Uncapped #19 | Dwarkesh Patel

First of all, we just didn't realize how much we didn't know about human evolution. Just like the story you learned in high school, all of it is like at least somewhat false about how when we're, who, what do you mean? Like, did it happen in Africa? Did it? A big chunk of it didn't. We have stuff right up to, you know, a certain amount of history though, right? Okay. Yeah. At least there's something we can hold on to. To our cash, I've been really looking forward to this. Thanks for making time for it. Thanks for having me on. So I want to start by talking about your thinking around the state of AI. You obviously are very close to it. You're a user of it. You have gone really deep with a lot of people who know it on many levels. And you recently wrote this really interesting blog post called why I don't think AGI is right around the corner. And I want to ask you a little bit about that interest this general topic. A lot of my guests so far, probably myself included, have been like a little breathlessly like, you know, this is, this is here. If, you know, we just sort of deployed all the AI research that we have, you know, or capabilities today, we would have, you know, insane GDP growth. I think you have a slightly different take than some of my other guests. So I wanted to start by asking you about how you see the current state of AI. I've been in similar position as you where I've also interviewed a lot of people who are breathlessly anticipating what's coming with AI. Sometimes in a very optimistic way of the AI researchers, in other cases, they're worried that like the world's going to end in two years. And I think what's changed my mind around how soon we're going to get to these super transformative outcomes is just trying to use these AI's to help me with very simple like script kitty kind of task for my own podcast. And so I have a lot of friends who think, look, if the reason the Fortune 500 isn't using AI all across their stack right now is because the management is too stodgy. It's like not being creative enough about how to get O3 into their workflows. And look, I'm like, I'm thinking a lot about how to use AI in my podcast post reflections that I've tried for a hundred hours to get it to be useful for me. And it hasn't been that useful. And I think it that's because it's just genuinely hard to get human life labor out of these models fundamentally because these models can't learn on the job in a way human can. And so what I think is really important is that when you're a user, you're not going to be able to get the right things and you're going to be able to do that. And I think what I think is really important is that you're going to be able to get the right things and you're going to be able to do that. And the models currently you just get like whatever they can do in a session, you talk to them for 30 minutes and then they totally lose awareness or understanding of how your business works, what your preferences are, et cetera. And a lot of tasks just require you to like, you do a five out of 10 job at something, then you like talk to your boss, you're like, go out to the consumer and then like you learn what didn't go wrong, you like ask yourself what didn't go well. And you just like keep iterating on that. And they just can't do this on the job kind of training, which I think is what makes humans valuable. Yeah, like there's a certain set, particularly within like language tasks, and maybe we can get over to coding, which is sort of like a whole different piece. But within language tasks, it seems like there is a limit still to like how good it can be. And so if, for example, even if you're trying to either pull out the most interesting segment from a podcast or caption it and make clips that are postable, you know, those clips need to be perfect. And like a human still, you know, you're going to trust a person more to do that, picking what's actually interesting might be kind of hard. But even there. So offline, we were having a conversation about how to how to write a tweet for something for your podcast, right? And then we were discussing, oh well, once you're out, you might be like, write it for a group chat. Maybe you can add that to like you think this is exactly what other ones are for, right? You're right, the tweet is like language in language out. Here's a system prompt. But like, why is it I assume you are getting damaged use out of just like having the AI write your tweets for you? And why is that not because right by the way, I'm focusing on this not because writing tweets is like the most important thing in the economy. This is just like, this is just like, this is the first thing they should be able to do, right? Why are we not delegating this to them yet? If you post something you like notice it doesn't do well, you have this. You can like think about what went wrong. You also have like this experience from what are your users want or what are your followers want that these guys can't pick up is actually a reasonably high. So like think about you posting something on Twitter or substack or whatever. Like that bar for you is actually going to be pretty high. There's a lower bar thing where, you know, language miles are really useful like customer support tickets, for example. You know, it works really well there because you don't need the language to be perfect. You don't need insane nuance and it's actually okay for a certain class of thing if it's right 97% of the time. And if it knows how to say, hey, I'm wrong here. So I do think that what we're describing here still does drive a lot of GDP growth in certain areas at least because you don't need the high bar everywhere. Yeah, I mean, I'd be curious to see what the actual numbers are on customer service employment. I think from what I understand they're not like down that much. Yeah, so it's interesting even in those areas, you're not seeing these transformers impacts. Yeah, I mean the venture view against that would be that it's like the beginning of this massively exponential curve and these things are like, you know, in the first half of the first inning, but all the curves are like this. And so three years from now, it's going to it'll show up. I agree with that maybe not just in three years, but I agree with that over the course of a decade, but that's just because I think this is such a big bottleneck to getting these models to be valuable that it has to be like people will want to solve it like right now, open AI or and the graphics revenues on the order of 10 billion ARR and that's a lot obviously but like McDonald's and calls make more money and those companies aren't AGI right so like if you have real AGI like trillions of dollars a year, that's what like humans around the world or an age of wages. Yeah, given that that is sort of the addressable market. Yeah, and given that this is one of the key bottlenecks to getting there. I just think a ton of effort would put into this problem. We've had a lot of progress in the past. So I'm not I'm not like one of these people was like AGI is not coming. Yeah, of course. I'm just saying like this this definitely needs to be solved before we get there. I guess maybe then to the broader thing about like being you know AGI PILD in general. Like you obviously spent a lot of time talking to certain churches. You're very close to that. Do you still feel as confident as ever in that even if your timelines are different than you know maybe what you've gotten from some of your guests. There is one thing we're really just going to observe, which is that like they have cracked a reason it just so happens that reasoning ended up being much easier than something we take for granted. Which is just that day in day out you're going to be picking up information in your workplace. But you know like you go back to Aristotle and his big take was like look what the thing that makes human special. Yeah, it from other animals is that we can reason the other animals can't and it's sort of funny that like these models just aren't that useful yet. They can't do almost anything except for the one thing they can do is reason given the fact that these sort of like ambiguous capabilities do come online. That makes me think that like continue learning might also be something that like in a 10 years. It's also like you sort of remember deep learning is like not that old. Yeah, 15 14 years old or at least thanks right Alex net is like that old when we started training these models. Yeah, like it's very possible to me that in a decade or two we find a solution is problem. It's very interesting to hear you continue using language that's like you know these models aren't that useful yet. Which part of me very much agrees with and part of me is you know it's like putting language to an experience that I have when I you know use them myself. The other side of it would be you know they are you know an amazing you know replacement for search in a lot of cases they do things like code generation in like a very effective way it seems like you know doctors are able to use them to like you know handle a lot of like like they're are these things that are I would say clearly working yes do you see it that way to where you like some things are highly useful. Yeah, no 100% I'm putting it more in the context of the real potential for AGI just like a genuine replacement for human labor. And so if it's on the scale of the internet I'm like oh wow this is so much shorter of what AGI could be so where this is clearly not that useful yet and sort of more tangible reason to expect this kind of change. But one is just that the amount of labor supply just dramatically increases so I think people often especially in tech focus on how it will make a specific industry more productive this narrow productivity improvements where you just like no imagine like a trillion people in the world for each specializing and each discovering your knowledge or we just get all these games compared advantage as a result but another is that because these minds are digital they have advantages even if they're safe same amount of intelligence specifically advantages in collaboration that humans just can't have because of the way our minds work. And one example of this is suppose this problem is solved we can like actually learn on the job now like a human can on the job learn from learn from their work and then so we're the course of 20 years they become master their craft incredibly valuable you want such person right you like picked up all this context and tech industry from running companies from investing in companies if we get models that have this human like capability not only could they learn on a single job copies of the model or deployed all through the economy they can amalgamate the learning from basically doing every single job in the economy at the same time and at that point even if you don't have further or are going to the innovations you would still have something that's functionally becoming a super intelligence you've got this like broadly deployed intelligence explosion yes they just one of the many ways in which the fact that they are digital just give them like emerging intelligence like answer something exactly yeah you're kind of describing a world where like the way that the impact happens is through a trillion new you know white collar workers there's another version of it where it never actually does exactly that but does this other thing which is just like a higher level of intelligence. intelligence than anything we've ever encountered. And it creates new paths and identifies new ways to do things that humans could still then do. Sometimes the way I think about this is the 400 IQ AI. Even if it can't do all the things that a person can do, it could help or fully identify new drugs, help us get to space more effectively, all sorts of innovations like that. - I think a good way to think about this is maybe China. So, okay, why has China been so successful in not only catching up in science technology, but in fact, in many ways surpassing America in a lot of key domains. And obviously China's full of lots of brilliant people. And so that does lead to the arguments that look, they have to have, I think the intelligence is a big part of it, but I think we're fundamentally just like, once you've hit a benchmark of intelligence, the scale is what makes China so successful, right? You have, just within manufacturing, there's a hundred million people who have built up, who are working in manufacturing in China, who have built up all this process knowledge in whatever subdomain is relevant to whatever's being built. So, that scale, I think is like, if China's graduating, I think like tens of millions of STEM graduates every single year, it's not that any one of them is super brilliant. It's just that each of them can specialize, right? In whatever radar technology that BYD needs or whatever production technologies needed. - I'm thinking about this out loud, but it opens up the question of would more impact happen from a trillion more super connected, super collaborative human level intelligence people, or from one just like demigods, level intelligence, who could like figure stuff out and tell us all what to do? - Do we have evidence in Silicon Valley history that it's more of the latter? It seems to me that, well, it depends, you know, there's the great man theory thing where it's like there are these special people who, you know, that's how the big bleats happen, whether it's, you know, like a Steve Jobs or Elon or whatever, where like there is some just outlier person who directs the resources that one person can pull greatness out. - Yes, I haven't seen them up close as you have, but it seems to me, I agree that they've had a huge impact people like Elon and Steve Jobs. It seems to me their impact has been more so a fact product of like just like you will do this otherwise I will throw a tantrum, which is good, right? Like you should throw a tantrum, and I'm gonna sleep in the office for years on end and just like get people in the right place at the right time, but it's less so like, only Elon can come up with how the fins on the space extra rocket should be designed. And therefore this, like his like over intelligence allows him to like design like across by different hardware verticals. - It's probably, it's definitely something that's more to do with the leading of people and the clarity of vision, I would say, than the probably like engineering genius for something like this. - Yes, yeah. But I mean, this is actually another way to illustrate why digital minds are such an upgrade. - Elon has obviously been super successful across so many different errors of technology. But of course he's just like one person. To the extent that you think there's something unique about him or about the small teams he had assembled like early SpaceX, early Tesla. If they were digital, you could just replicate them. Like early SpaceX team, replicate them 1,000 times, throw them out a thousand different hardware verticals and see what happens. Because you can't scale that with humans. - The other thing I'm thinking about now is we're talking is could a digital intelligence be like a leader of, you know, 10,000 people, you know? And I do think that coordinating large groups seems to be incredibly important to getting big societal things done. So now I'm wondering, could you have a digital, you know, a digital mind at the top or does it need to be a human? - I think it would have a much easier time with a digital. This is another one of the key advantage of a digital. Because right now Elon has the same 10 to the 15 flops in his head that every single other human has. Now this could be negative as well, right? Like, Xi Jinping also only has 10 to the 15 flops in his head that every other human has. The ratio of compute or deliberation happening at the top versus through the hierarchy is just so lopsided and that requires a lot of delegation. Now that's good in a sort of like free society sense. You don't want the president to have that much control over your life. But if you within these specific domains where we want like companies have this outer loop where they fail, they can just go down unlike a country. So there it might make sense to have more of a, have the company be the product of a single coherent vision. This is the founder mode idea, right? But obviously that's limited by the fact that if a company goes to a certain size, it just hard for a single person to monitor everything. If it wasn't AI, and especially if you could like, inference scale, mega Elon, the like, the thing that's like running on a huge data center that's dedicated to just him, mega Elon can read every pull request, every, every comms input output in the to the company. He can like, micromanage every single employee down to like the technician at the dealership. Especially I don't know, Elon have 10 years ago. It just will be like quite incredible. I guess what I'm wondering is that the AI COO under the human who is giving them superpowers or is that person, is that AGI rather actually leading? - I think in the near term, it will be like the latter. But over the long term, obviously, like if AI could be the AI CEOs basically in long term. - Yeah, yeah. And so it does seem like the thing they're lacking now is like this sort of more taste oriented stuff, right? They can do the research for you. Oh, three can go do the research for you. But then you're not gonna let it make the investment decision for you, right? That just comes down to your own unique insight into the field or your unique taste. And maybe like running company will work like this for a while where they can just curate a lot of information for you and then you still have to make the final call. Like it's just like the Steve Jobs model. Like if you make the final call, the designers come up with a bunch of different ideas. Eventually that will also go, but maybe the near term, that's what it looks like. - So maybe back on the AGI, all the time that you spent with researchers and companies and you really inspecting this and you're very truth seeking. You still do believe that AGI is gonna just like wake up. Like you think it's all gonna happen still or has anything as you've learned over the last year or so changed your mind and you're like, well, it's a really cool idea, but it's not for sure it's gonna happen to you. - Yeah, I think the biggest consideration there is the progress for the last 10 years in AI, I mean more than 10 years actually, but even going back to the 60s, the progress in AI period has been driven by increases in compute and especially in the deep learning era, it's been like stupendous increases and compute dedicated to frontier systems. If you just look at public announcements of how the compute runs are, it looks like the trend since, I think, 2012, 2016 has been 4x per year. So there's over 4 years, there's 160 x, the biggest system train from the one before in terms of the compute used. That physically cannot continue past this decade from how much energy is used to like what fraction of advance ships at TMCNC you need to procure, even raw fraction of GDP. So then you just need progress from other, the progress would just have to come from algorithmic progress or I mean it would just have to be that. And because this key input into AI progress would stop after the next five years, there is this dynamic that the yearly probability of AGI is like quite high now, not in the sense that it will happen, but there's like a decent chance every year until 2030. And then it just has sort of craters, 'cause then you're just like, okay, we'll just have to think hard about what's missing. We can't just throw more compute at the problem. - Yep, yep, that makes sense. Do you feel right now like AI is making us smarter or is it like increasing brain rot and do you think like over time that that goes in a particular direction? - Did you see the meter uplift study from the other day? Oh, super interesting. Meter is this organization that does e-vows on basically how AI is progressing. They had a very interesting result the other day. So they had open source developers who are working in repositories that have tens of thousands of stars. They did a randomize control trial with these people where they would issue them a random pull request that was open in these repositories and they'd work on it in one case just by themselves in another case with the help of like cursoring plot 7 or plot 3.7. And then they measured in the case where they're working with the AI, one, how much do you think you were sped up and then two, how much were they actually sped up? And the developers thought that they were 20% more productive as a result of AI. They were actually 19% less productive as a result of AI. It was really interesting to read the threads of the developers participated, trying to explain. So people who have looked at this who are even more bullish on AI are like, they think their experimental design was extremely robust. - Even like senior engineers misread themselves. - Especially as engineers. - Wow. - And the senior engineers, from what I remember, the biggest decreases in productivity. - Huh. - So these are people who experience in this watcher as they've been working on it for decades. And there's a couple explanations. One is just that, I think on the lot of domains, there is this common failure mode in intellectual work where you default to procrastinating by doing a thing which seems productive but is not moving the ball forward that much. So the classic example of this is in college, instead of like rereading the textbook, you just do the practice problems. And maybe using these AI tools and then like, going on social media for 30 minutes while you're waiting for the completion to complete, is another example of this? Why did this happen? So at least we're now to go out to your original question. It's like not obvious to me that it's making us smarter. - It's interesting the version for everybody is, I think a large number of people are having Chad's UBT sort of like give them guidance in life at this point. You know, I don't mean that in some like, huge philosophical way. Although maybe in some cases, but even just like day to day people like, here's my whole setup. This is everything about me like, what should I do? And that is a big influence. And so it's sort of, we kind of, to the extent that these models have quietly gripped a lot of people either through their decisions, through their engineering work, through whatever else. It's like pretty important that these get really good. - But have you found it useful with that kind of stuff? Just like, I mean, I mean, it is sort of like, okay, plan out a cute date for me or something. I use it some. I think I use it less than a lot of my friends. I think more I'm commenting that I think broadly a lot of people do use it for that. Even if you were I happen to be in the group that uses it a little bit less, I think a lot do. Right. Yeah. And even probably taking personal advice and things like that. Yeah. Which is probably good in a lot of cases. Like, I think it is very smart and people know how to use it and things like that. Right. But it just speaks to, even with engineering and then also on, you know, people use it using it and sort of like search and question asking for personal use that's got a lot of influence. It feels like it's made me smarter, especially when I interview people in domains where there's not a lot of just written down, like biology. This is a classic example. I was going to bring interview George Church is his famous pioneer in synthetic biology. And there most of my prep time was just dominated, talking to these models and then just telling them, teach this to me as if you're a secratic tutor. Don't move on in the explanation until you're satisfied that I have completely understood. I guess I'd be curious. Yeah. People in other domains have. Do you feel actually on biology in particular? Have you spent time with Patrick at ARC by chance? A little bit. Yeah. I feel like the bullishness that a lot of people in biology have for AI's ability to do like drug discovery and things like that seems very promising to me. Yeah. I don't know what you found as you'd like spent time in biology. One interesting question I had for these people is in biology, we can either employ models which think in thought space. Just like humans, they can come up with hypotheses and so forth. Or models which think in protein space or DNA space or capsid space. Humans are starting to, we can't like, I think G sounds really good here. Then let's do TNX. And so I'm curious if which one they think is a more promising or more useful complement to the current for agress and biology? Is it having better models I can think in the alpha-fold type stuff? Or is it just like have O3 come up with hypotheses and just like write them out in English? At least George Cerston to think it was the bio space like thinking in proteins or DNA or so forth. Because this is, while we have millions of life science PhDs who can like come up with ideas, being able to prune through them in simulation. He exactly like a digital cell kind of thing. Yeah. Yeah. Yeah. It is like the more useful complement there. Yeah. I mean that seems like that would be like a very unequivocally positive output for humans if we can sort of, you know, just wildly change biology and, you know, pharmacology and things like that. Yeah. I think the longer and I sort of worry about the fact that we like, we know ways in which things can just go horribly wrong. Like we have the equivalent of nuclear weapons but in different domains. So mirror life and biology. Apparently if you have life with the opposite chirality, there's just no defense. Like plausibly it could render many life forms on viable on earth. And so George Church was one of these people who wrote this letter saying life, look, this thing exists. Let's not work on it. But like I don't know over the course of 100 years. What's the equilibrium here? In physics, I didn't read this physicist. One of the things he's worked on is thinking about this thing called vacuum decay. And the TLDR is basically like, it might be plausible to just like literally destroy the universe. Well, what's the idea there? Look, I'm a podcaster. You're asking if that's fine. You're asking if quantum physics is on cell to my best. Apparently the quantum field theory, in a sort of what's called a meta-stable state where if it's like sort of having a huge valley and then a little bit of a hill and then we're in this like little rump here, it's possible to throw such a huge amount of energy. And what would happen is that this like bubble would expand at the speed of light, which would just be like total destruction. It sounds like some wild sci-fi thing. But it actually takes me to the next thing I wanted to ask you about, which is you have this wide range of guests and interests. So maybe first, outside of AI, what domains are you most interested in right now? Because I think I've seen you talk about politics in Russia and math and science and longevity. What are you interested in most right now? I'm interested in what your 2050 looks like. Obviously, your turn is an AI in order to understand what happens in 2050. But throughout history, there's never been a case where there's just been a single technology which explains why, say, the industrial revolution happened, right? It wasn't just that we made better textile machines. You have improvements in sector after sector, which are enabled by key innovations in specific sectors. But like, for example, I, yeah, it's important to learn about what's happening in bio and robotics, et cetera. I want to get into those fields. Also, I've just been interested in the fact that we are finally getting to a pace of change that we actually have seen before in history, but not for a long time. I've most recently interviewed this biographer of Stalin, Stephen Cawken. And I think Stalin is born in the 1870s. And you just think about his life from the 1870s onwards. No way, planes, airplanes, steamships, radio, telegraph, light bulbs, combustion. I mean, World War I is a crazy example where you start off the war. I think there were the Wright brothers at flown, but there weren't like many, there were like on the order of hundreds of planes in the world to, and there were no tanks. Like tanks was not a thing. And World War I ends with like, it's a tank war. It's a plane war. It's not that many years. Yes. You go from like almost no trucks to tens of thousands of trucks. Wow. It was a course of four years. And planes? Yes. So I think even planes were at that extremely low level before the war and the military used during obviously. Yeah. So I mean, the reason Germany thought it was going to win is because it had this like rail one network and there was this plan of how you could do this two front war and knock out both France and Russia at the same time, just by like you'd be amazing at railway or logistics. And I think the one mocha or whoever the leader of the German command was said at the end of the war, like we lost because of trucks, right? Like we didn't anticipate that like there was another way to ship enemy combatants to the front. Yeah. We're just going to see like this level of change across so many different sectors. There was somebody posted on Twitter, VACO posted something that I thought was an interesting point about you, whether or not it's true, but I'm curious how you react to it, which was it seems like you went through this evolution where you were learning a ton about AI and then it seems like you believed that AGI was coming and then your interest started expanding out into all these other things like geopolitics, biology, all these other areas. I'm guessing the, you know, which was sort of like saying, you know, the technology is what you know, creates, you know, moment for change, but then this backdrop of the world is what really influences it. Yeah. Like the chronology of your interests were a little bit different than the framing, but I'm curious just like how you think about that. Given what I do, I'm actually quite pessimistic about like being able to learn from other fields. I just know people who will like read some philosopher in the 19th century and they think like, oh, this like explains how Silicon Valley works or like this explains AI and I think you just like have to read the papers about AI. I just think it's very hard to generalize. You're saying to understand the technology, you have to, you have to read the papers. Yeah. And if people have this idea that like, I'll come up with my grand theory of history. Yeah. You're like, it's not philosophy, it's science. Yes. But just like in any domain, it's just very hard to have this like, I think there's, I know a couple of people who can do this and I find it really impressive. But what I noticed about them, they're just like not hand-wavy at all. So there are people who like, for example, if you're trying to model how AI will impact economic growth, one way is just to like read the sort of like first hand accounts of people going through it in the 1500s and like, oh, like let's read the biography of the Medici and so forth. And there's other people who are like, okay, let's look at the growth rates going back 10,000 years. What is like the long run secular trend? You know, like what are, what it actually explains what changed? Well, there's the endogenous growth theory where the key change is that population growth and put the more people come up with more ideas. Okay, well, AI is more people, they'll come up with more ideas, they'll be more specialization. So you just like, there's this very different mode of learning from other fields. Yeah. So, empirical and, I mean, empirical was maybe the wrong word, but just like very falsifiable and grounded versus I'm just going to read, I'm going to look at the library and just like read a bunch of random books. Totally. So how do your interests connect to each other then? Like are you following any particular threads or you know, there's one connect to another in a certain way? Like what's, what would you say is driving what creates various interests over time? Honestly, it's just a super bespoke, whatever I happen to be interested in that week. I'm just reading an interesting book. How are you spending your time? Like are you reading a lot? Like is it, do you learn mostly through readings? You're talking to people? Are those, are there other methods? Reading. Reading. I think there's a couple of people you learn a lot from talking to. In general, I've just sort of been disappointed about like, I mean, given the fact that I'm actually talking like some of the smartest people though. It's also really interesting, especially, so in some domains, people can be super like generated outside of domains. I've sort of been disappointed about the fact that look, you might hope that you could talk to some historian about World War One or about the history of oil or something. And then they'd have insights about like how this applies to AI. But really, those connections will likely come from you and not from them. When I was in during Daniel Jorgon who was the author of the prize, it's this book about, you know, like the 200 year history of oil. One thing that I was really interesting is that Drake discovers the first oil oil in Pennsylvania, I think in the 1850s. And then the Model T car, I think it's like 19 or five or something that like you finally have cars within total convenience and engines that people are using to transport all around the world. And this is sort of industrial case use of oil. Before that, most of oil was just wasted. It was only the kerosene component. So all of Rockefeller, all of that history that you sort of think about as a gilded age, like oil, bear, and stuff, that's happening when a small fraction of oil is being used just for lighting before the electrical light bulb was invented. And in fact, when the light bulb was invented, I remember getting my day strong on the Marble Sea and the light bulb, but it's like around that area, when the light bulb was invented, people are like, oh, oil is, like standard oil is gonna go above us, because what's the other use case of oil? I do find it interesting that it took more than 50 years to go from, we have discovered limitless energy in the earth to, here's a way to use billions of gallons of this stuff. And I think it has maybe interesting implications for AI, where, look, we have like, basically, it's sort of shocking how cheap AI is. I think, this is why I was finding confusing that people are like, optimizing on cost. Do I want like two cents for million tokens or point two cents for million tokens? So we have this like commodity that we could potentially use at an industrial scale. And we don't know how to, like, part of it's just technological, we don't know how to like, get those tokens from you more valuable. And part of it is just like, yeah, what do we do? Where's the internal combustion engine equivalent for AI? So you said, most of your learning comes from what you read rather than, you know, through your conversations with people. Yeah, I'm very lucky that there's maybe six to 12 people who have known for five years, almost all of them have out on the podcast, but who I'm in just extremely regular touch with. I've genuinely learned a lot of what I know from like this handful, this like this group chat. We went, and one sense of me was like, I know that it's just weird that I've like known for five years. And now they're also like super successful, but like we were all call us students at some point. Yeah, there's a lot to be said for that, you know, is that five closest friends. Yeah, it's a big deal. Yeah, I'll wait, how do you feel about this? Do you do learn more from talking to people? I think I learn more from talking to people. I also think that like, there's different sorts of things that you can be seeking, you know, like seeking the truth versus seeking a good decision are very related, but not exactly the same thing. There's a, for example, if you're trying to learn from people about how do you spot great talent and what can you pick up from somebody, you're not gonna get to the truth. You're just gonna get to techniques and things that have been useful for other people that you try to apply to yourself. So for that kind of thing, I wouldn't know how to read about it anyway. But on object level stuff, so if you want to learn about what's happening in robotics, is it, I just, I don't know, I've been sort of underwhelmed by how, - Yeah, I mean, the schools of thought to me would be either you can try to go learn it for real yourself. - Right. - Or if that hill is too high to climb, which, you know, for me, getting into robotics in like the white paper's way, I'm like, I would be, you know, kidding myself to think I could catch up and then get to the edge of anything and on any time scale that mattered. And so for me, there's the other move of, is there a way if you're gonna do it to shortcut the decisions somehow to somebody. And so who can you most trust to give you good information? - Yeah. - I think we're also in a lucky position where we have enough public output that we can sort of reach out to people and they'll say yes. This is a tougher position to be in if you're like 19 and I wanna like, I wanna learn about biology. I feel very lucky because a lot of people do great work in many different domains. Mine just happens to be public facing by default. So I get like more ability to reach out to people than I would do great work in private. - Exactly. Which does create this flywheel where if I do make good content, smart people will be willing to talk to me. That helps me make better content. I think that's actually more relevant to why the podcast grows than just like audience tells people. - So I mean, that's the flywheel you're saying? - Yeah. But for sort of moving on to a new topic, I wanted to ask about that's related to this. You mentioned a bunch of really interesting ideas that are around, you know, oil and history and Stalin and biology. And I'm curious if there's any other just ideas recently that have just really stuck out to you that you can't stop thinking about that have really gripped you. Just 'cause I love hearing about these. - The one that's been on my mind for a long time is I interviewed this geneticist of ancient DNA, David Reich. And what his lab and his research has revealed is that human history, first of all, we just didn't realize how much we didn't know about human evolution. Like the story you learned in high school, all of it is like at least somewhat false about how when we're who, - What do you mean? - Like did it happen in Africa? - Did it? - A big chunk of it didn't. Like there was this, there was a group that went out 400,000 years ago and then they mixed in back with the group that left our Africa 70,000 years ago. A lot of the evolution that led to this branch of humanity maybe just didn't even happen in Africa. When did it happen? - Wow. - We were like learning more stuff about it. And then the key thing we're learning is how did it happen? And it seems like we just see this pattern again and again in history, which is very disturbing. But like super recurring is that some small group will figure something out. And it's not clear from the genetic record what it is, right? Like 70,000 years ago, there's this group of one to 10,000 people in the Near East. So like where the Middle East, North Africa are right now. They figure something out and they wipe out every single other species of humans across all of Eurasia. - What? - Like there was half a dozen different species of humans or the Hobbits, obviously the Neanderthals. - The Hobbits? - I forget where they're like real biological names. But like I think they're called the Hobbits. - They can't be, yeah, yeah. That's good. - The Denysovans, they're all wiped out by this one group. Like it starts out with like one to 10,000 people. They expand all through 10,000 years ago, Anatolian farmers, also from the modern day Middle East, they expand out, kill off like 90% of the hunter gatherers in Europe, through Asia. This also happens again by the way, with a group that goes to the landbrush to America. They also keep doing this, they're like multiple waves and like one of the waves killed off the remaining ones. - Wow. - The only people who survived by the way, interestingly are that we have genetic evidence of is this group in the Amazon where because the Amazon is so densely, so dense to get through, like the genocide wasn't completed and so it was like more of an intermixing. Then 5,000 years ago, the Yamnaya, which is this group of like step nomads, they sweep through all of Eurasia again. Like and we're talking about like 90% death rates of the domestic population. So and not just like multiple continents, like all of Europe, the people who build the Stonehenge are killed off by these people. - This is insane. And they're pretty sure this is right. - Yes. - By the way, the way you learn why it's a genocide or why it was like violent is you look at the fraction of maternal versus paternal DNA that comes from the native population versus the invading population. And what they'll find is that the maternal DNA comes exclusively from the native population. All the paternal DNA comes from the invading population. - Wow. - Which means that the guys will kill off. - Wow. - And so India today is a mix of the original Indus Valley civilization from 3,500 years ago plus the Yamnaya. And so all of India is just like this like gradient basically, like North is more of this Yamnaya ancestry, South is this Indo Indus Valley civilization ancestry. - So basically just had like a lot of this wrong. - Yes, but you know what else is really interesting here? For hundreds of years anthropologists, archaeologists have been doing this Indiana Jones thing. We're gonna read the squirrels and we're gonna like think hard about how, you know, what they're trying to say here and read the literature or whatever. And it was just like so useless in comparison to one mathematician who went into this field and he was like, okay, let's just like look at the haplotypes and see how they compare. And just like totally redefined our understanding of basically all of history, going back millions of years, even to things that happened like 500 years ago, all of that is like totally we can re-understand it. All these mysteries about like, why did the Mycenaean civilization fall and who exactly were the Mycenaeans and Greece? Just like all around the world. - We have stuff right up to you know, a certain amount of history though, right? - Yes, okay. - Yeah, at least there's something we can hold on to. - But I just think it's a very interesting actually. It's like that, no, it was just like all this sort of like esoteric reading and understanding. - Right, like what was happening in the year 500? Like that we could have that super wrong for example. - Yes. - Oh, I mean, something really interesting we learned speaking of the year 500 is the Roman Empire, I think it's like, was it 540 or so? Is that there was basically the black death kills off like half, close to half of big fractions of the Roman Empire. And this is around when the Roman Empire falls. There were also previous flags like the Antonin flag in the second century, third century. - I interviewed somebody about this. I didn't realize the extent to which Rome actually has something as bad as a black death and how that contributed to their collapse. Because another bias we have in history is just to think about, oh, we had the four good emperors and then they did a really good job and then the next guy fucked it up. No, just like, there was a climate optimum during the quote unquote four good emperors where the bread basket was really. - Yeah, civilization's happened to fall after a certain amount of time it's like no, a thing happened. - Yeah, exactly. - I often wonder because over the last few hundred years, like we keep like learning a new thing, you know, something was completely wrong whether it was like, you know, the earth's round or gravity works this way or whatever else it is. And I'm like, we must still have big fundamental things wrong or human history and that kind of thing would be like a good example. My five year old recently, which was a funny question, was like, do you believe in Jupiter? And I was like, I do, but that's a great question. And I, there's, you know, you could ask me about something else in the universe and I like, I think we have it super wrong. but we must still have like big basics wrong. - Yeah, I feel like this is what I especially when I talk to physicists. We're just like very basic questions of like, is the universe infinite? - Yeah, we're not. - We have no idea. - And there's like, there's many different kinds of infinities it could be. But just, that very, like it just seems like so consequential. - Or like the fact that like time warps when you go to a different speed. I'm like, we just like can't possibly have it all exactly right? - Yeah. - It's just too crazy right? Like it just seem, or like dark matter. Like there's just like enough big stuff that I'm like, I bet we don't have it quite right. But that's like, that might sound like some, you know, PhD physicists listening and being like, what is the city at Jack talking about right now? Not that we have the answer, but I mean, many of them would agree that like, - Yeah, a lot of stuff has to be. - Yeah, okay, this kind of flows into the next thing I wanted to ask you about, which is your broad sort of perceptions of the way learning is happening. And you know, you've obviously been sort of like learning in public. You do a lot of self-directed sort of education and things like that. You know, but you're also, you've got a, you know, one foot sort of very tied to people in academia and at the top of research fields and things like that. And I'm curious sort of, you know, your opinion on sort of this transition that's happening where like a lot of learning and the way people think that the stuff should get consumed is self-directed and not part of the big institutions and things. The standards people have for like, is this thing true? Do I really believe it? Have just really degraded, especially in sort of podcast land to criticize my own tribe. People were just like, people were just fucking satiate. Whatever you say about academia, there is this idea like, okay, does this make sense? Like have you actually made like a clear argument? Like do you even have a clear like end statement or is it just like the thing that like people are saying? On the other hand, look, I mean, is it like net good for the world? If you read history and you read about the worst things that ever happened, the cultural revolution in China, the great terror in the Soviet Union, you can complain that Twitter has low average IQ that like the conversation is very dumb down. But you just don't need that many IQ points to realize the cultural revolution is bad. You just need some mechanism where you could have gone on and been like, why is Mao having us kill all the sparrows? Like isn't that actually really bad for, you know, like pest control and just like making fun of this defecation? And I think that actually has worked, right? Like woke was a thing for a second. And then I think social media contributed to that being less. And I think also making fun of Trump is like a thing that people do and has worked. And so on that, I just think like, getting rid of the worst accesses is much more important for making history go well than making sure that we can have these gigabrain, genius level takes all the time. And I think social media does have like a reasonably good job of helping us correct the worst accesses. On the sort of truth point, I think part of the issue is that like the legacy kind of media corporations, in my view have like lost quite a lot of trust from people, like myself included in many cases where they seem, you know, like they've got agendas and it seems like they are of like, you know, for profit and maximizing eyeballs. And so I'm like, you know, citizen journalism on Twitter, I'm sorry, I trust that completely. But I also don't trust, you know, like the institutions completely. So I don't know that one's like that much better than the other. - I disagree with this. - My attempt to do this thing has, actually giving me more respect for the media. In this, in a couple of ways, one, I think they genuinely are better at holding power to account than sort of independent creators. Like talking to somebody, an extremely powerful politician or business leader, and then asking tough questions, is a thing that the media will do? And often, it won't happen if they could just get to go on the podcast of their choice. And it's like, it's harder than it looks. - Yep. - And they're willing to uphold these standards when they do interviews. Now I mean, I agree that it can often be st. ammonia as when they do this. - Is that to do with the person or the institution? Like as an example, like, you know, Tucker Carlson, goes Fox News to Indy, same guy, theoretically. Like, does that change his truthiness? - I mean, I think this is another example of, like, his show and many others. This is not coming from a place where I'm like, making an optical political point. I'm a libertarian. I'm like, sort of close enough in embedding space to these people. But the standards of discourse in these new places are just abysmal. They'll just, if you just like, if you had a conversation, you could just like save one of a thousand things. And you're just like, pause on one of these. Like, what exactly do you mean here? Why do you think this? And I'm just like, oh, I heard a thing in a group chat or whatever. Whatever you say about the New York Times, they just like genuinely have fact checkers who will go through content. I know that like, there's many cases where they failed by their own standards, especially in texturinalism. And I like don't like their bias in these kinds of things. But just like, the standards are like an order of magnitude difference. - I mean, my view is we actually probably, at first with AI, it might have looked like that was going to be like a big problem for the New York Times and maybe in some ways it is. But I would actually, as Times gone on, I think we probably need these institutions more than ever. In a certain way, which is that, you know, like AI also now brings, you know, a whole new layer of fun to what's true with deep fakes and random content generation by bots everywhere. And so you kind of, at some point, do go back to needing somebody to like really hold the standards of like truth as much as possible. I would think that should make these institutions more necessary. - Yeah. And I think of like, we always had to compare AI against the counterfactual. I mean, this goes back to the, are they making us smarter or dumber? - Yes, do they hallucinate? But they are probably, I don't know, when I talk to an AI. - This, I feel like this is, do you remember like 10 years ago people were like, oh, we can't trust Wikipedia because anybody can edit it? - Yeah. - I feel like there was always a siaf. Like I think it was like, the reasonably trustworthy always. - Well, there's one version. - And we have this added to towards AI where like, oh, hallucinate. - Well, there's one version of AI where you're asking it directly. There's another version where somebody who has propaganda intentions uses it to their advantage. - Right. - Yeah. - We don't have a lot of time left and I wanted to get to the last topic. So up until you, all of my guests have been, you know, VCs are founders and, you know, I've been sort of using this as an excuse to kind of publicly learn from them. One of the things I wanted to sort of learn from you is about the way you've done podcasting 'cause you've done it as well as anybody I've seen. You were like, the one person I reached out to when I was getting started for advice, you gave me really good advice, which was to just, you know, basically all centered back to just like the authentic, follow your interests. Like, don't post stuff you're embarrassed to be like, all that stuff. And that was basically like the one North Star that I had. But I'm curious because you've had so much success with it. If you can kind of reflect it all about what's made it work or, you know, like, why has it played out the way that it has so far? - It's really hard to say from the inside. I feel extremely lucky that my job is, I get to sit down this morning and decide, what I want to learn about over the next few weeks. I'll interview the person who's the best in the world at that. I get to pepper them with questions for a few hours and then I get to repeat that week after week. I try to ask the questions that I gently want the answers to, including the questions I want to answer to, after having done two weeks of prepping that field and hopefully having had learned about it over the previous years. And so much content is very much like, give us the intro chapter of your book again, explain this very basic thing in your field. I think people just really appreciate the feeling of being a fly on the wall. Like, one of the reasons it's valuable to be St. St. Francisco is that you go to good dinners or events where you will miss a ton of context. Like, people will have, know a bunch of things and you won't know what they're talking about, but it sort of raises the bar and immersion learning works. I try to provide that kind of environment and whatever field I'm trying to learn about. And I think people appreciate like not being talked down to. Having a sense that like the host is actually interested in the questions they're asking. These are like, if they were having a private dinner, I think the dynamic replicate is if you were like at a private dinner party, well, you wouldn't like just be deferential. At a dinner party, you'd be like, you'd hassle them if you disagree with them about something. But there'd be a fun vibe and you wouldn't like, can you explain this concept for everybody else here? You wouldn't have that dynamic. - Somebody that I spoke to recently, who worked closely with Steve Jobs, who I'm actually gonna have on the podcast at some point soon, said something that I really liked, which was that one of the things that made Steve Jobs special was that the fundamentals of just operating day to day, the way you talk to people, the way you give feedback, the way you ask questions, it was just really good at those fundamentals. Before we started, I asked you about like, what have you learned about conversations? Because I see that as like a big sort of fundamental thing that everybody does that you're very practiced at. But you made a point, which is that actually what it is for you is preparation. That's the centerpiece and that's your fundamental. I still think that's highly applicable to everybody 'cause we're all going to interviews as either somebody on the candidate side or the employer, we're all meeting people for all sorts of things. But everybody's preparing for stuff. So what is your preparation like? Like what does that mean for you when you're saying I'm preparing really hard for this? And it's like the center of your YouTube. - In some sense, it's the very obvious stuff. It's if you're interviewing a researcher and if you'll read the key papers a couple of years ago for back when I was just like starting getting into AI and I was about to interview Ilya, I'm like, okay, I'm gonna like program the transformer. This is like how I'll learn about this. And then try to talk to me with researchers like could. If I'm interviewing a scholar and feel that interview them, this person who actually wrote a rebuttal to the power worker, which is this book about how Moses changed in New York City. That itself, I think is a 1500 word book or some like 1500 page book. I read that and I read like his rebuttal of that book and I read like review articles or whatever, like different things about like New York construction history. I just do this for, I try to do this for all my guests. So, but in some sense it's like very obvious. Just like read the things that could potentially be relevant. And then write down questions obviously. The thing I've changed over the last year is I've started using space repetition. Please repetition is this like tool where you basically write flashcards or yourself and this software serves them to you every couple of months. Like you have to be preparing for something well ahead of time. No, this is for consolidating knowledge across interviews. So if I do an interview, I'm actually going to like retain what I've learned. Because a lot of the concepts connect, I mean, especially with AI, with AI you're just like trying to predict what a future civilization of different kinds of beings will look like. And there's no domain of knowledge, which is not relevant to this question, right? So obviously technically, I stuff as relevant, but history, anthropology, even primatology, like, you know, like what happened between primates and humans, everything is relevant, it will come up in the interview. And so just having a cast through tools like this is extremely valuable. That's cool. So it's like you're like retaining a curriculum of all your work. It's the kind of thing where if you're reading a book, I think like, yeah, either you should have just like shouldn't read. At least if you think you're doing it for like learning, or you should have a very intensive practice around, I'm going to make the cards, I'm going to write through it. Like whatever thing is the equivalent to doing practice problems for the domain you're trying to study, because it's talking to me how often I will make a flashcard for a topic where I'm like, okay, this is like so basic. I don't write this down, but I just have to do something right now. And in a week later, I'm like, I was on the verge of forgetting it. And you just like think about how many books have you read in your life? Hundreds, right? How much have you like taken away from them or conversations, whatever other medium? The lack of efficiency here is really striking. And so I've been thinking a lot about how to make this a process where over the coming years, I can be like, I'm really getting better over time. I'm not just like doing the next thing. It's sort of the spiritual opposite of the idea of an AI that's always listening and remembering for you so that you don't have to remember any conversation you ever had. And it's sort of just like something on your person that is constantly listening and you can always go back to it, but everything's captured for you. You're like, it needs to get in my brain so that I can learn the next thing. This is exactly full circle from where we started, because people will say in response to the continual learning on the job training stuff that, oh, we'll just have an external memory system that it will be just like a document of like things that model has to do. And they'll, you know, Chad G.P.T. already has this. And I think a lot of cognition is just memory. Like it has to be on board. It has to be cash the whole time. There's a great place to end. Thank you so much for doing this. I hope you didn't mind being a guest and keep doing your amazing work. I love to watch you. It was super fun. Thanks for having me on.