve called me a biochemist.
0:40:00 We do very interdisciplinary work, so I end up getting called more than one thing.
0:40:02 Do you care what people call you?
0:40:04 I just like them to call me Bob.
0:40:11 Langer holds more than 1,500 patents, including those that are pending.
0:40:16 He runs the world’s largest biomedical engineering lab at MIT,
0:40:20 and he is one of the world’s most highly cited biotech researchers.
0:40:26 He also played a role in the founding of dozens of biotech firms, including Moderna,
0:40:29 which produced one of the most effective COVID vaccines.
0:40:33 One thing Langer is particularly known for is drug delivery.
0:40:40 That is, developing and refining how a given drug is delivered and absorbed at the cellular level.
0:40:44 A time-release drug, for instance, is the sort of thing we take for granted today,
0:40:46 but it took a while to get there.
0:40:52 One problem Langer worked on back in the 1970s was finding a drug delivery system
0:40:55 that would prevent the abnormal growth of blood vessels.
0:41:00 The chemical that inhibits the growth is quite large by biological standards,
0:41:05 and there was consensus at the time that a time-release wouldn’t work on large molecules.
0:41:07 But as Langer once put it,
0:41:11 I didn’t know you couldn’t do it because I hadn’t read the literature.
0:41:16 So he ran experiment after experiment after experiment
0:41:19 before finally developing a recipe that worked.
0:41:23 Decades later, thanks to all that failure,
0:41:26 his discovery played a key role in how Moderna
0:41:30 used messenger RNA to create its COVID vaccine.
0:41:40 So, in your line of work, when I say the word failure, what comes to mind?
0:41:44 Well, I mean, a lot of things, but I’d go back to my own career.
0:41:46 I failed at trying to get research grants.
0:41:48 My first nine research grants were turned down.
0:41:51 I’d send them to places like National Institutes of Health,
0:41:53 and they have study sections, reviewers.
0:41:56 Mine would go, just because of the work I was doing,
0:41:58 to what was called a Pathology B study section,
0:42:01 and they would review it, and they said,
0:42:03 well, Dr. Langer, you know, he’s an engineer.
0:42:05 He doesn’t know anything about biology or cancer.
0:42:07 I failed over and over again.
0:42:11 Other things, like I failed to get a job in a chemical engineering department
0:42:13 as an assistant professor, even.
0:42:14 Nobody would hire me.
0:42:15 They said actually the opposite.
0:42:17 They said, you know, chemical engineers
0:42:20 don’t do experimental biomedical engineering work,
0:42:23 so, you know, they should work on oil or energy.
0:42:27 When I first started working on creating these micro or nanoparticles
0:42:29 to try to get large molecules to be delivered,
0:42:34 I failed over 200 times, I mean, before I finally got something to work.
0:42:35 I could go on and on in my failures.
0:42:39 What kept you going during all this failure?
0:42:42 I really believed that if we could do this,
0:42:44 it would make a big difference in science,
0:42:45 and I hoped a big difference in medicine.
0:42:48 Secondly, as I did some of it, you know,
0:42:51 I could see some of these results with my own eyes.
0:42:53 You know, when we were trying to deliver
0:42:56 some of these molecules to stop blood vessel growth,
0:42:58 I could see we were doing this double blind,
0:43:01 but I could still see that we were stopping the vessels from growing.
0:43:03 That’s such a visual thing.
0:43:05 And I also developed these ways of studying
0:43:07 delivery out of the little particles
0:43:09 by putting certain enzymes in them
0:43:12 and putting dyes in a little gel
0:43:15 that would turn color if the enzymes came out.
0:43:16 And I could see that happening.
0:43:18 Like I said, the first 200 times
0:43:20 or first 200 designs or more,
0:43:21 it didn’t happen.
0:43:23 But finally, I came up with a way
0:43:25 where I’d see it come out after an hour,
0:43:27 after two hours, after a day,
0:43:28 after a second day,
0:43:30 up to over 100 days in some cases.
0:43:32 So I could see with my own eyes this was working.
0:43:35 So that made an enormous difference to me too.
0:43:39 But failing 200 times costs a lot of money
0:43:40 and obviously a lot of time.
0:43:43 Did you ever almost run out of one or the other?
0:43:45 The experiments I was doing weren’t that expensive,
0:43:48 especially the delivery ones initially
0:43:49 because they were in test tubes.
0:43:51 I worked probably 20-hour days.
0:43:54 And so the expense wasn’t that great.
0:43:56 And I’ve always been good at manufacturing time.
0:44:02 Now, let’s say someone is in a similar situation today
0:44:04 to where you were then with an idea
0:44:07 or a set of ideas that they believe in,
0:44:10 that they think they are right about,
0:44:12 they think it’s an important idea,
0:44:16 and yet they are failing and failing
0:44:17 to get the attention of the people
0:44:20 who can help manufacture success.
0:44:22 How do you think about the line?
0:44:24 I think of it sometimes as the line
0:44:26 between grit and quit, right?
0:44:28 Economists talk about opportunity cost.
0:44:30 Every hour you spend on something that isn’t working
0:44:31 is an hour you could spend on something
0:44:32 that is working.
0:44:34 But then psychologists talk about grittiness
0:44:37 and how useful it can be to stick things out.
0:44:38 Do you have anything to say to people
0:44:40 who might be wrestling with that?
0:44:41 Well, I think it’s a great question.
0:44:44 And I ultimately think it’s a judgment call
0:44:45 and we can never be sure of our judgment.
0:44:47 You like to try to think,
0:44:50 are these things scientifically possible?
0:44:51 I think that’s one thing.
0:44:54 Secondly, it’s good to get advice from people.
0:44:55 It doesn’t mean you have to take it,
0:44:57 but it’s good to get advice.
0:45:00 I certainly personally have always erred
0:45:02 on the side of, I guess, not quitting.
0:45:04 And maybe that’s sometimes a mistake.
0:45:05 I don’t think so.
0:45:08 I think it depends on what could happen
0:45:09 if you are successful.
0:45:11 You know, if you are successful,
0:45:13 could it make a giant difference in the world?
0:45:15 Could it help science a lot?
0:45:17 Could it help patients’ lives a lot?
0:45:19 And so if you really feel that it can,
0:45:20 you try that much harder.
0:45:22 If it’s incremental, sure,
0:45:24 then it’s much easier to quit.
0:45:26 Is that ability to persevere,
0:45:27 within yourself at least,
0:45:29 do you think that’s your natural temperament?
0:45:30 Is that something you learned?
0:45:32 Did you find incentives to lead you there?
0:45:34 I think for me, there are a couple of things.
0:45:37 One, I guess I’ve always been very stubborn.
0:45:38 My parents told me that.
0:45:39 But secondly,
0:45:42 I think there’s a whole thing with role models too.
0:45:44 When I was a postdoc,
0:45:46 the man that I worked with, Judah Folkman,
0:45:48 he experienced the same thing.
0:45:51 He had this theory that if you could stop blood vessels,
0:45:52 you could stop cancer.
0:45:55 And that was mediated by chemical signals.
0:45:57 And everyone told him he was wrong.
0:45:59 But I would watch him every day.
0:46:01 And he believed anything was possible.
0:46:02 And he kept sticking to it.
0:46:04 And of course, eventually he was right.
0:46:10 I think seeing his example probably also had a big effect on me.
0:46:15 Can you talk to me about how scientific failure is treated generally?
0:46:16 Let’s assume a spectrum.
0:46:21 And on one end of the spectrum is that every failure is written up and published
0:46:27 and perhaps even celebrated as having discovered a definitive wrong path to pursue.
0:46:30 So everybody coming after you can cross that off their list.
0:46:32 And on the other end of the spectrum,
0:46:34 every failure is hidden away,
0:46:38 which allows many other people to make the same failure.
0:46:40 Can you talk about where the reality is?
0:46:42 I think that’s an interesting question.
0:46:45 A lot of it even depends how you define failure.
0:46:47 You know, when you’re trying to learn about something,
0:46:49 you try different things.
0:46:52 And embedded in the scientific papers we write,
0:46:55 like when we wrote this paper in Nature in 1976,
0:46:58 which was the first time you could get small particles
0:47:02 to release large molecules from biocompatible materials,
0:47:05 well, some of the materials we used failed.
0:47:08 A lot of them did, actually, because they would either cause inflammation
0:47:12 or the drug would come out way too fast or not come out at all.
0:47:16 We found one fraction that worked and stopped blood vessels
0:47:19 and probably 50 or 100 that didn’t.
0:47:23 So the failures and successes are maybe in the same papers sometimes.
0:47:26 What I’ve tried to do, even to give more detail,
0:47:30 is you put all the data in, even if it makes for a very long thesis.
0:47:34 So not only are the graphs there and the papers,
0:47:37 but there’s even the raw data that people can look at and analyze.
0:47:40 And I try to get people to do as much of that as possible.
0:47:43 So I guess what I’m trying to say is that
0:47:46 the failures and successes are almost intertwined.
0:47:48 I’d like to hear you talk about
0:47:53 how failure is discussed or thought of in the lab.
0:47:55 Maybe it’s nothing overt, but I am curious,
0:47:58 especially when you bring in young people, researchers,
0:48:00 whether they’re, you know, postdoc or undergrad,
0:48:02 do you give pep talks about failure?
0:48:06 Do you kind of have a philosophy that you want to instill in these people
0:48:10 that failure is an essential component of research and success?
0:48:11 Yes.
0:48:11 Yes.
0:48:13 And I do.
0:48:17 And I, whether it’s my own talks or just meeting with students
0:48:20 and brainstorming with them about those things.
0:48:23 But to me, that research, scientific research,
0:48:25 I mean, you just fail way more than,
0:48:27 at least I do, way more than you succeed.
0:48:29 It’s just part of the process.
0:48:32 I mean, that’s experimentation and that’s okay.
0:48:37 A lot of your colleagues and students go on to start companies,
0:48:40 and that’s a whole different ball of wax.
0:48:43 How do you think about failure in the entrepreneurial process?
0:48:51 Obviously, the easy criteria is a successful company having a good financial exit, I suppose.
0:48:54 But I don’t necessarily think of it as just that way.
0:48:56 I mean, that’s certainly going to be important.
0:49:02 You know, I’ve been involved in things where you’ve advanced science and you learn some things,
0:49:03 and there’s degrees of success.
0:49:04 You just don’t know.
0:49:09 I’ve been pretty fortunate in the companies we’ve started in terms of the exits that they’ve had.
0:49:13 But I just think there’s no simple criteria.
0:49:17 I feel like we’ve turned out a lot of great scientists and entrepreneurs,
0:49:20 and not all their companies have had great financial exits.
0:49:24 But I think they’ve also created products that can change people’s lives.
0:49:27 And that, to me, is also very, very important, obviously.
0:49:28 That’s why we do it in the first place.
0:49:31 I have never done it for money, and I don’t think they do it for money.
0:49:34 They do it to try to make a difference in the world.
0:49:38 Do you think failure is, however, a different animal in the research sphere
0:49:40 as in the entrepreneurial sphere?
0:49:43 I would say yes, I think it is.
0:49:46 But I also think, you know, there’s different cultures, too.
0:49:49 I think the good thing about the United States culture,
0:49:53 maybe in contrast to some cultures, is failure is widely accepted.
0:49:58 I’ll give you one of my examples, actually, in the business sphere.
0:50:00 So I’m a big fan of chocolate.
0:50:03 Of eating it or making it or researching it?
0:50:05 Probably any part, but mostly eating it.
0:50:08 But at any rate, one of the books I read, and I’m actually not a fan of their chocolate,
0:50:10 is a book on Milton Hershey.
0:50:13 And so this really gets to your point on failure.
0:50:19 Milton Hershey, he had this idea when he was young, very young, of starting a candy company.
0:50:22 And I remember the first candy company, he went bankrupt.
0:50:24 You know, and he tried to raise more money, started another one.
0:50:29 I think, like, the first six or seven totally failed, but not the last one, obviously.
0:50:32 And he became a millionaire at a time when there weren’t very many.
0:50:33 Was that really failure?
0:50:38 Or was it just being an apprentice to trying to learn how to succeed?
0:50:41 And I think that’s true in a lot of things.
0:50:48 The reason I brought it up is I don’t think there’s a shame and failure in either area, or I hope there’s not.
0:50:51 I think you have to feel it’s okay.
0:50:53 And then you keep going on.
0:50:56 What do you think?
0:51:01 Would you like to live in a world where there’s no shame in failure?
0:51:09 Or do you think it’s important for failure to hurt, to burn, as one of our guests put it last week?
0:51:12 Maybe that creates a stronger incentive to succeed.
0:51:18 I’d love to know your thoughts on this question and on this series so far.
0:51:24 Send an email to radio at Freakonomics.com, or you can leave a review or rating in your podcast app.
0:51:30 Coming up next time on the show, we will dig deeper into the idea of grit versus quit.
0:51:35 When you’re failing, how do you know if it’s time to move on?
0:51:44 We just could not stop it from leaking, and I was no longer willing to just keep pouring more and more of my money into it.
0:51:48 He dumped me when I was 70, and I married him again at age 75.
0:51:49 You know, hope springs eternal.
0:51:50 This is a great idea.
0:51:52 You just have to raise a quarter million dollars.
0:51:59 Case studies in failure and in grit versus quit, including stories from you, our listeners.
0:52:02 That’s in the next part of our series on failure.
0:52:05 Until then, take care of yourself.
0:52:07 And if you can, someone else too.
0:52:11 Freakonomics Radio is produced by Stitcher and Renbud Radio.
0:52:14 This episode was produced by Zach Lipinski.
0:52:17 He and Dalvin Abawaji worked on the update.
0:52:21 It was mixed by Eleanor Osborne and Jasmine Klinger with help from Jeremy Johnston.
0:52:31 The Freakonomics Radio network staff also includes Alina Cullman, Augusta Chapman, Ellen Frankman, Elsa Hernandez, Gabriel Roth, Greg Rippin, Morgan Levy, Sarah Lilly, and Tao Jacobs.
0:52:40 You can find our entire archive on any podcast app, also at Freakonomics.com, where we publish transcripts and show notes.
0:52:45 Our theme song is Mr. Fortune by the Hitchhikers, and our composer is Luis Guerra.
0:52:55 The conversation that we had casually last year was a great conversation.
0:52:59 If we can essentially do something similar, that’ll be fantastic for our listeners.
0:53:01 I’ll try to remember what I said.
0:53:09 The Freakonomics Radio Network.
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0:53:15 Stitcher.

0:00:06 You’re one of the most successful investors that a lot of people have probably never heard of.
0:00:09 AI is the only market where the more I learn, the less I know.
0:00:11 And in every other market, the more I learn, the more I know.
0:00:13 The more I’m able to predict things, and I can’t predict anything anymore.
0:00:15 What scares you about the future?
0:00:18 That’s a big question.
0:00:22 I think in a couple of years, we’ll start thinking about it as we’re selling units of cognition.
0:00:27 AI is dramatically underhyped because most enterprises have not done anything in it.
0:00:31 And that’s where all the money is, all the changes, all the impact is, all the jobs, everything.
0:00:36 The people that I know who have been very successful or driven solely by money end up miserable.
0:00:38 Because they have money, and then what?
0:00:41 It’s just, what do you do? What fulfills you?
0:00:44 What are the most common self-inflicted wounds that kill companies?
0:00:46 I think that…
0:00:49 What do you think is the next wave?
0:00:53 I think it’s going to be an ongoing wave of…
0:00:55 And that’s coming, right? And that hasn’t even hit yet.
0:01:17 Welcome to the Knowledge Project Podcast.
0:01:20 I’m your host, Shane Parrish.
0:01:26 In a world where knowledge is powered, this podcast is your toolkit for mastering the best of what other people have already figured out.
0:01:34 My guest today is Elad Gill, who has had a front row seat to some of the most important technology companies started in the past two decades.
0:01:40 He invested early in Stripe, Airbnb, Notion, Coinbase, Andrel, and so many others.
0:01:45 He’s also authored an incredible book on scaling startups called High Growth Handbook.
0:01:49 In my opinion, he’s one of the most underrated figures in Silicon Valley.
0:01:58 In this episode, we explore how he thinks about startups, talent, decision-making, AI, and most importantly, the future of all of these things.
0:02:08 We talk about the importance of clusters, why most companies die from self-inflicted wounds, and what it really means to scale a company, and importantly, what it means to scale yourself.
0:02:11 It’s time to listen and learn.
0:02:22 You’ve had a front row seat at some of the biggest, I would say, surprises in a way, like Stripe, Coinbase, Airbnb, when they were just ideas.
0:02:25 What was the moment where you recognized these were going to be outliers?
0:02:28 So all three of those are very different examples, to your point.
0:02:31 I invested in Airbnb when it was probably around eight people.
0:02:33 Stripe was probably around the same size.
0:02:37 And then Coinbase only got involved with much later, when it was a billion-dollar-plus company.
0:02:40 And even then, I thought there was enormous upside on it, which luckily has turned out to be the case.
0:02:46 I think really, the way I think about investing in general is that there’s two dimensions that really matter.
0:02:52 The first dimension is what people call product-market fit, or is there a strong demand for whatever it is you’re building?
0:02:55 And then, secondarily, I look at the team.
0:02:58 And I think most early-stage people flip it.
0:03:00 They look at the team first, and how good is a founder?
0:03:02 And obviously, I’ve started two companies myself.
0:03:06 I think the founder side is incredibly important, and the talent side is incredibly important.
0:03:13 But I’ve seen amazing people get crushed by terrible markets, and I’ve seen reasonably mediocre teams do extremely well in what are very good markets.
0:03:16 And so, in general, I first ask, do I think there’s a real need here?
0:03:17 How is it differentiated?
0:03:18 What’s different about it?
0:03:20 And then I dig into, like, are these people exceptional?
0:03:22 How will they grow over time?
0:03:24 What are some of the characteristics of how they do things?
0:03:35 Let’s go into people’s second, but how do you determine product-market fit in a world where a lot of people are buying product-market fit almost through brute force or giving away product?
0:03:40 Yeah, there’s a lot of signals you can look at, and I think it’s kind of varied by type of business.
0:03:42 Is it a consumer business versus enterprise versus whatever?
0:03:46 For things like consumer businesses, you’re just looking at organic growth rate and retention.
0:03:47 Are people using it a lot?
0:03:48 Are they living in it every day?
0:03:49 That sort of thing.
0:03:51 That would be early Facebook, right?
0:03:52 The usage metrics were insane.
0:03:58 And then for certain B2B products, it could be rate of growth and adoption.
0:04:02 It could be metrics people call, like, NDR and a dollar retention or other things like that.
0:04:09 Honestly, if you’re investing before the thing even exists in the market, then you have to really dig into how much do I believe there’s a need here, right?
0:04:10 Or how much is there a customer need?
0:04:16 So I invested in Rippling and other related companies before there’s anything built, right?
0:04:18 Under the premise that this is something that a lot of people want.
0:04:21 And Notion is the same thing.
0:04:24 Actually, Notion was a rare example where I did it as a person investment.
0:04:26 I met Ivan, who’s a CEO over there.
0:04:33 And everything about him was so aesthetically cohesive in a very odd way.
0:04:38 The way he dressed, his hairstyle, the color scheme of his clothes, the color scheme of the app and the pitch deck.
0:04:41 The only other person I’ve seen that with is Jack Dorsey, who started Square and Twitter.
0:04:46 And there was this odd, almost pure embodiment of aesthetic.
0:04:50 And I just thought it was so intriguing and so cool.
0:04:53 And I’ve only seen two people like that before that I had to invest.
0:04:56 And it was just this immense consistency.
0:04:57 It was very weird.
0:05:01 And you see that, like, you go to his house and it’s like, it feels like him.
0:05:03 You know, everything, the company feels like him.
0:05:04 Everything feels like him.
0:05:05 It’s fascinating.
0:05:06 He’s done an amazing job with it.
0:05:09 It almost stands out to the point where you think it’s manufactured.
0:05:11 I think it’s genuine.
0:05:12 I think it’s almost the opposite.
0:05:14 You feel the purity of it.
0:05:16 You’re like, oh my gosh, there’s a unique aesthetic element here.
0:05:23 And that probably reflects some unique way of viewing the world or thinking about products or thinking about people and their usage.
0:05:25 Let’s come back to outliers.
0:05:27 So product market fit, outliers.
0:05:30 How do you identify an outlier team?
0:05:34 Yeah, you know, I think it really depends on the discipline or the area.
0:05:37 For tech, I think it’s very different than if you’re looking in other areas.
0:05:43 For an early tech team, I almost use like this Apple framework of Jobs, Wozniak, and Cook, right?
0:05:46 Steve Jobs and Steve Wozniak started Apple together.
0:05:51 Steve Jobs was known as somebody who really was great at setting the vision and direction, but also was just an amazing salesperson.
0:05:54 And selling means selling employees to join you.
0:05:55 It means raising money.
0:05:57 It means selling your first customers.
0:05:58 It’s negotiating your supply chain.
0:06:01 Those are all aspects of sales in some sense or negotiation.
0:06:06 And so you need at least one person who can do that unless you’re just doing a consumer product that you threw out there, right?
0:06:08 And it just grows and then people join you because it’s growing.
0:06:13 Then you need somebody who can build stuff and build it in a uniquely good way.
0:06:15 And that was Wozniak, right?
0:06:21 The way that he was able to hack things together, drop chips from the original design of Apple devices, etc., was just considered legendary.
0:06:25 And then as the thing starts working, you eventually need somebody like Tim Cook who can help scale the company.
0:06:30 And so you could argue that was Sheryl Sandberg in the early days of Facebook who eventually came on as a hire and helped scale it.
0:06:35 And Zuck was really the sort of mixture of the product visionary, the salesperson, etc.
0:06:42 Why did all these people concentrate in San Francisco almost or California?
0:06:47 How did that happen where you had Apple, you have Stripe, you have Coinbase, you have Facebook?
0:06:50 Walk me through that.
0:06:54 We were talking a little bit about this before we started recording about clusters of people.
0:07:07 Yeah, it’s really fascinating because if you look at almost every major movement throughout history, and that could be a literary movement, it could be an artistic movement, it could be a finance movement, economic schools of thought.
0:07:19 It’s almost always a group of young people aggregating in a specific city who all somehow find each other and all start collaborating and working together towards some common set of goals that reflect that.
0:07:26 So there was, you know, a famous literary school in the early 20th century in London.
0:07:35 That was, I think it was like Virginia Woolf and John Maynard Keyes and E.M. Forster and all these people all kind of aggregated and became friends and started supporting each other.
0:07:37 Or you look at the Italian Renaissance.
0:07:44 Similarly, in Florence, you had this aggregation of all these great talents, all in a timely manner coincident with each other.
0:07:51 Or Favism or Italian Futurism or Impressionism, Paris in the, you know, late 1800s.
0:07:52 And so that repeatedly happens for everything.
0:07:55 And similarly, that’s happened for tech.
0:07:58 And even within tech, we’ve had these successive waves, right?
0:08:03 Really, the founding story of Silicon Valley goes back to the defense industry and then the semiconductor industry, right?
0:08:06 Defense was HP and other companies starting off in the 40s.
0:08:13 You then ended up with Shockley Semiconductor and Fairchild Semiconductor in the early semiconductor companies, 50s, 60s.
0:08:24 And that kind of established Silicon Valley as a hub and as things moved from microprocessors to computers to software, people just kept stuff propagating across those waves from within the industry.
0:08:29 So one big thing is just you have a geographic cluster and you have that for every single industry.
0:08:32 You look at wineries and they’re clustered in a handful of places because of geography.
0:08:35 You look at the energy industry, it’s in a handful of cities.
0:08:38 Finance is in New York and Hong Kong and London.
0:08:48 So every single industry has clusters, Hollywood and Bollywood and, you know, Lagos and Nigeria for the main hubs for, you know, movie making in different regions.
0:08:51 So in Silicon Valley, obviously, we created this tech cluster.
0:09:00 But then even within the tech cluster, there are these small pockets of people that I mentioned earlier that somehow find each other and self-aggregate.
0:09:03 It’s funny, I was talking to Patrick Hollis and the founder of Stripe about this.
0:09:12 And he mentioned that when he was 18 and he showed up in Silicon Valley as a nobody, right, completely unknown, 18-year-old, nobody’s heard of him.
0:09:19 And during that six-month period that he was first here, he said he met all these people who are now giants of Silicon Valley.
0:09:25 And it was this weird self-aggregation of people kind of finding and meeting each other and talking about what each other’s working on.
0:09:28 Somehow this keeps happening.
0:09:29 And this happens through time.
0:09:33 And then right now in Silicon Valley, it’s happening in very specific areas.
0:09:33 It’s happening.
0:09:36 All the AI researchers all knew each other from before.
0:09:38 They were in the common set of labs.
0:09:39 They had common lineages.
0:09:43 All the best AI founders, which is different from the researchers, have their own cluster.
0:09:45 And all the SaaS people have their own cluster.
0:09:51 And so it’s this really interesting almost self-aggregation effect of talent finding each other and then helping each other over time.
0:09:54 And it’s just fascinating how that works.
0:09:57 How do you think about that in an era of remote work?
0:10:04 Remote work is generally not great for innovation unless you’re truly in an online collaborative environment.
0:10:11 And the funny thing is that when people talk about tech, they would always talk about how tech is the first thing that could go remote because you can write code from anywhere and you can contribute from anywhere.
0:10:13 But that’s true of every industry, right?
0:10:14 You look at Hollywood.
0:10:19 You could make a movie from anywhere, like you film it off-site anyhow or on-site in different places.
0:10:20 You could write a script from anywhere.
0:10:22 You could edit the musical score from anywhere.
0:10:23 You could edit the film from anywhere.
0:10:25 You could write the script from anywhere.
0:10:27 So why is everything clustered in Hollywood?
0:10:29 Nobody would ever tell you, oh, don’t go to Hollywood.
0:10:32 Go to Boise and, you know, you could work in the movie industry.
0:10:33 Or finance.
0:10:37 You could raise money from anywhere, come up with your trading strategy from anywhere.
0:10:39 Everything in finance is in a handful of locations.
0:10:41 And so tech is the same way.
0:10:43 And it’s because there’s that aggregation of people.
0:10:51 There’s the people helping each other, sharing ideas, trading things informally, learning new distribution methods that kind of spread, learning new AI techniques that spread.
0:10:54 There’s money around it that funds it specifically so it’s easier to raise money.
0:10:58 There’s people who’ve already done it before who can help you scale once the thing is working.
0:11:04 That’s the common complaint I hear in Europe for Google Start companies there is we can’t find the executives who know how to scale what we’re doing.
0:11:05 Oh, interesting.
0:11:09 And so I do think there are these other sort of ancillary things that people talk about.
0:11:13 The service providers, the lawyers who know how to set up startups, right?
0:11:16 Or the accountants who know how to do tax and accounting for startups.
0:11:18 Those things sound trivial, but they cluster.
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0:14:19 A big part of why Combinator is sort of like helping everybody with that stuff.
0:14:23 Yeah, why Combinator is a great example of taking out-of-network people, at least that
0:14:26 was the initial part of the premise, not the full premise, right?
0:14:29 Like people like Sam Altman or others who were very early in YC came out of Stanford, which
0:14:30 was part of the main hub.
0:14:34 But a lot of other people came out of universities that just weren’t on the radar for people who
0:14:36 tended to back things in Silicon Valley.
0:14:39 And so, you know, the early Reddit founders went to East Coast universities.
0:14:45 The Airbnb founders, two of them were out of RISD, the Rhode Island Institute of Design.
0:14:53 And so, YC early on was very good at taking very talented people who weren’t part of the core networks in Silicon Valley and basically
0:14:55 inserting them into those networks and helping them succeed.
0:14:58 Why do you think they’re still relevant today?
0:15:00 Why is YC still relevant today?
0:15:03 I think they’ve just done a great job of building sort of brand and longevity.
0:15:06 Gary, who’s taken over, is fantastic.
0:15:07 And so I think he brings a lot of that.
0:15:13 Let’s go back to first principles and really implement YC the way that, you know, we think
0:15:14 it can really succeed for the future.
0:15:18 And I think they do a really good job of two things.
0:15:21 One is plugging people in, as mentioned, particularly your SaaS company, you want to have a bunch of
0:15:23 customers instantly, your batch mates will help you with that.
0:15:32 But also, it teaches people to ship fast and to kind of force finding customers.
0:15:37 And so because you’re in this batch structure and you’re meeting with your batch every week
0:15:40 and you hear what everybody else is doing, you feel peer pressure to do it.
0:15:44 But also, it kind of shapes how you think about the world, what’s important, what to work on.
0:15:47 And so I think it’s almost like a brainwashing program, right?
0:15:49 Beyond everything else they do, which is great.
0:15:52 It sets a timeline that you have to hit and it brainwashes you to think a certain way.
0:15:58 One of the things that I see, which I think is maybe relevant, maybe not, you tell me, is
0:16:04 I like how it brings people together who are probably misfits or outliers in their own environment
0:16:08 and then puts them in an environment where ambition is the norm.
0:16:10 It’s not the outlier to have ambition.
0:16:12 Where shipping is the norm.
0:16:15 It’s not the outlier to ship.
0:16:21 And it sort of normalizes these things that maybe cause success or lead to an increased
0:16:22 likelihood of success.
0:16:26 It’s actually a very interesting question of what proportion of founders these days are
0:16:29 actually people who normally wouldn’t fit in, right?
0:16:34 So the sort of founder archetype of before it was rebellious people or people who could never
0:16:35 work for anybody else or whatever.
0:16:40 And then as tech has grown dramatically in market cap and influence and everything else,
0:16:45 it’s inevitable that the type of people who want to come out here and do things has shifted.
0:16:48 And then the perception of risk in startups has dropped a lot.
0:16:51 And so I actually think the founder mix has shifted quite a bit.
0:16:54 Like there isn’t as much quirkiness in tech.
0:16:55 And during COVID, it was awful.
0:16:56 It was very unquirky.
0:17:00 Because at that point, you know, there was a zero interest rate environment.
0:17:02 Money was abundant everywhere.
0:17:07 And the nature of people who joined or who showed up shifted.
0:17:11 And so I think we had two or three years where the average founder just wasn’t that great,
0:17:12 right?
0:17:13 On a relative basis to history.
0:17:18 And then as the AI wave was happening, you know, I started getting involved with a lot of
0:17:21 the generative AI companies maybe three-ish years ago, maybe three and a half years ago.
0:17:25 So before Chachapiti came out and before MidJourney and all these things kind of took off.
0:17:30 And the people starting those companies were uniquely good.
0:17:32 And you felt the shift.
0:17:38 You went from these kind of plain vanilla, me too, almost LARPers, to these incredibly driven,
0:17:45 mission-oriented, hyper-smart, very technical people who wanted to do something really big.
0:17:47 And you felt it.
0:17:48 It was a dramatic shift.
0:17:52 And if you look at it, there’s basically been three or four waves of talent coming through
0:17:53 the AI ecosystem.
0:17:55 And I should say gen AI because we had this whole wave.
0:17:59 We had 10 years, 15 years of other types of deep learning, right?
0:18:03 We had recursive neural networks and convolutional neural networks and GANs and all these things.
0:18:08 And that technology basis fundamentally has different capabilities than this new wave.
0:18:14 And so there’s this paper in 2017 that came out of Google called the Transformer Architecture.
0:18:19 And that is the thing that spawned this whole wave of AI right now that we’re experiencing.
0:18:20 And so it’s a new technology basis.
0:18:24 We took a step function and we’re doing new stuff that you couldn’t do before on the old
0:18:24 technologies.
0:18:29 That whole wave led to this really interesting set of companies.
0:18:33 And the first people in that wave were the researchers because they were closest to it.
0:18:38 And they could see firsthand what was actually happening in the technology, in the market, how they
0:18:39 were using it.
0:18:44 You know, the engineers at OpenAI used to go into the weights to query stuff, which then eventually
0:18:46 actually in some form is ChatGPT, right?
0:18:47 They were doing it before it existed.
0:18:51 There was also MENA at Google, which was basically an internal form of almost like ChatGPT.
0:18:55 So they kind of saw the future and they went to try and substantiate it.
0:18:59 And you could argue that the same thing happened in the internet wave in the 90s, right?
0:19:03 All the people working at the National Supercomputer Centers like Marc Andreessen and others saw the
0:19:04 future before anyone else.
0:19:06 They’re using email before anyone else.
0:19:08 They were browsing the internet before anyone else.
0:19:12 They were using FTP and file downloads and sharing music files before anyone else.
0:19:15 And so they knew what was coming, right?
0:19:17 They had a glimpse into the future.
0:19:20 It’s the old saying, the future is here is just not equally distributed.
0:19:22 For AI, we had the same thing.
0:19:25 We had these researchers who could tangibly feel what was coming.
0:19:28 And so the first wave of AI companies was researchers.
0:19:30 The second wave was infrastructure people.
0:19:31 We’re not closest.
0:19:34 And in this current wave, we’re now at the application people, the people who are building
0:19:36 applications on top of the core technology.
0:19:38 What do you think is the next wave?
0:19:43 I think it’s going to be an ongoing wave of kind of everything, right?
0:19:46 There’s still a lot to build, but I think we’ll see more and more application level companies.
0:19:51 We’ll see fewer what are known as foundation model companies, the people building the open
0:19:55 AIs or Anthropics or some of the Google core technologies or X.AI.
0:19:57 There will be specialized versions of that, right?
0:19:58 That’s all the language stuff, right?
0:20:04 It understands what you say and it can interpret it and it can generate text for you and do all these
0:20:04 things, right?
0:20:07 That’s all these LLMs, large language models.
0:20:10 There’s going to be the same thing done for physics and material science.
0:20:11 We’ve already seen it happening in biology, right?
0:20:13 So at that layer, there’s a bunch of stuff.
0:20:14 There’s the infrastructure.
0:20:16 What is the equivalent of cloud services?
0:20:18 And then there’s the apps on top.
0:20:20 And then in the apps, you have B2B and then you have consumer.
0:20:23 And so I think we’re going to see a lot of innovation across the stack.
0:20:25 But I think this next wave is a mix of B2B and consumer.
0:20:31 And then I think the wave after that is very large enterprise adoption.
0:20:38 And so I think AI is dramatically underhyped because most enterprises have not done anything
0:20:38 in it.
0:20:42 And that’s where all the money is, all the changes, all the impact is, all the jobs, everything,
0:20:43 right?
0:20:45 It’s a big 80-20 rule of the economy.
0:20:48 And that’s coming, right?
0:20:49 And that hasn’t even hit yet.
0:20:56 Are there any historical parallels to anything that you can think of that map to artificial
0:20:57 intelligence or AGI?
0:21:06 I think the thing that people misunderstand about artificial intelligence is that, you know,
0:21:09 people are kind of viewing it as what you’re selling as like a cool tool to help you with
0:21:10 productivity or whatever it is.
0:21:14 I think in a couple of years, we’ll start thinking about it as we’re selling units of cognition,
0:21:16 right?
0:21:20 We’re selling bits of person time or person equivalent to do stuff for us.
0:21:27 I’m going to effectively hire 20 bot programmers to write code for me to build an app, or I’m going
0:21:34 to hire an AI accountant, and I’m going to basically rent time off of this unit of cognition.
0:21:39 On the digital side, it really is this shift from you’re selling tools to you’re selling
0:21:42 effectively white-collar work.
0:21:47 On the robotic side, you’ll probably have some form of like robot minutes or something.
0:21:52 You’ll probably end up with some either human form robots or other things that will be doing
0:21:54 different forms of work on your behalf.
0:21:57 And, you know, potentially you buy these things or maybe you rent them, you know, it’ll be
0:21:59 interesting to see what business models emerge around it.
0:22:01 What scares you about the future?
0:22:03 That’s a big question.
0:22:04 Along what dimension?
0:22:07 Wherever you want to take it.
0:22:08 Like what scares you about AI?
0:22:10 Do you have any fears about AI?
0:22:14 I think that I have opposing fears.
0:22:20 In the short run, I worry that there’s the real chance to kind of strangle the golden goose,
0:22:20 right?
0:22:28 I do think AI and this wave of AI is the single biggest potential motivator for versions of global
0:22:31 advancements in health and education and all the things that really matter fundamentally.
0:22:37 And there’s some really great papers from the 80s that basically show that one-on-one tutoring,
0:22:42 for example, will increase performance by one or two standard deviations, right?
0:22:44 You get dramatically better if you have a one-on-one tutor for something.
0:22:49 And if you actually look through history and you look at how Alexander the Great was tutored
0:22:54 by Aristotle and all these things, there’s a lot of kind of prior examples of people actively
0:22:56 doing that on purpose for their kids if they can afford it.
0:23:00 This AI revolution is a great example of something that could basically provide that for every child
0:23:04 around the world as long as they have access to any device, which is most people at this
0:23:04 point, right?
0:23:05 Globally.
0:23:10 So from an education system perspective, a healthcare system perspective, it’s a massive
0:23:10 change.
0:23:13 So in the short run, I’m really worried that people are going to constrain it and strangle
0:23:17 it and prevent it from happening because I think it’s really important for humanity.
0:23:23 In the long run, there’s always these questions of, you know, at what point do you actually consider
0:23:24 something sentient versus not?
0:23:25 Is it a new life form?
0:23:26 Like, is there species competition?
0:23:29 You know, there’s those sorts of questions, right?
0:23:30 In the very long run.
0:23:32 Without robots, you could say, well, you just unplug the data center.
0:23:33 Who cares?
0:23:34 You know, it doesn’t matter.
0:23:38 If you do have robots and other things, then it gets a little bit harder, maybe.
0:23:43 At what point do you think we’re going to, AI is going to start solving problems that we
0:23:48 can’t solve in the sense of a lot of what it’s doing today is organizing logic on a human
0:23:49 level equivalent.
0:23:50 It’s not being like…
0:23:52 No, it’s already surpassed us on many things, right?
0:23:57 Like, just even look at how people play Go now and the patterns they learned off of AI,
0:23:58 which can beat any person at Go.
0:24:03 I mean, gaming is a really good example of that, where every wave of gaming advancements where
0:24:07 you pitted AI against people, people said, well, fine, they beat people at checkers, but
0:24:09 they’ll never beat them at chess.
0:24:11 And then they beat them at chess and say, well, fine, chess, but they’ll never beat them at
0:24:12 Go.
0:24:13 They beat them at Go.
0:24:16 And they’re like, well, what about complex games where there’s bluffing?
0:24:17 They’ll never beat them at poker.
0:24:18 And then Noam Brown had his poker paper.
0:24:20 And they say, well, okay, poker.
0:24:22 Well, they’ll never beat them at things like diplomacy, where you’re manipulating people
0:24:23 against each other.
0:24:27 And then, you know, a Facebook team solved diplomacy, right?
0:24:31 And so gaming is a really great example where you have superhuman performance against every
0:24:31 game now.
0:24:35 And you see that in other aspects of things as well.
0:24:40 I guess where my mind was going is in terms of mathematical problems.
0:24:43 I mean, we’ve solved a couple maybe that we haven’t been able to solve, but we haven’t
0:24:51 made real leaps or biology or health or longevity, like where, you know, here’s the, not the solution
0:24:56 maybe to Alzheimer’s because that’s like a big leap, but maybe it’s like, you’re not looking
0:24:57 in the right area.
0:24:59 You need to research in this area more.
0:25:01 Like when is that sort of advancement coming?
0:25:02 Yeah, I think it’s a really good question.
0:25:06 I mean, AI is already having some interesting advancements in biology, right?
0:25:12 The Nobel Prize this past year in biology went to Demas and a few other people who built
0:25:16 predictive models using AI about how proteins will fold, right?
0:25:20 And so I think it’s already being recognized as something that’s impacting the field at the
0:25:21 point where it gets a Nobel.
0:25:26 The hard part with certain aspects of biology and protein folding is a good counter example.
0:25:27 We actually have very good data.
0:25:31 You had tens of thousands or maybe hundreds of thousands of crystal structures.
0:25:34 You had solved structures for all these proteins and you could use that to train the model.
0:25:35 Right.
0:25:41 If you look at it, about half or more than half of all biology research in top journals is
0:25:42 not reproducible.
0:25:44 So you have a big data problem.
0:25:47 Half the data is false.
0:25:48 It’s incorrect.
0:25:49 Right.
0:25:53 And this is actually something that Amgen published a couple of years ago where they showed this
0:25:56 because they weren’t able to reproduce cancer findings in their lab because they’re trying
0:25:57 to develop a drug.
0:26:00 And they’re like, wait a minute, this thing we thought could turn into a drug isn’t real.
0:26:01 Right.
0:26:06 And so there’s this really big replication issue in certain sciences.
0:26:08 Isn’t that part of the advantage for AI then?
0:26:10 Like, I’m thinking out loud here.
0:26:10 Sure.
0:26:15 Like, if I uploaded all of the Alzheimer’s papers to AI.
0:26:16 Yeah.
0:26:18 And it would be like, these ones aren’t replicatable.
0:26:20 There’s mathematical errors here.
0:26:21 This looks like fraud.
0:26:24 But all of these things have generated future research.
0:26:28 So what you’re doing is you’re being like, oh, you’ve spent billions of dollars on this.
0:26:33 That’s likely, not like statistically, it’s probably not going to yield results.
0:26:35 You should focus your attention here.
0:26:37 And that would have a huge impact on…
0:26:37 Yeah.
0:26:40 I think there’s almost like three different things that are mixed in here.
0:26:42 One is just fraud.
0:26:44 You know, you fudged an image, you’re reusing it, whatever.
0:26:46 I think AI is wonderful for that.
0:26:51 And I actually think, and I’m happy to, if anybody who’s listening to this wants to get
0:26:55 sponsored, or maybe we should do a competition or something to basically build like fraud detectors
0:26:56 using AI or plagiarism detectors.
0:26:58 You could do it for liberal arts as well as sciences, right?
0:26:58 Yeah.
0:27:00 And I bet you’d uncover a ton of stuff.
0:27:05 Separate from that, there’s people publishing things that are just bad.
0:27:08 And the question is, is it bad because they ignored other data?
0:27:10 Did they throw out data points?
0:27:13 How would you know as an AI system, right?
0:27:15 That somebody threw out half their data to publish a paper.
0:27:21 And so there’s other issues around how science is done right now.
0:27:25 Or you just rush it and you have the wrong controls, but it still gets published because
0:27:26 it’s a hot field.
0:27:26 That happens a lot.
0:27:31 If you look during COVID, like there were so many papers that in hindsight were awful papers,
0:27:34 but they got rushed out because of COVID.
0:27:37 And unless somebody goes back and actually redoes the experiment and then publishes it,
0:27:40 they read it and it didn’t work, which nobody does because nobody’s going to publish it for
0:27:40 you.
0:27:42 How do you know that it’s not reproducible?
0:27:45 And so that’s part of the challenge in biology.
0:27:48 And so the biology problem isn’t, can an AI model do better?
0:27:49 I’m sure it could.
0:27:54 The biology problem is how do you create the data set that actually is clean enough and has
0:27:56 high enough fidelity that you can train a model that then goes and cleans everything
0:27:57 else up, right?
0:27:58 And it’s doable.
0:27:59 Like all these things are very doable.
0:28:00 You just have to go and do it.
0:28:01 And it’s a lot of work.
0:28:04 If you look at things like math and physics and other things like that, people are just
0:28:06 starting to train models against that now.
0:28:09 So I do think we’ll, in the coming years, see some really interesting breakthroughs there.
0:28:15 Do you think that’ll be rapid or do you, like how will those breakthroughs happen?
0:28:17 Yeah, it’s kind of the same thing.
0:28:22 You kind of need to figure out what’s the data set you’re using, what kind of model and
0:28:25 model architecture you’re using, because different architectures seem to work better or worse for
0:28:26 certain types of problems as well.
0:28:30 Like the protein folding ones have three or four different types of models that often get
0:28:32 mixed in, at least traditionally.
0:28:35 A lot of them have moved to these transformer backbones, but then they’re augmented by other
0:28:36 things.
0:28:40 So it’s a little bit of like, do you have enough and the right data?
0:28:43 Do you have the right model approach?
0:28:44 And then can you just keep scaling it?
0:28:48 Walk me through why I’m wrong here.
0:28:51 Like, I’m just, you know, what came to mind when you were saying this is like, we’re training
0:28:53 AI based on data.
0:28:55 So it’s like, here’s how we’ve solved problems in the past.
0:28:57 This is how you’re likely to solve it in the future.
0:29:02 But if I remember correctly, DeepMind trained Go by just being like, here are the rules.
0:29:06 We’re not actually going to show you people that have played before.
0:29:10 And that led to the creativity that we now see.
0:29:12 Yeah, that’s called self-play.
0:29:15 And as long as you have enough rules, you can do it.
0:29:18 You need a utility function you’re working against, right?
0:29:21 And so in the context of a game, it’s winning the game.
0:29:23 And there’s very specific rules of the game.
0:29:24 You know when to flip over the Go piece.
0:29:26 You know what winning means, right?
0:29:30 And so it’s easy to train against that because you have a function to select against.
0:29:31 This game you did well.
0:29:32 This game you did badly.
0:29:35 Here’s positive feedback or negative feedback to the model.
0:29:37 They’re starting to do that more and more.
0:29:40 So if you look at the way people are thinking about models now and scaling them, there’s three
0:29:41 or four components to it.
0:29:42 One is ongoing data scale.
0:29:44 Second is the training cluster.
0:29:46 People always talk about all the money they’re spending on GPUs.
0:29:48 The third is reasoning modules.
0:29:53 And that’s the new stuff from OpenAI in terms of 01 and 03 and all these things.
0:30:01 There’s other forms of time of inference-related optimizations and how do you do them and some
0:30:03 aspects eventually of this self-play.
0:30:09 And some of the places where that may really come into focus soon is coding because you
0:30:11 can push code and you can see if it runs and you can see what errors are thrown.
0:30:16 And there’s more stuff you can do in domains where you have a clear output you’re shooting
0:30:18 for and that you can test against it.
0:30:19 And there’s rapid feedback.
0:30:21 And there’s rapid feedback.
0:30:21 And that’s the key.
0:30:25 How quickly can you get feedback to keep training the system and iterating?
0:30:27 What happens when I give an AI a prompt?
0:30:30 Like what happens on the inside of that?
0:30:32 What’s the difference between a good prompt and a bad prompt?
0:30:37 Like does it basically take my prompt and break it into reasoning steps that a human would
0:30:38 use?
0:30:42 Like first I do this, second I do this, third I do this, and then I give the output.
0:30:47 And then the follow-on to this is like what can we do to better prompt AI to get better
0:30:47 outcomes?
0:30:48 Yeah, great question.
0:30:53 So a lot of the people working on agents have basically built what you’re describing, which
0:30:59 is something that will take a complex task, break it down into a series of steps, store those
0:31:01 steps, and then go back to them as you get output.
0:31:03 So you’re actually chaining a model.
0:31:07 You’re pinging it over and over with the output of the prior step and asking it now to do the
0:31:07 next step.
0:31:10 So one approach to that is you literally break it up into 10 pieces.
0:31:15 If it’s a simple problem and you’re just like write me a limerick with XYZ characteristics,
0:31:19 then the model can just do that in a single sort of call to the model.
0:31:24 But if you’re trying to do something really complex, you know, book me a flight or find
0:31:25 me and book me a flight to Mexico.
0:31:26 It’s like, okay, first I need to find the flight.
0:31:30 And so that means I need to go to this website and then I need to interact with the website
0:31:30 and pull the data.
0:31:32 Then I need to analyze that information.
0:31:34 And then I have to figure out what fits with your trip.
0:31:37 And then I, you know, I go through the booking steps and then I get the confirmation.
0:31:41 So it really depends on what you’re asking the model to do.
0:31:44 When I think of a model, though, I don’t think of an agent.
0:31:46 I just think, well, I can’t AI do that.
0:31:51 Like, why do I need a specific type of AI to book a flight to Mexico?
0:31:53 Why can’t ChatGPT just do it?
0:32:02 ChatGPT in its current form, or at least in the simplest form, is effectively interrogating
0:32:05 a mix of like a logic engine and a knowledge corpus, right?
0:32:10 It’s like a thing that will look at what it knows and based on that, provide you with some
0:32:11 output.
0:32:14 That’s a little bit different from asking somebody to take an action.
0:32:18 And that’s similar to if I was talking to you and I said, hey, where’s a nice place to
0:32:19 go?
0:32:23 And you didn’t say, oh, you should go to Cabo or you should go to wherever, right?
0:32:27 That’s different for me saying, hey, could you get me there, right?
0:32:30 And you have to go to the computer and load up the website and book it for it.
0:32:32 It’s the same thing for AI, right?
0:32:37 And so right now we have AIs that are very capable at understanding language, synthesizing
0:32:44 it, manipulating it, but they don’t have this remembrance of all the steps that they’ve
0:32:45 taken and will take.
0:32:49 And so you need to overlay that as another system on top of it.
0:32:53 And you see this a lot in the way your brain works, right?
0:32:56 You have different parts of your brain that are involved with vision and understanding
0:32:57 it.
0:32:59 You have different parts of your brain for language.
0:33:01 You have different parts of your brain for empathy, right?
0:33:05 You have mirror neurons that help you empathize with somebody or relate to them.
0:33:10 So your brain is a bunch of modules strung together to be able to do all sorts of complex
0:33:11 tasks, be they cognitive or physical.
0:33:16 And one could assume that over time you end up with roughly something like that as well
0:33:18 for certain forms of AI systems.
0:33:21 How are you using AI today?
0:33:23 I use it a lot.
0:33:32 I use it for everything from, you know, like I’ll go to a conference and I’ll dump the
0:33:36 names of the attendees in and ask like, who should I chat with based on these criteria?
0:33:38 And could you pull background on them?
0:33:41 You know, obviously a lot of people use it for coding right now or coding related tasks.
0:33:45 I use it for a lot of what I’m known as like regexes, regular expansions.
0:33:49 It’s like if I want to pull something out of certain types of data, I’ll do that sometimes.
0:33:53 So there’s all sorts of different uses for it.
0:33:56 What have you learned about prompting that more people should know?
0:34:03 I think a lot of people, and I’m by no means like a, you know, there’s these people whose
0:34:05 jobs are called prompt engineering and that’s all they do.
0:34:11 I think fundamentally a lot of it just comes down to like, what are you specifically asking
0:34:12 and can you create enough specificity?
0:34:16 And sometimes you can actually add checks into the system where you say, go back and double
0:34:19 check this just to make sure that you didn’t omit something because there are enough errors
0:34:22 sometimes depending on which model you’re using and for what use case and everything else that
0:34:28 if you put in simple safeguards of, hey, generate a table of XYZ as output, but then go back
0:34:31 and double check that these two things are true, I think it’s helped me clean up a lot of things
0:34:32 that would normally have been errors.
0:34:35 It’s almost like adding a test case.
0:34:36 Yeah, yeah.
0:34:40 Basically, if you think about it as like a smart intern, you know, often with your intern,
0:34:43 you say, okay, go do this thing, but why don’t you double check these three things about it?
0:34:47 And as the models get more and more capable, they’ll be less like an intern and more like
0:34:51 a junior employee, and then they’ll be like a senior employee, and then they’ll be like
0:34:54 a manager and they’ll kind of, you know, as the models get better and better and the
0:34:56 capabilities get stronger, you’ll see all these other things emerge.
0:34:59 Where do you see the bottlenecks today?
0:35:02 And like what comes to mind for me are different aspects of AI.
0:35:09 So you have, from going all the way up the stack, you have electricity, you have compute,
0:35:12 you have LLMs, you have data.
0:35:17 Where do you see the bottlenecks being, where’s the biggest bang for the buck?
0:35:19 Like what’s preventing this from going faster?
0:35:22 You know, it’s a really interesting question.
0:35:27 And I think there’s people who are better versed than I am in it because there’s this ongoing
0:35:30 question of when does scaling run out for which of those things, right?
0:35:34 When do we not have enough data to generate the next versions of models or do we just use
0:35:35 synthetic data and will that be sufficient?
0:35:37 Or how big of a training cluster can you actually get to economically?
0:35:42 You know, how do you fine-tune or post-train a model and at what point does that not yield
0:35:43 as many results?
0:35:46 That said, each one of these things has its own scaling curves.
0:35:48 Each one of these seems to still be working quite well.
0:35:52 And then if you look at a lot of the new reasoning stuff that OpenAI and others have been working
0:35:53 on, Google’s been working on some stuff here as well.
0:35:59 When you talk to people who work on that, they feel that there’s still enormous scaling loss
0:36:00 for that still left, right?
0:36:02 Because those are just brand new things that just rolled out.
0:36:06 And so these sort of reasoning engines have their own big curve to climb as well.
0:36:11 So I think we’re going to see two or three curves sort of simultaneously continue to inflect.
0:36:19 Is this the first real revolution where incumbents have an advantage?
0:36:23 And I say that because data costs money, compute costs money, power costs money.
0:36:24 Yeah.
0:36:30 And it sort of favors the Googles, the Microsofts, the people with a ton of capital.
0:36:31 Yeah.
0:36:36 I think in general, every technology wave has a differential split of outcome for incumbents
0:36:36 versus startups.
0:36:40 So the internet was 80% startup value.
0:36:41 It was Google.
0:36:42 It was Amazon.
0:36:45 You know, it was all these companies we now know and love.
0:36:46 Meta, you know.
0:36:54 And then mobile, the mobile revolution was probably 80% incumbent value or 90%, right?
0:36:59 And so that was mobile search was Google and mobile CRM was Salesforce and mobile whatever
0:37:00 was that app you were already using.
0:37:05 And the things that emerged during that revolution of startups were things that took advantage of the
0:37:07 unique characteristics that were new to the phone.
0:37:08 GPS.
0:37:09 So you had Uber.
0:37:11 Everybody has a camera.
0:37:12 You have Instagram, et cetera, right?
0:37:17 And so the things that became big companies in mobile that were startups were able to do
0:37:20 it because they took advantage of something new that the incumbents didn’t necessarily have
0:37:21 any provenance over.
0:37:26 Crypto was 100% or roughly 100% startup value, right?
0:37:29 It’s Coinbase and it’s the tokens and everything else.
0:37:33 So you kind of go through wave by wave and you ask, what are the characteristics that make
0:37:34 something better or worse?
0:37:38 And if you actually look at self-driving, which was sort of an earlier AI revolution in some
0:37:43 sense, the two winners, at least in the West, seem to be Tesla, which was an incumbent car
0:37:47 maker in some sense, by the point that they were willing to step out, and Google through
0:37:47 Waymo.
0:37:51 So two incumbents won in self-driving, which I think is a little bit under discussed because
0:37:54 we had like two dozen self-driving companies, right?
0:37:58 Wouldn’t that make sense, though, because they have the most data in the sense of like
0:38:04 Tesla acquires so much data every day and now the way that they’ve set up full self-driving,
0:38:07 my understanding is it’s gotten really good in the last six months.
0:38:12 One of the reasons is they stopped coding, basically, and they started feeding the data into AI and
0:38:15 having the AI generate the next version effectively.
0:38:19 Yeah, a lot of the early self-driving systems were basically people writing a lot of kind of
0:38:20 edge case heuristics.
0:38:23 So you’d almost write a rule if X happens, you do Y or some version of that.
0:38:26 And they moved a lot of these systems over to just end-to-end deep learning.
0:38:31 And so this modern wave of AI has really taken over the self-driving world in a really strong
0:38:33 way that’s really helped these things accelerate, to your point.
0:38:36 And so Waymo similarly has gotten dramatically better recently.
0:38:38 So I think all that’s true.
0:38:43 I guess it’s more of a question of when does that sort of scale matter and why wasn’t there
0:38:46 anybody who was able to partner effectively with an existing automotive company?
0:38:49 What in other things happen in the market?
0:38:52 For this current wave of AI, it really depends on the layer you’re talking about.
0:38:56 And I think there’s going to be enormous value for both incumbents and startups.
0:39:01 On the incumbent side, it really looks like the foundation model companies are either paired
0:39:04 up or driven by incumbents.
0:39:05 Maybe one or two kind of examples.
0:39:09 So, you know, OpenAI is roughly partnered with Microsoft.
0:39:10 Microsoft also has its own efforts.
0:39:13 Google is its own partner in some sense, right?
0:39:16 Amazon has partnered with Anthropic.
0:39:20 Obviously, Facebook has Llama, the open source model.
0:39:26 But I think for three of the four, and then there’s X.EI, which, you know, is Elon Musk’s just
0:39:32 sort of ability to execute in such an insane way that’s really driving it and access to capital
0:39:33 and all the rest.
0:39:38 But if you look at it, and I wrote a blog post about this maybe two, three years ago, which
0:39:40 is basically, what’s the long-term market structure for that layer?
0:39:46 And it felt like it had to be an oligopoly or, you know, at most an oligopoly.
0:39:48 And the reason was this point that you made about capital.
0:39:52 And back then, it costs, you know, tens of millions to build a model.
0:39:57 But if you extrapolated the scaling curve, you’re like, every generation is going to be a few
0:39:58 X to 10 X more.
0:40:02 And so, eventually, you’re talking about billions, tens of billions of dollars, not that many
0:40:03 people can afford it.
0:40:06 And then you ask, what’s the financial incentive for funding it?
0:40:10 And the financial incentive for the cloud businesses is their clouds, right?
0:40:14 If you look at Azure’s last quarter, I think it was like a $28 billion quarter or something
0:40:14 like that.
0:40:19 I think they said that 10 to 15% of the lift on that was from AI being sold on the cloud.
0:40:21 So, that’s what?
0:40:23 One and a half to three billion, a quarter, right?
0:40:28 So, the financial incentive for Microsoft to fund open AI is it feeds back into its cloud.
0:40:30 It feeds back in other ways, too, but it feeds back to its cloud.
0:40:36 And so, I don’t think it’s surprising that the biggest funders of AI today, besides sovereign
0:40:39 wealth, has been clouds because they have a financial incentive to do it.
0:40:40 And people really miss that.
0:40:45 So, I think that that is part of what really helped lock in this oligopoly structure early
0:40:49 is you had enormous capital scale going to a handful of the best players through these
0:40:49 cloud providers.
0:40:52 And so, the venture capitalists would put hundreds of millions of dollars into these companies.
0:40:54 The clouds put tens of billions in.
0:40:55 Yeah.
0:40:56 And that’s the difference.
0:41:04 And I guess the optimism there is that I can go use the full scale of AWS or Azure or
0:41:07 Google and just rent time.
0:41:09 So, I don’t need to make the capital investments.
0:41:10 I don’t need to run the data center.
0:41:11 I don’t need to.
0:41:13 Well, you could have done that either way, right?
0:41:16 You didn’t have to take money from them because they’re happy to be a customer.
0:41:17 That’s what I’m saying, right?
0:41:21 So, like the optimism is like you can compete with them now because you’re just competing
0:41:22 on ideas.
0:41:24 You have access to the structure.
0:41:25 Yeah.
0:41:25 Yeah.
0:41:28 And you would have done that no matter what, just given that everything moved to clouds,
0:41:30 like these third-party clouds that you can run on.
0:41:35 So, that’s enabling, but at least for these sort of language models, they’re increasingly
0:41:37 just a moat due to capital scale.
0:41:41 Do you think that we just end up with like three or four and they’re all pretty much equivalent?
0:41:43 Yeah, I’m not sure.
0:41:45 I think you can imagine two worlds.
0:41:47 World one is where you have an asymptote.
0:41:51 Eventually, things kind of all flatline against some curve because you can only scale a cluster
0:41:53 so much, you only have so much data or whatever.
0:41:56 In which case, eventually, things should converge really closely over time.
0:42:00 And in general, things have been converging faster than not across the major model platforms
0:42:01 already.
0:42:07 Where a second world is, if you think about the capability set built into each AI model,
0:42:12 if you have something that’s far enough ahead and it’s very good at code and it’s very good
0:42:16 at data labeling and it’s very good at doing a lot of the jobs that allow you to build the
0:42:20 next model really fast, then eventually you may end up with a very strong positive feedback
0:42:25 loop for whoever’s far enough ahead that their model always creates the next version of the
0:42:26 model faster than anybody else.
0:42:28 And then you maybe have liftoff, right?
0:42:32 Maybe that’s the thing that ends up dramatically far ahead because every six months becomes more
0:42:33 important than the last five years.
0:42:38 And so, there’s another world you could imagine where you’re in a liftoff scenario where there’s
0:42:41 a feedback loop of the model effectively creating its next version.
0:42:47 So, GPT-5 or 7 or whatever, GPT-7 would create GPT-8, which would help create GPT-9, which
0:42:48 would even faster create GPT-10.
0:42:54 And at that point, you have an advantage, but the advantage is expanding at the velocity at
0:42:55 which you’re creating the next model.
0:42:55 Correct.
0:43:00 Because GPT-10 perhaps is so much more capable than 9 that everybody else is at 9, it’s already
0:43:00 building 11.
0:43:04 And it can build it faster, smarter, et cetera, than everybody else.
0:43:09 And so, it really comes down to what proportion of the model building task or model training
0:43:12 and building task is eventually done by AI itself.
0:43:16 Spring is here and you can now get almost anything you need delivered with Uber Eats.
0:43:17 What do we mean by almost?
0:43:20 You can’t get a well-groomed lawn delivered, but you can get chicken Parmesan delivered.
0:43:21 Sunshine?
0:43:21 No.
0:43:22 Some wine?
0:43:23 Yes.
0:43:29 What do you think of Facebook?
0:43:34 They’ve spent, I don’t know, 50, 60 billion and they’ve basically given it away to society.
0:43:35 Yeah.
0:43:35 Yeah.
0:43:39 I’ve been super impressed by what they’ve done with Llama.
0:43:40 I think open source is incredibly important.
0:43:43 And why is open source important?
0:43:45 It does a couple of things.
0:43:51 One is it levels a playing field for different types of uses of this technology and it makes
0:43:53 it globally available in certain ways that’s important.
0:43:59 Second, it allows you to take out things that you may not want in there and because it’s
0:44:01 open weights and it’s open source.
0:44:08 So, if you’re worried about a specific political bias or a specific cultural outlook, because
0:44:13 it’s really interesting if you look at the way people talk about norms and what should be
0:44:17 built into models and safety and all the rest, it’s like, who are you to determine all
0:44:22 of global norms with your own values, right?
0:44:25 That’s a form of cultural imperialism if you think about it, right?
0:44:28 You’re basically imposing what you think on everybody else.
0:44:32 And so, open source models gives you a bit more leeway in terms of being able to retrain
0:44:39 a model or have it reflect whatever norms of your country or your region or whatever lens
0:44:40 on that you want to take.
0:44:42 So, I think it’s also important from that perspective.
0:44:49 As an investor, what’s the ROI on a $1,600 billion open source model?
0:44:53 How do you think through what Facebook is trying to do or accomplish?
0:44:57 Is it just like, I don’t want the competitors to get too far ahead?
0:45:00 I don’t know how meta specifically is thinking about it.
0:45:03 So, I think I’d be sort of talking out of turn if I just made some stuff up.
0:45:09 I think that in general, there’s been all sorts of times where open source has been very important
0:45:10 strategically for companies.
0:45:15 And if you actually look at it, almost every single major open source company has had a
0:45:17 giant institutional backer.
0:45:20 IBM was the biggest funder of Linux in the 90s as a counterbalance to Microsoft.
0:45:27 And the biggest funders of all the open source browsers are Apple and Google with WebKit.
0:45:31 And you just go through technology wave after technology wave, and there’s always a giant
0:45:32 backer.
0:45:35 And maybe the biggest counter to that is Bitcoin and all the crypto stuff.
0:45:40 And you could argue that they’re their own backer through the token, right?
0:45:43 So, Bitcoin financially effectively has fueled the development of Bitcoin.
0:45:49 It’s kind of paid for itself in some sense as an open source tool or open source sort of
0:45:50 form of money.
0:45:52 You know, I don’t know why AI would be different.
0:45:58 I, a couple years ago, was trying to extrapolate who is the most likely party to be the funder
0:46:00 of open source AI.
0:46:04 And back then, I thought it would be Amazon, because at the time, they didn’t have a horse
0:46:07 in the race like Microsoft and Google, or maybe be NVIDIA.
0:46:12 And Meta was kind of on the list because of all the money they have, but in prowess and
0:46:15 engineering and fair, and, you know, they have a lot of great things, but they weren’t the
0:46:17 one I would have guessed as the most likely.
0:46:19 They were on the list, but they weren’t the most likely.
0:46:23 And then there’s other players with tons of money, and tons of capabilities.
0:46:25 And the question is, are they going to do anything?
0:46:26 What does Apple do?
0:46:27 What does Samsung do?
0:46:31 You know, there’s like half a dozen companies that could still do really interesting things
0:46:32 if they wanted to.
0:46:33 And the question is, what are they going to do?
0:46:39 How would you think about sort of the big players and who is best positioned for the
0:46:41 next two to three years?
0:46:42 How would you rank them?
0:46:44 In terms of AI or in terms of other things?
0:46:46 In terms of AI.
0:46:47 Yeah.
0:46:53 Like who’s most likely to accrue some of the advantages of AI?
0:46:59 Yeah, it’s kind of hard because AI is the only market where the more I learn, the less I
0:47:00 know.
0:47:02 And in every other market, the more I learn, the more I know.
0:47:05 And the more predictive value, or the more I’m able to predict things.
0:47:06 And I can’t predict anything anymore.
0:47:09 You know, I feel like every six months, things change over so rapidly.
0:47:13 You know, fundamentally, there’s a handful of companies in the market that are doing very
0:47:14 well.
0:47:21 Obviously, there’s Google, there’s Meta, there’s OpenAI, there’s Microsoft, Anthropic, and AWS
0:47:24 or Anthropic, X.AI.
0:47:27 You know, Mistral has done some interesting things over time.
0:47:30 So I think there’s like a handful of companies that are the ones to watch.
0:47:32 And the question is, how does this market evolve?
0:47:33 Does it consolidate or not?
0:47:34 Like what happens?
0:47:37 How do you think about regulation around AI?
0:47:38 Yeah.
0:47:43 So there’s basically like three or four forms of AI safety that people talk about and they
0:47:44 kind of mix or complate them.
0:47:47 The first form of AI safety is almost what I call like digital safety.
0:47:48 It’s like, will the thing offend you?
0:47:51 Or will there be hate content or other things?
0:47:55 And there’s actually a lot of rules that already exist around hate speech on the internet or hate
0:47:58 speech in general or, you know, what’s free speech or not and how you should think about
0:47:58 all these things.
0:48:00 So I’m less concerned about that.
0:48:01 I think people will figure that out.
0:48:06 There’s a second area, which is almost like physical safety, which is will you use AI to
0:48:07 create a virus?
0:48:09 Will you use AI to derail a train?
0:48:10 You know, et cetera.
0:48:15 And similarly, like when I look at the arguments made about how it will create a biological
0:48:18 virus and et cetera, et cetera, like you can already do that, right?
0:48:24 The protocols for cloning and PCR and all this, it’s all on the internet.
0:48:25 It’s all posted by major labs.
0:48:26 It’s in all the textbooks.
0:48:30 Like that’s not new knowledge that people can’t just go and do right now if they really wanted
0:48:30 to.
0:48:33 So I don’t know why that matters in terms of AI.
0:48:41 And then the third area is sort of this existential safetyism, like AI will become self-aware and
0:48:42 destroy us, right?
0:48:45 And when people talk about safety, they mix those three things.
0:48:48 They conflate them and therefore they say, well, eventually maybe something terrible happens
0:48:50 here, so we better shut everything else down.
0:48:52 While other people are just saying, hey, I’m worried about hate speech.
0:48:56 And so I think when people talk about safety, they have to really define clearly what they
0:48:56 mean.
0:49:00 And then they have to create a clear view of why it’s a real concern.
0:49:04 It’s sort of like if I kept saying, I think an asteroid could at some point hit the earth
0:49:06 and therefore we better do X, Y, Z.
0:49:07 We should move the earth or whatever.
0:49:11 You know, it’s just at some point these things get a little bit ridiculous in terms of safetyism.
0:49:16 There’s actually a broader question societally of like why has society become so risk averse
0:49:17 in certain ways and so safety centric?
0:49:20 And it impacts things in all sorts of ways.
0:49:22 I’ll give you a dumb example.
0:49:28 After what age does the data suggest that a child doesn’t need a special seat?
0:49:29 They can just use a seatbelt.
0:49:33 I think it’s like 10 or 12, isn’t it?
0:49:37 Well, so in California, for example, the law is up until age eight.
0:49:37 Okay.
0:49:40 You have to be in a booster seat or a car seat or whatever.
0:49:46 If you actually look at crash data, real data, and people have now reproduced this across
0:49:48 multiple countries, multiple time periods.
0:49:49 It’s the age of two.
0:49:50 Oh, wow.
0:49:53 So for six extra years, we keep people in booster seats and car seats and all that,
0:49:55 at least against the data, right?
0:49:55 Okay.
0:49:58 The Freakonomics podcast actually had a pretty good bit on this.
0:50:02 And there’s like multiple papers now that reproducibly show this retrospectively.
0:50:03 You just look at all the crashes.
0:50:04 That’s crazy.
0:50:04 Yeah.
0:50:05 So why do we do it?
0:50:05 Safety.
0:50:07 But it’s not safe.
0:50:08 Exactly.
0:50:09 But it’s positioned as safe.
0:50:11 As a parent, of course, you want to protect your children.
0:50:11 No, seriously, right?
0:50:14 And so, but then it has other implications.
0:50:17 It’s like you can’t easily transport the kids in certain scenarios because you don’t have
0:50:22 the car seat or, you know, you can only fit so many car seats in a car and it’s a pain
0:50:22 in the butt.
0:50:24 And do you upgrade the car if you want more kids?
0:50:25 And can you afford it?
0:50:27 And, you know, so it has all these ramifications.
0:50:33 And it’s because I think, A, it’s lucrative for the car seat companies to sell more car seats
0:50:34 for longer, right?
0:50:35 You get an extra six years on the kid or whatever.
0:50:39 Parents will, of course, say, I want safety no matter what.
0:50:44 And certain legislatures are happy to just, you know, legislate it.
0:50:48 So I think there’s lots and lots and lots of examples of that in society if you start picking
0:50:51 at it and you realize it pervades everything.
0:50:52 It pervades aspects of medicine.
0:50:55 It pervades things like AI now.
0:50:56 It’s just, it’s everywhere.
0:51:03 There’s one in Ottawa that I see on mornings when there’s schools around the, I don’t know,
0:51:04 five or six blocks of a school.
0:51:07 They basically have crossing guards everywhere now.
0:51:13 So it’s basically, even for high schools, like, so kids can’t walk to school on their
0:51:19 own, which I think you think, oh, well, how do you argue with that, right?
0:51:23 Like, and then I was thinking about this the other day because I was driving and, you know,
0:51:27 I got stopped by one of these people and I was like, we’re just teaching kids that like
0:51:28 they don’t even have to pay attention.
0:51:30 They can look at their phone.
0:51:32 The crossing guard is going to save them.
0:51:36 And then if the crossing guard is not like they’re, we’re not developing ownership or
0:51:37 agency in people.
0:51:39 How do you think about that?
0:51:44 I think it’s, I think it’s really bad for society at scale.
0:51:48 I mean, it’s kind of like, there was a different wave of this, which was, you know, 10, 15 years
0:51:53 ago with fragility and microaggressions and everything can offend you and you need to be super fragile
0:51:53 and all this stuff, right?
0:51:55 Which I think is very bad for, for kids.
0:51:57 And I think that has a lot of mental health implications.
0:52:03 The wave we’re in now, which is basically taking away independence, agency, risk-taking.
0:52:09 I think that has some really bad downstream implications in terms of how people act, what they consider
0:52:14 to be risky or not, and what that means about how they’re going to act in life and also their
0:52:15 ability to actually function independently.
0:52:16 So I agree.
0:52:20 I think, I think all those things are things that we’ve accumulated over the last few decades
0:52:21 that are probably quite negative.
0:52:27 You’re one of the most successful investors that a lot of people have probably never heard
0:52:27 of.
0:52:32 One of the things that you’ve said is that most companies die from self-inflicted wounds and
0:52:33 not competition.
0:52:38 What are the most common self-inflicted wounds that kill companies?
0:52:40 Yeah, I think there’s two or three of them.
0:52:43 You know, it depends on the stage of the company.
0:52:47 For a very early company, the two ways that they die is the founders start fighting and
0:52:51 the team blows up, or they run out of money, which means they never got to product market
0:52:51 fit.
0:52:54 They never figured out something that they could build economically that people would care
0:52:54 about.
0:52:58 So for the earliest stages, that’s, that’s roughly everything.
0:53:04 Every once in a while, you have some competitive dynamic, but the reality is most incumbent companies
0:53:05 don’t care about startups.
0:53:10 And startups have five, six years before an incumbent wakes up and realizes it’s a big deal and then
0:53:11 tries to crush them.
0:53:13 And sometimes that works.
0:53:16 Sometimes you just end up with capped outcomes.
0:53:19 So for example, you could argue Zoom and Slack got capped by Microsoft launching stuff into
0:53:24 teams in terms of taking parts of the market or creating a more competitive market dynamic
0:53:25 for them.
0:53:29 You know, the other types of self-inflicted wounds, honestly, sometimes people get very competitor
0:53:31 centric versus customer centric.
0:53:33 Go deeper on that.
0:53:35 I mean, there’s a lot of examples of that.
0:53:41 Sort of like if you focus on your, your competitor too much, you stop doing your own thing.
0:53:46 You stop building that thing the customer actually wants and you lose differentiation relative to
0:53:47 your competitor.
0:53:51 Or you start doing things that can hurt your competitor, but they don’t necessarily help
0:53:51 you.
0:53:54 And sometimes your competitor will retaliate.
0:54:00 An example of that would be in the pharmaceutical distribution world.
0:54:05 You know, 20 years ago, there was roughly three players that really mattered of any scale.
0:54:08 And they used to go after each other’s market share really aggressively, which eroded all the
0:54:10 pricing, which meant they were bad businesses.
0:54:16 And at some point, I think one of them decided to stop competing for share, but just protect
0:54:17 itself.
0:54:20 And then the others copied it and suddenly margins went way up in the industry, right?
0:54:24 They stopped being as focused on banging on each other and more just like, let me just
0:54:26 build more services for my customers and let’s just focus on our own set.
0:54:30 We’re all going to win a lot more that way, right?
0:54:33 In some cases, yeah, if you have an oligopoly market, that’s usually where it ends up.
0:54:38 Eventually, this is why people are so worried about collusion, right?
0:54:42 Eventually, the companies decide, hey, we should be in a stable equilibrium instead of beating
0:54:44 up on each other and shrinking margins.
0:54:49 Scaling a company often means scaling the CEO.
0:54:56 What have you learned about the ways that successful CEOs scale themselves and things that get in the
0:54:57 way?
0:54:58 Yeah, I think it’s two or three things.
0:55:02 One is figuring out who else you need to fill out your team with and how much can you trust
0:55:03 them and all the rest.
0:55:08 And so one piece of it is very innovative founder CEOs always want to innovate and so
0:55:10 they reinvent things that they shouldn’t reinvent.
0:55:14 Like sales is like effectively process engineering that’s been worked through for decades.
0:55:16 You don’t need to go reinvent sales.
0:55:18 You know, you just hire a sales team and it’ll work just fine.
0:55:21 So one aspect is getting out of your own way on reinvention.
0:55:23 There’s certain things you want to rethink, but many of them you don’t.
0:55:27 Part of it is hiring people who are going to be effective in those roles and more effective
0:55:28 than you might be.
0:55:31 Often you end up finding people who are complementary to you.
0:55:39 Now that really breaks down during CEO succession because what happens is often the CEO will promote
0:55:43 the person who’s their complement as the next CEO instead of finding somebody like them who
0:55:47 can innovate and push on people and drive new products and new changes.
0:55:52 And so often you see companies have a golden age under a founder and then decay.
0:55:56 And the decay is because the founder promoted their lieutenant who was great at operations
0:56:00 or whatever, but wasn’t a great product thinker or technology vision like themselves.
0:56:04 And so that’s actually a failure mode for like longer term related areas.
0:56:08 You could argue such a Microsoft is a good example of somebody who has more of a founder mindset.
0:56:09 I’m going to reinvent things.
0:56:10 I’m going to rethink things.
0:56:11 I’m going to do these crazy deals.
0:56:14 They backed open AI at GPT-2, which is like a huge risk.
0:56:16 They’ve done all sorts of really smart acquisitions.
0:56:22 So like that’s an example of somebody who actually did a, they did a smart succession there in
0:56:25 terms of finding somebody who’s a bit more like product founder mentality.
0:56:35 You know, in terms of other ways that CEOs fail is they listen too much to conventional wisdom
0:56:37 on how to structure their team.
0:56:42 And really the way you want to have your team function at a large organization is based
0:56:43 on the CEO.
0:56:45 What does the CEO need?
0:56:46 What are the compliments they need?
0:56:47 What is the structure they need?
0:56:52 And if you were to plop out that person and plop in a different CEO, that structure probably
0:56:53 shouldn’t work like half the time.
0:56:57 There’s some types of people where there’s lots of commonalities, particularly if it’s people
0:57:01 who came up the corporate ladder and they’re all used to doing things the same way.
0:57:05 But if you’re more of a founder CEO and you’re going to have your quirks and you’re going
0:57:08 to have your obsessions and you’re going to have all these things that founders often
0:57:11 have, you need an org structure that reflects you.
0:57:13 And so like Jensen from NVIDIA talks about this, right?
0:57:15 The claim is he has like 40 direct reports.
0:57:19 He claims that, you know, he doesn’t do many one-on-ones or things like that.
0:57:22 And the focus is more on finding very effective people who’ve been with him for a while and
0:57:23 who can just drive things, right?
0:57:25 And then he sort of deep dives in different areas.
0:57:31 That’s a very different structure from how Satya’s running Microsoft or Larry Ellison
0:57:32 has run Oracle over time.
0:57:36 Or, you know, you look at these other sort of giants of industry and management and everything
0:57:36 else.
0:57:40 And so I think you really need an org structure that reflects you.
0:57:43 Now, there’s going to be commonalities and there’s only so many reports most people can
0:57:44 handle and all the rest of it.
0:57:48 But I do think you kind of want to have the team that reflects your needs versus the generic
0:57:49 team that could reflect anybody’s needs.
0:57:54 Is that the problem with sort of a lot of these business leadership books that are written
0:57:59 about a particular person in style that they have and then people read them and they try
0:58:02 to implement them, but it’s not genuine to who they are?
0:58:03 I think that’s very true.
0:58:07 And it really depends on whether you’re talking about the generic case of, hey, it’s a big
0:58:12 company and you’re at a related large company that’s 100 years old that’s been run a certain
0:58:12 way.
0:58:17 Like, I wouldn’t be surprised if you could roughly interchange the CEOs of a subset of the pharma
0:58:19 companies in terms of the org structure.
0:58:23 They may not have the chemistry with the people or the trust or whatever, but like the org
0:58:25 structures are probably reasonably similar.
0:58:28 That’s probably pretty different than if you looked at, you know, how Oracle has been run
0:58:32 over time versus Microsoft over time versus Google over time versus whoever.
0:58:38 When you say that, I think the wording you use like conventional wisdom, CEOs should pay less
0:58:39 attention to conventional wisdom.
0:58:46 Do you mean that in the sense of the, I guess the nomenclature that Brian Jeske came out
0:58:47 was founder mode?
0:58:55 Yeah, I think, um, I think we lived through a decade or so, maybe longer where a lot of
0:59:01 forces came into play in the workplace that were not productive to the company actually obtaining
0:59:02 its missions and objectives.
0:59:08 And a lot of that was all the different forms of politics and bring your whole self to work
0:59:11 and all these things that people are talking about, which I don’t want somebody’s whole
0:59:11 self at work.
0:59:18 You know, I remember at Google, um, for Halloween, uh, and maybe we should edit this part out,
0:59:20 but there’s somebody who would show up and ask those chaps every Halloween.
0:59:23 And you’re like, I don’t want to see that.
0:59:24 Like I’m in a work environment.
0:59:26 Why is this, why is this engineer walking around like this?
0:59:26 Yeah.
0:59:27 Yeah.
0:59:30 And then the second you start bringing kids to work, you’re like, I sure as hell don’t
0:59:31 want this guy walking around.
0:59:31 Right.
0:59:32 Yeah.
0:59:33 And that’s bring your whole self to work.
0:59:34 Like, why would you do that?
0:59:37 You actually should bring your professional self to work.
0:59:40 You should bring the person who’s going to be effective in a work environment and can work
0:59:44 with all sorts of diverse people and be effective and doesn’t bring all their mores
0:59:47 and values and everything else in the workplace that don’t have a place in the workplace.
0:59:49 There’s a subset of those that do, but many don’t.
0:59:54 We lived through a decade where not only were those things encouraged, but the traditional
0:59:58 conventionalist executives brought that stuff with them.
1:00:00 And I think it was probably bad for a lot of cultures.
1:00:02 It defocused them from their mission.
1:00:04 It defocused them from their customers.
1:00:06 It defocused them from doing the things that were actually important.
1:00:11 And the first person to speak out against that was Brian Armstrong that I remember like
1:00:13 in a very public and visible way.
1:00:17 And then Toby Luque followed him not long after.
1:00:20 And they said, no, the workplace is not about that.
1:00:22 It’s about X, Y, and Z.
1:00:24 And if you don’t like it, like basically leave.
1:00:24 Yeah.
1:00:29 And was that the moment where we started to go back to founder mode effectively?
1:00:31 I think it took some time.
1:00:33 I think Brian was incredibly brave for doing that.
1:00:33 Totally.
1:00:35 And he got a lot of flack for it.
1:00:35 And I think it-
1:00:36 They tried to cancel him.
1:00:39 They tried to cancel him aggressively, which was sort of the playbook, right?
1:00:42 Oh, and this was happening inside of companies too, right?
1:00:44 You’d say something and you’d get canceled for it.
1:00:47 And so you can have a real conversation around some of these things.
1:00:48 And again, that just reinforced it.
1:00:52 And I think Brian stepping forward made a huge difference.
1:00:54 To your point, Toby, I think did it really well.
1:01:00 I still sometimes send the essay that he wrote for that to other people where he had a few
1:01:04 central premises, which is we have a specific mission and we’re going to focus on that.
1:01:05 We’re not focusing on other things.
1:01:07 We’re not a family.
1:01:07 We’re a team.
1:01:08 Yeah.
1:01:09 Right?
1:01:12 The family is like, hey, your uncle shows up drunk all the time.
1:01:14 You kind of tolerate it because it’s your uncle.
1:01:18 If somebody showed up drunk at work all the time, you shouldn’t tolerate that, right?
1:01:19 You’re not a family.
1:01:20 You’re a sports team.
1:01:22 You’re trying to optimize for performance.
1:01:26 You’re trying to optimize for the positive interchange within that team.
1:01:30 And you want people pulling in the direction of the team, not people doing their own thing,
1:01:31 which is a family, right?
1:01:35 And so I think there was a lot of these kind of conversations or discussions that were more
1:01:40 like it’s a family and bring yourself to work and all the holisticness of yourself.
1:01:44 And it’s actually, well, no, you probably shouldn’t show up at work drunk and, you know,
1:01:45 look at bad things on the internet.
1:01:49 You know, you should focus on your job and you should focus on good collaboration with your
1:01:50 co-workers and things like that.
1:01:57 You’re around a lot of outlier CEOs, not only in the context of you know them, but
1:01:58 you hang out with them.
1:01:59 You spend a lot of time with them.
1:02:03 What are sort of the common patterns that you’ve seen amongst them?
1:02:06 Are there common patterns or is everybody completely unique?
1:02:10 But I imagine that at the core, there’s commonality.
1:02:11 Yeah.
1:02:13 You know, this is something I’ve been kind of riffing on lately, and I don’t know if it’s
1:02:15 quite correct, but I think there’s like two or three common patterns.
1:02:19 I think pattern one is there are a set of people who are, and by the way, all these people
1:02:24 are like incredibly smart, you know, incredibly insightful, et cetera, right?
1:02:28 So they all have a few common things.
1:02:30 But I do think there’s two or three archetypes.
1:02:32 I think one of them is just the people who are hyper-focused.
1:02:34 They don’t get involved with other businesses.
1:02:36 They don’t do a lot of angel investments.
1:02:38 They don’t, you know, do press junkets that don’t make sense.
1:02:40 They just stay on one track.
1:02:44 And a version of that was Travis from Uber.
1:02:47 I knew him a little bit before Uber, and I’ve, you know, run into him once or twice since
1:02:50 then, but like, he was always just incredibly focused.
1:02:52 He used to be an amazing angel investor.
1:02:55 I think he made great investments, but he stopped doing it with Uber, and he just focused on Uber.
1:02:59 And as far as I know, he never sold secondary until he left the company, right?
1:03:02 He was just hyper-focused on making it as successful as possible.
1:03:04 So that’s one class of ArchType.
1:03:12 There’s a second class, which I’d view as people who are equally smart and driven, but a bit more,
1:03:15 polymathic may be the wrong word, but they just have very broad interests, and they express
1:03:17 those interests in different ways while they’re also running their company.
1:03:22 And often they have a period where they’re just focused on their company, and then they
1:03:23 add these other things over time.
1:03:28 And so examples of that, I mean, obviously Elon Musk is now that, right?
1:03:29 In terms of all that.
1:03:35 Patrick Collison is that he’s running a biology institute, or his Sobana and the other Patrick
1:03:38 are running it alongside him called ARK.
1:03:44 Brian Armstrong is now running a longevity company in parallel to Coinbase, or he has somebody
1:03:45 running it.
1:03:51 So there’s a lot of these examples of people doing X2, X3, and doing it in other fields.
1:03:56 Honestly, that’s a little bit of a new development relative to what you were allowed to do before,
1:04:01 because there’s both activist investors who try to prevent that, and public markets in
1:04:02 particular.
1:04:07 But also, it was just a different mindset of how do I show impact over time?
1:04:12 Are these people going from the first one, hyper-focused, to this?
1:04:16 Or were they always sort of, I don’t want to use the word dabble because it really understates
1:04:19 how focused they are on their businesses.
1:04:24 But are they always like that, and as they get larger, it scales differently?
1:04:30 Or is it, no, we’ve gone from sort of the first, which is this hyper-focus, to the second?
1:04:36 I think it’s more like when you talk to them, the way that they think about the world and
1:04:40 the set of interests they have is a little bit different from the first group of folks.
1:04:43 And I’m not talking about Travis specifically, because I didn’t know him well enough to have
1:04:44 a perspective on that.
1:04:49 But I just mean more generally, I’ve noticed that they have this commonality of when you
1:04:55 talk to them very early, they’re like 20 years old or whatever, and you meet them, the set
1:04:57 of interests that they have is very, very broad.
1:05:02 And they tend to go very deep on each thing that they get interested in, whether it benefits
1:05:03 them or not.
1:05:04 They just go deep on it, right?
1:05:05 Because it’s interesting.
1:05:09 They’re driven by a certain form of interestingness, in addition to being driven by impact.
1:05:13 And then I think there’s a third set of people who end up with outside successes.
1:05:16 And sometimes that’s just product market fit.
1:05:18 And then they grow into the role, you know?
1:05:24 And so there’s some businesses that just have either such strong network effects or just such
1:05:26 strong liftoff early on.
1:05:29 And they’re obviously very smart people and all the rest of it, but you don’t feel that
1:05:33 same drive underlying it or that same need to do big things.
1:05:34 It’s almost accidental.
1:05:37 And you sometimes see that.
1:05:39 Would you say that’s more luck?
1:05:41 I don’t know.
1:05:45 I mean, say somebody is really good at product market fit, but they’re not that aggressive.
1:05:47 And once they hit a certain level, they’re not that ambitious.
1:05:49 Part of it too is like, what’s your utility curve?
1:05:50 Like, what do you care about in life?
1:05:52 Do you care about status?
1:05:53 Do you care about money?
1:05:54 Do you care about power?
1:05:55 Do you care about impact?
1:05:57 Do you do things because it’s interesting?
1:05:58 Like, why do you do stuff?
1:06:04 And imagine people where that is a big part of everything they do, right?
1:06:07 Because I think the average person may have mixes of that, but they’re also just happy
1:06:08 going to their kids and hanging out, you know?
1:06:10 And like, it’s a different life, right?
1:06:16 Like, the average Google engineer is not going to be this insanely driven, hyper, you know,
1:06:17 hyper drive person anymore.
1:06:20 What do you think keeps people going?
1:06:25 I mean, a lot of people become successful and maybe they hit whatever number they have in
1:06:29 their head that they can like retire comfortably or live the life they want to live and they
1:06:31 become complacent.
1:06:32 Maybe not intentionally.
1:06:36 I mean, they’re not thinking that way, but they take their foot off the gas and, you
1:06:39 know, all of a sudden I’m focused on 10 different things instead of one thing.
1:06:45 And then there’s another subset of people that are like, they just blow right by that and
1:06:46 they keep going.
1:06:50 And whether it’s a hundred million or a billion or a 10 billion or, you know, in Elon’s case,
1:06:53 a hundred billion or more, but they keep going.
1:06:54 Yeah.
1:06:55 It’s back to what’s your utility, like, what do you care about?
1:06:56 What’s your utility function?
1:06:57 What’s driving you?
1:07:02 And based on what’s driving you, like the people that I know who have been very successful
1:07:03 or driven solely by money end up miserable.
1:07:07 Because they have money and then, and then what?
1:07:08 It’s never enough.
1:07:09 What do you do then?
1:07:10 Well, it’s not just never enough.
1:07:12 It’s just, what do you do?
1:07:13 What fulfills you?
1:07:15 You can already buy everything you could ever buy.
1:07:17 Like what fulfills you?
1:07:22 And you also see versions of this where you see people who make it and then they don’t know
1:07:23 what to do with themselves.
1:07:24 I think I mentioned this earlier.
1:07:28 There’s one guy I know who’s incredibly successful and he spends all his time buying domain names.
1:07:34 You’re like, well, is that fulfilling or, you know, it’s almost like what’s your meaning or purpose?
1:07:41 I feel like the people who end up doing these other things have some broader meaning or purpose driver even very early on.
1:07:43 And obviously people want to win and all the rest.
1:07:47 There’s this really good framework from Naval Ravikant.
1:07:53 And so in the 90s, John Doerr, who’s one of the giants, the legends of investing, used to ask founders,
1:07:54 are you a missionary or mercenary?
1:07:59 And of course, the question that you were expected to say is, I’m a missionary, right?
1:08:02 I’m doing it because it’s the right work to do and all this.
1:08:09 And Naval’s framework is like, when you’re young, of course, you’re at least half, if not more, mercenary.
1:08:10 Yeah.
1:08:11 You want to make it.
1:08:11 You’re hungry.
1:08:12 You don’t have any money.
1:08:13 You need to survive.
1:08:16 You know, you’re driven because of that in part.
1:08:23 And then in the middle phase of your career or life, you’re more of a missionary if you’re not a zero-sum person, right?
1:08:24 You suddenly can have a broader purpose.
1:08:25 You can do other things.
1:08:26 You can engage.
1:08:28 And then he’s like, late in your life, you’re an artist.
1:08:30 You do it for the love of the craft, right?
1:08:42 I much prefer that framework of the people that I see who do the most interesting big things over time fall into that latter category where always there is some mercenary piece.
1:08:45 Of course, you want to have money to survive and all this stuff.
1:08:49 And then that morphs into you become more mission-centric.
1:08:52 And then over time, you just do it for the love of whatever the thing you’re doing is.
1:08:54 And those are the people that I see that become happy over time.
1:08:58 What’s the difference between success and relevance?
1:09:03 Yeah, it’s a great question because there’s lots of different ways to define success.
1:09:06 Success could mean I have a million Instagram followers.
1:09:09 It depends on your own version of success, right?
1:09:13 So, societally, one of the big versions of success is a big financial outcome.
1:09:16 One could argue a bigger version of that is like a happy family.
1:09:18 You know, like there’s lots of versions of success.
1:09:26 Relevance means that you’re somehow impacting things that are important to the world and people seek you out because of that.
1:09:28 Or alternatively, you’re just impacting things, right?
1:09:32 But usually, people end up seeking you out because of that for a specific thing.
1:09:37 And the amazing thing is that there’s lots and lots of people who’ve been successful who are no longer relevant.
1:09:44 You just look at the list of even the billionaires or whatever metric you want to use and like how many of those people are actually sought out.
1:09:45 Yeah.
1:09:47 Because they’re doing something interesting or important.
1:09:51 And so, there’s this interesting question that I’ve been toying with, which is,
1:09:55 are there characteristics to people who stay relevant over very long arcs of time?
1:09:59 People are constantly doing interesting things, right?
1:10:05 One could argue Sam Altman has sort of maintained that over a very long arc between YC and the early things he was involved with the investing side.
1:10:08 And then, of course, now OpenAI and other areas.
1:10:12 Patrick is obviously doing that between Stripe and Arc and other areas.
1:10:16 And there’s people with longer arcs than that, right?
1:10:21 Marc Andreessen invented the browser and then there was one of the key people behind that.
1:10:26 And then started multiple companies, including Netscape, which was a giant of the internet.
1:10:28 And then started, you know, one of the most important venture firms in the world.
1:10:33 And so, that’s a great example of a very, very strong arc over time.
1:10:36 Or Elon Musk is a very strong arc over time, right?
1:10:38 From Zip2 to PayPal to all the stuff he’s done now.
1:10:41 So, the question is, what do those people have in common?
1:10:42 Peter Thiel, right?
1:10:48 Think of all the stuff he’s done across politics and the Thiel Fellows and the funds and Palantir and Facebook and all this stuff.
1:10:57 The commonality that stands out to me across all those people is they tend to be pretty polymathic.
1:10:58 So, they have a wide range of interests.
1:11:04 They tend to be driven by a mix of stuff, not just money.
1:11:07 So, of course, money is important and all the rest.
1:11:10 But I think for a subset of people, it’s interestingness.
1:11:11 For a subset, it’s impact.
1:11:12 For a subset, it’s power.
1:11:14 For whatever it is, but there’s usually a blend.
1:11:17 And for each person, there’s a different spike across that.
1:11:21 And the other, I think, commonality is almost all of them had some form of success early.
1:11:31 Because the thing that people continue to underappreciate is kind of like the old Charlie Mungerism that the thing he continues to underappreciate is the power of incentives, right?
1:11:34 The thing I continue to underappreciate is the power of compounding.
1:11:40 And you see that in investing and financial markets, but you also see that in people’s careers and impact.
1:11:46 And the people who are successful early have a platform upon which they can build over time in a massive way.
1:11:50 They have the financial wherewithal to take risks or fund new things.
1:11:53 And importantly, they’re in the flow of information.
1:11:58 You start to meet all the most interesting people thinking the most interesting things.
1:12:03 And you can synthesize all that in this sort of pool of ideas and thoughts and people.
1:12:08 This is full circle back to almost where we started, right?
1:12:17 Like how important is that flow of information to finding the next opportunity, to capitalizing on other people’s mistakes, to staying relevant?
1:12:19 Yeah, there’s two types of information.
1:12:24 There’s information that’s hidden.
1:12:27 And there’s information that…
1:12:30 So I’ll give you an example, right?
1:12:36 When I started investing in generative AI, all these early foundation model things, et cetera, basically nobody was doing it.
1:12:39 And it was all out in the open, right?
1:12:41 GPT-3 had just dropped.
1:12:43 It was clearly a big step function from two.
1:12:46 If you just extrapolated that, you knew really, really interesting things were going to happen.
1:12:48 And people were using it internally in different ways at these companies.
1:12:54 And so it was in plain sight that GPT-3 existed out there, but very few people recognized that it was that important.
1:12:56 And so the question is why, right?
1:12:57 The information was out there.
1:13:05 There’s other types of information that early access to helps impact how you think about the world.
1:13:07 And sometimes that could just be a one-on-one conversation.
1:13:10 Or sometimes, again, they could be doing things out in the open.
1:13:16 And so, for example, all the different things that Peter Thiel talked about and it cites on like 10 years ago ended up being true.
1:13:19 Not all, but a lot of them, right?
1:13:21 So wait, let me go through some of these.
1:13:29 So there’s, I found, I found information that is publicly available that you haven’t found.
1:13:33 There’s, I weigh the information differently than you do.
1:13:33 Yeah.
1:13:35 So I weigh the importance of it differently.
1:13:40 And then there’s access where I have access to information that you don’t have.
1:13:42 Are there other types of information advantage?
1:13:47 No, because I think the one where you interpret it differently that you mentioned has all sorts of aspects to that.
1:13:48 Go deeper on that.
1:13:50 Well, do you have the tooling to do it?
1:13:52 Do you need a data scientist, right?
1:13:53 It’s all the algorithmic trading stuff.
1:13:57 All the information’s out there, but can you actually make use of it?
1:14:00 There’s, do you have the right filter on it?
1:14:04 Do you pick up or glean certain insights or make intuitive leaps that other people don’t?
1:14:08 You know, there’s all the different, it’s sort of like when people talk about Richard Feynman, the physicist.
1:14:14 And they said, with other physicists who won Nobel Prizes, they’re like, oh yeah, I could understand how that person got there.
1:14:16 It’s this chain of logical steps and maybe I could have done that.
1:14:19 They’re like with Feynman, he just did these leaps and nobody knew how he did it.
1:14:26 And so I do think there’s people who uniquely synthesize information in the world and come to specific conclusions.
1:14:34 And those conclusions are often right, but people don’t know how they got there.
1:14:40 You’re bringing it back to clusters and all the stuff about information and how to think about it and how to interpret it.
1:14:41 It’s all about being in a cluster.
1:14:44 How do you go about constructing a better cluster?
1:14:55 Like if you take the presumption that the material that goes into my head, whether I’m reading, you know, that’s one way I’m conversing, I’m searching.
1:15:03 How do I improve the information quality through a cluster or not that my raw material is built on later?
1:15:05 Yeah, I think it’s a few things.
1:15:09 And I think different people approach your processes in different ways.
1:15:15 And this is back to the best people somehow tend to aggregate or maybe best is the wrong word.
1:15:24 There’s a bunch of people with common characteristics, a subset of whom become very successful, that somehow repeatedly keep meeting each other quite young in the same geography.
1:15:26 And again, it’s happened throughout history.
1:15:33 And so, A, there’s clearly some attraction between these people to talking to each other and hanging out with each other and learning from each other.
1:15:37 And sometimes you meet somebody and you’re like, wow, I just learned a ton off of this person in like 30 minutes.
1:15:43 And this was a great conversation versus, okay, yeah, that was nice to meet that person.
1:15:44 They’re nice or whatever, you know.
1:15:56 And I feel like a lot of folks who end up doing really big interesting things just somehow meet or aggregate towards these other people and they all tell each other about each other and they hang out together and all the rest.
1:16:00 And so, I do think there’s sort of self-attraction of these groups of people.
1:16:04 Now, the internet has helped create online versions of that.
1:16:12 There’s been a lot of talk now about these IOI or gold medalist communities where people do like math or coding competitions or other things.
1:16:17 Scott, the CEO of Cognition, is a great example of that where he knows a lot of founders in Silicon Valley.
1:16:19 And one of the reasons they all know each other is through these competitions.
1:16:25 And there’s a way to aggregate people growing up all over the country or all over the world who never would have connected.
1:16:26 And then they connect through these competitions.
1:16:29 And so, that’s become a funnel for a subset of people.
1:16:37 So, the move towards the internet, I think, has actually created a very different environment where you can find more like-minded people than you ever could before, right?
1:16:39 Because before, how would you find people?
1:16:42 And how would you even know to go to Silicon Valley?
1:16:46 Do you think it’s true that if I change your information flow, I can change your trajectory?
1:16:53 And if so, what are the first steps that people listening can take to get better information?
1:17:00 If you want to work in a specific area and be top of your game in that area, you should move to the cluster for whatever that is.
1:17:00 Yeah.
1:17:02 So, if you want to go into movies, you should go to Hollywood.
1:17:05 If you want to go into tech, you should go to Silicon Valley, if you want to, you know, etc.
1:17:11 And the whole, hey, you can succeed at anything from anywhere is kind of true, but it’s very rare.
1:17:13 And why make it harder for yourself?
1:17:14 Yeah.
1:17:15 Why play on hard mode?
1:17:15 Yeah.
1:17:19 How do you think about that in terms of companies and remote work?
1:17:31 Like, we were talking about this a little bit before we hit record in the sense of, you know, one of the things that people lose is the culture of the company and feeling part of something larger than themselves.
1:17:36 How does that impact the quality of work we do or the information flow we have?
1:17:42 There’s no more water cooler conversation where, like, hey, you know, in that presentation, you should have done this, not that.
1:17:42 Yeah.
1:17:43 No, that’s a great point.
1:17:44 I think it’s interesting.
1:17:53 If a company is really young and still very innovative, I think a lot of remote work tends to be quite bad in terms of the success of the company.
1:17:54 Now, that doesn’t mean it won’t succeed.
1:17:55 It just makes it much harder.
1:18:00 And a company I backed, I don’t know how long ago now, 14 years or something like that, was GitLab.
1:18:02 Which has done quite well.
1:18:03 It’s a public company now, et cetera.
1:18:08 And they were one of the very first remote first companies.
1:18:10 And so when I backed them, it was like four people or something.
1:18:11 I can’t remember, four or five people.
1:18:13 They were fully remote.
1:18:14 They stayed remote forever.
1:18:17 And they built a ton of processes in to actually make that work.
1:18:18 And they were brilliant about it.
1:18:24 And they actually have all this published on their website where you can go and you can read hundreds of pages about everything they’ve done to enable remote work.
1:18:30 Everything from, like, how they thought about salary bans based on location on through to processes and all the rest.
1:18:37 And it was a very quirky, it may still be, culture where I’d be talking to the CEO and he’d say, oh, this conversation is really interesting.
1:18:43 And he dropped the link to our Zoom into a giant group chat and random people just start popping in while we’re talking.
1:18:44 Oh, wow.
1:18:45 You know, and you’re like, who are these people?
1:18:48 Like, we’re just talking about should you do a riff and like 30 people just joined.
1:18:49 Like, is this a good idea?
1:19:01 It was a very, and it probably still is, very innovative, very smart culture, very process driven, you know, very just excellent at saying, okay, if we’re going to be remote, let’s put in place every single control to make that work.
1:19:03 So they’re very smart about that.
1:19:06 I have not seen many other companies do anything close to that.
1:19:13 And so I think for very early companies, the best companies I know are almost 100% in person.
1:19:15 And there’s some counter examples of that.
1:19:16 And crypto has some nuances on that.
1:19:18 And, you know, which is a little bit different.
1:19:22 But for a standard AI, tech, SaaS, et cetera, that’s generally the rule.
1:19:29 As a company gets later, you’re definitely going to have remote parts of your workforce, right?
1:19:30 Parts of your sales team are remote.
1:19:32 Although really, they should be at the customer site, right?
1:19:35 Remote should mean customer site or home office or something, right?
1:19:37 It shouldn’t mean truly remote.
1:19:42 But, and you always, even 10 years ago or whatever, would make exceptions, right?
1:19:48 You’d say, well, this person is really exceptional and I know them well and they’re moving to Colorado and we’ll keep this person because we know that they’re, you know, as productive.
1:19:51 They’re more productive than anybody else on the team, even if they’re not going to be in the office every day.
1:19:57 Later stage companies, there’s this really big question of like, how much of your team do you want to be remote?
1:19:58 How many days a week?
1:20:07 And then is enforcing a lack of remote policy just also enforcing that you’re prioritizing people who care about the company more than they care about other things.
1:20:07 Right.
1:20:12 And each CEO needs to come and make a judgment call about how important that is.
1:20:15 How much does that impact how they can participate in global talent?
1:20:17 Because that’s often the question or concern.
1:20:19 So there’s like a set of trade-offs.
1:20:26 I mean, the argument for it, I guess, is like it’s more flexible for employees if that is part of what you’re optimizing for.
1:20:31 But we can also hire world-class talent that we might not be able to hire otherwise.
1:20:31 Yeah.
1:20:34 And I don’t know if I 100% buy that, but it’s possible.
1:20:40 I’ve been in the sauna at the gym with a number of people on like Microsoft Teams calls.
1:20:42 Yeah, you can see people who are clearly not working.
1:20:50 Now, the flip side of that is, you know, there are certain organizations that you knew people weren’t working very hard at before things went remote, right?
1:20:58 Like some of the big tech companies before COVID, you’d go in and it’d be pretty empty until like 11 and then people would roll in for lunch and then they’d leave at like 2.
1:21:08 And so one argument I make sometimes is that big tech is effectively a big experiment in UBI, universal basic income, for people who went to good schools, right?
1:21:12 You’re literally just giving money to people for not doing very much in some cases.
1:21:19 Do you think that that’s starting to change and the complacency maybe that caused that is starting to go away as we get into this?
1:21:23 Like it seems like we had this, everybody was super successful.
1:21:26 They all had their own area, but now we have a new race.
1:21:27 Like we have to get fit again.
1:21:32 You know, it’s kind of like the person who goes to the gym and never breaks a sweat.
1:21:35 If you’re talking about fitness, you know, they lift away and they’re like, I’m going to get on my phone now.
1:21:37 That’s what I feel like has basically happened.
1:21:47 And so I think the reality is if you look at what Musk did at Twitter, where they cut 80% or whatever it was, I wouldn’t be surprised if you could do things that are pretty close to that at a lot of the big tech companies.
1:21:49 That’s fascinating.
1:21:57 One of the things that we talked about was sort of how the best in any field, there’s sort of like 20 people who are just exceptional.
1:21:58 Go deeper on that for me.
1:21:59 Yeah.
1:22:00 So we were talking about clusters, right?
1:22:01 So there’s geographic clusters.
1:22:03 Like, hey, all of tech is happening in one area.
1:22:07 And honestly, all of AI is happening in like, you know, a few blocks, right?
1:22:09 If you were to aggregate it all up.
1:22:14 So there’s these very strong cluster effects at the regional level.
1:22:20 And then as we mentioned, there’s groups of people who keep running into each other who are kind of the motive force for everything.
1:22:29 And if you look at almost every field, there’s at most a few dozen, maybe for very big fields, a few hundred people who are roughly driving almost everything, right?
1:22:36 You look at cancer research, and there’s probably 20 or 30 labs that are the most important labs where all the breakthroughs come out of it.
1:22:40 Not just that, the lineage of those labs, the people they came from was in common.
1:22:47 And the people who end up being very successful afterwards are all come from one of those, or mainly all come from those same labs.
1:22:49 You actually see this for startups, right?
1:22:54 My team went back and we looked at where do all the startup founders come out of school-wise.
1:22:58 And three schools dominate by far in terms of big outside outcomes.
1:23:01 Stanford is number one by far, and then MIT and Harvard.
1:23:07 And then there’s a big step down, and there’s a bunch of schools that have some successes, Berkeley and Duke and a few others.
1:23:10 And then there’s kind of everything else, right?
1:23:15 And so there are these very strong rules of like lineage of people as well, right?
1:23:19 And oddly enough, you see this in religious movements, right?
1:23:20 The lineage really matters.
1:23:23 Schools of yoga, the lineage really matters.
1:23:25 Like all these things, the lineage really matters.
1:23:30 And so what you find is that in any field, there’s a handful of people who drive that field.
1:23:32 And a handful, again, could be in the tens or maybe hundreds.
1:23:33 And that’s true in tech.
1:23:40 Like, you know, there was probably early on 20, 30, whatever, maybe 100 at most AI researchers who were driving much of the progress.
1:23:42 There’s a bunch of ancillary people, but there’s a core group.
1:23:45 That’s true in areas of biology.
1:23:46 That’s true in finance.
1:23:49 That’s, you know, and eventually most of these people end up meeting each other, right?
1:23:53 In different forms, and some become friends, and some become rivals, and some become both.
1:23:56 But it’s surprising how small these groups are.
1:24:03 And a friend of mine and I were joking that we must be in a simulation because we keep running into the same people
1:24:05 over the 10 or 20-year arc who keep doing the big things.
1:24:06 Yeah.
1:24:10 Does that mean those people are almost perpetually undervalued?
1:24:14 Especially if it’s not a CEO and they’re running their own show, if it’s a researcher.
1:24:22 If you take the hypothesis that maybe there’s only 20 people, 20 great investors, or, you know,
1:24:28 20 great researchers, or 20 great whatever, but they’re employees of somebody else,
1:24:30 then they’re perpetually undervalued?
1:24:35 Because it’s like, no matter how much I’m paying you, it’s almost not enough.
1:24:38 Because you’re going to drive this forward.
1:24:39 Yeah, it depends on how you define greatness.
1:24:40 Yeah.
1:24:42 If somebody is the world’s best kite flyer.
1:24:43 Yeah.
1:24:45 No, seriously, though, right?
1:24:48 Like, there’s going to be a handful of people who are the best at every single thing.
1:24:50 But there’s not a ton of economic value created by that.
1:24:52 Yeah, and so that’s the question, right?
1:25:00 And so, you know, part of the question is, what is the importance of each person relevant
1:25:02 to an organization or field?
1:25:05 And then are they properly recognized or rewarded relative to those contributions?
1:25:06 And if not, why not?
1:25:07 And if so, then great.
1:25:10 And so I think there’s a separate question, right?
1:25:12 Of rewards, effectively.
1:25:16 And rewards could be status, it could be money, it could be influence, it could be whatever it is.
1:25:19 What else have you guys learned about investing in startups?
1:25:25 So you had these clusters like, oh, you know, most people come from Stanford, MIT, or Harvard.
1:25:26 Yeah.
1:25:30 What are the other things that you’ve picked up that you were like, oh, that’s surprising
1:25:34 or counterintuitive or challenges an existing belief that I had?
1:25:37 Oh, I mean, I’ll give you one that challenges and then I’ll give you one that I think is consistent.
1:25:40 Maybe I’ll start with a consistent one, which is back to clusters.
1:25:44 We take all of market cap of companies worth a billion dollars or more that are private.
1:25:48 And every quarter or two, we basically look at geographically where are they based, right?
1:25:52 And traditionally, the US has been about half of that globally.
1:25:54 The Bay Area has been about half of that.
1:25:59 So 25% of all private technology wealth creation happens in one place, right?
1:26:00 In one city.
1:26:03 If you add in New York and LA, then you’re at like 40% of the world.
1:26:04 Wow.
1:26:05 Right?
1:26:06 And LA is mainly SpaceX and Android.
1:26:07 Yeah.
1:26:10 So it’s very concentrated, right?
1:26:14 That’s why when I see venture capitalists build these global firms with branches everywhere,
1:26:15 you’re like, why?
1:26:19 You know, like from a research allocation perspective, unless you’re just trying to, you know, have
1:26:20 a specific footprint for reasons.
1:26:27 And if you look at AI, it’s like 80 to 90% of the market cap is all in the Bay Area.
1:26:29 Right?
1:26:30 And so it’s a super cluster.
1:26:33 And you see that going the other way.
1:26:37 Like for fintech, a lot of the value of fintech was split between New York and the Bay Area.
1:26:37 Yeah.
1:26:41 So one aspect of it is these things are actually more extreme than you’d think for certain areas.
1:26:50 And space and defense is roughly all, or was Southern California until SpaceX moved some of its operations.
1:26:53 The counterintuitive thing is more tactical things.
1:26:58 So, you know, there’s a few things that people say a lot in Silicon Valley that just aren’t correct.
1:27:05 So if you look, for example, there’s this thing that you should always have a co-founder or an equal co-founder.
1:27:11 And if you look at the biggest successes in the startup world over time, they were either solo founders or very unequal founders.
1:27:15 So that, and there’s kind of examples to that, of course, but that was Amazon, right?
1:27:16 Jeff Bezos was the only founder.
1:27:19 Microsoft, it was unequal.
1:27:21 And eventually the other founder left.
1:27:26 You know, you kind of go through the list and there aren’t that many where there was true equality, you know.
1:27:30 But it’s now kind of this myth that you should be equal with your co-founder.
1:27:32 And I think there’s negative aspects to doing that.
1:27:37 A second thing is, that’s a little bit counterintuitive, is reference checks on founders.
1:27:42 So if you do a, if you get a positive reference check on someone, then it’s positive.
1:27:46 If you get a negative reference check on a founder, it’s usually neutral.
1:27:50 Unless people are saying they’re ethically bad or there’s some issue with them or whatever.
1:27:52 But there’s two reasons for that.
1:27:55 One is I think product market fit trumps the founder fidelity.
1:27:58 And so like, you could be kind of crappy, but if you hit the right thing, you can do really well.
1:28:01 But the other piece of it is it’s contextual.
1:28:11 Like somebody who’s kind of lazy and not great in one environment may actually be much better when they have their, when they’re responsible and they need to drive everything.
1:28:18 And, you know, as an example of that, there was somebody I worked with at Twitter who was a very nice person, but never really seemed that effective to me.
1:28:20 He was always kind of hanging out, drinking coffee, chatting.
1:28:24 And then a few years later, I met up with him and he was running a very successful startup.
1:28:25 And I said, what happened?
1:28:27 I mean, I said it nicer than that, right?
1:28:28 Yeah, of course.
1:28:29 Like, hey, like, it’s so interesting.
1:28:30 You built this great company.
1:28:31 Like, you know.
1:28:32 He said, you know what?
1:28:34 I finally feel like my ass is on the line.
1:28:36 And that’s why I’m working so hard.
1:28:37 And that’s why I’m so, you know.
1:28:43 Now, in general, I think that the true giant outside success archetype is somebody who can’t tolerate that.
1:28:44 Right.
1:28:44 Right.
1:28:46 They’re always on and they can’t help it.
1:28:52 But there are examples where the context of the organization and the context of your situation really shapes what you do.
1:28:59 When you invested in Andrel, what was your, you mentioned you had criteria and they checked it all.
1:28:59 Sure.
1:29:05 What was your mental, oh, if I’m going to invest in a tech forward defense company, it needs to have X, Y, Z.
1:29:06 What was that criteria?
1:29:18 Yeah, so Andrel happened in a unique moment in time where Google had just shut down Maven and defense had suddenly become very unpopular in Silicon Valley and people were making arguments that ethically you shouldn’t support the defense industry.
1:29:26 And all the stuff that I thought was pretty ridiculous, because if you cared about Western values and you wanted to defend them, of course you needed defense tech.
1:29:34 So I started looking around to see who’s building interesting things in defense because if the big companies won’t do it, then what a great opportunity for a startup, right?
1:29:36 It seemed like a good moment in time.
1:29:45 And it felt like there was four or five things that you needed in order to build a next-gen defense tech company because there was a bunch of defense tech companies that just never worked or hit small scale.
1:29:49 Number one is you needed a why now moment for the technology.
1:29:53 What is shifting in technology that the incumbents can’t just tack it on, right?
1:30:00 Because the way the defense industry works is there’s a handful of players called primes who sell directly to the DoD and they subcontract out everything else, right?
1:30:10 And if you’re not a prime and you don’t have a direct relationship, then you end up in a bad spot in terms of being able to really win big programs and survive as a company or succeed.
1:30:14 So number one is what is the technology why now that creates an opening?
1:30:17 For Anduril, it was initially machine vision and drones, which were new things.
1:30:22 Two is, are you going to build a broad enough product portfolio that you can become a prime?
1:30:23 Right.
1:30:25 Which they did from day one.
1:30:32 Third is, do you have connectivity slash ability to, you know, really focus on faster sales cycle?
1:30:40 Fourth is, can you raise enough money that you’ll last long enough that you can put up with really long timelines to actually get to these big programs of record?
1:30:43 And I think Anduril did their first program of record in something like three and a half years.
1:30:44 It was remarkably fast.
1:30:48 I think it was the fastest program of record since the Korean War or something, which is super impressive.
1:30:55 And then lastly, the way that the business model for the defense industry works is this cost plus.
1:30:56 Oh, yeah.
1:31:03 So you basically make, say, 5% to 12% on top of whatever your cost to work the product out is.
1:31:04 And that includes your labor.
1:31:06 That includes every component.
1:31:10 And that’s why there’s a very big incentive in the defense industry to overrun on time.
1:31:11 Yeah.
1:31:14 Because you’ve charged 10% on that time, right?
1:31:16 So if something’s late, you make more money.
1:31:18 And not have a cost incentive at all.
1:31:19 You have no cost incentive.
1:31:24 That’s why you have a $100 screw, because you make $5 on the screw that costs $100 instead of using a $0.10 screw, right?
1:31:25 Yeah.
1:31:32 And so the cost plus model is extremely bad if you want efficient, fast-moving defense industry, right?
1:31:36 And they were really focused on trying to create a more traditional hardware margin business,
1:31:44 where an example would be if Lockheed Martin sold a drone to the government for a million dollars and made 5% cost plus, they’d make $50K.
1:31:52 If Anderil sold a $100,000 drone with the same capabilities of the government and had a 50% hardware margin, they’d make $50,000 too.
1:31:54 But the government could buy 10 of them for the same price.
1:31:55 Yeah.
1:31:59 So the government gets 10 times the hardware or the capability set.
1:32:08 Anderil gets 10 times as much margin if, again, that structure works, and everybody basically wins, right?
1:32:11 And so I just thought that business model shift was really important.
1:32:16 Why now, though, in the sense of why wouldn’t the defense industry encourage more competition?
1:32:19 They know they’re paying cost plus.
1:32:21 They know the screw shouldn’t be $100.
1:32:26 Like, why didn’t they encourage this way before Anderil?
1:32:32 Yeah, I think at the time, cost plus was viewed as the most fair version of it because you’re like, oh, just give me your bill of materials and I know exactly what it costs.
1:32:33 And then you’ll just get a fixed margin.
1:32:35 And so that’s more fair.
1:32:41 And I know from my budgeting perspective, really, like how much budget I need to ask for, how much.
1:32:41 Yeah.
1:32:46 And I think in hindsight, maybe it worked in that moment in time, but it no longer seems applicable.
1:32:50 And then the other thing that’s happened in the defense industry is there’s been massive consolidation over the last 30 years.
1:32:56 And so a lot of the growth of these companies came through M&A, and so you had fewer and fewer players competing for the same business.
1:33:00 And so that also means that it’s back to the oligopoly market structure that we talked about earlier.
1:33:03 How do you see defense changing in the future?
1:33:07 Like, is it less about ships and more about cyber and drones?
1:33:16 And how do we see the future of defense spending in a world where what used to dominate is these like billion dollar ships?
1:33:28 And now we’re in a world of asymmetry where, you know, for a couple million bucks, I might be able to hire the best cyber attack team in the world, or I might be able to buy a thousand drones.
1:33:30 Or how do you think about that?
1:33:33 Like, how do you think about defense in the next five, 10 years?
1:33:44 Yeah, I mean, in general, defense is inevitably going to move to these highly distributed drone-based systems as a major component of any branch of the military.
1:33:54 And it’s not just because it’s faster, cheaper, et cetera, et cetera, but also there’s certain things that you can’t do with a human operator inside the cockpit.
1:34:05 So, for example, you have a plane, the G-forces that a human piloted plane can tolerate is much lower than if you’re just a drone and you don’t have to worry about people inside the…
1:34:09 Plus, we must be at a point where AI can outperform a human fighter pilot, I would imagine.
1:34:11 I haven’t kept up on defense.
1:34:20 Yeah, there’s a few different contracts, both in Europe and the U.S., that are moving ahead around autonomous flight and autonomous drones and all the rest of it, autonomous capabilities in general in the air.
1:34:31 You know, I think the thing that people have stuck to so far is if there’s any sort of decision that is involved with, like, killing somebody or hurting something, then you need a human operator to actually trigger it.
1:34:37 And so that way you’re not turning over control to a fully autonomous system, which I think is smart, right?
1:34:42 You don’t want the thing to do the targeting and go after the target and make all these mistakes, right?
1:34:44 You want a human to make that decision.
1:34:48 But we exist in a world where not everybody is going to follow those roles.
1:34:50 That’s true.
1:34:56 And then the question is, what’s the relative firepower of that group of people and how do you deal with them and, you know, what do you do to retaliate and everything else?
1:35:01 I mean, in general, one could argue warfare has gotten dramatically less bloody.
1:35:04 Oh, wait, go deeper on that.
1:35:17 Well, if you think about the type of warfare that happened 150 years ago, or imagine if some equivalent to the Hooties was constantly shooting at your ships 100 years ago, what do you think the response would have been?
1:35:19 Do you think you would have said, ah, don’t worry about it?
1:35:29 Obviously, we’ve become much more civilized in our approach and very thoughtful about the implications of certain ways that people used to fight battles and all the rest of it.
1:35:32 But the way that we deal with problems today is very different from how we used to deal with them.
1:35:38 Is there an equivalent to Andrel, but in the software space, from a defense perspective?
1:35:42 And I mean that as like cyber weapons or cyber defense.
1:35:43 Who’s the best?
1:35:45 Yeah, I’ve been looking around for that for a while.
1:35:49 I don’t think I’ve seen anything directly yet, but it may exist and I may just have missed it.
1:35:52 But I do think things like that are coming.
1:35:58 And you do see some AI security companies emerging, which are basically using AI to deal with phishing threats or other things.
1:36:03 You could argue material security is doing that, but there’s people working across pen testing and other areas right now as well.
1:36:05 This has been a fascinating conversation.
1:36:09 We always end with the same question, which is what is success for you?
1:36:12 Yeah, you know, I’ve been noodling on that a lot recently.
1:36:25 And I think if I look at the frameworks that exist and certain Eastern philosophies or religions, it’s almost like there are these expanding circles that change with time as you go through your life, right?
1:36:35 Early on, you’re focused more on yourself and your schooling and then you kind of add work and then you add your family and community and then you add society.
1:36:40 And then eventually you become a sadhu and you go off and you meditate in a cave in the forest or whatever.
1:36:44 And different people weigh those different circles differentially.
1:36:49 And, you know, a big transition I’m making right now probably is I’ve been focused a lot on work and family.
1:36:55 And the thing I’m increasingly thinking about are like, what are positive things I can do that are more society level?
1:36:56 Thank you.
1:36:57 This was awesome conversation.
1:36:58 Oh, no, thanks so much for having me on.
1:36:59 It was really great.
1:37:04 Thanks for listening and learning with us.
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