Leopold Aschenbrenner - China/US Super Intelligence Race, 2027 AGI, & The Return of History

21:30You know, you almost didn't want to talk about AI or AGI, you know, it was kind of dirty word, right? And then 2023, you know, people saw chat, you'd be cheaper the first time in the side, you'd be four and it's like exploded, right? And it triggered this kind of like, you know, and, you know, huge sort of capital expenditures from all these firms and, and, and, you know, the explosion revenue from Nvidia and so on. And, you know, things have been quiet since then, but, you know, the next thing has been in the event. And I sort of expect sort of every generation, these kind of like, GeForce is to intensify, right?

21:56It's like, people see the models. There's like, you know, people haven't counted through them, so they're going to be surprised. And they'll be kind of crazy. And then, you know, revenue is going to accelerate, you know, suppose you do hit the 10 billion, you know, I know this year, suppose it, like, just continues on the sort of doubling trajectory of, you know, like, every six months of revenue doubling, you know, it's like, you're not actually that far from 100 billion, you know, maybe that's like 26. And so, you know, at some point, you know, like, you know, sort of what happened to Nvidia is going to happen to BigTak, you know, like, their stocks, they're, you know, that's going to explode.

22:25And I mean, I think a lot more people are going to feel it, right? I mean, I think the, I think 2023 was the sort of moment for me where it went from kind of AGI's sort of theoretical abstract thing and you'd make the models to like, I see it. I feel it. And like, I see the path. I see where it's going. I like, I think I can see the cluster which trained on like the rough combination of algorithms, the people, like, how it's happening. And I think, you know, most of the world is not, you know, most of the people feel it are like right here, you know, right? But, but, you know, I think a lot more of the world is going to start feeling it.

22:56And I think that's going to start being kind of intense. Okay. So right now, who feels it, you can, you go on Twitter and there's these GPT wrapper companies like, whoa, GBD4, I mean, so, so various on the wrapper companies, right? Because like, they're the ones that are going to be like the wrapper companies are betting on stagnation, right? The wrapper companies are betting like, you have these intermediate models and take sure to integrate them. And I'm kind of like, I'm really bearish because I'm like, we're just going to sonic boom you, you know, and we're going to get the unhauled when we start to get the drop in remote worker.

23:21And then, you know, your stuff is not going to matter. Okay. Sure. Sure. So that's done. Now, who, so the SF is being attention now or the this crowd here is paying attention. Who is going to be paying attention in 2026, 2027? And presumably, these are years in which hundreds of billions of cat bucks is being spent on the eye. I mean, I think the national security state is going to be starting to pay a lot of attention. And I, you know, I hope we get to talk about that. Okay. Let's talk about it now. What happened? Yeah. Like, well, what is the sort of political reaction immediately? Yeah. And even like internationally, like, what people see, like right now, I don't know if like Xi Jinping, like, reads the news and sees like, yeah, I don't know.

23:58I'm like, oh my god. Again, I'm in a new score on that. What are you doing about this comrade? Yeah. So what happens when the, like what, what, what, the, he's like, sees a remote replacement and it has $100 billion in revenue. There's a lot of businesses that have $100 billion in revenue. And people don't like aren't staying up all night talking about it. The question, I think the question is when, when does the CCP and when does the sort of American national security establishment realize that super intelligence is going to be absolutely decisive for national power? Right. And this is where the sort of intelligence explosion stuff comes in, which you know, we should also talk about later.

24:32You know, it's sort of like, you know, you have a GI, you have the sort of drop in room worker that can replace, you know, you are me at least that sort of remote jobs, you know, kind of jobs. And then, you know, I think fairly quickly, you know, I mean, I've got to fault, you know, you turn the crank, you know, one or two more times, you know, and then you get a thing that's smarter than humans. But I think even even more than just turning the cramp a few more time, crank a few more times, you know, I think one of the first jobs to be automated is going to be that of sort of an AI researcher engineer.

24:59And if you can automate AI research, you know, I think things can start going very fast. You know, right now there's already this trend of, you know, half an order of magnitude a year of algorithmic progress, you know, suppose, you know, at that point, you know, you're going to have GPU fleets and the tens of millions for inference, you know, or more. And you're going to be able to run like 100 million human equivalents of these sort of automated AI researchers. And if you can do that, you know, you can maybe do, you know, decades worth of sort of ML research progress in a year, you know, you get the some sort of 10X speed up.

25:29And if you can do that, I think you can make the jump to kind of like AI that is vastly smarter than humans, you know, within a year, a couple years. And then, you know, that broadens, right? So you have this, you have this sort of initial acceleration of AI research, that broadens to like you apply R &D to a bunch of other fields of technology. And the sort of like extremes, you know, at this point, you have like a billion, just super intelligent researchers, engineers, technicians, everything, you're just perfectly competent, all the things, you know, they're going to figure out robotics. So we talked about it being a software problem.

25:59Well, you know, you have, you have a billion of super smart, smarter than the smartest human researchers, AI researchers, and your cluster, you know, at some point during the intelligence explosion, they're going to be able to figure out robotics, you know, and then again, that expands. And, you know, I think if you play this picture forward, I think it is fairly unlike any other technology in that it will, I think, you know, a couple years of lead could be utterly decisive and say like military competition, right? You know, if you look at like GoFour1, right? GoFour1, you know, like the Western coalition forces, you know, they had, you know, like a hundred to one kill ratio, right?

26:34And that was like, they had better sensors on their tanks, you know, and they had, they had better precision, more precision missiles, right? Like GPS, and they had, you know, stealth, and they had sort of a few, you know, maybe 20, 30 years of technological lead, right? And they, you know, just completely crushed them. Super intelligence applied to sort of broad fields of R &D, and then, you know, the sort of industrial explosions while you have the robots, you're just making lots of material, you know, I think that could compress, I mean, basically you can press kind of like a century worth of technological progress, it's less than a decade.

27:03And that means that, you know, a couple years could mean a sort of GoFour1 style like, you know, advantage in military affairs. And, you know, including like, you know, a decisive advantage that even like preempts, right? Suppose like, you know, how do you find the stealth in nuclear submarines? Like right now, that's a problem of like, you have sensors, you have the software, like the tech where they are, you know, you can do that, you can find them, you have kind of like millions or billions of like mosquito -like, you know, sized drones, and you know, they take out the nuclear submarines, they take out the mobile launchers, they take out the other nukes.

27:34And anyway, so I think enormously destabilizing, enormously important for national power. And at some point, I think people are going to realize that not yet, but they will. And when they will, I think there will be sort of, you know, I don't think it will just be the sort of AI researchers in charge. And, you know, I think on the, you know, the CCP is going to, you know, have sort of an all -out effort to like infiltrate American AI labs, right? You know, like billions of dollars, thousands of people, you know, full force of the sort of, you know, Ministry of State Security. CCP is going to try to, you know, like outbuild us, right?

28:05Like, they, you know, they're, you know, power in China, you know, like the electric grid, you know, they added a US is, you know, a complete, like they added as much power in the last decade, as they sort of entire US electric goods. So like the undergave out cluster, at least the hundred gigawatts is going to be a lot easier for them to get. And so I think sort of, you know, by this point, I think it's going to be like an extremely intense sort of international competition. Mm -hmm. Okay, so in this picture, one thing I'm uncertain about is whether it's more like what you say, where it's more of an implosion of you have developed an AGI and then you make it into an AI researcher.

28:41And for a while, a year or something, you're only using this ability to make hundreds of millions of other AI researchers. And then like the thing that comes out of this, yeah, really frenetic process is a super intelligence. And then that goes out in the world. Then is developing robotics and helping you take over other countries and whatever. I think it's a little bit more, you know, it's a little bit more gradual, but it's sort of like it's an explosion that starts narrowly. It's can do cognitive jobs. You know, the highest RRI used for cognitive jobs is make the AI better, like solve robotics, you know, and as you solve robotics, now you can do R &D and you know, like biology and other technology, you know, initially you start with the factory workers, you know, they're wearing the glasses and the air pods, you know, and the AI is instructing them, right?

29:23Because you kind of make any worker into a skilled technician and then you have the robots come in. And anyway, so it sort of expands, as process expands. Metas, Ray Vans or a cobbleman to their lava. Well, you know, like whatever, like, you know, the fabs in the US, the constraints of skilled workers, right? You have, you have the, even if you don't have robots that you have the cognitive super intelligence and you know, it can kind of make them all into skilled workers immediately. But that's, you know, it's a very repurion. Robots will come soon. Sure. Okay. So suppose that this is actually how the tech progresses in the United States, maybe because these companies are already experiencing hundreds of billions of dollars of AI revenue.

29:54And at this point, you know, companies are borrowing, you know, hundreds of billions of more in the corporate debt markets, you know, but why is a CCP bureaucrat, some 60 year old guy, he looks at this and he's like, oh, it's like co -pilot has gotten better now. But why are they now, I mean, it's much more than co -pilot has gotten better now. I mean, I mean, I don't know. Yeah. So to shift the production of an entire country, to dislocate energy that is otherwise being used for consumer goods or something and to make it at all feed into the data centers. What? Part of this whole story is you realize the super intelligence is coming soon, right?

30:32And I guess you realize it. Maybe I realize it. I'm not sure how much I realize it. But will the national security apparatus in the United States and will the CCP realize it? Yeah. I mean, look, I think in some sense, this is a really key question. I think we have sort of a few more years of mid game, basically, and where you have a few more 2023s and that I think the trend lines will become clear. I think you will see some amount of the sort of COVID dynamic. COVID was like February of 2020. It honestly feels a lot like today, where it's like, it feels like this utterly crazy thing is about, is impending, is coming.

31:13You kind of see the exponential. And yet most of the world just doesn't realize. The mayor of New York is like, go out to the shows. And this is just Asian racism or whatever. But at some point, the exponential is some point people saw it. And then, like, just kind of easy, radical reactions came. Right. So by the way, what were you doing during COVID? We're writing February. Freshman, sophomore, what? Junior. But still, like, we're like 17 year old junior or something. And then you like, did you short the market or something? Yeah. Yeah. Did you sell it the right time? Yeah. So there will be like a March 2020 moment, the thing that was COVID, but here, now then you can like make the analogy that you make in a series that this will then cause the reaction of like, we got to do the men hand project for America here.

32:10I wonder where the politics of this will be like because the difference here is it's not just like we need the bomb to beat the Nazis. It's we're building this thing that's making all our entry prices rise a bunch and it's automating a bunch of our jobs and the climate change stuff like people are going to be like, oh my god, it's making climate change worse. And it's helping big tech. Like, politically, this doesn't seem like a dynamic where the national security apparatus or the the president is like, we have to step on the gas here and like make sure America wins. Yeah. I mean, again, I think a lot of this really depends on sort of how much people are feeling it, how much people are seeing it.

32:49You know, I think there's a thing where, you know, kind of basically our generation, right? We're kind of so used to kind of, you know, basically peace and like, you know, the world, you know, American, Germany, and nothing matters. But, you know, this sort of like extremely intense and these extraordinary things happening in the world and like intense international competition is like very much this historical norm. Like in some sense, it's like, you know, sort of this there's this sort of 20 year very unique period. But like, you know, the history of the world is like, you know, like in World War II, right?

33:21It was like 50 % of GDP went to, you know, like, you know, Warped Iron production, the gas barred over 60 % of GDP, you know, and in, you know, I think Germany, Japan over 100 % World War I, you know, UK, Japan, sorry, UK, France, Germany, all barred over 100 % of GDP.

33:38And, you know, I think this sort of much more was on the line, right? Like, you know, and, you know, people talk about World War I being sort of destructive and you know, like 20 million Soviet soldiers dying and like 20 % of Poland. But, you know, that was just the sort of like that happened all the time, right? You know, like seven years war, you know, like whatever 20, 30 % of Prussia died, you know, like 30 years war, you know, like, I think like, you know, up to 50 % of like large swath of Germany died.

34:06And, you know, I think the question is, will these sort of like, will people see that the stakes here are really, really high? And that basically, sort of like history is actually back. And I think, you know, I think the American National Security State thinks very seriously about stuff like this. They think very seriously about competition with China. I think China very much thinks of itself on this historical mission, that you're driven nation of the Thai and Asian nation. It's a lot about national power. I think a lot about like the world order. And then, you know, I think there's a real question on timing, right?

34:36Like, do they, do they start taking this seriously, right? Like, when the intelligence explosion is already happening, like, quite late, or do they start taking this seriously, like two years earlier? And that matters a lot for how things play out. But at some point, they will. And at some point, they will realize that this will be sort of utterly decisive for, you know, not just kind of like some proxy or somewhere, but, you know, like whether liberal democracy can continue to thrive, whether, you know, whether the CCP will continue existing. And I think that will activate sort of forces that we haven't seen in a long time.

35:07The great conflict, the great power conflict thing definitely seems compelling. I think just all kinds of different things seem much more likely when you think from a historical perspective, when you zoom out beyond the liberal democracy that we've been living in, I had the pleasure to live in America, let's say 80 years, including dictatorships, including all, obviously, war, famine, whatever. I was reading the Gulagarca pelago, and one of the chapters begins with Sojini Sen saying, if you would have told Russian citizens under the Zars that because of all these new technologies, we wouldn't see some great Russian revival or becomes a great power and the citizens are made wealthy.

35:44But instead, what you would see is tens of millions of Soviet citizens tortured by millions of beasts in the worst possible ways, and that this is what would be the result of the 20th century. They wouldn't have believed you. They'd have called you a slanderer. Yeah, and you know, the, you know, the possibilities for dictatorship with superintelligence are sort of even crazier, right? I think, you know, imagining of a perfectly loyal military and security force, right? That's it. No more rebellions, right? No more popular uprisings, you know, perfectly loyal, you know, you have, you know, perfect lie detection, you know, you have a surveillance of everybody, you know, you can perfectly figure out who's the dissentor, weed them out, you know, no Gorbachev would have ever risen to power, who had some doubts about the system, you know, no military coup would have ever happened.

36:27And I think you, I mean, you know, I think there's a real way in which, you know, part of why things have worked out is that, you know, ideas can evolve and, you know, there's sort of like some, some sense in which sort of time heals a lot of wounds and time, you know, and solves, solves, you know, a lot of debates and a lot of people had really strong convictions, but you know, a lot of those have been overturned by time because there's been this continued pluralism and evolution. I think there's a way in which kind of like, you know, if you take a CCP -like approach to kind of like truth, truth is what the party says, when you supercharge that with superintelligence, I think there's a way in which that could just be like locked in and trying for, you know, a long time.

37:03And I think the possibilities are pretty terrifying. You know, your point about, you know, history and sort of like living in America for the past eight years, you know, I think this is one of the things I sort of took away from growing up in Germany is a lot of this stuff feels more visceral, right? Like, you know, my mother grew up in the former East, my father in the former West, they like met shortly after the wall fell, right? Like the end of the Cold War was this sort of extremely pivotal moment for me because, you know, it's the reason I exist, right? And then, you know, growing up in Berlin and, you know, the former wall, you know, my great grandmother, who was, you know, still alive, was very important in my life.

37:37She was born in 34, you know, grew up, you know, during the Nazi area, during, you know, all that, you know, then World War II, you know, like, saw the fire bombing of Dresden from the sort of, you know, country cottage or whatever were, you know, the days kids were, you know, then, and then, you know, then spends most of her life in sort of the East German communist dictatorship, you know, she'd tell me about, you know, in like 54 when there's like the popular uprising, you know, and Soviet tanks came in, you know, her husband was telling her to get home really quickly, you know, get off off the streets, you know, had a, had a son who tried to, you know, ride a motorcycle across, across the Iron Curtain and then was put in the Stasi Prison for a while.

38:13You know, and then finally, you know, when she's almost 60, you know, it's the first time she lives in, you know, a free country, and a wealthy country. And, you know, when I was a kid, she was, she, the thing she always really didn't want me to do was like get involved in politics because like joining a political party was just, you know, it was a very bad connotation for her. Anyway, and she sort of raised me when I was young, you know, and so it, you know, it doesn't feel that long ago, it feels very close. Yeah. So I wonder when we're talking today about the CCP. Listen, the people in China who will be doing the prod, their version of the project will be AI researchers who are somewhat westernized, who interact with either got educated in the West or have colleagues in the West.

39:02Are they going to sign up for the CCP project that's going to hand over control to Xi Jinping? What's your sense on? I mean, it's just like fundamentally, they're just people, right? Like you can't you like convince them about the dangerous super intelligence. Will they be in charge, though? I mean, so since this is, I mean, this is also the case, you know, in the US or whatever, this is sort of like rapidly depreciating influence of the lab employees. Like right now, the sort of AI lab employees have so much power, right? Over this, you know, like, you saw this in November event so much power, right?

39:36But both, I mean, both they're going to get automated and they're going to lose all their power. And it'll just be, you know, kind of like a few people in charge with their sort of armies of automated eyes, but also, you know, it's sort of like the politicians and the generals and the sort of national security state, you know, a lot of, you know, it's, I mean, there's sort of, this is the sort of some of these classic scenes from the Oppenheimer movies, you know, the scientists built it and then it was kind of, you know, and the bomb was shipped away and it was out of their hands. You know, I actually, yeah, I think I actually think it's good for like lab employees to be aware of this is like, you have a lot of power now, but, you know, maybe not for that long and you know, use it wisely.

40:09Yeah, I do, I do think they would benefit from some more, you know, organs of representative democracy. What do you mean by that? Oh, I mean, I, you know, the sort of the, in the opening of board events, you know, employee power is exercising a very sort of direct democracy way. Right. Like, that's how some of how that went about, you know, I think it really highlighted the benefits of representative democracy and having some of the liberal organs. Interesting. Yeah. Let's go back to the 100 billion revenue, whatever, as these companies, the dollar cluster. Yeah, the companies are deploying, we're trying to build clusters that are this big.

40:36Yeah. Where are they building it? Because if you say it's the amount of energy that would require for a small or medium size US state, is it then Colorado, gets no power in something in the United States? Or is it happening somewhere else? Oh, I mean, I think the, I mean, in some sense, this is the thing that I was trying to find fun is, you know, you talk about Colorado, gets no power, you know, the easy way to get the power would be like, you know, displaced less economically useful stuff, you know, it's like whatever, buy up the aluminum smelting plant and you know, that has a gigalot and you know, we're going to replace it with the data center because that's important.

41:02I mean, that's not actually happening because a lot of these power contracts are really sort of long -term locked in, you know, there's obviously people don't like things like this. And so it's sort of, it seems like in practice what it's, what it's requiring, at least right now is building new power. The, that might change. And I think that that's when things get really interesting when it's like, no, we're just educating all of the power to the AGI. Okay, so right now it's building new power. Ten gigawatt, I think quite doable. You know, it's like a few percent of like US natural gas production.

41:28You know, I mean, when you have the Ten gigawatt, Clinton and Cluster, you have a lot more inference. So that starts getting more, you know, I think a hundred gigawatt, that starts getting pretty wild. You know, that's, you know, again, it's like over 20 percent of US electricity production. I think it's pretty doable, especially if you're willing to go for like natural gas. But I do, I do think, I do think it is incredibly important, incredibly important that these clusters are in the United States. And why does it matter it's in the US? I mean, look, I think there's some people who are, you know, trying to build clusters elsewhere.

41:59And you know, there's like a lot of free flowing middle eastern money that's trying to build clusters elsewhere. I think this comes back to the sort of like national security question we talked about earlier. Like would you, I mean, would you do them a patent project in the UAE, right? And I think, I think basically like putting, putting the clusters, you know, I think you can put them in the US. You can put them in sort of like allied democracies. But I think once you put them in kind of like dictatorships with or tearing to katorships, you kind of create this, you know, irreversible security risk, right?

42:23So I mean, one cluster is there much easier for them to actually trade the weights. You know, they can like literally steal the AGI, the super intelligence. It's like they got a copy of the, you know, of the atomic bomb, you know, and they just got the direct replica of that. And it makes so much easier to them. I mean, we're ties to China. You can ship that to China. So that's a huge risk. Another thing is they can just seize the compute, right? Like maybe right now they just think of this, I mean, in general, I think people, you know, I think the issue here is people are thinking of this as they, you know, chat to BT, big tech product clusters.

42:48But I think the clusters being planned now, you know, three to five years out, like it will be the like AGI super intelligence clusters. And so anyway, so like when things get hot, you know, they might just seize the compute. And I don't know, supposedly put like, you know, 25 % of the compute capacity in these sort of middle eastern katorships, well, they seize that. And now it's sort of a ratio of compute of three to one. And you know, we still have some more. But even like even, even only only 25 % of compute there, like I think it starts getting pretty hairy, you know, I think three to one is like not that great of a ratio.

43:15You can do a lot with that amount of compute. And then look, even even if they don't actually do this, right? Even they don't actually seize the compute, even they actually don't steal the weights. There's just a lot of implicit leverage you get, right? They get the C .D. the AGI table. And you know, I don't know why we're giving a thorough tour of taryndic tatorships to see that the AGI table. Okay. So there's going to be a lot of compute in the Middle East if these deals go through. First of all, who is it just like every single big tech company is just trying to figure out what it would be like?

43:44I don't know. I'm some. Okay. Okay. I guess there's reports. I think Microsoft or yeah, yeah, yeah, yeah. Which we'll get into. So they, UAE gets a bunch of compute because we're building the clusters there. And why? So let's say they have 25 % of why does a compute ratio matter? Is it if it's about them being able to kick off the intelligence explosion? Isn't it just some threshold where you have a hundred million AGI researchers or you don't? I mean, you do a lot with, you know, 33 million extremely smart scientists. And, you know, and again, a lot of the stuff, you know, so first of all, it's like, you know, that might be enough to build the crazy bio weapons, right?

44:22And then you're in a situation where like, now, wow, we've just like they stole the weights. They seized the compute. Now they can make, you know, they can build these crazy new WMDs that, you know, will be possible super intelligence. And then you just kind of like proliferated the stuff and you know, it'll be really powerful. And also, I mean, I think, you know, three three acts on compute isn't actually that much. And so the, you know, the, you know, I think I think I worry a lot about is, I think everything, I think that risk is situation is if we're in some sort of like really tight neck, feverish international struggle, right?

44:57If we're like really close with the CCP and we're like months apart, I think the situation we want to be in, we could be in if we played our cards right, there's a little bit more like, you know, the US, you know, building the atomic bomb versus the German project way behind, you know, years behind. And if we have that, I think we just have so much more wiggle room like to get safety right, we're going to be building like, you know, there's going to be these crazy new WMDs, you know, things that completely undermine, you know, nuclear deterrence, you know, intense competition. And that's so much easier to deal with if, you know, you're like, you know, it's not just, you know, you don't have somebody right on your tail, you gotta go, go, go, you gotta go, maximum speed, you have no wiggle room, you're worried that at any time they can overtake you.

45:34I mean, they can also just try to outbuild you, right? Like they can might, they might literally win, like China might literally win if they can steal the weights because they can, they can outbuild you. And they maybe have less caution, both, you know, good and bad caution, you know, kind of like whatever unreasonable regulations we have. Or you're just in this really tight race. And I think it is that sort of like if you're in this really tight race, the sort of feverish struggle, I think that's when sort of there's the greatest peril of self -destruction. So then presumably the companies that are trying to build clusters in the Middle East realize this, what is it just that it's impossible to do this in America?

46:05And if you want American companies to do this at all, then you do it in Middle East or not at all. And you just like, I'm trying to build the three gorgeous damn cluster. I mean, this is a few reasons. One of them is just like, people aren't thinking about this is the AI super intelligence cluster. They're just like, ah, you know, like cool clusters from my, you know, for my Chachi B. But they're building in the plans right now are clusters, which are ones that are like, because if you're doing ones where inference, presumably get like spread them out across the country or something. But the ones that are building, they realize we're going to do one training run in this thing we're building.

46:32I just think it's harder to distinguish between inference and training compute. And so people can claim it's training compute, but I think they might realize that actually, you know, this is going to be useful for it. Yep. Sorry. They might say it's inference compute and actually it's useful for training compute too. Okay. So this isn't that a data and things like that. Yeah. The future of training, you know, like RL looks a lot like inference, for example, right? Or or you just kind of like end up connecting them, you know, in time, you know, it's like a lot raw material. You know, it's like, you know, it's it's it's placing your uranium refining facilities there.

46:57Sure. Okay. So a few reason, right? One is just like, they don't think about the CZ AI jihad cluster. I know there's just like easy money from the Middle East, right? Another one is like, you know, people saying some people think that, you know, you can't do it in the US. And you know, I think we actually face this sort of real system competition here because again, some people think it's only autocracies that can do this that can kind of like top down, mobilize the sort of industrial capacity, the power, you know, get the stuff done fast. And again, this is the sort of thing, you know, we haven't faced in a while.

47:26But you know, during the Cold War, like we really there was this sort of intense system competition, right? Like East West Germany, it was this, right? Like West Germany kind of like liberal democratic capitalism versus kind of, you know, communist state plan. And you know, now it's obvious that the sort of, you know, the free world would win. But you know, even even as late as like 61, you know, Paul Samuelson was predicting the Soviet Union would would outgrow the United States because they were able to sort of mobilize industry better. And so yeah, there's some people who, you know, they ship post about loving America by day, they're betting against America.

47:57They're betting against the liberal order. And I think I basically just think it's a bad bet. And the reason I think it's a bad bad is I think the stuff is just really possible in the US. And so that's make it possible in the US. There's some amount that we have to get our act together, right? So I think there's basically two paths to doing it in the US. One is you just got to be willing to do natural gas. And there's ample natural gas, right? You put your cluster in West Texas, you put it in, you know, Southwest Pennsylvania by the, you know, Marcelo Shail, Tengue cluster super easy, honey or gigawatt cluster, also pretty doable.

48:24You know, I think, you know, natural gas production in the United States is, you know, almost doubled in a decade. You do that, you know, one more time with the next, you know, seven years or whatever, you know, you could power multiple trillion dollar data centers. But the issue there is, you know, a lot of people have sort of these made these climate commitments. They're not just government. It's actually the private companies themselves, right? The Microsoft, the Amazon's and so on. They have these climate commitments. So they won't do natural gas. And, you know, I admire the climate commitments, but I think at some point, you know, the national interest and national security kind of is more important.

48:55The other path is like, you know, you can do this sort of green energy mega projects, right? You do the solar and the batteries and the, you know, the SMRs and geothermal. But if we want to do that, there needs to be sort of a sort of broad, eregulatory push, right? So like, you can't have permitting take a decade, right? So you got to reform for, you got to like have, you know, blanket Nika Nipa exemptions for this stuff. You know, there's like a name state level regulations, you know, that are like, yeah, you could build, you know, you can build the solar panels and batteries next to your data center, but it'll still take years because, you know, you actually have to hook it up to the state electrical grid.

49:27You know, and you have to like use governmental powers to create rights of way to kind of like, you know, have multiple clusters and connect them, you know, and have thick cables basically. And so look, I mean, ideally, we do both, right? I mean, we do natural gas and the broad regulatory agenda. I think we have to do at least one. And then I think this possible stuff is just possible in the United States. Yeah. I think a good analogy for this, by the way, before the conversation, I was reading, there's a good book about World War II industrial mobilization, you know, it's called Freedom's Forge.

49:54Yeah. And I guess when we think back on the period, especially if you're from, if you read like the Patrick calls and fast and the progress study stuff, it's like, the Hatsuit capacity back then and people just got you done. But now it's a cluster of. Well, in the case, no, so it's, it was really interesting. So you have people who are from the Detroit Auto industry side, like Knutson, who are running mobilization for the United States. And they were extremely competent. Yeah. But then at the same time, you had labor organization agitation, which is actually very analogous to the climate pledges and climate change concern.

50:29We have today. Yeah. Where they would have these strikes while literally into 1941, that would cost millions of man hours worth of time when we're trying to make tens of millions, sorry, tens of thousands of planes a month or something. And they would just debilitate factories before, you know, trivial like pennies on the dollar kind of concessions from capital. And it was concerns that, oh, the auto companies are trying to use the pretext of a potential war to actually prevent a paying labor that money deserves. And so the what climate changes today, like you think, I fuck America's fucked, like we're not going to be able to build this shit.

51:12Like if you, you feel like a kneebar or something, but I didn't realize how debilitating labor was in like, right? It was just, you know, before at the, you know, it's sort of like 39 or whatever, the American military was in total shambles, right? You read about it. And it reads a little bit, like, you know, the German military today, right? It's like, you know, military expenditures, I think were less than 2 % of GDP, you know, all the European countries that gone even in peace time, you know, like above 10 % of GDP sort of this like rapid mobilization, there's nothing, you know, like we're making kind of like no planes, there's no military contracts.

51:39Everything had been starved during the Great Depression. But there was this latent capacity. And you know, at some point, the United States got their act together. I mean, the thing I'll say is I think, you know, the supplies sort of the other way around too, to basically to China, right? And I think sometimes people are, you know, they kind of count them out a little bit and they're like the export controls and so on. And you know, they're able to make some nanomere chips now. I think there's a question of like, how many could they make? But, you know, I think there's at least a possibility that they're going to be able to mature that ability and make a lot of some nanomere chips.

52:06And there's a lot of latent industrial capacity in China. And they are able to like, you know, a lot of power fast. And maybe that is an activated fray high yet. But at some point, you know, the same way the United States and like, you know, a lot of people in the US and the United States government is going to wake up, you know, at some point the CCP is going to wake up. Yep. Okay. Going back to the question of presumably companies, if are they blind to the fact that there's going to be some sort of, well, okay, so they realize that there's going, they realize scaling is a thing, right? Obviously, their whole plans are contingent on scaling.

52:37And so they understand that we're going to be in 2020, building the China -Kiwad data centers. And at this point, that the people who can keep up are big tech just potentially at like the edge of their capabilities. Yeah. Then sovereign wealth fund fund a thing. Yeah. And also big, major countries like America, China, whatever. Yeah. So what's their plan? If you look at like these AI labs, what's their plan given the this landscape? Do they not want the leverage of having being in the United States? I mean, I think, I don't know. I think, I mean, one thing the Middle East does offer is capital, but it's like America has plenty of capital, right?

53:14It's like, you know, we have trillion dollar companies. Like, what are these Middle Eastern states? They're kind of like trillion dollar oil companies. We have trillion dollar companies and we have very deep financial markets. And it's like, you know, Microsoft could issue hundreds of billions dollars of bonds and they can pay for these clusters. I mean, look, I think another argument being made, and I think it's worth taking seriously, is an argument that look, if we don't work with the UAE or with these Middle Eastern countries, they're just going to go to China, right? And so, you know, they're going to build data centers.

53:40They're going to poor money and AI, regardless. And if we don't work with them, you know, they'll just support China. And look, I mean, I think, I think there's some merit to the argument. And in the sense that I think we should be doing basically benefit sharing with them, right? I think we should talk about this later, but I think basically sort of on the road to AGI, there should be kind of like two tiers of coalitions. It should be the sort of narrow coalition democracies, that's sort of the coalition that's developing AGI. And then this should be a broader coalition where we kind of go to other countries, including, you know, dictatorships, and we're willing to offer them, you know, and we're willing to offer them some of the benefits of the AI, some of the sharing.

54:13And so it's like, look, if the UAE wants to use AI products, if they want to run, you know, meta recommendation engines, if they want to run, you know, like the last generation models, that's fine. I think, right, of course, they just like wouldn't have had this seat at the AGI table, right? And so it's like, yeah, they have some money, but a lot of people have money. And, you know, the only reason they're getting this sort of course, the AGI table, the only reason we're giving, these dictators will have this enormous amount of leverage over this extremely national security relevant technology is because we're, you know, we're kind of getting them excited and offering it to them.

54:48You know, I think the other, yeah. Who specifically is doing this? Like just the companies who are going there to fundraise, or like, this is the AGI is happening, and you can fund it or you can. It's been reported that it's been reported that, you know, Sam is trying to raise, you know, seven trillion or whatever, for a chip project. And, you know, it's unclear how many of the clusters will be there and so on. But it's, you know, definitely, definitely stuff is happening. I mean, look, I think another reason I'm a little bit, at least, suspicious of this argument of like, look, if the US doesn't work with them, they'll go to China is, you know, I've heard, I heard from multiple people, and this wasn't, you know, from a time I'd open AI, and I haven't seen the memo, but I have heard from multiple people that, you know, at some point several years ago, open AI leadership had sort of laid out a plan, the fund in sell AGI by starting a bidding war between the governments of, you know, the United States, China and Russia.

55:32And so, you know, it's kind of surprising to me that they're willing to sell AGI to the Chinese and Russian governments, but also there's something that sort of feels a bit eerily familiar about kind of starting the spitting war and then kind of like playing them off each other. And while, you know, you don't do this, China will do it. So, anyway, interesting. Okay, so that's pretty fucked up, but given that that's okay. So, suppose that you were right about, we ended up in this place because we got one, the way one of our friends put it is that the Middle East has like no other place in the world, billions of dollars or trillions of dollars up for persuasion and, and, and we have the first time all of that.

56:11Like, you know, the Microsoft board, it's only the dictator. Yeah, but, so let's say you're right that you shouldn't have gotten them excited about AGI in the first place, but now we're in a place where they are excited about AGI. And they're like, fuck, we want us to have GPD -5 where we're going to be off building superintelligence. This item's a piece thing doesn't work for us. And if you're in this place, don't they already have the leverage? Aren't you like, and as you might as well. I don't think, I think the UAE on its own is not competitive, right? It's like, I mean, they're already x4 controlled.

56:40Like, you know, we're not, you know, there's like, you're not actually supposed to ship and video chips over there, right? You know, it's not like they have any of the leading AI labs, you know, it's like, they have money, but you know, it's actually hard to just translate money into like, but the other things you've been saying about laying out your vision is very much there's just almost industrial process. You put in the compute and then you put in the algorithms. Yeah. You add that up and you get AGI on the other end. Yeah. If it's something more like that, then the case for somebody being able to catch up rapidly seems more compelling than if it's some disposal.

57:09Well, well, if they can steal the algorithms and if they can steal the way it's not really that's really where sort of, I mean, we should talk about this. This is really important. And I think, you know, so like, yeah, how easy would it be for for an actor to steal the things that are like the not the things that are released about Scarlett Johansson's voice, but the RL things are talking about the unhobblings. I mean, I mean, I mean, extremely easy, right? You know, I, you know, deep mind, even like, you know, they don't make a claim that it's hard, right? Deep mind put out there like whatever frontier safety, something and they like lay out security levels and they let you know, security level zero to four and four is the original and resistant to state actors and they say we're at level zero, right?

57:47And then, you know, I mean, just recently, there was like an indictment of a guy who just like stole the code a bunch of like really important AI code and went to China with it. And you know, all he had to do to steal the code was that, you know, copy the code and put it into Apple notes and the next word it is PDF and that got past their monitoring, right? And Google is the best security of any of the ILOBs probably because they have the you know, the Google infrastructure. I mean, I think, I don't roughly, I would think of this as like, you know, security of a startup, right? And like, what is security of a startup look like?

58:13Right? You know, it's not that good. It's easy to steal. So, it's even if that's the case. Yeah. A lot of your posts was making the argument that, oh, you know, why are we going to get the intelligence explosion because if we have somebody with the intuition of an Alec Radford, yeah, to become able to come up with all these ideas. Yeah, that intuition is extremely valuable and you scale that up. But if it's a matter of these, if it's just in the code, that like if it's just the intuition, then that's not going to be just in the code, right? And also because of X -Word Controls, these countries are going to have slightly different hardware, you're going to have to make different trade -offs and probably rewrite things to be able to be compatible with that, including all these things.

58:57It's just a matter of getting the right pen drive and you plug it into the gigawatt data center and X -Word just damn and then there's a few different things. So one threat model is just stealing the weights themselves. And the weights one is sort of particularly insane, because they can just steal the literal end product, just like make a replica of the atomic bomb and then they're just ready to go. And I think that one is just extremely important around the time we have AI and super intelligence. Because it's China can build a big cluster. By default, we'd have a big lead because we have the better scientists, but we make the super intelligence, they just steal it.

59:30They're off to the races. Wates are a little bit less important right now because who cares if they steal the GP -4 weights, right? Like whatever. And so we still have to get started on weight security now because, look, if we think AI by 27, this stuff is going to take a while. And it's not just going to be like, oh, we do some access control. If you actually want to be resistant to Chinese espionage, it needs to be much more intense. The thing though that I think people aren't paying enough attention to is the secret, says you say. And I think the compute stuff is sexy. We talk about it, but I think people underrate the secrets because they're, I think the half -in -order of magnitude a year just by default are vaguely like progress.

1:00:12That's huge. If we have a few year lead by by default, that's 10, 30, X 100, X Bayer cluster, if we protected them. And then there's this additional layer of the data wall, right? And so we have to get through the data wall. That means we actually have to figure out some sort of basic new paradigm, sort of the AlphaGo step two, right? AlphaGo step one is learns from human imitation. AlphaGo step two is the sort of self -play RL. And everyone's working on that right now. And maybe we're going to crack it. And, you know, if China can't steal that, then they, then they, you know, then they're stuck.

1:00:42If they can't steal it, they're off to the race. But whatever that thing is, is it like literally, I can write down on the back of a napkin? Because if it's that easy, then why is it that hard for them to figure it out? And if it's more about the intuitions, then you just have to hire Alec Radford, like what are you copying down? Well, there's a few layers to this, right? So I think at the top is kind of like sort of the, you know, fundamental approach, right? And sort of like, I don't know, on pre -training, it might be, you know, like, you know, unsupervised learning, next token protection, train on the entire internet.

1:01:11You actually get a lot of juice out of that already. That one's very quick to communicate. Then there's like, there's a lot of details that matter. And you were talking about this earlier, right? It's like probably the way that thing people are going to figure out is going to be like somewhat obvious. There's going to be some kind of like clear, you know, not that complicated thing that'll work. But there's going to be a lot of details to getting that right. But if that's true, then again, why, why are we even, why do we think that getting state -level security in the Starbastell prevent China from catching up?

1:01:36If it's just like, oh, we know some sort of self -play RL, we're required to get pass a data wall. And if it's as easy as you say in the some fundamental signs, I mean, again, but it's going to be sold by a 2027, you say, like, right? It's like not that hard. I just think, you know, the US and the sort of, I mean, all the leading ant labs in the United States and they have this huge lead. I mean, by default, you know, China actually has some good LLM. So why do they have good LLM? They're just using this sort of open source code, right? You know, Lama or whatever. And so the, the, I think people really underrate the sort of both the sort of divergence on algorithmic progress and the lead the US would have by default.

1:02:06Because by the, you know, all this stuff was published until recently, right? Like, Shenzhella scaling laws were published. You know, there's a bunch of MOU papers. There's, you know, Transformers and, you know, all that stuff was published. And so that's why open source is good. That's why China can make some good models. That stuff is now, I mean, at least they're not publishing it anymore. And, you know, if we actually kept it secret, it would be this huge edge. To your point about sort of like some tacit knowledge and like Bradford, you know, there's, there's another layer at the bottom that is something about like, you know, large scale engineering work to make these big training ones work.

1:02:33I think that is a little bit more tacit technology. So I think that, but I think China will be able to figure that out. That's like sort of engineering slap. They're going to figure out how to figure that out, but not how to get the RL thing working. I mean, look, I don't know, Germany during World War II, you know, they went down the wrong path. They did heavy water. And that was wrong. And there's actually, there's an amazing anecdote in in the making of the atomic bomb on this, right? So, so secrety is actually one of the most contentious issues, you know, early on as well. And, you know, part of it was sort of, you know, zillard or whatever really thought, you know, this sort of nuclear chain reaction was possible.

1:03:06And so the atomic bomb was possible and you went around and it was like, eyes is going to be of enormous strategic importance, military importance. And a lot of people didn't believe it or they're kind of like, well, maybe this is possible, but you know, I'm going to act as if it's not possible. And, you know, science should be open and all these things. And anyway, in some of these early days, so there had been some sort of incorrect measurements made on graphite as a moderator. And that Germany had. And so they thought, you know, graphite was not going to work. We have to do heavy water. But then for me, made some new measurements on graphite.

1:03:37And they indicated that graphite would work. You know, this is really important. And then, you know, zillard kind of assaulted Fermi with the kind of another secrecy appeal. And Fermi was just kind of, he was pissed off, you know, at a temper tantrum. You know, he was like, he thought it was absurd. You know, like, come on, this is crazy. But, you know, zillard persisted. I think they're up to another guy, a pegrim. And then Fermi didn't publish it. And, you know, that was just in time because Fermi not publishing and meant that the Nazis didn't figure out graphite would work. They went down this path of heavy water.

1:04:07And that was the wrong path. That was one of the sort of, you know, this is a key reason why the sort of German project didn't work out. They were kind of way behind. And, you know, I think we face a similar situation on are we, are we just going to instantly leak the sort of how do we get pass the data while what's the next paradigm? Or are we not? So, and the reason this would matter is if there's like a being one year ahead would be a huge advantage in the world where it's like you deploy AI over time. And then just like, ah, they're going to catch up anyway. I mean, I interviewed Richard Rhodes, the guy who wrote making an atomic bomb.

1:04:39Yeah. And one of the anecdotes he had was when, so they'd realized America had the bomb. Obviously, we dropped it in Japan. Yeah. And Bariah goes, the guy who ran the NKBD. Yeah. And just a famously ruthless guy, just evil. And he goes to, I forgot the naive, but the guy, the Soviet scientist was running their version of the man on project who says, Comrade, you will get us the American bomb. Yeah. And the guy says, well, listen, their implosion device actually is not optimal. We should make it a different way. And Bariah says, no, you will get us the American bomb or your family will be camped us.

1:05:13But the, the thing that's relevant about that anecdote is actually the Soviets would have had a better bomb if they hadn't copied the American design at least initially. And which suggests that often in history, this is something that's not just really manhand project, but there's this pattern of parallel invention where because the tech tree implies that the certain thing is next, in this case, it's up play or all whatever, then people are just like working on that and like, people are going to figure out around the same time. There's not, there's not going to be that much cap and who gets it first.

1:05:46Yeah. But it wasn't like famously that a bunch of people were invented something like the light bulb around the same time. Yeah. And so forth. Yeah. So, but is it just that, like, yeah, that might be true, but it'll, with the one year or the six months or whatever. Two years makes all the difference. I don't know if it'll be two years though. I mean, I actually, I mean, I actually think if we locked down the labs, we have much better scientists were way ahead, it would be two years. But even I think even, I think, I think whether you, I think, yeah, I think even six months a year would make huge difference.

1:06:08And this gets back to the sort of intelligence exclusion and it's like a year might be the difference between, you know, a system that's sort of like human level and a system that is like vastly superhuman, right? It might be like five, five, you know, even on the current pace, right? We went from, you know, I think on the math benchmark recently, right? Like, you know, three years ago on the math benchmark, we, you know, that was, you know, this is sort of come really difficult high school competition math problems. You know, we were at, you know, a few percent couldn't solve anything. Now it's solved.

1:06:36And now it's sort of at the normal pace of AI progress. You didn't have sort of a billion super intelligent resources researchers. So like a year is a huge difference. And then particularly after superintelligence, right? Once this is applied to sort of lots of elements of R &D, once you get the sort of like industrial explosion with robots and so on, you know, I think a year, you know, a couple years might be kind of like decades worth of technological progress. And might, you know, again, it's like go for one, right? 20, 30 years of technological lead, totally decisive. You know, I think it really matters.

1:07:02The other reason it really matters is, you know, suppose, suppose they steal the weight, suppose they steal the algorithms and, you know, they're close on our tails. Suppose we still pull out a head, right? We just kind of, we were a little bit faster, you know, we're three months ahead. I only think the sort of like role in which we're really knack and knack, you know, you only have a three months lead are incredibly dangerous, right? And we're in this like fever struggle. We're like, if they get ahead, they get to dominate, you know, sort of maybe they get a decisive advantage. They're building clusters like crazy.

1:07:32They're willing to throw all caution to the wind. We have to keep up. There's some crazy new WMDs popping up. And then we're going to be in the situation where it's like, you know, crazy new military technology, crazy new WMDs, you know, like deterrence and mutually disturbed instruction, like keeps changing, you know, every few weeks. And it's like, you know, completely unstable volatile situation is incredibly dangerous. So it's I think I think, you know, both both from just the technologies are dangerous from the alignment point of view, you know, I think it might be really important during the intelligence explosion to have this sort of six month, you know, wiggle room to be like, look, we're going to like dedicate more compute to alignment during this period because we have to get it right.

1:08:04We're feeling uneasy about how it's going. And so I think in some sense, like one of the most important inputs, so whether we will kind of destroy ourselves or whether we will get through this just incredibly crazy period is whether we have that buffer. Why so before we go further object level in this, I think it's very much worth noting that almost nobody, at least nobody I talk to, thinks about the geopolitical implications of AI. And I think I have some object level disagreements that we'll get into, but at least things I want to iron out, I may not disagree in the end. But the basic premise that obviously, if you keep scaling and obviously people realize that this is where intelligence is headed, it's not just going to be like the, the same old world where like what model are we deploying tomorrow and what is the latest like, well, if people on Twitter like, oh, did you beat four of us going to share your expectations or whatever.

1:09:02You know, coitus are really interesting because before a year or something, when March 2020 hit, we, it became clear to the world like, president, CEO, media, average person. There's other things happening in the world right now, but the main thing we as a world with are dealing with right now is COVID. Soon on AGI. Yeah. Okay. And then so this is the quiet period. You know, you got vacation, you know, you want to, yeah, you want to have, you know, maybe like now is the last time you can have some kids. You know, my girlfriend sometimes complains that, you know, that I know when I'm like, you know, off doing work or whatever, she's like, I'm not spending time with her.

1:09:41She's like, you know, she's threatened to replace me with like, you know, GPG 6 or whatever. And I'm like, GP6 will also be too busy. Okay. Anyway, so what's the answer to the question of why why why why are not the people talking national security? I made this mistake with COVID, right? So I, you know, February of 2020 and I, you know, I thought just it was going to sweep the world and all the hospitals would collapse and it would be crazy. And then, and then, you know, and then it'd be over. And you know, a lot of people thought this kind of the beginning of COVID, they shut down their offices a month or whatever.

1:10:11I think the thing I just really didn't price in was the side of the reaction, right? And, and within weeks, you know, Congress spent over 10 % of GDP on like COVID measures, right? The entire country was shut down. I was crazy. And so I know I didn't price it in with COVID sufficiently. I don't know why do people underrate it. I mean, I think there's there's a, there's a sort of way in which being kind of in the trenches actually kind of, I think, um, gives you a less clear picture of the trend lines. You know, you actually have to zoom out that much only like a few years, right? But, you know, you're in the trenches, you're like trying to get the next model to work, you know, there's always something that's hard.

1:10:47You know, for example, you might underrate algorithmic progress because you're like, ah, things are hard right now or, you know, data wall or whatever. But, you know, you zoom out just a few years and you actually try to like count up how much algorithmic progress made in the last, you know, last few years and it's enormous. Um, but I also just don't think people think about this stuff. Like I think smart people really underrate espionage, right? And, you know, I think part of the security issue is I think people don't realize like how intense state level espionage can be, right? Like, you know, this is really company had had software that could just zero click hack any iPhone, right?

1:11:18They just put in your number and then it's just like straight download of everything, right? Like the United States infiltrated in ARGAF, the atomic weapons program, right? Wild, you know, like, are you about sex? Yeah, yeah, yeah. You know, the, you know, intelligence agencies have just stockpiles of zero days, you know, when things get really hot, you know, I don't know, I'm able to send special forces, right? To like, you know, get go to the data center or something that's, you know, or, you know, I mean, China does this. They threaten people's families, right? And they're like, look, if you don't cooperate, if you don't give us the intel, um, there's a good book, you know, along the lines of the Gulag or develop, you know, the inside the aquarium, which is by a Soviet GRU defector.

1:11:57GRU was like military intelligence, earlier I remember this book to me. And, um, you know, I think reading that is just kind of like shocked that I have a 10 sort of state level SV nausea. The whole book was about like, they go to these European countries and they try to like get all the technology and recruit all these people to get the technology. Um, I mean, yeah, maybe one anecdote, you know, so when, so the spy, you know, this eventual defector, you know, so he's being trained, he goes to the kind of GRU spy academy. And so then to graduate from the spy academy, sort of before your center broad, you kind of had to pass a test to show that you can do this.

1:12:30And the test was, you know, you had to in Moscow, recruit a Soviet scientist and recruit them to give you information, sort of like you would do in the foreign country. Um, but of course, for whomever you recruited, the penalty for giving away, sort of secret information was death. And so to graduate from the Soviet spy, the GRU spy academy, you had to condemn a countryman to death. Um, states do this stuff. I, I started reading the book on, because I saw it in the series. Yeah. And I was actually wondering the fact that you use this anecdote. Yeah. And then you're like, and I have a book recommended by Ilya, is this some sort of, is this some sort of easter egg?

1:13:13We'll leave that for an exercise with a reader. Um, okay. So the beatings will continue until them are all improved.

1:13:22So suppose that we live in the world in which these secrets are locked down. But China still realizes that this progress is happening in America. So in that world, especially if they realize, and I guess it's a very interesting question. It probably won't be locked down. Okay. But suppose it's probably going to live in the bad world. Yeah. It's going to be really bad. Hmm. Why are you so confident that they won't be locked down? I mean, I'm not confident that won't be locked down. But I think it's just, it's not happening. And so tomorrow, the lab leaders get the message. How hard, like, what do they have to do?

1:13:59They get the more security guards? They like air gap. The, what do they do? So again, I think, I think basically it's, you know, I think people, there's kind of like two, two reactions there, which is like it's, you know, we're already secure. Yeah. Not. And there is, you know, fatalism. It's impossible. And I think the thing you need to do is you kind of got to stay ahead of the curve of basically how egi pillows the CCP. Yeah. Right. So like right now, you've got to be resistant to kind of like normal economic espionage. They're not, right? I mean, I probably wouldn't be talking about the stuff that the labs were, right?

1:14:28Because they wouldn't want to wake them up more, the CCP. But they're not. You know, this is like, this stuff is like really trivial for them to do right now. I mean, it's also, anyway, so they're not resistant to that. I think it would be possible for private company to be resistant to it. Right? So, you know, both of us have, you know, friends in the kind of like quantitative trading world, right? And, and, you know, I think actually those secrets are shaped kind of similarly where it's like, you know, you know, they've said, you know, yeah, if I got on a call for an hour with somebody from a competitor firm, I could, most of our alpha would be gone.

1:14:56And that's sort of like, that's the like list of details of like really how to how to make your own. I'm not worried about that for the soon. You're not worried about that for the soon. Well, anyway, and so, so all of a good we gone, but in fact, they're alpha persists, right? And, you know, often, often for many years and decades. And so this doesn't seem to happen. And so I think there's like, you know, I think there's a lot you could go if you went from kind of current startup security, you know, you just got to look through the window and you can look at the slides. So, you know, it's, it's kind of like, you know, good private sector security hedge funds, you know, the wakeable treats, you know, customer data or whatever.

1:15:25Um, I'd be good right now. The issue is, you know, basically the CCP will also get more a GI build. And at some point, we're going to face kind of the full force of, you know, the Ministry of State security. And again, you're talking about smart people underwriting espionage and sort of insane capability of the states. I mean, this stuff is wild, right? You know, they can get like, you know, there's papers about, you know, you can find out the location of like where you are in a video game map, just from sounds, right? Like states can do a lot with like electromagnetic emanations, you know, like, you know, at some point, like, you got to be working from a sketch, like, your cluster needs to be air gap and basically be a military base.

1:16:00It's like, you know, you need to have, you know, intense kind of security clearance procedures for employees, you know, they have to be like, you know, all their shit is monitored, you know, they're, you know, they basically have security guards, you know, it's, you know, you, you can't use any kind of like, you know, other dependencies, it's all gotta be like intensely vetted, you know, all your hardware has to be intensely vetted. Um, and, you know, I think basically if they actually really face the full force of state level espionage, I don't really think this is a thing private companies can do.

1:16:27Both, I mean, empirically, right? Like, you know, Microsoft recently had executives emails hacked by Russian hackers and, you know, government emails they've hosted hacked by government actors, but also, um, you know, it's basically there's just a lot of stuff that only kind of, you know, the people behind the security curators is no, and only the deal with, um, and so, you know, I think it's actually kind of resist the sort of full force of espionage, you're gonna need the government. Anyway, so I think basically we could, we could do it by always being ahead of the curve. I think we're just gonna always be behind the curve.

1:16:53Um, and I think, you know, maybe unless we get the sort of government project. Okay, so going back to the naive perspective of, we're very much coming at this from, there's gonna be a race in the CCP, we must win. And listen, I understand like, back people are in charge of the Chinese government, like, good CCP and everything. Um, but just stepping back in a sort of galactic perspective, humanity is developing a GI. And do we want to come at this from the perspective of, we need to be China to this are our super intelligent, Jupiter brain descendants won't know, which I like, China will be some like distant memory that they have America to.

1:17:30So shouldn't it be a more the initial approach, just come to them like, listen, we, this is super intelligence. So this is something like, we come from a cooperative perspective. Why, why immediately sort of rush into it from a hawkish competitive perspective? I mean, look, I mean, one thing I want to say is like a lot of the stuff I talk about in the series is, you know, is sort of primarily, you know, descriptive, right? And so I think that on the China stuff, it's like, you know, yeah, and some ideal world, you know, we, we, you know, it's just all, you know, merry go around in cooperation.

1:18:01But again, it's sort of, I think, I think people wake up to a GI. I think the issue particular on sort of like, can we make a deal, can we make an international treaty? I think it really relates to sort of what is the stability of sort of international arms control agreements? Right? And so we did very successful arms control on nuclear weapons in the 80s, right? And the reason that was successful is because the sort of new equilibrium was stable, right? So you take go down from, you know, whatever, 60 ,000 nukes to 10 ,000 nukes, you know, when you have 10 ,000 nukes, you know, basically breakout, breakout doesn't matter that much, right?

1:18:32Suppose the other guy now tried to make 20 ,000 nukes, well, it's like, who cares, right? You know, like, it's still mutually short destruction. Suppose a rogue state kind of went from zero nukes to one nukes. It's like, who cares, we still have way more nukes than you? I mean, it's still not ideal for destabilization, but it, you know, it'd be very different if the arms control agreement had been zero nukes, right? Because if I had been zero nukes, then it's just like one rogue state makes one nuk. The whole thing is destabilized. Breakout is very easy. You know, your adversary state starts making nukes.

1:18:57And so basically, when you're going to sort of like very low levels of arms or when you're going to kind of, in your industry, very dynamic technological situation, arms control is really tough because because breakout is easy. You know, there's, there's, I mean, there's some other sort of stories about this in sort of like 1920s, 1920s, 1930s. You know, it's like, you know, all the European states had done this armament and Germany was kind of did this like crash program to build the Luftwaffe. And that was able to like massively destabilize things because not that, you know, they were the first, they were able to like pretty easily build kind of a modern, you know, Air Force because the other didn't really have one and that, you know, that really destabilized things.

1:19:29And so I think the issue with EGI and super intelligence is the explosiveness of it, right? So if you have an intelligence explosion, if you're able to go from kind of EGI to super intelligence, if that super intelligence is decisive, like either, you know, like a year after because you developed some crazy WMD or because you have some like, you know, super hacking ability that lets you kind of, you know, completely deactivate the sort of enemy arsenal. That means like suppose, suppose you're trying to like put in a break, you know, like we both, we're both going to like cooperate and we're going to go slower, you know, on the cost of AGI or whatever, you're just going to be such an enormous incentive to kind of race ahead and break out.

1:20:03So what we're just going to do in the intelligence explosion explosion, if we can get three months ahead, we win. I think that makes it basically, I think any sort of arms control agreement that comes as situation where it's close, very unstable. That's really interesting. This is very analogous to kind of a debate I had with Rose on the podcast where he argued for nuclear disarmament. But if some country tries to break out and starts developing nuclear weapons, the six months or whatever that you would get is enough to get international consensus and invade the country and prevent them from getting nukes.

1:20:37And I thought that was sort of, that's not a say like a little bit. But it's really tough. Yeah. But so it's on this, right? So like maybe it's a bit easier because you have AGI and so like, you can monitor the other person's cluster or something like data centers, you can see them from space, actually, you can see the energy draw they're getting. There's a lot of things as you were saying, there's a lot of ways to get information from an environment if you're really dedicated. And also because unlike a nukes, the data centers are nukes you have obviously the submarines, planes, you have bunkers, mountains, whatever, you have some different places.

1:21:10A data center that you're 100 gigawatt data center, we can blow that shut up if you're like we're concerned, right? Like just some cruise missile or something. Yeah. It's like very wonderful to sabotage. That gets to the sort of, I mean, that gets to the sort of insane vulnerability of this period, post super intelligence, right? Because basically, I think so, you love the intelligence explosion. You have these vastly superhuman things on your cluster, but you're like, you haven't done the industrial explosion yet. You don't have your robots yet. You haven't covered the desert in robot factories yet.

1:21:35And that is the sort of crazy moment where, say the United States is ahead, the CCP is somewhat behind. There's actually an enormous incentive for a strike, right? Because if they can take out your data center, they know you're about to have just this command and decisive lead. They know if we can just take out the data center, then we can stop it. And they might get desperate. And so I think basically we're going to get into a position. It's actually, I think it's going to be pretty hard to defend early on. I think we're basically going to be in a position where protecting data centers with like the threat of nuclear retaliation.

1:22:05It's like maybe sounds kind of crazy though, you know, it's the inverse of the LA's are we going to the data centers are nearly running. New clear deterrence for data centers. I mean, this is a, you know, Berlin, you know, in the like late 50s, early 60s, both Eisenhower and Kennedy multiple times kind of made the threat of full on nuclear war against the Soviets if they tried to encroach on West Berlin. It's sort of insane. It's kind of insane that that went well. But basically, I think that's going to be the only option for the data centers. It's a terrible option. This whole scheme is terrible, right?

1:22:32Like being being being in this like neck and neck race, sort of at this point is terrible. And you know, it's also you know, I think I have some uncertainty basically on how easy that decisive advantage will be. I'm pretty confident that if you have super intelligence, you have two years, you have the robots, you're able to get that 30 year lead. Look, then you're in this like goal for one situation, you have your like, you know, millions or billions of like mosquito sized drones that can just take it out. I think there's even a possibility you can kind of get a decisive advantage earlier. So, you know, there's these stories, you know, about these as well about, you know, like colonization and like the sort of 1500s where it was that, you know, these like a few hundred kind of spaniards were able to like topple the Aztec empire, you know, couple, I think a couple other empires as well.

1:23:09You know, each of these had a few million people. And it was not like godlike technological advantage. It was some technological advantage. It was, I mean, it was some amount of disease. And then it was kind of like cunning strategic play. And so I think there's a, there's a possibility that even sort of early on, you know, you have them gone through the full industrial explosion yet. You have super intelligence, but you know, you're able to kind of like manipulate the imposing generals, claim you're allying with them. Then you have you have some, you know, you have sort of like some crazy new bio weapons.

1:23:33Maybe maybe there's even some way to like pretty easily get a paradigm that like activates enemy nukes. Anyway, so I think the stuff could get pretty wild. Here's what I think we should do. I really don't want this volatile period. And so, a deal with China would be nice. It's going to be really tough if you're in this unstable equilibrium. I think basically we want to get in position where it is clear that the United States that a sort of coalition of democratic allies will win. Just clear the United States are declared to China. You know, that will require having locked down the secrets that will require having built the 100 gigawatt cluster in the United States and having done the natural gas and doing what's necessary.

1:24:08And then when it is clear that the democratic coalition is well ahead, then you go to China and then you offer the Medieval. And you know, China will know they're going to win. This is going to be, they're very scared of what's going to happen. We're going to know we're going to win, but we're also very scared of what's going to happen because we really want to avoid this kind of like break net break neck race right at the end. And where things could really go awry. And, you know, and then, and then so then we offer the Medieval. I think there's an incentive to come to the table. I think there's a sort of more stable arrangement you can do.

1:24:37It's a sort of an Adams for peace arrangement. And we're like, look, we're going to respect you. We're not going to like, we're not going to use super intelligent against you. You can do what you want. You're going to get you're like, you're going to get your slice of the galaxy. We're going to like, we're going to benefit share with you. We're going to have some like compute agreement where it's like, there's some ratio of compute that you're allowed to have. And that's like, enforced with her like, opposing a eyes or whatever. And we're just not going to do, we're just not going to do this kind of like volatile sort of WMD arms race to the death.

1:25:03We're good. And sort of it's like a new world order that's US led that sort of democratic led, but that respects China. Let's do what they want. Okay, there's so much to do so much there. First on the galaxy's thing, I think it's just a funny anecdote. I want to kind of want to tell it. And this we're at an event, the number -specting chat on House rules here. I'm not revealing anything about it. But we're talking to somebody, or a leopold was talking to somebody influential. Afterwards, that person asked the group. Leopold told me that he wants, he's not going to spend any money on consumption until he's ready to buy galaxies.

1:25:39And he goes, the guy goes, I honestly don't know if you meant galaxies, like the brand of private plane galaxy or the physical galaxies. And there was an actual debate. Like, you went away to the restroom and there was an actual debate among people who are very influential about, he can't amend galaxies. And I've learned that other people who knew you better, be like, no, he means galaxies. I mean, the galaxies. I think it'll be interesting. I mean, yeah, I think there's, I mean, there's two ways to buy the galaxies. One is like at some point, you know, it's like post -Sufferntaleged. See, oh, they're surprised.

1:26:12I love, okay, so what happens is he's out there. I'm laughing at Asam. I'm not even saying good thing. People were like, gaffing this debate. And then, so Leopold comes back. And the guy, somebody's like, oh, Leopold, we're having this debate about whether you meant you want to buy the galaxy or you want to buy the other thing. And Leopold assumes they must mean not the brightest. They're the galaxy or the actual galaxy. But do you want to buy the property rights of the galaxy? Or actually just send out the probes right now? Exactly.

1:26:46Oh my god. All right. Back to China. There's a whole bunch of things I could ask about that plan about whether you're going to get incredible promise. Yeah. Yeah. You will get some part of the galaxies, whether they care about that. I mean, you have your eyes helping you enforce stuff. Okay, sure. We'll leave that aside. That's a different rabbit hole. The thing I want to ask is, but it has to be the thing we need, the only way this is possible is if we lock it down. I see. If we don't lock it down, we are in this fever struggle greatest peril mankind will have ever seen. So, but given the fact that in during this period, instead of just taking their chances and they don't really understand how this AI governance scheme is going to work, where they're going to check, whether we have that you get the galaxies.

1:27:31Yeah. The data centers, they can't be built underground. They have to be built above ground. Taiwan is right off the coast of us. They need the chips from there. Yeah. Why aren't we just going to invade? Listen, we don't want like worst case scenario is they win the super intelligence, which they're on track to do anyways. Wouldn't this instigate them to either invade Taiwan or blow up the data center in Arizona or something like that? Yeah. Yeah. I mean, look, I mean, talk about the data center one. And then, you know, you probably have to like, threaten nuclear retaliation to protect that. They might also just blow it up.

1:27:57There's also maybe ways they can do it without sort of attribution, right? Like you suck. Sucksnet. Sucksnet. Yeah. I mean, this is, I mean, this is part of, we'll talk about this later, but you know, I think, I think we need to be working on the sucks net for the Chinese project. But but by the audience, Taiwan, I mean, Taiwan, the Taiwan thing, the, you know, I talk about, you know, EGI, about, you know, 27 or whatever. Do you know about the like terrible 20s? No. I mean, sort of in the sort of Taiwan watchers circles, people often talk about like the late 2020s, like maximum period of risk for Taiwan, because sort of like, you know, military modernization cycles and basically extreme fiscal tightening on the military budget in the United States over the last decade or two, as meant that sort of we're in this kind of like, you know, trough in the late 20s of like, you know, basically overall naval capacity.

1:28:42And you know, that sort of when China is saying they want to be ready. So it's already kind of like it's kind of pitching, you know, there's some sort of like, you know, parallel timeline there. Yeah, look, it looks appealing to invade Taiwan. I mean, maybe not because they, you know, basically remote cutoff of the chips. And so then it doesn't mean they get the chips, but it just means they, they, you know, it's just, it's, you know, the machines are deactivated. But look, I mean, imagine if during the cold war, you know, all of the world's uranium deposits had been in Berlin, you know, and Berlin was already, I mean, almost multiple times in this caused nuclear war.

1:29:13So God help us all. The growth had a plan after the, after the war, yeah, that the plan was that the America would go around the world and getting the rights to every single uranium deposit. Because it didn't realize how much uranium there was in the world. And that this was the thing that was feasible. Not realizing, of course, that there's like huge deposits in the Soviet Union itself. Right. Um, okay, extremely too. There's a, there's always a, there's a lot of East German workers who kind of got screwed. Oh, interesting. Got cancer. Okay. So the framing we've been talking about that we've been assuming.

1:29:45Yeah. And I'm not sure I buy yet is that the United States, yeah, this is our leverage. This is our data center, the China is the competitor. Right now, obviously, that's not the way things are progressing. Private companies control these AIs. They're deploying them. It's a market -based thing. Uh -huh. Why, why will it be the case that the, it's like the United States, it has this leverage or is doing this thing versus China is doing this thing? Yeah. I mean, look, look on the, on the project, you know, I mean, there's sort of descriptive and prescriptive claims or normative positive claims. I think the main thing I'm trying to say is, you know, look, we're at, we're at these SF parties or whatever.

1:30:21And I think people talk about AGI and they're always just talking about the private AI labs. And I think I just really want to challenge that assumption. It just seems like seems pretty likely to me, you know, as we've talked about for reasons we've talked about that look like the national security state is going to get involved. And, um, you know, I think there's a lot of ways this could look like, right? Is it, is it like nationalization? Is it a public -private partnership? Is it a kind of defense contract or like rematialism? Is it a sort of government project that suits up all the people?

1:30:48And so there's a spectrum there. Um, but I think people are just vastly underreading, um, the chances of this more or less looking like a government project. Um, and look, I mean, look, if, if, you know, it's sort of like, you know, do you, do you think, do you think like we all have literal, like, you know, when we have like literal superintelligence on our cluster, right? And it's like, you know, you have a hundred billion, they're like, sorry, that you have a billion, like superintelligence scientists, they can like hack everything, they can like, stuck the Chinese data centers, you know, they're starting to build the Robo armies, you know, you like, you really think that'll be like a private company.

1:31:18And the government would be like, oh my god, what is going on? You know, like, yeah. So suppose there's no China. Suppose there's people like Iran, North Korea, who theoretically at some point will go to do superintelligence, but they're not on our heels and they don't have the ability to be on our heels. In that world, are you advocating for the national project or do you prefer the private path forward? Yeah. So I mean, two responses to this one is, I mean, you still have like Russia, you still have these other countries. Um, you know, you've got to have Russia proof security, right? It's like, you, you can't, you can't just have Russia steal all your stuff.

1:31:49And like, maybe their clusters aren't going to be as big, but like, they're still going to be able to make the crazy bio weapons and the, you know, the musculosized dreams so on, you know, and so on. And so I mean, I think, I think, I think the security component is just actually a pretty large component of the project in the sense of like, I currently do not see another way where we don't kind of like instantly proliferate this to everybody. Um, and so, yeah. So I think it's sort of like, you still have to deal with Russia, you know, Iran with Korea. And you know, like, you know, Saudi and Iran are going to be trying to get it because they want to screw each other and you know, Pakistan and India because they want to screw each other.

1:32:20There's like this enormous destabilization still. Um, that said, look, I agree with you. If, if, you know, if, you know, by some, somehow things that checking out differently, I'm like, you know, Igei would have been in 2005, um, you know, sort of like unparalleled, you know, American, and Germany. Um, I think there would have been more scope for less government involvement. Um, but again, you know, as we were talking about earlier, I think that would have been sort of this like very unique moment in history. And I think basically, you know, almost all other moments in history, there would have been the sort of great power comp competitor.

1:32:48So, um, okay, so let's get into this debate. So my position here is if you look at the people who are involved in the man hand project itself, yeah, many of them regretted their participation. As you said, now we can infer from that that we should sort of start off with a cautious approach to the nationalized ASI project. Then you might say, well, listen, obviously, they were, did they regret their participation because of the project or because of the technology itself. I think people will regret it. But I think it's, it's about the nature of the technology and it's not about project. I think they also probably had a sense that different decisions would have been made if it wasn't some conservative effort that everybody had agreed to participate in that if it wasn't in the context of this, we need to race to be to the Germany and Japan.

1:33:36You might not develop, so that's a technology part, but also like you wouldn't actually like hit them. It's like, you know, it's like the sort of the destructive potential, the sort of military potential. It's not, it's not because of the project. It is because of the technology and that will unfold regardless. I think this underrates the power of modeling. You imagine you go through like the 20th century in like, you know, a decade. It's just the sort of, the sort of yes, great. Actually, let's actually run that example. So, as you actually, there was some reason that the 20th century would be run through in one decade.

1:34:06Do you think the cause of that should have been, sort of, then like the technologies that happened through the 20th century shouldn't have been privatized? That it should have been a more sort of concerted government led project. You know, look, there is a history of just dual use technologies, right? And so I think AI in some sense is going to be dual use in the same way. And so there's going to be lots of civilian uses of it, right? Like nuclear energy itself, right? There's like, you know, there's the government project to develop the military angle of it. And then, you know, it was like, you know, then the government worked with private companies.

1:34:38There's a sort of like real like flourishing of nuclear energy and told you know, the environmental stopped it. You know, um, um, um, planes, right? Like Boeing, right? Actually, you know, the Manhattan project wasn't the biggest defense R &D project during World or two. It was the B29 bomber, right? Because they needed the bomber that had long enough range to reach Japan, to destroy their cities. Um, and then, you know, Boeing made some Boeing made that, B Boeing made the B47 made the B52, you know, the plane the US military uses today. And then they used that technology later on to, um, to, you know, build the 707 and this sort of the, but what is later on mean in this context?

1:35:10Because in the other, like, I get what it means after a war to privatize. But if you have the government has ASI, let me just let me back up an explain my concern. Yeah. So you have the only institution in our society, which has a monopoly on violence. Yes. Um, and then we're going to give the give it some, uh, in a way that's not broadly deployed access to the ASI. Yeah. The counterfactual and this may be sound silly, but listen, we're going to go through hired higher levels intelligence. Yeah. Private companies will be required by regulation to increase their security. Yeah. But they'll still be private companies and they're deployed this and they're going to release the AGI, now McDonald's and JP Morgan and some random startup are now more effective organizations because they have a bunch of AGI workers.

1:35:55And it'll be sort of like the industrial revolution in the sense that the benefits were widely diffused. If you don't end up in a situation like that, then the, I mean, even backing up, like, what is it retrying to why do we want to win against China? We want to win against China because we don't want a top down authoritarian system to win. Yeah. Now if the way to beat that is that the most important technology that humanity will have has to be controlled by a top down government, like what was the point? Like, maybe, so let's like run our cards with privatization. That's the way we get to this classic liberal market -based system we want for the AGI.

1:36:32Yeah. All right. So a lot of talk about here. Yeah. I think, yeah, maybe I'll start a bit about like actually looking at what the private world would look like. And I think this is part of where the sort of there's no alternative comes from. And then let's look like look at like what the government project looks like, what checks and balances look like and so on. All right. Private world. I mean, first of all, okay, so right, like a lot of people right now talk about open source. And I think there's this sort of misconception that like AGI development is going to be like, oh, it's going to be some like beautiful decentralized thing.

1:36:57And you know, like, you know, some goody community of coders who gets to like, you know, collaborate on it. That's not how it's going to look like. Right? You know, it's, you know, the $100 billion trillion dollar cluster. It's not going to be that many people that have it. The algorithms, you know, it's like right now open source is kind of good because people just use the stuff that was published. And so they basically, you know, the algorithms were published or, you know, as mistroll, they just kind of like leave deep mind and you know, take all the secrets out of them and they just kind of replicated it.

1:37:18But that's not going to continue being the case. And so, you know, the sort of like open source will turn, I mean, also people say stuff like, you know, 1026 flops, it will be in my phone or, you know, it's, you know, it won't, you know, it's like, Moore's law is really slow. I mean, AGI chips are getting better. But like, you know, the $100 billion dollar computer will not cost, you know, like $1 ,000, you know, within your lifetime or whatever, less time for me. So, it's going to be, it's going to be like two or three, you know, big players on the private world. And so look, a few things. So first of all, you know, you talk about the sort of like enormous power that sort of super intelligence will have and the government will have.

1:37:57I think it's pretty plausible that the alternative world is that like one AI company has that power, right? And it's basically, we're talking about lead, you know, it's like, what? I don't know, opening eyes is six months lead. And then, you know, so then you're not talking, you're talking about basically, you know, the most powerful weapon ever. And it's, you know, you're kind of making this like radical bet on like a private company CEO is the benevolent dictator. No, no, no, this is not necessarily like any other thing that's privatized. We don't count on that being benevolent. We just look, think of, for example, somebody who manufactures industrial fertilizers, right?

1:38:26This, the person with this factory, if they went back to an ancient civilization, they could like blow up Rome, they could probably blow up Washington DC. And I think in their series, you talk about Tyler Collins phrase of modeling through. And I think even with privatization, people sort of underrate that there are actually a lot of private actors who have the ability to like, there's a lot of people who control the water supply or whatever. And we can count on cooperation and market based incentives to basically keep a balance of power. Sure. I get the things are proceeding really fast. Yes.

1:38:58But we like, we have a lot of historical governance. This is the thing that works best. So look, I mean, I mean, what do we do with nukes, right? The way we keep the sort of nukes in check is not like, you know, a sort of beefed -up second amendment where like each state has their own like little nuclear arsenal and like, you know, Dario and Sam of their own little nuclear arsenal. No, no, it's like, it's institutions. It's constitutions. It's laws. It's courts. And so, so I don't actually, I'm not sure that this, you know, I'm not sure that the sort of balance of power analogy holds. In fact, you know, sort of the government having the biggest guns was sort of like an enormous civilizational achievement, right?

1:39:30Like Lundfrieden in the sort of Holy Roman Empire, right? You know, if somebody from the town over kind of committed a crime on you, you know, you didn't kind of start a sort of, you know, a big battle between the two towns, no, you take it to a court of the Holy Roman Empire and they would decide. And it's a big achievement. Now, the thing about, you know, the industrial fertilizer, I think the key difference is kind of speed and often seafence balance issues, right? So it's like 20th century and, you know, 10 years and a few years. That is an incredibly scary period. And it is incredibly scary, you know, because it's, you know, you're going through just the sort of enormous array of destructive technology and the sort of like enormous amount of like, you know, basically military event.

1:40:07I mean, you would have gone from, you know, kind of like, you know, you know, bayonets and horses to kind of like tank armies and fighter jets in like a couple of years and then from, you know, like, you know, and then to like, you know, nukes and, you know, ICBMs and still, you know, it's just like in a matter of years. And so it is sort of that speed that creates, I think basically the way I think about it is there's going to be this initial just incredibly volatile and incredibly dangerous period. And somehow we have to make it through that. And that's going to be incredibly challenging. That's where you need the kind of government project.

1:40:37If you can make it through that, then you kind of go to like, you know, now we can, now, you know, the situation has been stabilized. You know, we don't face this imminent national security threat. You know, it's like, yes, there were kind of WMDs that came along the way. But either we've managed to kind of like have a sort of stable off in defense balance, right? Like I think bi -weapons initially are a huge issue, right? Like an attacker can just create like a thousand defense aesthetic, you know, viruses and spread them. And it's like going to be really hard for you to kind of like make it a fence against each.

1:41:00But maybe at some point you figure out the kind of like, you know, universal defense against every possible virus. And then you're in a stable situation again on the off in defense balance, or you do the thing, you know, you do with planes where it's like, you know, there's certain capabilities that the private sector isn't allowed to have. And you've like figured out what's going on, restrict those. And then you can kind of like let, let, you know, you let this sort of civilian, civilian uses, yeah, I'm skeptical of this because well, sorry, I mean, the other important thing is, so I talked about this sort of, you know, maybe it's like, it's, it's a, you know, it's, you know, it's one company with all this power.

1:41:32And I think it's like, I think it is unprecedented because it's like the industrial fertilizer guy cannot overthrow the US government. I think it's quite plausible that like the AI company is super intelligence can overthrow the US company. But there will be multiple AI companies, right? And I buy the one that could be ahead. So it's not obvious that it'll be multiple. I think it's again, if there's like a six monthly, maybe, maybe there's two or three, but if there's two or three, then what you have is just like the crazy race between these two or three companies, you know, it's like, you know, whatever, Demis and Sam, they're just like, I don't want to let the other one win.

1:41:57And, and they're both developing their nuclear arsenals and the real, it's just like also like, come on, the government is not going to let these people know, are they going to let like, you know, is Dario going to be the one developing the kind of like, you know, you know, super hacking Stuxnet and like deploying against the Chinese data center. The other issue though is it won't just, if it's two or three, it won't just be two or three. There'll be two or three and I'll be China and Russia and North Korea because the private and the private lab world, there's no way they'll have security. That is good enough.

1:42:22I think we're also assuming that somehow if you nationalize it, like the security just, especially in the world, the rate that did this stuff is priced in by the CCP, that now you've like got a nail down and I'm not sure why we would expect that to be the case. But on this, the only one who does stuff. So, if it's not Sam or Dario who's, we don't want to trust them to be benevolent dictator, whatever. Now, but here we're counting on, if it's because you can cause a coup, the same capabilities are going to be true of the government project, right? And so the model president in 2020, 2025, but Donald Trump will be the person that you don't trust Sam or Dario to have these capabilities.

1:43:03And why? Okay, I agree that I'm worried if the Sam or Dario have a one -year lead on ASI in that world, then I'm concerned about this being privatized. But in that exact same world, I'm very concerned about Donald Trump having the capability and potentially for living a world where the take office floor that you anticipate, in that world, I'm like very much I want the private companies. So, like in no part of this matrix, obviously true that the government led project is better than the private project. Talk about the government project a little bit and checks and balances. In some sense, I think my argument is a sort of birken argument, which is like American checks and balances have held for you know, over 200 years and through crazy technological revolutions, you know, the US military could kill like every civilian in the United States.

1:43:45But you're going to make that argument, the private public balance of power itself for hundreds of years and then like, but yeah, why is it housed? Because the government has the biggest guns. And it's never before has a single CEO or a random nonprofit board had the ability to launch nukes. And so again, it's like, you know, what is the track record of the government checks and balances or is the track record of the private company checks and balances? Well, the I lab, you know, like first -stress test, you know, what really badly, you know, that didn't really work, you know. I mean, even worse in the sort of private company world.

1:44:14So, it's both like, it is not just that it is like the two private companies and the CCP and they just like instantly have all the shit. And then it's, you know, they probably won't have good enough internal control. So it's like, not just like the random CEO, but it's like, you know, rogue employees that can kind of like use these super intelligences to do whatever they want. And this won't be sure of the government, like the rogue employees won't exist on the project. Well, the government actually like, you know, has decades of experience and like actually really cares about the stuff. I mean, it's like they deal with nukes.

1:44:41They deal with really powerful technology. And it's, you know, this is like, this is the stuff that the national security state cares about. You know, again, to the guy, let's talk about the government checks and balances a little bit. So, you know, what are checks and balances in the government world? First of all, I think it's actually quite important that you have some amount of international coalition. And I talked about these sort of two tiers before. Basically, I think the inner tier is a sort of modeled on the cool back agreement, right? This was like Churchill and Roosevelt. They kind of agreed secretly.

1:45:05We're going to like pull our efforts on nukes, but we're not going to use them against each other and we're not going to use them against anyone else with their consent. And I think basically look, bring in, bring in the UK, they have deep mind, bringing in the kind of like Southeast Asian states who have the chips of the blockchain, bring in some more kind of like NATO close democratic allies for, you know, talent and industrial resources. And you have this sort of like, you know, so you have, you have those checks and balances in terms of like more international countries at the table. Sorry, somewhat separately, but then you have the sort of second tier of coalitions, which is the sort of Adams for peace thing where you go to a bunch of countries, including like the UAE and you're like, look, we're going to basically like, you know, there's a deal with so much like the NPT stuff where it's like, you're not allowed to like do the crazy military stuff, but we're going to share the civilian applications.

1:45:45We're in fact going to help you and share the benefits and you know, sort of kind of like this new sort of post super intelligence world order. All right, US checks and balances, right? So obviously, Congress is going to have to be involved, right? Appropriate and trillions of dollars. I think probably ideally you have, Congress needs to kind of like confirm whoever's running this. So you have Congress, you have like different factions of the government, you have the courts, I expect the first amendment to continue being really important. And maybe that, I think that sounds kind of crazy to people, but I actually think again, I think these are like institutions that will help us test of time in a really sort of powerful way.

1:46:16You know, eventually, you know, this is why honestly, alignment is important is like, you know, the eyes, you program the eyes to follow the constitution. And it's like, you know, why does the military work? It's like generals, you know, are not allowed to follow unlawful orders. They're not allowed to follow unconstitutional orders. You have the same thing for the eyes. So what's wrong with this argument? Well, you say, listen, maybe you have a point in the world where we have extremely fast takeoff. It's like one year from age, I do ASI. Yeah. And then you have the like, years after the side where you have this like extraordinary, I think that's not possible.

1:46:46Maybe you have a point. Yeah. We don't know. You have these arguments will like get into the weeds on them about why that's a more likely world, but like maybe that's not the world we live in. Yeah. And in the other world, I'm like very on the side of making sure that these things are privately held. Now, why? I mean, I'm not sure. Yeah. Yeah. Yeah. So when you nationalize, yeah, that's a one -way function. You can't go back. Why not wait until we have more evidence on which of those worlds we live in? Why? I think like rushing on the nationalization might be a bad idea while we're not sure. And yeah, okay, let's just run it at first.

1:47:19I mean, I don't expect us to nationalize tomorrow. If anything, I expected to be kind of with COVID where it's like kind of too late. Like ideally you nationalize it early enough to like actually lock stuff down. It'll probably be kind of chaotic. And like, you're going to be trying to like do this crash program lock stuff down and it'll be kind of late. It'll be kind of clear what's happening. We're not going to nationalize when it's not clear what's happening. I think the whole bow of the whole story is institutions have held up well. First of all, the battery almost broken a bunch of times.

1:47:44It's like this is this is this is room. This is the end break. The first time we're the argument that some people who are say that we shouldn't be that concerned about nuclear war say or it's like lesson we have the nuke for 80 years. Yeah. And like we've been fine so far. So the risk must be low. And then the answer to that is no. Actually, it is a really high risk. And the reason we've avoided is like people have gone through a lot of effort to make sure that this thing doesn't happen. I don't think that giving government ASI without knowing what that implies is going through a lot of effort.

1:48:12And I think the base rate like you you can talk about America. I think America is very exceptional. Yeah. Not just in terms of dictatorship, but in terms of every other country in history has had a complete drawdown of wealth because of war revolution and something America is very unique in not having that. And the historical base rate we're talking about green power competition. I think that has a really big that's something we haven't been thinking about the last 80 years, but it's really big. Yeah. Dictatorship is also something that is just the default state of mankind. Yeah. And I think relying on institutions which in an ASI world like there's a fundamentally right now if the government tried to overthrow there's a it's much harder if you don't have the ASI right like there's people who have a K -A -R -4 -15s and I there's like things that make it hard and crush the R -15s.

1:48:59No, I think it actually be pretty hard. The reason I was being non -Mina Afghanistan is pretty hard. Yeah, I agree. But like I'm good. I agree. I agree. I'm the ASI. Yeah, I think it's just like easier if you have what you're talking about with that institution. With the constitution, their legal restraints, their courts, their checks and balances. The crazy bet is the bet which are like private companies. The same thing, by the way, it isn't the same thing true of NUX where we have these institutional agreements about non -proliferation and whatever. And we're still very concerned about that being broken and somebody getting NUX and like you should stay up that night worrying about that.

1:49:31That's a very situation. But ASI is going to be a really precarious situation as well. And like given given how precarious NUXR we've done pretty well. And so what is privatization in this world even mean? I mean, I think the other thing is like what happened after? I mean, the other thing you know, whether the government project is good or not. And it's like I have very mixed feelings about this as well. Again, I think my primary argument is like, you know, if you're at the point where this thing has like vastly superhuman hacking capabilities. If you're at the point where this thing can develop, you know, bio weapons, you know, like, increasing bio weapons.

1:50:01One that are like targeted, you know, can kill everybody but the hand Chinese or you know, that, you know, you know, would wipe out, you know, entire countries where you're talking about like building robo armors. You're talking about kind of like drone swarms that are, you know, again, the mosquito sized drones that could take it out, you know, the United States National Security State is going to be intimately involved with this. And this will, you know, the labs, whether, you know, and I think again, the government, a lot of what I think is the government project looks like, it is basically a joint venture between like, you know, the cloud providers between some of the labs and the government.

1:50:31And so I think there is no world in which the government isn't intimately involved in this like crazy period. The very least basically, you know, like the intelligence agencies need to be running security for these labs. So they're already kind of, like, they're controlling everything, they're controlling access to everything. Then they're going to be like, probably again, if we're in this like really volatile international situation, like a lot of the initial applications, it'll, it'll suck. It's not what I want to use ASI for will be like, trying to somehow stabilize this crazy situation. Somehow we need to prevent like proliferation of like some crazy new WMDs and like the undermining of mutually assured destruction to kind of like in a North Korea and Russia and China.

1:51:07And so I think, you know, I basically think your world, you know, I think there's much more spectrum than you're acknowledging here. And I think basically the world in which it's private labs is like extremely heavy government involvement. And really what we're debating is like, you know, what form of government project, but it is going to look much more like, you know, the national security state than anything it does look like, like a startup as it is right now. And I think the, yeah. Look, I think something like that makes sense. I would be, if it's like the Manhattan project that I'm very worried where it's like this is part of the US military.

1:51:39Where I've had some more like, listen, you got to talk to Jake Sullivan before you like run the next training line. He's like Lockheed Martin scunkwards part of the US military. It's like, they call the shots. Yeah, I don't think that's great. I think that's bad. I think it would be bad if that happened with ASI. And like, what is what is the scenario? What is the alternative? Okay, so it's closer to my end of the spectrum where, yeah, you do have to talk to Jake Sullivan before you can launch an extraening cluster. Yeah, but there's many companies who are still going for it. Yeah. And the government will be intimately involved in the security.

1:52:10Yeah. The, but the like three different companies are trying to launch the stocks net attack. Yeah. What he is launching launching. Okay. So, are you deactivating the Chinese data? I think this is a similar to the story you can tell about. There's a lot of like literally the big tech right now. Yeah. I think Sasha, if you wanted to, he probably like could get his engineers like, what are the zero days and windows and the companies and the guy and like, well, how do we get in full straight the president's computer? So that like, we can shut down. No, no, no, like right now I'm saying Sasha could do that right?

1:52:40Because he knows he's shut down. What do you mean government wouldn't let them do that? Yeah, I think there's a story you could tell where like they could pull up a coup or whatever, but like I think there's like multiple companies. Okay, okay, I agree. I'm just saying like something closer to.

1:52:57So, you the government is there's like multiple companies going for it. Yeah. But the AI is still broadly deployed and alignment works in the sense that you can make sure that it's not you, the system level prompt is like you can't help people make bio weapons or something. But these are still broadly deployed. So that I mean, I expect the AI to be broadly deployed. I mean, first of all, even if it's a government project, yeah, I mean, look, I think first of all, like I think the matter is the world, you know, open sourcing their eyes, you know, that are two years behind or whatever. Yeah, super valuable role.

1:53:24They're going to like, you know, and so there's going to be some question of like either the offense defense balance is fine. And so like even if they open source to your old AI is it's fine or it's like there's some restrictions on the most extreme due use capabilities like, you know, you don't let private company sell kind of crazy weapons. And that's great. And that will help with the diffusion. And, you know, you know, after the government project, you know, there's going to be this initial tense period, hopefully that's stabilized. And then look, yeah, like Boeing, they're going to go out and they're going to like make do all the flourishing civilian applications and, you know, like nuclear energy, you know, it'll like all the civilian applications will have their day.

1:53:55I think part of my argument here is that, and how does that proceed, right? Because in the other world, there's existing stocks of capital that are worth a lot of the questions. There'll be still be Google clusters. And so Google, because they got the contract from the government, there'll be the ones that control the AI. But like, why are they trading with anybody else? Why is there a random start -up get like the same, it'll be the same companies that would be doing it anyway. But in this, in this world, they're just contracting with the government or like their DPA for all their compute goes to the government.

1:54:21And but it's very natural. After you get the assailant, we're building the robot armies and building fusion reactors or whatever, that the that's government will get to build robot armies. Yeah, that one worried. Or like the fusion reactors and stuff. It's what we do with this because it's a situation we have today. Because if you already have the robot armies and everything, like the existential society doesn't have some leverage where it makes sense with the government to, yeah, the given the sense that there's like, have a lot of capital that the government wants. And there's other things like why was Boeing privatized after?

1:54:52The biggest guns. The government has the biggest guns and the waiver regulated institutions, constitutions, legal restraints. Okay, so tell me what privatizers should look like in the assailant world afterwards. Afterwards, like the Boeing example, right? It's like, you have this government. Who gets it? Like Google, Microsoft, and we're selling it too. Like they already have a robot factory. And then why are they selling it to us? Like they already have their they don't need like our, this is a chum change in the assailant world. Because we didn't get like the the ass I broadly deployed throughout the stake -off.

1:55:17So we don't have the fusion reactors and whatever advanced decades of advanced science that you're talking about. So like it just what what are they trading with us for? Trading with whom for? Everybody who was not part of the project. They got the technology that's decades ahead. Yeah, I mean, like that's a whole nother issue. Like how does like economic distribution work or whatever? I don't know. That'll be rough. Yeah. I don't, basically, I'm kind of like, I don't see the alternative. The alternative is you like over turn a 500 year civilizational achievement of Lonfleet. You basically instantly leak the stuff to the CCP.

1:55:49And either you like barely scrape out ahead. And but you're in this fever struggle. You're like proliferating crazy WMDs. It's just like enormously dangerous situation, enormously dangerous on alignment. Because you're in this kind of like crazy race at the end. And you don't have the ability to like take six months to get alignment. Right. You know, alternative is, you know, I'll try to it is like you aren't actually bundling your efforts to kind of like win the race against the authoritarian powers. And you know, yeah. And so you know, I don't like it. You know, I wish I wish the thing we use the ASI for is to like, you know, cure the diseases and do all the good in the world.

1:56:25But it is my prediction that sort of like by the in the end game, what will be at stake will not just be kind of cool products. But what will be at stake is like whether the liberal democracy survives, like whether the CCP survives, like what the world order for the next century will be. And when that is at stake, forces will be activated that are sort of way beyond what we're talking about now. And like, you know, in in this sort of like crazy race at the end, like the sort of national security implications will be the most important. You know, sort of like, you know, world were true. It's like, yeah, you know, nuclear energy had its day.

1:57:01But in the initial kind of period, when, you know, when this technology was first discovered, you had to stabilize the situation, you had to get new, because you had to do it right. And then and then the civilian applications have the day. I think of closer analogy to what this is because nuclear, I agree the nuclear energy is a thing that happens later on and it's like dual use in that way. But it's something that happened like literally a decade after nuclear weapons were developed. Yeah, because where, where, is with AI, like the immediately all the applications are unlocked and it's closer to literally, I mean, this is analogy people actually make a context of AGI is like, assume your society had 100 million more John winnowements.

1:57:34Yeah. Yeah. And I don't think like if that was literally what happened. Yeah. Tomorrow, you just have 100 million more of them. Yeah. The approach should have been, well, some of them will convert to ISIS and we need to like really be really careful about that. And then like, oh, you know, like, what if I've mentioned them are born in China and then we, like, if we got to nationalize the John winnowements, I'm like, though, I think it'll be generally a good thing. And I'd be concerned about one power had getting like all the John winnowements. I mean, I think the issue is this sort of like bottling up in the sort of intensely short period of time.

1:58:00Like this enormous sort of like, you know, unfolding of technological progress of an industrial explosion. And I think we do worry about the 100 million John winnowements. And it's like, rise of China. Why are we worried about the rise of China? Because it's like 100 billion people and they're able to do a lot of industry and do a lot of technology. And but it's just like, you know, the rise of China times like, you know, 100 because not just 100 one billion people. It's like a billion super intelligent crazy, you know, crazy things. So and and in like, you know, very short period. Let's talk practically because if the goal is we need to beat China.

1:58:32Part of that is protecting. That's one of the goals, right? Yeah, I agree. One of the goals is we China. And also that managed this incredibly crazy story period. Right. Right. So part of that is making sure we're not leaking our own secrets to them. Yep. Part of that is a lot of cluster. I mean, building the trillion dollar cluster. That's right. Yeah, but like, you're using your whole point, the Microsoft and release corporate bonds that are I think Microsoft can do the like hundreds of billions of billion dollar cluster. Yeah. I think that I think the trillion dollar cluster is closer to a national after I thought that earlier point was that American capital markets are deep and they're good.

1:59:01They're pretty good. I mean, I think the trillion I think it's possible. It's private. It's possible. But it's going to be like, and you know, at this point, we have it, AGI that's a dribbling accelerating productivity. I think the trillion dollar cluster is going to be planned before before the AGI. It's it's I think I think I think it's sort of like you get the AGI on the like 10 gigawatt cluster like intelligence. Maybe you have like one more year where you're kind of doing some final and hobbling to fully unlock it. Then you have the intelligence explosion. And meanwhile, the like trillion dollar clusters almost finished.

1:59:25And then you like, and then you do your super intelligence and your trillion dollar cluster or you run it on your trillion dollar cluster. And by the way, you have not just your trillion dollar cluster, but like, you know, hundreds of millions of GPUs and inference clusters everywhere. And this isn't result. Like I think private. But even in this world, I think private companies have the capital and can raise capital. Then you will need the government force to do it fast. Yeah. Well, I was just about to ask like, wouldn't it be the like we know a company's are on track to be able to do this and be trying to, if they're unhindered by, yeah, climate pledges or whatever.

1:59:52Well, that's part of what I'm saying. So if that's a whole case, and if it's all going to be all kinds of practical difficulties of like, will the AI researchers actually join the AI effort? If they do, yeah, there's going to be three different teams at least who are currently doing private pre -training on different different companies. Yeah. Now who decides at some point, you're going to have to be like, you're like, you'll know the hyper parameters of the trillion dollar cluster parameters. Like who decides that? And just like merging extremely complicated research and development processes across very different organizations.

2:00:28Yeah. This is somehow supposed to speed up America against the Chinese. Like, why don't we just let it in? Brain and deep mind merge and it was like a little messy. It was pretty messy. And it was also the same company and also much earlier on in the process. Pretty similar, right? Same code, different code bases and like lots of different infrastructure and different teams. And it was like, you know, it wasn't like, it wasn't the smoothest of all processes. But you know, deep mind is doing I think very well. I mean, look, you give the example of COVID and the COVID example is like, listen, we woke up to it.

2:00:53Maybe it was laid, but then we deployed all this money. And COVID response to government was a cluster fuck over. And like the only part of it that was worked is I agree Warp Street was like enabled by the government. It was literally just giving the permission that you can actually do. We were also taking making like the big, contra -comments or whatever. But it was like fundamentally was like a private sector like effort. Yeah. That was the only part of COVID that worked. I mean, I think, I think, again, I think the project will look close to Operation Warp Speed. And it's not even, I mean, I think you'll have all the companies involved in the government project.

2:01:21I'm not that sold that merging is that difficult. You have one. You select one code base and you run free training on like GPUs with one code base. And then you do the sort of second RL step on the other code base with TPUs. And I think it's fine. I mean, to the topic of like, well, people sign up for it. It went sign up for it today. I think this would be kind of crazy to people. But also, I mean, this is part of the like secrets thing. You know, people, gather parties or whatever. You know, you know, this, you know, I don't think anyone has really gotten up in front of these people and been like, look, you know, the thing you're building is the most important thing for like the national security of the United States for like whether, you know, like, you know, the free world will have another century ahead of it.

2:02:00Like this is the thing you're doing is really important. Like for your country, for democracy. And you know, don't talk about the secrets. And it's not just about, you know, deep mind or whatever it's about, it's about, you know, these really important things. And so, you know, I don't know, like again, we're talking about the Manhattan Project, right? This but you know, at some point it was like clear that this stuff was coming. It was clear that there was like sort of a real sort of like exigency on the military national security front. And, you know, I think a lot of people come around on the like whether it'll be competent.

2:02:32I agree. I mean, this is again, where it's like a lot of this stuff is more like predictive in the sense. I think this is like reasonably likely. And I think not enough people are thinking about it. You know, like a lot of people think about like AI lab politics or whatever. But like nobody has a plan for the project. You know, it's like, you know, like, sure, maybe you were pessimistic about it. And like, wait, don't have a plan for it. We need to do it very soon because AGI is upon us. Yeah. Then fuck the only capable, competent, technical institutions capable of making AI right now are private companies.

2:02:58Let's go. We're going to play that leading role. It'll be a sort of a partnership basically. But the other thing is like, you know, again, we talked about World or two. And, you know, American unpreparedness, the being of World or two is complete, you know, complete shambles, right? And so there is a sort of like very company. I think America has a very deep bench of just like incredibly competent managerial talent. You know, I think that, you know, there's a lot of really dedicated people. And, you know, I think basically a sort of operational warp speed, public private partnership, something like that, you know, is sort of what I imagine it would look like.

2:03:25Yeah. I mean, the recruiting the talent is an interesting question because the same sort of thing where initially for the Manhattan Project, you had to convince people we've got to beat the Nazis and you got to get on board. I think a lot of them maybe regretted how much they accelerated the bomb. And I want, I think this is generally a thing of war where, I mean, I think they're also wrong to regret it. But yeah, and why? What's the reason for regretting it? I think there's a world in which you don't have the way in which nuclear weapons were developed after the war was pretty explosive because there was a precedent that you actually can use nuclear weapons.

2:04:06Then because of the race that was set up, you immediately go to the H bomb. I mean, my view is, again, this is, this is related to the view on AI and maybe some of our disagreement is like, that was inevitable. Like, of course, like, you know, there was this, you know, world war. And then obviously there was the, you know, Cold War right after, of course, like, you know, the military and the kind of angle of this would be like, you know, pursued with ferocious intensity. And I don't really think there's a world in which that doesn't happen, which is like, ah, we're all not going to build nukes.

2:04:33And also, just like, nukes went really well. I think that could have gone terribly, right? You know, like, you know, again, I mean, this sort of, I think this is like not physically possible with nukes, this sort of pocket nukes for everybody. But I think sort of like WMDs that are sort of proliferated and democratized. And like, all the countries have it, like, the US leading on nukes. And then sort of like building this new world order that was kind of US -lad or at least sort of like a few great powers and a non -proliferation regime for nukes, a partnership and a deal that's like, no military sort of application of nuclear technology.

2:05:01But we're going to help you with the civilian technology. We're going to enforce safety norms on this in the world that worked, worked. And it could have gone so much worse. Okay, so I'm zooming out. I don't know if we can work. I mean, this is, I mean, I say this a bit in the piece, but it's like actually the A -bomb, you know, like the A -bomb and Hiroshima and Hiroshima and Hiroshima, it was just like, you know, the fire bombing, yeah. Fire bombing. I think the thing that really changed the game was like the super, you know, the H -bombs and ICBMs. And then I think that's really when it took it to like a whole new level.

2:05:27I think part of me thinks when you say we will tell the people for the free world to survive, we need to pursue this project. It sounds similar to World War II is, let me show you. So World War II is a sad story, obviously, in this fact that it happened, but also like the victory is sad in the sense that what you bring goes into protect Poland. And at the end, the USSR, which is, you know, as your family knows, is incredibly brutal, ends up occupying half of Europe. And the like part of like the, we're protecting the free world, that's where we gotta rush the AI. And like if we end up with the American AI Leviathan, I think there's a world where we look back on this where it has the same sort of twisted irony that Brinn going into World War II had about trying to protect Poland.

2:06:22Look, I mean, I think there's going to be a lot of unfortunate things that happened. I'm just like, I'm just hoping we make it through. I mean, to the point of it's like, I really don't think the pitch will only be the sort of like, you know, the race. I think the race will be sort of a backdrop to it. I think the sort of general like, look, it's important that democracy's shaped this technology. We can't just like leak this stuff to, you know, North Korea is going to be important. I think also for the just safety, including alignment, including the sort of like creation of new WMDs, I'm not currently sold.

2:06:48There's another path, right? So it's like, if you just have the breakneck race, both internationally, because you're just instantly leaking all this stuff, including the weights and just, you know, the commercial race, you know, Demis and Dario and Sam, you know, just kind of like they all want to be first. And then it's incredibly rough for safety. And then you say, okay, safety regulation. But you know, it's sort of like, you know, the safety regulation that people talk about, it's like, oh, well, NIST, and they take years and they figure out what the expert consensus is. And then they, it's not what's going to happen with the project as well.

2:07:15But I think, I mean, I think the sort of alignment angle during the intelligence explosion, it's going to, you know, it's not a process of like years of bureaucracy. And then you can kind of write some standards. I think it looks much more like basically a war. And like you have a fog of war, it's like, look, is it safe to do the next room, you know, and it's like, ah, you know, like, you know, we're like three rooms into the intelligence explosion. We don't really understand what's going on anymore. You know, the, you know, like a bunch of our like generalization scaling curves are like kind of looking not great.

2:07:42You know, some of our like automated eye researchers that are doing alignment are saying it's fine, but we don't quite trust them. In this test, you know, the, like they started doing naughty things and then we like hammered it out and then it was fine. And like, ah, should we should we go ahead? Should we take, you know, another six months also, by the way, you know, like China just all the weights are we, you know, they're about to like deploy the room or army. Like, what do we do? I think it's this, I think it is this crazy situation. And, you know, basically you, you were lying much more on kind of like a sane chain of command than you are on sort of some like, you know, the liberative regulatory scheme.

2:08:15I wish you had, you were able to do the liberative regulatory scheme. And this is the thing about the private companies too. I don't think, you know, they'll claim they're going to do safety, but I think it's really rough when you're in the commercial race and their startups, you know, and startups startups startups, you know, I think they're not fit to handle WMDs. Yeah, I'm coming closer to your position. But part of me also, so with responsible scaling policies, I was told that people who are advancing that, that the way to think about this, because they know I'm like a libertarian type of person.

2:08:49And the way they approached me about it was that fundamentally, this is a way to protect market based development of AGI. In the sense that if you didn't have this at all, then you would have this sort of misuse and then you would have to be nationalized. Yeah. And the RSPs are a way to make sure that through this deployment, you can still have a market based order. But then there's these safeguards that make sure that things don't go off the rails. And I wonder if the, it seems like your story seems self -consistent. But it does feel, I know this was never your position. So I'm not like, I'm not looping you into this, but a sort of a modern Bailey almost in the sense of, well, look, here's what I think about RSP type stuff for sort of safety regulation that's happening now.

2:09:39I think they're important for helping us figure out what world we're in and like flashing the warning signs on our coast. Right? And so the story we've been telling is sort of like, you know, sort of what I think the modal version of this decade is. But it's like, I think there's lots of ways it could be wrong. I really, you know, we should talk about the data while more. I think there's like, again, I think there's a world where the stuff stagnates, right? There's a world where we don't have AGI. And so I basically, the RSP thing is like preserving the optionality. Let's see how this stuff goes.

2:10:04But like, we need to be prepared. Like if the red lights start flashing, if we're getting the automated eye researcher, then it's crunch time. And then it's time to go. I think, okay, I can be on the same page on that that we should have a very, very strong fryer on a pursuing and market base way unless you're right about what the explosion looks like. They tell us a lotion. And so like, I don't move yet. But in that world where like really does seem like Alec Radford can be automated. And that is the only bottleneck to getting TSI. Okay, I think we can leave it at that. I can, I can, yeah, I am somewhat of the way there.

2:10:41Okay. Okay. Yeah. I hope it goes well. It's going to be, ah, very stressful. And again, right now is the chill time. Go to your location. It's funny to look out over. I'm just like, this is San Francisco. Yeah. Yeah. And opening eyes right there, you know, and the graphics there. I mean, again, this is kind of like, you know, it's like, you guys have this enormous power over how it's how it's going to go for the next couple of years. And that power is depreciating. Yeah. Um, who's you guys? Like, you know, people at labs. Yeah. Yeah. Um, but it is this sort of crazy world. And you're talking about like, you know, I feel like you talk about like, oh, maybe the nationalized is soon.

2:11:17It's like, you know, almost nobody like really like feels it sees what's happening. And it's it's, I think this is the thing that I find stressful about all the stuff is like, look, maybe I'm wrong. Like if I'm right, we're in this crazy situation where there's like, you know, a few hundred guys, like paying attention. Um, and it's, it's daunting. I went to Washington a few months ago. Yeah. And I was talking to some people who are doing AI policy stuff there. Yeah. And I was asking them how likely the thing nationalization is. Yeah. And they said, oh, you know, like, it's really hard to nationalize stuff.

2:11:49It's been a long time since we've done it. Yeah. It's these very specific procedural constraints on what kinds of things can be nationalized. And then I was asked, well, like ASI. So that means because there's there's constraints that defense production actor, whatever, that that won't be nationalized. There's this Supreme Court would overturn that. And they're like, yeah, I guess that would be nationalized. That's the short summary of my past or my view on the project.

2:12:21Okay. So before we go further on the AI stuff, let's just back off. Okay. You, uh, we begin the conversation. I think people we confuse you graduate, valedictorian of Columbia when you were 19. Uh -huh. So you got to college when you were 15. Right. And you grew, you grew to Germany. Yeah. You got to college of 15. Yeah. How the fuck did that happen? I really wanted out of Germany. I, you know, I went to kind of a, you know, German public school. It was a, it was not a good environment for me. Um, and, you know, I mean, in what sense is just like, no peers, like the dark, yeah, look, I mean, it wasn't, yeah, it was, you know, there's, I mean, there's also just a sense in which, um, sort of like, there's this particular sort of German cultural sense.

2:13:02I think in the US, you know, there's all these like amazing high schools and like sort of an appreciation of excellence. And in Germany, there's really this sort of like Paul Poppies in German, what was right where it's, um, you know, you're the curious kid in class and you want to learn more instead of the teacher being like, ah, that's great. They're like, they kind of present you for it. And they're like trying to crush you. And, um, I mean, there's also like, there's no kind of like elite universities for undergraduate, which is kind of crazy. Um, um, so, you know, the sort of, you know, there's sort of like, basically like the meritocracy was kind of crushed in Germany at some point.

2:13:31Um, also, I mean, there's a sort of incredible sense of, you know, complacency, um, you know, across the board. I mean, one of the things that always puzzles me is like, you know, even just going to a US college was just kind of like radical act. And like, you know, it doesn't seem radical to anyone here because it's like, ah, this is obviously the thing you do. And you can go to Columbia, you go to Columbia, but it's, you know, it's very unusual. And it's, it's, it's wild to me because it's like, you know, this is where stuff is happening. You can get so much of a better education and, you know, like America's were, you know, it's where, where, where, where all the stuff is.

2:14:03And, people don't do it. And, and so, um, yeah, anyway, so I, you know, I skipped a few grades and, and, you know, I think, um, at the time, it seemed very normal to me to kind of like go to college and go to America. I think, um, you know, now one of my sisters is now like turning 15, you know, and so then I, you know, and I look at her and I'm like, now I understand how many mothers I had. And as you get to college, you're like presumably the only 15 -year -old. Yeah, yeah. I was just like normal for you to be a 15 -year -old. Like, what was the initial years like? It felt so normal at the time.

2:14:35Yeah, so, yeah, it's like, now I understand why my mother's worried. And, you know, I think, you know, I worked, I worked on my parents for a while, you know, eventually I was, you know, I persuaded them. No, but yeah, it felt, felt very normal at the time. And it was great. It was also great because I, you know, I actually really like college, right? In some sense, it sort of came at the right time for me, where, you know, I, um, I mean, I, you know, for example, I really appreciated the sort of like liberal arts education and, you know, like the core curriculum and reading sort of core works of political philosophy and literature.

2:15:02And, um, you did what you con and, I mean, my majors were math and statistics and economics. Um, um, but, you know, Columbia is a sort of pretty heavy core curriculum and liberal arts education. And honestly, like, you know, I shouldn't have done all the majors. I should have just, I mean, the best courses were sort of the courses where it's like, there's some amazing professor and it's some history class and it's, um, I mean, that's, that's honestly the thing I would recommend people spend their time on in college. Was everyone professor or class that stood out that way? I mean, a few, there's like a class by Richard Betts, um, on, uh, Warpiece and Strategy, Adam Tuss obviously fantastic.

2:15:37Um, and you know, has written very reverting bucks. Yeah. Yeah. You should have them on the podcast by the way. I tried. Okay. I tried. I think you tried for me, but you gotta give them on the pod. Yeah. Oh, it'd be so good. Um, okay. So then in a couple of years, yeah, we were talking to Tyler Conn recently and he said that when the way we for, he first encountered you, yeah, was you wrote this paper on economic growth and existential rest and he said, I, when I found him, I couldn't believe that a 17 year old had written it. I thought if this was a MIT dissertation, I'd be impressed. So you were like, what, what, how did you go from?

2:16:15You're, I guess we've been junior then. You're writing, you're, you're writing, you know, pretty novel, economic papers. Uh, why did you get interested in this, this kind of thing and what, like, what was the process to get in that? I don't know. I just, you know, I get interested in things. In some sense, it's sort of like, um, it feels very natural to me. It's like, I get excited about a thing. I read about it. I immersed myself. I think I can, you know, I can learn information very quickly and understand it. Um, the, um, I mean, I think to the paper, I mean, I think one actual, um, at least for the way I work, I feel like sort of moments of peak productivity matter much more than sort of average productivity.

2:16:50I think there's some jobs, you know, like CFO or something, you know, like average productivity really matters. But I think there's sort of, uh, I often feel like I have periods of like, you know, there's some, there's a couple months where there's sort of net for lessons. And I'm like, you know, and the other times I'm sort of computing stuff in the background. And at some point, you know, like writing the series, this is also kind of someone, it's just like you, you write it and, and it's, it's like, it's really flowing. And, um, that's sort of what ends up matter. I think even for CEOs, it might be the case that the peak productivity is very important.

2:17:17There's one of our, uh, following chat and mouse rules, one of our friends in a group chat has pointed out how many famous CEOs and founders have been bipolar manic, which is very much the peak, um, like the call option on your productivity is the most important thing you get it by just increasing the volatility through bipolar. Uh -huh. Okay, so that's interesting. And so you get interested in economics first. First of all, why economics? Like you could read about anything at this moment. Like you, if you wanted, you know, you could, you kind of got a slow start on them, all right? You wasted all these years on Econ.

2:17:52There's an alternate world where you're like on the super alignment team at 17 instead of 21 or whatever it was. Um, um, I mean, in some sense, I'm still doing economics, right? You know, what is, what is straight lines on a graph? I'm looking at the log, log plots and like figuring out what the trends are. Yeah. And like thinking about the feedback loops and it could be on R &S control dynamics. And, you know, it's, I think it is a sort of a way of thinking that I find very useful. Yeah. Um, and, um, you know, like what, you know, Dario and Ilya seeing scaling early. In some sense, that is a sort of very economic way of thinking.

2:18:25Also, the sort of physics, like empirical physics, you know, a lot of them are physicists. I think the economists usually can't code well enough and that's their issue. But I think it's that sort of way of thinking. I mean, the other thing is, you know, I thought they were sort of, um, you know, I thought a lot of sort of like core ideas of economics. I thought we're just beautiful. Um, and, um, you know, in some sense, I feel like I was a little duped, you know, where it's like actually, econ academia is kind of decadent now. You know, I think that, you know, for example, the paper I wrote, you know, it's sort of, I think the takeaway, you know, it's a long paper, it's 100 pages of math or whatever.

2:18:56I think the core takeaway, I can, you know, kind of give the core intuition for in like, you know, 30 seconds and it makes sense. And it's, and it's like, you don't actually need the math. Yeah. I think that's the sort of the best pieces of economics are like that. We do the work, but you do the work, um, to kind of uncover insights that weren't obvious to you before. Once, once you've done the work, it's like some sort of like mechanism falls out of it that like, makes a lot of crisp and intuitive sense that like explains some facts about the world that you can then use an argument. And I think, you know, I think, you know, like a lot of econ 101 like this and it's great.

2:19:25A lot of econ in the, you know, the 50s and the 60s, you know, uh, was like this. And, um, you know, Chad Jones papers are often like this. I really like Chad Jones papers for this. No, I think, um, you know, why did I ultimately not pursue econ academia was, um, number of reasons. One of them was Tyler Cowan. Um, um, um, um, you know, he kind of took me aside and he was kind of like, look, I think you're one of the like top young economists I've ever met. But also you should probably not go to grad school. Oh, interesting. Yeah. I didn't realize that. Well, yeah. And it was, it was good because you kind of introduced me to the, you know, I don't know, like the Twitter weirdos or just like, you know, I think the takeaway from that was kind of, um, you know, got to move out less one more time.

2:20:03Wait, Tyler, did you see the Twitter weirdos? A little bit. Yeah. Or just kind of like the sort of brought you like the 60 year old, the old economist to energy to that Twitter. Yeah. Well, you know, I had been, I, so I went from Germany, you know, completely, you know, on the periphery, it was kind of like, you know, a US -lead institution and sort of got some vibe of like sort of, you know, your parent to crackically, you know, US society. And then sort of, yeah, basically this sort of like, there was a sort of directory then to being like, look, I, you know, find the true American spirit. I got to come out here.

2:20:30But the other reason I didn't become economist was because at least Econ academia was sort of, I think, sort of, Econ academia has become a bit decadent. And maybe it's just ideas getting harder to find and maybe it's sort of things, you know, and the sort of beautiful, simple things have been discovered. But like, you know, like, what are Econ papers these days? You know, it's like, you know, it's like, uh, uh, 200 pages of like empirical analyses on what happened when, you know, like Wisconsin bought, you know, 100 ,000 more textbooks on like educational outcomes. And I'm really happy that work happened.

2:20:55I think it's important work, but I think it is not in covering, covering these sort of like fundamental insights and sort of mechanisms in society. Or, you know, it's like, even the theory work is kind of like, here's a really complicated model. And the model spits out, you know, if the Fed does X, you know, then why happens? You have no idea what that hat, why that happened? Because it's like, gazillion parameters and they're all calibrated in some way and it's some computer simulation. You have no idea about the validity, you know, yeah. So I think I think the sort of, you know, the most important insights are the ones where you have to do a lot of work to get them.

2:21:23But then there's sort of this crisp intuition. Yeah. The P versus NP of sure. Yeah. That's really interesting. So just going back to your time in college, yeah. You say that peak productivity kind of explains the, yeah, this paper and things. But the valedictorian, that's getting straight A's or whatever is very much, uh, uh, average productivity phenomenon. All right. So there's one award for the highest GPA, which I want. But the valedictorian is like, I'm on the people, which have the highest GPA and selected by faculty. So it's not just peak productivity. It's just, it's just, I generally just loved this stuff.

2:22:01You know, I just, I was curious and I thought it was really interesting and I love learning about it. And, and I love kind of like it made sense to me. And, you know, it was very natural. And so, you know, I think I'm, you know, I'm not, you know, I think one of my faults is I'm not that good at eating glass or whatever. I think there's some people who are very good at it. I think the sort of, like, the sort of moments of peak productivity come when I, you know, I'm just really excited and engaged and, and, and, and, and, and, uh, love it. And, you know, I, uh, you know, if you take the like courses, you know, that's what you got in college.

2:22:29Yeah. It's, it's, it's the Bruce Banner code in Avengers, you know, I'm always angry. I'm always excited. I'm always curious. That's I'm always to be quite. So it's interesting, by the way, when you were in college, I was also in college. I think you were despite being a year younger than me. I think you're, you're, you're ahead in college than me or at least two, maybe two years ahead. Um, and we met around this time. Yeah. Yeah. Yeah. We also met, I think through the Tyler Cowan universe. Yeah. Yeah. And it's very insane how small the world is. Yeah. I think I, did I reach out to you? I must have about, I don't know, when I had a couple of videos that they had a couple hundred views or something.

2:23:11Yeah. It's a small world. Yeah. I mean, this is the crazy thing about the eye world, right? It's kind of like, it's the same few people at the KSF parties and they're the ones, you know, running the models that deep mind and you know, open the eye and then traffic and, and, um, you know, I mean, I think some other friends of ours have mentioned this who are now later in their career and very successful that, you know, they actually met all the people who are also kind of very successful in Silicon Valley now. Like, you know, when they're when they're in their, you know, when before the 20s or really 20s, the, um, I mean, look, I actually think, um, you know, and why is it a small world?

2:23:43Um, I mean, I think one of the things is some amount of like, you know, some sort of agency. And I think in a funny way, um, this is a thing I sort of took away from the sort of Germany experience where it was, I mean, look, I, I, I, it was crushing. I really didn't like it. And it was like, it was such an unusual move to kind of skip grade and such an unusual move to come to the United States. You know, a lot of these things I did were kind of unusual moves. And, um, you know, there's some amount where like, just like just trying to do it. And then it was fine and it worked. Um, that kind of reinforced like, you know, you don't, you don't just have to kind of conform to what the opportune window is.

2:24:21You can just kind of like try to do the thing. The thing that seems right to you. And like, you know, most people can be wrong. I know things like that. And I think that was kind of a, you know, valuable kind of like early experience. That was sort of formative. Okay. So after college, what did you do? I did econ research for a little bit, you know, an Oxford and stuff. And then, uh, then I worked at future fund. Yeah. Okay. So, and so, tell me about it. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. If you took future fund was that, you know, it was a foundation that was, uh, you know, funded by San Benken Freed.

2:24:51I mean, we were our own thing, you know, we were based in the Bay. Um, you know, at the time, this was then sort of a early 22. Um, it was, uh, it was just like incredibly exciting opportunity, right? It was that basically like a startup, you know, foundation, which is like, you know, it doesn't come along that, that often that, you know, we thought would be able to give away billions of dollars. You know, thought would be able to kind of like, you know, remake how philanthropy is done, you know, from first principles. Um, thought would be able to have, you know, this like great impact, you know, we caused as we focused on where, you know, biosecurity, uh, you know, AI, um, uh, you know, finding exceptional talent and putting them to work on hard problems.

2:25:27Um, and you know, like a lot of the stuff we did, I was, I, I was really excited about, you know, like academics who would, you know, usually take six months with send us emails like, ah, you know, this is great. This is so quick and, you know, and straightforward. You know, in general, I feel like I've often find that with like, you know, a little bit of encouragement, a little bit of sort of empowerment, kind of like removing excuses, making the process easy, you know, you can kind of like get people to do great things. Um, I think like the future fund that I think is a context for people who might not realize, yeah, not only where you guys planning on deploying billions of dollars, but it is a team of four people.

2:26:00Yeah, yeah, yeah. So you at 18 are on a team of four people that is in charge of deploying billions of dollars. Yeah. I mean, just, I mean, yeah, I'm future fund, you know, the, um, yeah, I mean, the, you know, so that was, that was sort of the heyday, right? Um, and then obviously, you know, when, when in sort of, you know, November of 22, um, you know, it was kind of revealed that Sam was this, you know, giant fraud. Um, and from one day to the next, you know, the whole thing collapsed. Um, that was just really tough. Um, I mean, you know, obviously, it was devastating. It was devastating. Obviously, for the people at their money and FTX, you know, closer to home, you know, all the, you know, all these grantees, you know, we'd want it to help them.

2:26:38And we thought they were doing amazing projects. And so, but instead of helping them, we ended up saddling them with like a giant problem. Um, you know, personally, it was, you know, it was just a startup, right? And so I, you know, I'd worked 70 hour weeks every week for, you know, basically a year on this to kind of build this up. You know, we're a tiny team. Um, and then from one day to the next, it was all gone and not just gone, it was associated with this giant fraud. Um, and so, you know, that was incredibly tough. Um, yeah. And there were, were there any signs early on that SPF was, yeah.

2:27:09And like, obviously, I didn't know he was a fraud. And the whole, you know, I would have never worked there. You know, um, and, you know, we weren't, you know, we were a separate thing. We weren't with the working with the business. Um, I mean, I think I do think there were some takeaways for me. I think one takeaway was, um, you know, I think there's a, um, I had this tendency. I think people in general have this tendency to kind of, like, you know, give successful CEOs the pass on their behavior because, you know, they're successful CEOs and that's how they are. And that's just successful CEO things.

2:27:36And, um, you know, I didn't know Stan McPronfried was a fraud, but I knew SPF and I knew he was extremely risk -taking, right? I knew he, um, he was narcissistic. Um, he didn't tolerate disagreement well, you know, sort of by the end, he and I just like didn't get along well. And sort of, I think the reason for that was like, there's some biosecurity grants you really liked because they're kind of cool and flashy. And at some point I'd kind of run the numbers and it didn't really seem that cost effective and I pointed that out and he was pretty unhappy about that. Um, and so I knew his character.

2:28:09Um, and I think, you know, I feel like one takeaway for me was, um, was, um, you know, like, I think it's really worth paying attention to people's character including like people you work for and successful CEOs. Um, and, um, you know, that can save you a lot of pain down the line. Mm -hmm. Okay. So after that, up to excellent clothes and you're out. Um, and then you got into, you went to OpenAI, the super alignment team had just started. I think you were, you were like part of the initial team. And so what was the original idea? What was compelling about that for you to join? Yeah, totally. Um, so I mean, what was the goal of the super alignment team?

2:28:51Um, you know, the um, alignment team at OpenAI, you know, at, you know, other labs sort of like several years ago kind of had done sort of basic research and they developed RLHF, reinforcement learning from your feedback. And um, that was a sort of a, you know, ended up being really successful technique for controlling sort of current generation of AI models. Um, what we were trying to do was basically kind of be the basic research bet to figure out what is the successor to RLHF. And the reason we needed that is, you know, basically, you know, RLHF probably won't scale to superhuman systems. Um, RLHF relies on sort of human Raiders who kind of thumbs up, thumbs down, you know, like the model said something, it looks fine, looks good to me.

2:29:26At some point, you know, the superhuman models, the superintelligence, it's kind of right, you know, a million lines of, you know, crazy complex code, you don't know at all what's going on anymore. And so how do you kind of steer and control these systems? How do you hide side constraints? Um, you know, the reason I joined was, um, I thought this was an important problem. And I thought it was just a really solvable problem, right? I thought this was basically, you know, there's, I think there's a, I still do, I mean, even more so do, I think there's a lot of just really promising sort of ML research on alignment on sort of aligning superhuman systems.

2:29:55Um, and maybe we should talk about that a bit more later. But, um, so, and it was so solvable, you solved it in the year. Well, yeah, over. I think that's all right. There was a look open. Now I wanted to do this like really ambitious effort on alignment. And, you know, it was backing it. And, you know, I liked a lot of the people there. And so I was, you know, I was really excited. And I was kind of like, you know, I think there was a lot of people, um, sort of, alignment. There's always a lot of people kind of making hay about it. And, you know, I appreciate people highlighting the importance of the problem.

2:30:24And I was just really into, like, let's just try to solve it. And let's do the ambitious effort. You know, let's do the, you know, Operation Warp Speed for solving alignment. And, um, it seemed like an amazing opportunity to do so. Mm -hmm. Okay. And, uh, now basically the team doesn't exist. I think the head of it has left. Yeah. I both had to put it up left. Yeah. And, and Ilia, that's what the news of the last week. What happened? Why did the thing break down? I think opening eyes sort of decided to take things in a somewhat different direction. Meaning what? I mean, that super alignment isn't the best way to frame the, uh, no, I mean, look, obviously, sort of after the November board events, you know, there were personnel changes.

2:31:02I think Ilia leaving was just incredibly tragic for opening her eye. And, you know, I think some amount of repartization, I think some amount of, you know, I mean, there's been some reporting on the superliming compute commitment. You know, there's this 20 % compute commitment as part of, you know, how a lot of people recruited, you know, it's like, we're going to do this ambitious effort in alignment. And, um, you know, some amount of, you know, not keeping that and, and deciding to go in a different direction. Hmm. Okay. So now, Yana's left, Ilia has left. So this team itself has dissolved, but you were the sort of first person who left or was forced to leave.

2:31:36You were the information reported that you were fired for leaking. What happened? Is this accurate? Yeah. Um, look, why don't I, why don't I tell you what they claim my leak and you can tell me what you think? Yeah. So opening, I did claim to employees that I was fired for leaking. And, you know, I and others have sort of pushed them to say what the leak is. And so here's their response in full. Um, you know, sometime last year, I had, um, uh, written a sort of brainstorming document on preparedness on safety and security measures we need in the future on the path to AGI. And I shared that with three external researchers for feedback.

2:32:10So that's it. That's the leak. Um, you know, I think for context, it was totally normal at opening, I had the time to share sort of safety ideas with external researchers for feedback. Um, you know, it happened all the time. You know, the doc was sort of my ideas, you know, before I shared it, I reviewed it for anything sensitive. Um, uh, the internal version had a reference to a future cluster, but I redacted that for the external copy. Um, you know, there's a link in there to some, to some slides of mine, internal slides. Um, you know, that was a dead link to the external people I shared it with, you know, the slides weren't shared with them.

2:32:44And, um, so obviously I, um, I pressed them to sort of tell me what is the confidential information in this document. And what they came back with was a line in the doc about planning for AGI by 2728 and that setting timelines for preparedness. Um, you know, I wrote this doc, you know, a couple months after the super alignment announcement, we'd put out, you know, this sort of four year planning horizon, I didn't think that planning horizon was sensitive. You know, it's, it's the sort of thing. Sam says publicly all the time. Um, I think sort of John said it. Yeah, I guess. A couple weeks ago.

2:33:18Um, anyway, so that's it. That's it. So that's a pretty thing for, uh, if the cause was leaking, that seems pretty thin. Was there anything else to it? Yeah. I mean, so that was, that was the leaking claim. I mean, you can say a bit more about sort of what happened. Sure. Yeah. Um, so one thing was, um, last year I had a written a memo, internal memo about opening I security, thought it was, you know, greediously insufficient, you know, I thought it wasn't sufficient to protect the theft of model rates or, or key algorithmic secrets from foreign actors. Um, so I wrote this memo. I shared it with a few colleagues, a couple members of leadership, um, who sort of mostly said it was helpful.

2:33:57Um, but then, you know, a couple weeks later, a sort of major security incident occurred. Um, and that prompted me to share the memo with a couple members of the board. Um, and so after I did that, you know, days later, it was made very clear to me that leadership was very unhappy with me having shared this memo with the board. Um, you know, apparently the board had hassled leadership about security. Um, and then I got sort of an official HR warning for this memo, uh, you know, for sharing it with the board. Uh, the HR person told me it was racist to worry about CCPS view and not. Um, and they said it was sort of unconstructive.

2:34:31Um, and you know, look, I think I probably wasn't at my most diplomatic, you know, I definitely could have been more politically savvy. Um, but, you know, I thought it was a really, really important issue and, um, you know, the security incident had been really worried. Um, anyway, and so I guess the reason I bring this up is when I was fired, it was sort of made very explicit that the security memo was a major reason for my being fired. Um, you know, I think it was something like, you know, the reason that this is a firing and out of warning is because of the security memo. Um, the you sharing it with the board.

2:35:01The warning I'd gotten for the security memo. Um, anyway, and I mean, some other, you know, what might also be helpful context is the sort of questions they asked me when they fired me. So, you know, this was a bit over a month ago. I was pulled, you know, aside for a chat with a lawyer, you know, that quickly turned very adversarial. And, you know, the questions were all about my views on AI progress on AGI on the level security appropriate for AGI. On, you know, whether government should be involved in AGI on, you know, whether I and super alignment were loyal to the company. Um, on, you know, what I was up to during the opening, I board events, you know, things like that.

2:35:43And, um, you know, then they, you know, chatted to a couple of my colleagues and then they came back and told me I was fired. And, you know, they'd gone through all of my digital artifacts from the time, you know, time out opening messages, docs and that's when they found, you know, the leak. Um, yeah. And so anyway, so the main claim they made was this leaking allegation. You know, that's what they told employees. Um, they, uh, you know, the security memo. Um, there's a couple other allegations they threw in. One thing they said was that I was on forthcoming during the investigation, because I didn't initially remember who I'd share the doc with.

2:36:14The sort of preparedness brainstorming doc only that I had sort of spoken to some external researchers about these ideas. And, um, you know, look, the doc was over six months old. You know, I'd spent the day on it. Um, you know, it was a Google doc. I shared with my open and email. It wasn't that, you know, a screenshot or anything. I was trying to hide. Um, it simply didn't stick because it was such a non -issue. Um, and then they also claimed that I was engaging on policy in a way that they didn't like. Um, and so what they cited there was that I had spoken to a couple external researchers, you know, somebody that I think tank.

2:36:44Um, about my view that a AGI would become a government project, you know, as we discussed. You know, in fact, I was speaking to lots of sort of people in the field about that at the time. I thought it was a really important thing to think about. Um, anyway, and so they found, you know, they found a DM that I'd written to like a friendly colleague, you know, five or six months ago, where I related this and, you know, they cited that. Um, and, you know, I had thought it was well within Open Eye Norms to kind of talk about high -level issues on the future of AGI with external people in the field.

2:37:11So that's, that's what they led. So that's what happened. Um, you know, I've spoken to kind of a few dozen former colleagues. Colleagues about this, you know, since, um, I think the sort of universal reaction is kind of like, you know, that's insane. Um, I was sort of surprised as well, you know, I, I had been promoted just a few months before. Um, I think, you know, I think I, I think I was comment for the promotion case at the time was something like, you know, the appolds amazing. We're lucky to have him. Um, but look, I mean, I think the thing I understand and I think it's some sense is reasonable is like, you know, I think I ruffled some feathers.

2:37:45And, you know, I think I was, I was probably kind of. Knowing at times, you know, it's like, I security stuff. And I kind of like repeatedly raised that and maybe not always in the most diplomatic way. Um, you know, I didn't sign the employee letter during the board events, you know, despite pressure to do so. Um, anywhere what one of like eight people or something. I'd like, yeah, I guess that I think the sort of two senior most people didn't sign were Andre and yeah, new. Since last. Um, and you know, I mean, on the letter, by the way, I, um, by the time on sort of Monday morning, when that letter was going around, I think probably was appropriate for the board to resign.

2:38:22I think they'd kind of like lost too much credibility and trust with the employees. Um, but I thought the letter had a bunch of issues. I mean, I think one of them was it just didn't call for an independent board. I think it's sort of like basics of corporate governance, having an independent board. Anyway, you know, it's other things, you know, I am in sort of other discussions. I pressed leadership for sort of opening eye to abide by its public commitments. Um, you know, I raised a bunch of tough questions about whether it was consistent with the opening eye mission and consistent with the national interest to sort of partner with authoritarian dictatorships to build the core infrastructure for AGI.

2:38:56Um, so you know, look, you know, it's a free country, right? So what I love about this country, you know, we talked about it. Um, and so I, they have no obligation to keep me on staff. Um, and, you know, I think in some sense, I think it would have been perfectly reasonable for them to come to me and say, look, you know, we're taking the company in a different direction. You know, we disagree with your point of view. Um, you know, we don't trust you enough to sort of tow the company line anymore. And, um, you know, thank you so much for your work at OpenAI, but I think it's time to part ways. I think that would have made sense.

2:39:27I think, you know, we did start sort of materially diverging on sort of views on important issues. I'd come in very excited in line with OpenAI, but that sort of changed over time. And, um, look, I think I think there would have been a very amicable way to part ways. Um, and I think it's a bit of a shame that it sort of, this is the way it went down. Um, you know, all that being said, I think, um, you know, I really want to emphasize. Um, there's just a lot of really incredible people at OpenAI and, um, it was an incredible privilege to work with them. And, um, you know, overall, I'm just extremely grateful for my time there.

2:40:00Mm -hmm. When you left, now that there's, now there's been reporting about an NDA that former employees have to sign in order to have access to their Vesor Decree. Did you sign some, such NDA? No, um, I should say. A situation is a little different and that it was sort of, I was basically right before my cliff. Mm -hmm. Um, but then, you know, they still offered me the equity, um, um, but I didn't want to sign a non disparagement, you know, freedom is priceless. And how much was the equity? Like, uh, close to a million dollars. Mm -hmm. So it was definitely a thing you were, you and other reservoir era, that this is like, a choice that OpenAI is explicitly offering you.

2:40:40Yeah. And presumably the person on OpenAI is staff knew that we're offering them equity, but they had to sign this NDA that has these conditions that you can't, for example, give the kind of statements about your thoughts on AGI and OpenAI that you're giving on this podcast right now. Like, I don't know what the whole situation is. I certainly think sort of vested equity is pretty rough if you're conditioning that on the NDA. It might be a somewhat different situation if it's a sort of separate agreement. Right. But in OpenAI employee who had signed it, presumably could not give the podcast that you were giving today.

2:41:11Quite possibly not. Yeah. I don't know. Okay. So analyzing the situation here, I guess if you were to, yeah, the board thing is really tough because if you were trying to defend them, you would say, well, listen, you were just kind of going outside the regular chain command. And maybe there's a point there, although the way in which the person from HR thinks that you have an adversarial relationship with the, or you're supposed to have an adversary relationship with the board where to, to give the board some information, which is relevant to. Whether OpenAI is fulfilling its mission and whether or can do that in a better way is part of the leak as if the board is that is supposed to ensure that OpenAI is following its mission is a sort of external actor.

2:41:59That seems pretty. I mean, I think, I think, I mean, to be clear, the leak allegation was just that sort of document. Right. This is just sort of a separate thing that they said, and they said, I wouldn't have been fired if not for the security memo. They said, they said you wouldn't have been fired. The reason this is a firing and not a warning is because of the warning you had gotten for the security memo. Oh, before you left the incidents with the board happened where Sam was fired and then rehardic CEO, and now he's on the board. Now, Ilya and Jan, who are the heads of the super lineman team in Ilya, who is a co -founder of OpenAI, obviously the most significant in terms of stash or a member of OpenAI from a research detective.

2:42:37They've left. It seems like, especially with regards to super lineman stuff, and just generally the OpenAI, a lot of the sort of personnel drama has happened over the last few months. What's going on? Yeah, just a lot of drama. Yeah, so why is there so much drama? I think there would be a lot less drama, all OpenAI claimed to be with building chat, GPT, or building business software. I think where a lot of the drama comes from is OpenAI really believes they're building AGI, right? And it's not just a claim that make for marketing purposes, whatever, there's this report that Sam is raising $7 trillion for chips, and it's like, that stuff only makes sense if you really believe in AGI.

2:43:22And so I think what gets people sometimes is sort of the cognitive dissonance between sort of really believing in AGI, but then sort of not taking some of the other implications seriously. You know, it's going to be incredibly powerful technology, both for good and for bad, and that implicates really important issues, like the national security issues we spoke about. Like, you know, are you protecting the secrets from the CCP? Like, you know, does America control the core AI infrastructure, or does it, you know, a Middle Eastern dictator control the core AI infrastructure? And then, I mean, I think the thing that, you know, really gets people is the sort of tendency to kind of then make commitments, and sort of like, you know, they say they take these issues really seriously, they make big commitments on them, but then sort of frequently don't follow through.

2:44:05Right. So, you know, again, as mentioned, there's this commitment around superlime compute, you know, sort of 20 % of compute for this long term safety research effort. And I think, you know, you and I could have a totally reasonable debate about what is the appropriate level of compute for superlimate. But that's not really the issue. The issue is that this commitment was made, and it was used for group people, and you know, it was, it was very public. And it was made because, you know, there's a recognition that there would always be something more urgent than a long term safety research effort, you know, like some new product or whatever.

2:44:35And then, in fact, they just, you know, really didn't keep the commitment. And so, you know, there was always something more urgent than long term safety research. I mean, I think, I think another example of this is, you know, when I raised these issues about security, you know, they would, they would tell me, you know, securities are number one priority. But then, you know, invariably when it came time to sort of invest serious resources, when it came time to make tradeoffs to sort of take some pretty basic measures, security would not be prioritized. And so, yeah, I think it's the cognitive dissonance, and I think it's the sort of unreliability that causes a bunch of the drama.

2:45:12So let's zoom out, talk about the part, a big part of the story, and also a big motivation of the way in which must proceed with regards to geopolitics and everything is that once you have the AGI pretty soon after you proceed to ASI because super intelligence because you have these AGI's which can function as researchers. They may also work with researchers, researchers into further A你看 progress, and within a matter of years, maybe less you go to something that is like super intelligence and and then from there, then you can do up. According to your story to all this research and development and the Robolle addiction.

2:45:50Okay.

2:45:56But there's, it was capital field. the story for any reasons. Yes. At a high level, it's not clear to me that this input output model of research is how things actually happen in research. We can look at economy -wide, right? Patrick Hollison others have made this point that from compared to 100 years ago, we have 100x more researchers in the world. It's not like progress is happening 100x faster. So it's clearly not the case that you can just pump in more population into research and you get higher research on the other end. I don't know why it would be different for the researchers themselves.

2:46:31Okay, great. So this is getting into some good stuff. I have a kind of classic agreement I have with Patrick and others. So obviously inputs matter, right? So it's like United States produces a lot more scientific and technological progress than Lichtenstein, right, or Switzerland. And even if I made Patrick Hollison dictator of Lichtenstein or Switzerland, and Patrick Hollison was able to implement his utopia of ideal institutions, keeping the town full fix. He's not able to do some crazy high school immigration thing or whatever, some crazy genetic breeding scheme or whatever he wants to do.

2:47:05Keeping the town full fix but amazing institutions. I claim that still, even if you made Patrick Hollison dictator of Switzerland, maybe you get some factor, but Switzerland is not going to be able to outcompute the United States and scientific and technological cars. Obviously, magnitude is matter. Okay. No, I'm not sure I agree with this. There's been many examples in history where you have small groups of people who are part of like Bell Labs or Skunk Works or something. It's a couple hundred researchers. Open AI, right? A couple hundred researchers. They do make... Highly selected though, right?

2:47:34You know, it's like saying that's part of the theory. That's part of the theory. Patrick Hollison is a dictator. He's going to do a good job of this. Well, yes, if you can highly select all the best AI researchers in the world, you might only need a few hundred. But if you, yeah, that's the talent pool. It's like you have the, you know, 300 best AI researchers in the world. But there's, there has been, it's not a case of from 100 years to now. There haven't been population has increased massively. A lot of the work, in fact, you would expect the density of talent to have increased in the sense that malnutrition and other kinds of debilit, poverty, whatever that have debilitated past talent at the same sort of level as no longer debilitated in the way.

2:48:06So to the 100x point, right? So I don't know if it's 100x, I think it's easy to inflate these things, probably at least 10x. And so people are sometimes like, ah, you know, like, you know, come on, ideas haven't gotten them a charter to find. You know, why would you have needed this 10x increase in research effort? Um, whereas to me, I think this is an extremely natural story. And why is it a natural story? It's a straight line on a log log plot. This is sort of a, you know, deep learning researchers dream, right? What is this log log plot? On the x axis, you have log cumulative research effort.

2:48:31On the y axis, you have some log GEP or ooms of algorithmic progress or, you know, log transistors per square inch or, you know, in the sort of experience curve for solar, kind of like, you know, whatever the log of, you know, the price for a lot of solar. And, um, it's extremely natural for that to be a straight line. You know, this is sort of a class, yeah, it's a classic. And, um, you know, it's basically the first thing is very easy. Then basically, you know, you have to have log increments of cumulative research effort to find the next thing. Um, and so, you know, in some sense, I think this is a natural story.

2:49:00Um, now one objection kind of people then make is like, oh, you know, isn't it suspicious, right? That like ideas, you know, well, we increased research effort 10x and ideas also just got 10x harder to find. And so it perfectly, you know, equilibrates. Um, and today I say, you know, it's just, it's an equilibrium. It's an adageous equilibrium, right? So it's like, you know, isn't it a coincidence that supply equals demand, you know, on the market clears, right? And that's, and the same thing here, right? So it's, um, you know, ideas getting, how much ideas have gotten harder to find as a function of how much progress you've made.

2:49:31Um, and then, you know, what the overall growth rate has been is a function of how much ideas have gotten harder to find in ratio to how much you've been able to, like, increase research effort. What is the sort of growth? Yeah, it's a lot of cumulative research effort. So in some sense, I think the story is sort of like fairly natural. And you see this, you see this not just economy -wide, you see it in kind of experience curve for all sorts of individual technologies. Um, so I think there's some process like this. I think it's totally possible that, you know, institutions have gotten worse by some factor.

2:49:56Obviously there's some sort of exponent of diminishing returns on more people, right? So like serial time is better than just paralyzing. Um, but still, I think it's like clearly inputs matter. Yeah, I agree. But if the coefficient of, uh, how fast they diminish as you grow the input is high enough, then the, uh, and the abstract, the fact that inputs matter is in that relevant. Okay. So I mean, we're talking to very high level, but just like, take it down to the actual concrete thing here, opening eye has a staff of at most low hundreds, who are directly involved in the algorithmic progress in future models.

2:50:32If it was really the case that you could just arbitrarily scale this number, and you could have much faster algorithmic progress. And that would result in much, much better AIs for opening eye, basically. Then it's not clear why opening eye doesn't just go out and hire every single person with 150 IQ, of which there are hundreds of thousands in the world. And my, my story there is, there's transaction costs to managing all these people that don't just go away if you have a bunch of AIs that there, these tasks aren't easy to parallelize. And I think you, I'm not sure how you would explain the fact of like, why does an opening eye go on a recruiting binge of every single genius in the world?

2:51:12Okay. So let's talk about the opening eye example and let's talk about the automated AI researchers. So I mean, the opening eye case, I mean, just, you know, it's kind of like, look at the inflation of like AI researcher salaries over the last year. I mean, I think like, I don't know, I don't know what it is, you know, 4x, 5x. It's kind of crazy. So they're clearly really trying to recruit the best AI researchers in the world. And you know, I don't know, it's, they do find the best AI researchers in the world. And I think my response to your thing is like, you know, almost all of these 150 IQ people, you know, if you just hired them tomorrow, they wouldn't be good AI researchers.

2:51:38They wouldn't be an Alec Radford. But they're willing to make investments that take your subpoena out of the four, they're the data centers they're buying right now will come online in 2026 or something. Why wouldn't they be able to make every 150 IQ person some of them won't work out. Some of them won't have the traits we like. Yeah. But some of them by 2026 will be amazing AI researchers. Why, why aren't they making that bet? Yeah. And so sometimes this happens, right? Like smart physicists have been really good at AI research. You know, it's like all the anthropocopin. But, but, but like if you talk to, I had Dario in the podcast, I'm like, they have this very careful policy of like, we're not going to just hire arbitrarily.

2:52:10We're going to be extremely selective. We're making sure there's training is not as easily scalable, right? So training is very hard. You know, if you just hired, you know, 100 ,000 people, it's like, you, I mean, you couldn't train them all. If you're really hard to train them all, you know, you wouldn't be doing any AI research, like, you know, there's, there's huge costs of bringing on a new person training them. This is very different with AI's, right? And I think this is, it's really important to talk about the sort of like advantages they eyes will have. So it's like, you know, training, right?

2:52:33It's like, was it take to be an Alec Radford? You know, we need to be in a really good engineer, right? They are, they're going to be an amazing engineer. They're going to be amazing at coding. You can just train them to do that. They need to have, you know, not just be good engineer, but have really good research intuitions and like really understand deep learning. And this is stuff that, you know, I like Radford, you know, somebody like him is acquired over years of research over just like being deeply immersed in deep learning, having tried lots of things himself and failed. They eyes, you know, they're going to be able to read every research paper ever written, every experiment ever run at the lab, you know, like gain the intuitions from all of this.

2:53:04They're going to be able to learn and parallel from all of each other's experiment, you know, experiences. You know, I don't know what else, you know, it's like, what does it take to be now like Radford? Well, there's a, there's a sort of cultural acclimation aspect of it, right? You know, if you hire somebody new, there's like, politicking, maybe they don't fit in. Well, in the I case, you just make replicas, right? There's a like motivation aspect for it, right? So it's like, you know, I like, you know, I could just like duplicate Alec Radford. And before I run every experiment, I haven't spent like, you know, a decades worth of human time, like double checking the code and thinking really careful, be careful about it.

2:53:33I mean, first of all, on how that many Alec Radford's and, you know, he wouldn't care, and he would not be motivated. But you know, the eyes, I can just be like, look, I have a hundred million of you guys. I'm just going to put you on just like really making sure this code is cracked. There are no bugs. This experiment is thought through every hyper parameters crack. Um, final thing I'll say is, you know, the 100 million human equivalent AI researchers, that is just a way to visualize it. So that doesn't mean you're going to have literally a hundred million copies. You know, so there's trade offs you can make between serial speed and in parallel.

2:54:02So you might make the trade off is, look, we're going to run them at, you know, 10X, 100 X serial speed. It's going to result in fewer tokens overall because of sort of inherent trade offs. But, you know, then we have, I don't know what the numbers would be, but then we have, you know, a hundred thousand of them running at a hundred X human speed. And thinking and, you know, and there's other things you can do on coordination, you know, they can kind of like share latent space that tend to each other's contacts. There's basically this huge range of possibilities of things you can do. The 100 million thing is more, I mean, another illustration of this is, you know, if you kind of run the math in my series, and it's basically, you know, 2728, you have this automated AI researcher, um, you're going to be able to generate an entire internet's worth of tokens every single day.

2:54:39So it's clearly sort of a huge amount of like intellectual work that you can do. I think the analogous thing there is today we generate more patents in a year than during the actual physics revolution in the early 20th century. They were generating across like half a century or something. And are you making more physics progress in a year today than you? So yeah, you're going to generate all these tokens. Yeah. Are you generating as much, codified knowledge as humanity has been able to generate into initial creation that internet? Internet tokens are usually final output, right? The sort of a lot of these tokens, if we talked, we talked about the unhobbling, right?

2:55:14And I think of a kind of like, you know, a GPN token is sort of like one token of my internal monologue. Yeah. Right. And so that's how you do the math on human equivalence. You know, it's like 100 tokens a minute. And then, you know, humans working for X hours and, you know, what is the, what is the equivalent there? I think this goes back to something we're talking about earlier where well, I haven't we seen the huge revenues from people often ask this question that if you took GPD for back 10 years and you said people this, they think this is going to automate. This is already automated half the jobs.

2:55:42And so there's a sort of a modisponance modus tollens here where part of the explanation is like, oh, it's like just on the verge, you need to do these unhobblings. And part of that is I probably true. Right. But there is another lesson to learn there, which is that just looking at face value at a set of abilities, there's probably more sort of hobblings that you don't realize that are hidden behind the scenes. I think the same will be true of the AGI is that you have running as AI researchers. I think a lot of things I basically agree, right? I think I think my story here is like, you know, I talk about, I think there's going to be some long tail, right?

2:56:14And so maybe it's like, you know, 26, 27 year, like the proto -automated engineer and it's really good at engineering. It doesn't have the research intuition yet. You don't quite know how to put them to work. But you know, the sort of even the underlying pace of AI progress is already so fast, right? In three years from not being able to do any kind of like math at all, it's now crushing, crushing these math competitions. And so you have the initial thing and like 26, 27, maybe the sort of auto, it's an automated research engineer. It speeds you up by 2x. You go through a lot more progress in that year.

2:56:40By the end of the year, you figured out like the remaining kind of unhobblings, you've like got a smarter model and you know, maybe then that thing or maybe it's two years, you know, and that thing, just like that thing really can do automate 100%. And again, you know, they don't need to be doing everything. They don't need to be making coffee, you know, they don't need to like, you know, maybe there's a bunch of, you know, tacit knowledge and a bunch of other fields. But you know, AI researchers that AI labs really know the job of an AI researcher. And it's in some sense, it's a sort of there's lots of clear metrics.

2:57:05It's all virtual. There's code. It's things you can kind of develop and train for. So I mean, another thing is how do you actually manage a million AI researchers? Humans, the sort of comparative ability we have that we've been especially trained for is like working in teams. And despite this fact, we have for thousands of years, we've been learning about how we work together in groups. And despite this management is a cluster far right? It's like most companies are badly managed. It's it's really hard to do this stuff. Yeah. For AI's the, the sort of like we talk about age, I, but it'll be some bespoke set of abilities, some of which will be higher than humans, I'm going to be at human level.

2:57:49And so it'll be some bundle and we'll need to figure out how to put these bundles together with their human overseer's with the equipment and everything. And the idea that as soon as you get the bundle, you'll figure out how to get like just shove millions of them together and manage them. I'm just very skeptical of like any other revolution, a technological revolution in history has been very piecemeal. It's much more piecemeal than you would expect on paper if you just thought about what is the industrial revolution? Well, we dig up coal that powers a steam engine. You use the steam engine to run these real world that helps us get more coal out.

2:58:29And there's sort of like factorial store you can tell where like a six, six hours, you can be pumping thousands of times more coal. But in real life, it takes centuries often, right? In fact, the electrification, there's this famous study about how to initially to electrify factories. It was decades before after electricity to change from the pull, pull these and water wheel based system that we had for steam engines to one that works with more spread out electrical motors and everything. I think this will be the same kind of thing. It might take like decades to actually get millions of years of research to work together.

2:59:04Okay. Great. This is great. Okay. So a few responses to that. First of all, I mean, I totally agree with the kind of like real world bottlenecks type of thing. I think this is sort of, you know, I think it's easy to underrate. You know, basically what we're doing is we're removing the labor constraint. We automate labor and we like kind of exploit technology. But you know, there's still lots of other bottlenecks in the world. And so I think this is part of why the story is that kind of like starts pretty narrow at the thing where you don't have these bottlenecks. And then only over time as we let it kind of expand to the broader areas.

2:59:30Yeah, this is part of why I think it's like initially this sort of I research explosion, right? It's like, yeah, I research doesn't run into these real world bottlenecks. It doesn't require, you know, like plow field or dig up coal. It's just you're just doing I research the other thing. You know, the other thing about like in your model, yeah, research, yeah, you're it's not complicated like go about flipping a burger. It's just the I research. I mean, because people make these arguments like, oh, you know, AGI won't do anything because it can't flip a burger. Like, yeah, we'll be able to flip burger, but it's going to be able to algorithmic progress, you know, and then and then and then when it does all the market progress, we'll figure out how to flip a burger.

3:00:06For a big robot. You know, look, the the other thing is about, you know, again, these sort of quantities are lower bound, right? So it's like, this is just like, we can definitely run a hundred million of these. Probably what will happen is one of the first things we're going to try to figure out is how to like again run like, you know, translate quantity into quality, right? And so it's like even at the baseline rate of progress, you're like quickly getting smarter and smarter systems, right? If we said it was like, you know, four years between the preschool and the high school, right? So I think, you know, pretty quickly, you know, there's probably some like simple algorithmic changes you find, you know, instead of one Alec Radford, you have a hundred, you don't even need a hundred million.

3:00:40And then and then you get even smarter systems. And now these systems are, you know, they're capable of sort of creative, complicated behavior. You don't understand maybe there's some way to like use all this test time compute in a more unified way rather than all these parallel copies. And, you know, so there won't just be quantitatively superhuman though. Pretty quickly become qualitatively superhuman. You know, it's sort of like, um, look, like, you know, you're a high school student, you're like trying to wrap yourself, wrap your mind around kind of standard physics. And then there's some like super smart professor who is like quantum physics, it all makes sense to him.

3:01:08And you're just like, what is going on? And sort of, I think pretty quickly, you kind of enter that regime, just given even the underlying pace of AI progress. But even more quickly than that, because you have the sort of accelerated force of now this automated AI research, I agree that over time, you would, I'm not denying that ASI is the best possible. I'm just like, how is this happening in a year? Like you, okay, first of all, I think the story is sort of like, basically, I think it's a little bit more continuous. You know, right? Like, I think already, you know, like I talked about, you know, 2526, you're basically going to have models as good as a college graduate.

3:01:40And I, you know, I don't know where the unhobbling is going to be. But I think it's possible that even then you have kind of the proto -automated engineer. So there's, I think there is a bit of like a smear, kind of an AGI smear, or whatever, where it's like, there's sort of unhobblings that you're missing. There's kind of like ways of connecting them you're missing. There's like some level intelligence you're missing. But then at some point, you are going to get the thing that is like a 100 % automated Alec Radford. And once you have that, you know, things really take off, I think. Yeah. Okay.

3:02:06So let's go back to the unhobblings. Yeah. Is there, we're going to get a bunch of models by the end of the year. Is there something, let's suppose we didn't get some capacity by the end of the year. Yeah. Is there some such capacity which lacking would suggest that AI progress is going to take a longer than you are projecting? Yeah. I mean, I think there's, there's two kind of key things. There's the unhobbling and there's the data wall. Right. I think we should talk about the data wall for a moment. I think the data wall is, you know, even though kind of like all of this stuff has been about, you know, crazy AI progress.

3:02:32I think the data wall is actually sort of underrated. I think there's like a real scenario where we're just stagnating. Yeah. You know, because we've been running this tailwind of just like, it's really easy to bootstrap. And you just do unsupervised learning next token prediction. It learns these amazing world models like, bam, you know, great model. And you just got to buy some more compute, you know, do some simple efficiency changes. And again, like so much of deep learning, all these like big gains on efficiency have been like pretty dumb things, right? Like, you know, you got a normalization layer, you know, you know, you fix the scaling laws, you know, and these already have been huge things.

3:03:01Let alone kind of like obvious ways in which these models aren't good yet. Anyway, so data wall, big deal, you know, I don't know, some like put some numbers on this, you know, some like you do common crawl, you know, online is like, you know, 30 trillion tokens, llama three, you trained on 15 trillion tokens. So you're basically already using all the data. And then, you know, you can get somewhat further by repeating it. So there's an academic paper by, you know, Bozbara and some others that does scaling laws for this. And they're basically like, yeah, you can repeat it sometime. After 16 times of repetition, just like returns, basically go to zero.

3:03:32You're just completely screwed. And so I don't know. So you can get another 10X on data from rep, you say like llama three, and GP4, you know, llama three is already kind of like at the limit of all the data, you know, maybe you can get 10X more by repeating data. You know, I don't know, maybe that's like at most a 100X better model than GP4, which is like, you know, 100X effective compute from GP4 is, you know, not that much. You know, if you do half an order magnitude a year of compute, half an order magnitude a year of algorithmic progress, you know, that's kind of like two years from GP4. So, you know, GP4 finished pre -treating in 22, you know, 24.

3:04:04So I think one thing that really matters, I think we won't quite know by end of the year, but, you know, 25, 26, are we cracking the data wall? Okay, so suppose we had three orders of magnitude less data in common crawl on the internet than we just happened to have now. And for decades, the internet, other things we've been rapidly increasing the stock of data that humanity has. Yeah. Is it your view that for contingent reasons, we just happen to have enough data to train models that are just powerful enough at 4 .5 level where they can kick off the self -play RL loop. Yeah. Or is it just that we, you know, if it had been three UMS higher, then it would probably sort of been slightly faster.

3:04:50Yeah. In that world, we would have been looking back at like, oh, how hard it would have been to like kick off the RL explosion with just 4 .5, but we would have figured it out. And then so in this world, we would have gotten to GP3 in and we'd have to kick us on sort of RL explosion. But we would have still figured it out. This sort of, we didn't just like, glock out on the amount of data we happen to have in the world. I mean, three UMS is pretty rough, right? Like, three UMS, if less data means like six UMS, six UMS, less compute model and to chill scaling laws. You know, that's basically a capping out at like GP2, really bad.

3:05:18So I think that would be really rough. I think you do make an interesting point about the contingency. You know, I guess earlier we were talking about this sort of like, when in the sort of human trajectory are you able to learn from yourself? And so, you know, if we go with that analogy, again, like if you've only gotten the preschooler model, it can't win from itself. If you've only gotten the elementary schooler model, can't learn from itself. And maybe GP4, smart high schoolers, really where it starts. Ideally, you have a somewhat better model, and then it really is able to kind of like learn from itself, or learn by itself.

3:05:46So I think there's, I think maybe one UMS data, I would be like more iffy, but maybe still doable. Yeah, I think I would feel chiller if we had one or two. It would be an interesting exercise to get probably distributions of HEI contingent on across like, it was a data. Yeah. Okay. The thing that makes you skeptical of this story is that the things, it totally makes sense for free training work so well. Yeah. These other things, their stories of in principle, why they ought to work. Like a humans can learn this way. And so on. Yes. And maybe they're true. But I worry that a lot of this case is based on sort of first principles, evaluation of how learning happens, that fundamentally we don't understand how humans learn.

3:06:30And maybe there's some key thing we're missing. Yeah. On the sort of sample efficiency, yeah, humans actually, maybe there's, you say, well, the fact that these things are way of less sample efficient in terms of learning, then humans are suggested there's a lot of room for improvement. Yeah. Another perspective is that we are just on the wrong path altogether. Right? That's why there's a sample inefficient when it comes to pre -trading. Yeah. So, yeah, I mean, just like, there's a lot of like, first -wants -all -sarguments that can't top of each other. Yeah. You get these unhopplings and then you get to HEI.

3:07:00Yeah. Then you, because of these reasons where you can stack all these things on top of each other, you get to ASI. Yeah. And I'm worried that there's too many steps of this. Yeah. Sort of first -wants -all thinking. I mean, we'll see, right? I mean, on the sort of sample efficiency thing, again, sort of first principles, but I think again, there's this clear sort of missing middle. And so, you know, and sort of like, you know, people hadn't been trying. Now people are really trying, you know, and so it's sort of, you know, I think often again in deep learning, something like the obvious thing works.

3:07:32And there's a lot of details to get right. So it might take some time, but it's now where people are really trying. So I think we get a lot of signal in the next couple years. You know, on hobbling, I mean, what is the signal on hobbling that I think would be interesting? I think the question is basically, like, are you making progress on this test time compute thing? Right? Like, is this thing able to think longer or rise in than just a couple hundred tokens? Right? That was unlocked by Chain of Thought. And on that point in particular, yeah, the many people who have longer time lines have come on the podcast, have made the point that the weight is trained this long rise in RL.

3:08:04It's not, I mean, I earlier were talking about like, well, they can think for five minutes, but not for longer. Yeah. But it's not because they can't physically output an hour's worth of tokens. Yeah. It's just really, at least from what I understand what they say. Right? Like, even like Gemini has like a million in context. And the million of context is actually great for consumption. And it solves one important on hobbling, which is the sort of onboarding problem, right? Which is, you know, a new coworker, in your first five minutes, like a new smart high school intern, first five minutes, not useful at all.

3:08:32A month in, you know, much more useful, right? Because they've like looked at the monorepone, understand how the code works, and they've like read your internal docs. And so, being able to put that in context, great, solves this onboarding problem. Yeah, but they're not good at sort of the production of a million tokens yet. Yeah. Right. But on the production of a million tokens. Um, there's no public evidence that there's some easy loss function where you can... And GPT -4 has gotten a lot better since... It's actually, so the GPT -4 gains since launch, I think are a huge indicator that there's like, you know, so you talked about this with Johnson and One of the podcasts.

3:09:04John said this was mostly post -training gains. You know, if you look at the sort of LMSIS scores, you know, it's like 100 -ELo or something. It's like a bigger gap than between Claw3Obis and Claw3Hycoup. And the price difference between those is 60X. But it's not really... It's not really authentic. It's like better in the same chat box. Right? Like, you know, from like, you know, 40%. Right. But that's the fact. The crux is like whether like, because I'd be able to... But I think it indicates that clearly there's stuff to be done on hobbling. I think the interesting question is like, this time of year from now, you know, is there a model that is able to think for like, you know, a few thousand tokens coherently, collectively, agentically?

3:09:39And I think probably there's, you know, again, this is what I'd feel better if we had an Umurtune more data, because it's like, the scaling just gives you this sort of like tailwind, right? We're like, for example, tools, right? Tools, I think, you know, talking to people who try to make things work with tools, you know, actually sort of GPT -4 is really when tools start to work. And it's like, you can kind of make them work with GP3 .5, but it's just really tough. And so it's just like having GP4, you can kind of help it learn tools in a much easier way. And so just a bit more tailwind from scaling.

3:10:07And then, yeah, and does, I don't know if it'll work, but it's a key question. Oh, yeah. I think it was a good place to sort of close that part where we know what the crux is and what the progress, what evidence would that look like. On the agitist superintelligence, maybe it's the case that the gains are really easy right now, and you can just sort of lose an aliex rat for a given compute budget, and it comes out the other end with something that is an additive, like change the part of the code. This is a compute multiplier, change the part. What other parts of the world? Maybe there's an interesting way to ask this.

3:10:45Yeah. How many other domains in the world are like this, where you think you could get the equivalent of, in one year, you just throw enough intelligence across multiple instances, and you would just come out the other end with something that is remarkably decades centuries ahead. Yeah. Like you start off with no flight, and then you're the right brother is a million instances of GPT -6, and you come out the other end with Starlink. Yeah. Like, is that your model of how things work? I think you're exaggerating the timelines a little bit, but I think it decades are worth a progress in a year or something.

3:11:22I think that's a reasonable problem. So I think this is where, you know, basically the sort of automated AI research here comes in, because it gives you this enormous headwind on all the other stuff. Right? So it's like, you know, you automate AI research with your sort of automated aliex rat foods, you come out the other end, you've done another five ooms, you have a thing that is vastly smarter, not only is it vastly smarter, you've been able to make it good at everything else. You're solving robotics. The robots are important, right? Because for a lot of other things, you do actually need to try things in the physical world.

3:11:52I mean, I don't know, maybe you can do a lot in simulation. Those are the really quick worlds. I don't know if you saw the last Nvidia GTC, it was all about the digital twins, and just having all your manufacturing processes in simulation. I don't know. Again, if you have these super intelligent cognitive workers, can they just make simulations of everything, kind of off -of -load style, and then make a lot of progress in simulation possible? I also just think you're going to get the robots. Again, I agree about there are a lot of real world bottlenecks. And so, it's quite possible that we're going to have crazy drone forms, but also lawyers and doctors still need to be humans because of regulation.

3:12:31But I think you start narrowly, you broaden, and then the world's in which you let them lose, which again, because of these competitive pressures, we will have to let them lose in some degree on various national security applications. I think like a white rapid progress is possible. The other thing though is it's sort of, you know, basically in the sort of an explosion after there's kind of two components. There's the A in the production function, the growth of technology, and that's massively accelerated by you. Now, you have a billion super intelligent scientists, and engineers, and technicians, you know, superbly competent and everything.

3:13:01You also just automated labor, right? And so, it's like, even without the whole technological explosion thing, you have this industrial explosion, at least if you let them lose, which is like, now you can just build, you can cover Nevada, and like, you know, you start with one robot factory as producing more robots. And basically, it's like just the cumulative process because you've taken labor out of the equation. Yeah, that's super interesting. Yeah, although when you increase the K or the L without increasing the A, you can look at the Soviet Union or China, where they rapidly increase inputs.

3:13:35And that does have the effect of being geopolitically game -changing, where it is remarkable, like you go to Shanghai over a sex -quase decade. I mean, they throw up these crazy cities in a decade. Right, right. And that's the closest thing to like, people talk about 30 % growth rates or whatever, for a million years, 10 % years. It's totally possible. Yeah, right. And that's just, yeah. But without productivity gains, it's not like the industrial revolution, where like you're, from the perspective of, you're looking at a system from the outside, you're goods have gotten cheaper, they can manufacture more things.

3:14:03But, you know, it's not like the next century is coming at you. Yeah, it's both. It's both. So, it's, you know, both that are important. The other thing I'll say is like, and all this stuff, I think the magnitudes are really, really important. Right. So, you know, we talked about a 10X of research effort or maybe 10, 30X over a decade, you know, even without any kind of like self -improvement type loop. You know, we talk, the sort of even in the sort of cheap before -to -age -i story, we're talking about an order of magnitude of effective compute increase the year. Right, half an order of magnitude, a compute, half an order of magnitude of algorithmic progress.

3:14:32That sort of translates into effective compute. And so, you're doing a 10X a year, right, basically on your labor force, right? So, it's like, it's a radically different world if you're doing a 10X or 30X in a century versus a 10X a year on your labor force. So, the magnitudes really matter. They also really matter on the sort of intelligence explosion, right? So, like, just the automated iResearch part. So, you know, one story you could tell there is like, well, ideas get harder to find, right, algorithmic progress is going to get harder. Yeah, right now you have the easy ones, but in like four or five years, there'll be fewer easy ones.

3:15:00And so, the sort of automated iResearchers are just going to be what's necessary to just keep it going, right? Because it's gotten harder. But that's sort of, it's like a really weird knife edge assumption economics, where you assume it's just not... But isn't that the equilibrium story you were just telling with why the economy as a whole has 2 % economic growth because you just pursued on the equal... I guess you're saying by the time you get the equilibrium here, is it's like way faster, at least, you know, and it's at least, and it depends on the sort of exponents. But it's basically, it's the increase, it's like, suppose you need to like 10X effective research effort in AI research in the last, you know, four or five years to keep the pace of progress.

3:15:30We're not just getting a 10X, you're getting, you know, a million X or a hundred thousand X. There's just magnitudes really matter. And the magnitudes, just basically, you know, one way to think about this is that you have kind of two exponentials. You have your sort of like, normal economy that's growing at, you know, 2 % a year, and you have your like AI economy, and that's going at like 10X a year. And it's starting out really small, but sort of eventually it's gonna, it's just, it's way faster, and eventually it's gonna overtake, right? Even if you have... You can almost sort of just do the simple revenue extrapolation, right?

3:15:57If you think your AI economy, you know, that has some growth rate, I mean, it's a very simplistic way and so on, but there's this sort of 10X a year process, and that will eventually kind of like, you're gonna transition the sort of whole economy from as it broadens from the sort of, you know, 2 % a year to the sort of much faster, a growing process. And I don't know, I think that's very like, consistent with historical chain, you know, stories, right? There's this sort of like, you know, there's this sort of long run hyperbolic trend, you know, it manifested in the sort of like, sort of change in growth mode in the Yostrov revolution, but there's just this long run hyperbolic trend.

3:16:31And, you know, now you have the sort of... Now you have that, another sort of change in growth mode. Yeah, yeah. I mean, that was one of the questions I asked, Tyler, when I had them on the podcast, is that you do go from... The fact that after 1776, you go from a regime of negligible economic growth, 2%, yeah, it's really interesting. It shows that, I mean, from the perspective of somebody in the middle ages, or before, 2 % is equivalent to the sort of 10%. Yeah. I guess you're projecting you grew in higher for the AI economy, but... Yeah, I mean, it depends. I think, again, and there's all this stuff, you know, I have a lot of uncertainty, right?

3:17:02So, a lot of the time I'm trying to kind of tell the modal story, because I think it's important to be kind of concrete and visceral about. Sure. And, you know, I have a lot of uncertainty, basically, over how the 2030s play out. And basically, the thing I know is it's going to be working crazy. But, you know, exactly what, you know, where the bottlenecks are, and so on, I think that will be kind of like... So, I let's talk through the numbers here. You hundreds of millions of AI researchers. So, right now, GPD40 turbos, like, 15 bucks, for a million tokens outputted, and a human thinks 150 tokens a minute, or something.

3:17:37And if you do the math on that, I think it's for an hour's worth of human output. It's like 10 cents or something. Mm -hmm. Now, people... For example, they're not a human worker. Cheaper than a human worker. Oh, yeah. But they can't do the job yet. That's right. That's right. But probably the time you were talking about models that are trained on the 10 gigawatt cluster, then you have something that is forwarders of magnitude, more experiments of, yeah, inference, three orders of magnitude, something like that. So, that's like $100 an hour of labor. And now you're having hundreds of millions of such laborers.

3:18:10Is there enough compute to do with the model that is a thousand times bigger, this kind of labor? Great. Okay. Great question. So, I actually don't think inference costs for frontier models are necessarily going to go up that much. So, I mean, one historical data point is... But isn't the test time sort of thing that it will go up even higher? I mean, we're just doing per token, right? Yeah. And then I'm just saying, you know, if suppose each model token was the same as sort of a human token thing at 100 tokens a minute. So, it's like, yeah, it'll use more. But the sort of... If you just... The token calculations is already pricing that in.

3:18:40The question is like per token pricing, right? And so, like, GB3 when it launched was like actually more expensive than GB4 now. And so, over just like, you know, vast increases in capability gains, inference costs has remained constant. That's sort of wild. And I think it's worth appreciating. And I think it gestures that sort of an underlying pace of algorithmic progress. I think there's sort of more theoretically grounded way to why inference costs would stay constant. And it's the fourth following story, right? So, on Chichelle's scaling laws, right? You know, half of the additional compute you allocate to bigger models.

3:19:13And half of it you allocate to more data, right? But also, if we go with the sort of basic story of half an order of year more compute and half an order of magnitude of year of algorithmic progress, you're also kind of like, you're saving half an order of magnitude of year. And so, that kind of would exactly compensate for making the model bigger. The caveat on that is, you know, obviously not all training efficiencies are also inference efficiencies. You know, a bunch of the time they are, separately you can find inference efficiencies. So, I don't know, given this historical trend, given the sort of like, you know, baseline sort of theoretical reason, you know, I don't know.

3:19:45I think it's not crazy baseline assumption that actually these models, the frontier models are not necessarily going to get more expensive, proto -can. Oh, really? Yeah. Like, okay, that's, that's wild. We'll see, we'll see. I mean, the other thing, you know, maybe they get, you know, even if they get like 10x more expensive, then you know, you have 10 million instead of 100 million. So, it's like, it's not really, you know, like, but okay, so part of the intelligence version is that each of them has to run experiments. That are GBD4 sized. Uh -huh. And the result, so that takes up a bunch of compute.

3:20:14Yes. Then you can solidate the results of experiments. And what is the synthesized way? I mean, you have a much bigger influence street anyway than your training. Sure. Okay. But I think the experiment compute is a constraint. Yeah. Okay. I'm going back to maybe a sort of bigger fundamental thing we're talking about here. We're projecting, in the series you see, we should denominate the probability of getting to AGI in terms of orders and magnitude of effective compute. Effective here, accounting for the fact that there's a compute, quote -unquote compute multiplier if you have better algorithm.

3:20:49Yes. And I'm not sure that it makes sense to be confident that this is a sensible way to project progress. It might be, but I'm just like, I have a lot of uncertainty about it. It seems similar to somebody trying to project when we're going to get to the moon. And they're like looking at the Apollo program in the 40s or something and they're like, we have some amount of effective jet fuel. And if we get more efficient engines then we have more effective jet fuel. And so we're going to like probability of getting to move the moon based on the amount of effective jet fuel we have. And I don't deny that jet fuel is important to launch rockets.

3:21:28But that seems like an odd way to denominate when you're going to get to the moon. Yeah. Yeah. Yeah. So I mean, I think these cases are pretty different. I don't know. I don't think there is a sort of clear, I don't know how rocket science works. But I didn't get the impression that there's some clear scaling behavior with the amount of jet fuel. I think the, I think in AI, you know, first of all, the scaling laws, they've just helped. And so if you, a friend of mine, point of this out, and I think it's a great point, if you kind of concatenate both these sort of original Kaplan scaling laws paper that I think went from 10 to the negative 9 to 10 pedophile days.

3:22:01And then concatenate additional compute from there to kind of GP4. You assume some algorithmic progress. It's like the scaling laws of hell, you know, like probably over 15 ohms, you know, I know this rough calculate probably maybe even more, it held for a lot of rooms. They held for the specific law function, which they're trained on, which is, training makes token. Whereas the progress you are forecasting, which we required for further progress, in capabilities. Yeah, it was specifically, we know that scaling can't work because of the data wall. And so there's some new thing that has to happen.

3:22:31And I'm not sure whether the, you can extrapolate that same scaling curve to tell us whether these hobblings will also, like it's, is this not on the same graph? The hobblings are just a separate thing. Yeah, exactly. So this is sort of like, you know, it's, yeah. So I mean, a few, few things here, right? Okay, so the, on the, on the effective compute scaling, the, you know, in some sense, I think it's like people center the scaling laws because they're easy to explain in the sort of like, why is scaling matter? The scaling laws like came way after people, or at least, you know, like Dario Ilya realized that scaling mattered.

3:23:01And I think, you know, I think that almost more important than the sort of loss curve is just like, just in general, make, you know, there's this great quote from Dario on your, on your, on your podcast, it's just like, you know, Ilya was like, for models, they just want to learn, you know, you make them bigger, they learn more. And, and that just applied just across domains, generally, you know, all the capabilities. And so, and you can look at this in benchmarks. Again, like you say, headwind, data wall, and I'm sort of bracketing that and talking about that separately. The other thing is on hobblings, right?

3:23:27If you just put them on the effective compute graph, these on hobblings would be kind of huge, right? So like, I think, what does it even mean? Like, what is it? What is on the y -axis here? Like, say MLPR on the benchmark or whatever, right? And so, you know, like, you know, we mentioned this sort of, you know, the, the LMSIS differences, you know, Arleach F, you know, again, as good as 100x more, chain of thought, right? Chain of just going from this prompting chain, a simple algorithmic chain can be like, 10x effective compute increases on like, math benchmarks. I think it's like, you know, I think this is a useful to illustrate that on hobblings are large.

3:23:57But I think they're like, I kind of think of them as like slightly separate things. And the kind of the way I think about is that like, at a per token level, I think GP4 is not that far away from like a token of my internal monologue, right? Even like 3 .5 to 4 took us kind of from like, bottom of the human range to the top of the human range, unlike a lot of, you know, on a lot of, you know, kind of like high school tests. And so it's like a few more 3 .5 to 4 jumps per token basis, like per token intelligence. And then you've got to unlock the test time, you've got to solve the onboarding problem, make it use a computer.

3:24:26And then you're getting real close. I'm reminded of, again, the story might be wrong, right? It is strikingly plausible. I agree. And so I think actually, I mean, the other thing I'll say is like, you know, I say this 2027 timeline. I think it's unlikely, but I do think there's worlds that are like AGI next year. And that's basically if the test time compute overhang is really easy to crack. If it's really easy to crack, then you do like four rooms of test time compute, you know, from a few hundred tokens to a few million tokens, you know, quickly. And then, you know, again, maybe it's, maybe it only takes one or two to 3 .5 to four jumps per token, like one or two of those jumps for token, plus uses test time compute.

3:25:00And you basically have the proto automated engineer. So I'm reminded of Stephen Pinker releases his book on what is it? The better angels of our nature. And it's like a couple of years ago or something. And he says the secular decline in violence and war and everything. And you can just like plot the line from the end of World War II. And in fact, before World War II, then these are just aberrations, whatever. And basically, as soon as it happens, you crane Gaza. The everything is like. So basically the ASI and crazy global console. Right. ASI and crazy. So I think this is a sort of thing that happens in history, where you see history align and you're like, oh my gosh.

3:25:43And then just like as soon as you make that prediction, yeah, who was that famous author? But you might. So yeah, just, you know, again, people are predicting deep learning with how to wall every year. Right. Maybe one year they're right. But it's like gone a long way and hasn't had a wall. I don't have that much more to go. And, you know, so yeah. I guess I think this is a sort of plausible story. And I let's just run with it and see what it implies. Yeah. So we were talk in your series, you talk about alignment from the perspective of this is not about some doomer scheme to get the point zero on personal probability distribution where things don't go off the rails.

3:26:19It's more about just controlling the systems, making sure they do what we intend them to do. If that's the case, and we're going to be in the sort of geopolitical conflict with China. And part of that will involve, and what we're worried about is then making the CCP boss that go out and take the red flag of Mao across the galaxies or something. Then shouldn't we be worried about alignment as something that if in the wrong hands, this is the thing that enables brainwashing sort of dictatorial control. This seems like a worrying thing. This should be part of this sort of algorithmic secrets we keep hidden, right?

3:26:59How to align these models? Because that's also something the CCP can use to control their models. I mean, I think in the world where you get the democratic coalition, yeah, I mean, also just alignment is often dole use, right? Like Arleichep, you know, it's like alignment to develop. It was great. You know, it was a big win for alignment, but it's also, you know, obviously makes these models useful. Right. The, um, but yeah, so yeah, alignment enables the CCP bots. Alignment also is what you need to get the, you know, get the sort of, you know, whatever, USAIs like follow the constitution and like this is Baylot, you know, unlawful orders and, you know, like respect separation of powers and checks and balances.

3:27:31And, um, so yeah, you need alignment for whatever you want to do. It's just, it's the sort of underlying technique. Tell me what you make of this take. I'm in the string with this a little bit. Okay. So fundamentally, there's many different ways a future could go. Yeah. There's one path in which the Eliezer type crazy AIs with the nanobots take the future and turn everything into gray goo or paper that path of the decision tree is circumscribed. Uh -huh. And then so the more you stop alignment, the more it is just different humans and divisions they have. And of course, we know from history that things don't turn out the way you expect.

3:28:07So it's not like you can decide the future, but it will appear beauty of it. Right. Yeah. You want exactly the error crotch. Exactly. But for the perspective of anybody who's looking at the system, it will be like, I can control where this thing is going to end up. Enter the more you solve alignment and the more you circumscribe, the, different futures that are the results of AI will, the more that accentuate the conflict between humans and their visions of the future. Yep. And so in the world, the alignment is solved. Right. And the world in which alignment is solved is the one is the world in which you have the most sort of human conflict over where to take AI.

3:28:42Yeah. I mean, it's, you know, by removing the worlds in which the AIs take over, then like, you know, the remaining worlds are the ones where it's like the humans decide what happens. And then as we talked about, there's a whole lot of, yeah, a whole lot of worlds and how that could go. And I worry. So when you think about alignment, and it's just controlling these things, yeah, just think a little forward. And there's worlds in which hopefully, you know, human descendants or some version of things in the future merge with super intelligences and they have the rules of their own, but they're in some sort of law and market based order.

3:29:14Uh huh. I worry about if you have things that are conscious and should be treated with rights. Uh huh. If you read about what aligns schemes actually are, and then you read these books about what actually happened during the cultural revolution, what happened when Stalin took over Russia. And you have a very strong monitoring from different instances where one everybody's tacit watching each other. You have brainwashing, you have red teeming where you have the spice stuff you were talking about where you try to convince somebody you're on like a defector and you see if they defect with you. Yeah.

3:29:48And if they do, then you realize they're an enemy and then you take and listen, I maybe I'm stretching the analogy too far. Yeah. But the way, like the ease of this, these alignment techniques actually map on something you could have read about during like mouse culture revolution. It is a little bit troubling. Yeah. I mean, look, I think sent you in the eyes a whole other topic. I know if we want to talk about it. I agree that like it's going to be very important how we treat them. You know, in terms of like what you're actually programming these systems to do. Again, it's like alignment is just it's a technical, it's a technical problem.

3:30:21Technical solution enables the CCP bots. I mean, in some sense, I think the, you know, I almost feel like the sort of model and also about talking about checks and balances is sort of, you know, like the Federal Reserve or Supreme Court justices. And there's a funny way in which they're kind of this like very dedicated order. Supreme Court justices. And it's amazing. They're actually quite high quality. Yeah. Right. And they like really smart people. They really believe in the Constitution. They love the Constitution. They believe in their principles. They have, you know, these, these, these wonderful, you know, back, you know, and yeah, they have different persuasions, but they have sort of, I think very sincere kind of debates about what is the meaning of the Constitution?

3:30:53You know, what is the best actuation of these principles? You know, I guess that's good. You know, by the way, our conversation sort of skodis or arguments like this podcast, you know, but when I run out of high quality content, I mean, I think there's going to be a process of like figuring out what the Constitution should be. I think, you know, this Constitution has like worked for a long time. You start with that. Maybe eventually things change enough that you want edits to that. But anyway, you want them to like, you know, for example, for the checks and balances, they like, they really love the Constitution and they believe in it and they take it really seriously.

3:31:20And like, look, at some point, yeah, you are going to have like AI police and AI military, but I think sort of like, you know, being able to ensure that they like, you know, believe in it in the way that like Supreme Court justice does or like in the way that, like a Federal Reserve, you know, uh, uh, official takes their job really seriously. Yeah. Yeah. And I guess a big open question is whether if you do the project or something like the project, I'm sorry. The other important thing is like a bunch of different factions need their own a eyes, right? And so it's, I, it's really important that like each local party gets to like have their, you know, and like whatever, crazy, you might totally disagree with their values, but it's like, it's really important that they get to like have their own kind of like superintelligence.

3:31:54And again, I think it's that these sort of like classical liberal processes play out, including like different people of different persuasions and so on. And I don't mean, the advisors might not make them, you know, wise, they might not follow the advice or whatever, but I think it's important. Okay. So speaking of alignment, you seem pretty optimistic. So let's run through the source of the optimism. Yeah. I think there you laid out different worlds in which we could get AI. Yeah. There's one that you think is low probability of next year where a GPD4 plus scaffolding plus unhoplings gets you to AGI.

3:32:26Not GP4, you know, like, I'm sorry, sorry. So GP4, yeah. Yeah. And there's ones where it takes much longer. There's ones where it's something that's a couple years. A little world. Yeah. So GPD4 seems pretty aligned in the sense that I don't expect to go off. The rails. Yeah. Maybe what scaffolding things might change. Yeah. Yeah. So the and you maybe you'll keep turn at there's cranks to keep going up. And one of the cranks gets you to ASI. Yeah. Is there any point at which the sharp left turn happens? Is it when you start? Is it the case that you think plausibly when they act more like agents?

3:33:01This is a thing to worry about. Yeah. Is there anything qualitatively that you expect to change with regards to alignment perspective at these cranks? So I don't know if I believe in this concept of sharp left turn, but I do think there's basically, I think there's important qualitative changes that happen between now and kind of like somewhat superhuman systems, kind of like early on the intelligence explosion. And then important qualitative changes that happen from like early and intelligence explosion to kind of like true superintelligence and all its power and might. And let's talk about both of those.

3:33:27And so okay, so the first part of the problem is one, you know, we're going to have to solve ourselves, right? We have to kind of have to line the like initial AI and the intelligence explosion, you know, the sort of automated outcryd for. I think there's kind of like, I mean, two important things that change from GPD 4, right? So one of them is, you know, if you believe the story on like, you know, synthetic data or L or self play to get past the data wall. And if you believe this on hobbling story, you know, at the end, you're going to have things, you know, they're agents, right? Including they do long term plans, right?

3:33:56They have long, long, you know, they're somehow they're able to act over long horizons, right? But you need that, right? That's a sort of prerequisite to be able to do the sort of automated high research. And so, you know, I think there's basically, you know, I basically think sort of pre training is sort of alignment neutral in the sense of like, it has all these representations, it has good representations, you know, as as representations of doing bad things, you know, but but there's, there's, it's not like, you know, scheming against you, whatever. I think this sort of misalignment can arise once you're doing more kind of long horizon training, right?

3:34:25And so you're training, you know, again, two simplified example, but to kind of illustrate, you know, you're training an AI to make money. And, you know, if you're just doing that with reinforcement learning, you know, it's, you know, it might learn to commit fraud or lie or to see you or seek power simply because those are successful strategies in the world, right? So maybe, you know, RL is basically it explores, maybe it figures out like, oh, it tries to like hack and then it gets some money and that made more money, you know, and then if that's successful, if that gets reward, that's just reinforced.

3:34:51So basically, I think you're there sort of more serious misalignments kind of like misaligned long -term goals that could arise between now and or that sort of necessarily have to be able to arise if you're able to get long horizon system. That's one. What you want to do in that situation is you want to add side constraints, right? So you want to add, you know, don't lie, don't deceive, don't commit fraud. And so how do you add those side constraints, right? The sort of basic idea you might have is like Arleach F, right? You're kind of like, yeah, it has a goal of like, you know, make money or whatever, but you're watching what it's doing.

3:35:20It starts trying to like, you know, lie or deceive or fraud or whatever, break the law. You're just kind of like thumbs down, don't do that. You want to have reinforced that. The sort of critical issue that comes in is that these eye systems are getting superhuman, right? And they're going to be able to do things that are too complex for humans to evaluate, right? So again, even early on, you know, in the intelligence explosion, the automated AI researchers and engineers, you know, they might write millions, you know, billions, trillions of lines of complicated code, you know, they might be doing all sorts of stuff.

3:35:45You just like, don't understand anymore. And so, you know, in the million lines of code, you know, is it somewhere kind of like, you know, hacking hacking or like, exaltrating itself or like, you know, trying to go for the nukes or whatever, you know, like, you don't know anymore, right? And so this sort of like, you know, thumbs up, thumbs down, pure RLHF doesn't fully work anymore. Second part of the picture, and the, we should maybe talk more about this. First part of the picture, I think it's going to be like, there's a hard technical problem of what do you do sort of post RLHF, but I think it's a solvable problem.

3:36:11And it's like, you know, there's various things in bullish on. I think there's like ways in which deep learning has shaped that favorite way. The second part of the problem is you're going from your like initial systems intelligence explosion to like super intelligence in you. It's like, many of them ends up being like, by the end of it, you have a thing that's vastly smarter than humans. I think the intelligence explosion is really scary from an alignment point of view, because basically if you have the strap intelligence explosion, you know, less than a year or two years or whatever, you're going, say in the period of a year, from systems where like, you know, failure would be bad, but it's not kind of strapped back to like, you know, saying a bad word.

3:36:44It's like, you know, it's something goes awry to like, you know, failure is like, you know, an extra -traded itself, it starts hacking the military. It can do really bad things. You're going less than a year from sort of a world in which like, you know, it's some descendant of current systems and you kind of understand it and it's like, you know, has good properties. The something that potentially has a very sort of alien and different architecture, right, after having gone through another decade of amalgamances. I think one example there that's very salient to me is legible and faithful chain of thought, right?

3:37:12So a lot of the time when we're talking about these things, we're talking about, you know, it has tokens of thinking and then it uses many tokens of thinking and, you know, maybe we bootstrap ourselves by, you know, it's pre -trained at lunch that think in English and we do something else on top so it can do the sort of longer chains of thought. And so, you know, it's very plausible to me that like for the initial automated alignment researchers, you know, we don't need to do any complicated mechanism interpretability and just like literally read what they're thinking, which is great. You know, it's like huge advantage, right?

3:37:41However, I'm very likely not the most efficient way to do it, right? There's like probably some way to have a recurrent architecture. It's all internal states. There's a much more efficient way to do it. That's what you get by the end of the year. You know, you're going this year from like RLHF plus plus some extension works to like it's vastly superhuman. It's like, you know, it's it's it's it's to us like, you know, an expert in the field might be to like an elementary school or middle school or and so, you know, I think it's this sort of incredibly sort of like hairy period for alignment. The thing you do have is you have the automated air air researchers to also do alignment.

3:38:18And so in this world, yeah, why are we optimistic that the project is being run by people who are thinking, I think so here's here's here's here's here's something to think about. Okay. The opening eye, yeah, you start off with people who are very explicitly thinking about exactly these kinds of things. Yes, right? But are they still there? No, no, but you're still here. Here's the thing. No, no, even the people who are here, even like the current leadership is like exactly these things are going to find them in interviews and their blog post talking about. And what happens is when as you were talking about when some sort of trivial and y 'all talked about it, this is not just you, we all talked about his tweet thread when there is some tradeoff that has to be made to us, we need to do this flashy release this week and not next week because whatever Google I always the next week.

3:39:08So we're going to get it. And then the tradeoff is made in favor of the the less the more careless decision. When we have the government or the national security advisor, the military, whatever, which is much less familiar with this kind of discourse. There's a nationally thinking in this way about, huh, I'm worried the chain of thought is unfaithful. And how do we think about the features that are represented here? Why should we optimistic that a project run by people like that will be thoughtful about these kinds of considerations? I mean, they might not be. You know, I agree. I think a few thoughts, right?

3:39:52First of all, I think the private world, even if they sort of nominally care is extremely tough for alignment. A couple of reasons. One, you just have the race between the sort of commercial labs, right? And it's like, you don't have any headroom there to like be like, ah, actually, we're going to hold back for three months, like get this right. And you know, we're going to dedicate 90 % of our compute to automated alignment research instead of just like pushing the next zoom. The other thing though is like in the private world, you know, China has stolen your way, China has your secrets, they're right on your tails.

3:40:16You're in this fever struggle. No room at all for maneuver. They're like the way it's like absolutely essential to get alignment right. And you get it during this intelligence explosion, you got it right is you need to have that room to maneuver and you need to have that clear lead. And you know, again, maybe you've made the deal or whatever, but I think you're an incredibly tough space, tough spot if you don't have this clearly. So I think the sort of private world is kind of rough there. On like whether people take it seriously, you know, I know I have some faith in sort of sort of normal mechanisms of a liberal society.

3:40:46If alignment is an issue, which you know, we don't fully know yet, but sort of the science will develop, we're going to get better measurements of alignment, you know, and the case will be clear and obvious. I worry that there's, you know, I worry about worlds where evidence is ambiguous. And I think a lot of a lot of the most scary kind of intelligence explosion scenarios are worlds in which evidence is ambiguous. But again, it's sort of like I if evidence is ambiguous, then that's the world's in which you really want the safety margins. And that's also the world's in which kind of like running the intelligence explosion is sort of like, you know, running a war, right?

3:41:15It's like uh, the evidence is ambiguous. You know, make these really tough trade -offs and you like you better have a really good chain of command for that. And it's not just like, you know, you're going to be like, wow, let's go. You know, it's cool. Yeah. Let's talk a little bit about Germany. Uh -huh. We're making the analogy to World War II. And you've made a really significant many hours ago.

3:41:38The fact that throughout history, World War II is not unique. At least when you make a proportion to the size of the population. Yeah. But these other sorts of catastrophes where some significant proportion of the population has been killed off. Yeah. After that, the nation recovers and they get back to their heights. So what's interesting after World War II is that Germany, especially, and maybe Europe as a whole, obviously they experienced fast economic growth in the direct aftermath because of catch up growth. But subsequently, we just don't think of Germany as we're not talking about Germany potentially launching an intelligence closure.

3:42:27And they're going to get it and see it at the EI table. We were talking about Iran and North Korea and Russia. We were talking about Germany, right? Look at their allies. Yeah. Yeah. But so what happened? I mean, the World War II and now it didn't like come back to the Southern years wars on thing, right? Yeah. Yeah. I mean, look, I'm generally very bearish on Germany. I think in this context, I'm kind of like, you know, it's a little bit, you know, I think you're underwriting a little bit. I think it's probably still one of the top five most important countries in the world. You know, I mean, Europe overall, you know, it still has, I mean, it's GDP that's like close to the United States, the size of GDP, you know, and there's things actually that Germany is kind of good at, right?

3:43:02Like state capacity, right? Like, you know, the, the roads are good and they're clean and they're all maintained and, and, you know, in some sense, the sort of a lot of this is the sort of flip side of things that I think are bad about germ, right? So in the US, it's a little bit like there's a bit more of a sort of wild west feeling to the United States, right? And it includes the kind of like crazy bursts of creativity, it includes like, you know, political candidates that are sort of, you know, there's a much broader spectrum and, you know, much, you know, like both an Obama and a Trump is somebody you just wouldn't see in the sort of much more confined kind of German political debate.

3:43:34You know, I wrote this blog post at some point, you're a political stupor about this. But anyway, and so there's this sort of punctilial sort of rule following that is like good in terms of like, you know, keeping your kind of state capacity functioning. But that is also, you know, I think I kind of, I think there's this sort of very constrained view of the world in some sense. You know, and that includes kind of, you know, I think after World War II, there's a real backlash against anything like elite, you know, and, you know, again, no, you know, no elite high schools or elite colleges and sort of what by the law, excellence isn't cherished, you know, there's a, yeah, why is that the logical intellectual, I think to rebel against if you're trying to overcorrect from the Nazis, yeah, why is it because the Nazis were very much into leadism?

3:44:22What's I don't understand why that's a logical sort of kind of reaction? I know maybe it's sort of a kind of reaction against the sort of like whole like Aryan race and that sort of that sort of thing. I mean, I also just think there is a certain amount in what amount certain, I mean, look, look at sort of World War I end of World War I, resented World War II for German, right? And sort of, you know, a common narrative is that the piece of Versailles was too strict on Germany. But, you know, the piece imposed after World War II was like much more strict, right? It was a complete, you know, the whole country was destroyed, you know, it was, you know, you know, and all the, most of the major cities, you know, over half of the housing stock had been destroyed, right?

3:44:56Like, you know, in some birth cohorts, you know, like 40 % of the men had died. Half of the population displaced. Oh, yeah, I mean, almost 20 million people were displaced, right? Huge, crazy, right? You know, like, and the borders are way smaller than the Versailles border. Yeah, exactly. And sort of a complete imposition of a new political system. Right. And, you know, on both sides, you know, and, yeah, so it was, but in some sense, that worked out better than the post -World War I piece, where then there was this kind of resurgence of German nationalism and, you know, in some sense, the thing that has been a pattern.

3:45:30So it's sort of like, it's unclear if you want to wake the sleeping beast. I do think that at this point, you know, it's gotten a bit too sleepy. Yeah. I do think it's an interesting point about we underrate the American political system. And I've been making the same correction myself. Yeah. There's, there was this book about burdened by a Chinese economist called China's World View. Yeah. And overall, I was a big fan, but they made a really interesting point in there. Yeah. Which was the way in which candidates rise up through the Chinese hierarchy for politics, for administration. In some sense, it's like for, you're not going to get some Marjorie Taylor green or somebody running.

3:46:10Right. Right. Right. Don't get that in Germany either. Right. Yeah. But you're, he explicitly made the point in the book that that also means we're never going to get a hand -recissing drill Barack Obama. Right. In China. We're going to get like, by the time they end up in the charge of the, the, the polyurethal, polyurethal, there'll be like some 60 -year -old Democrat who's never like ruffled any feathers. Yeah. Yeah. Yeah. I mean, I think, I think there's something really important about the sort of like very raucous people's bait. And I mean, yeah, in general, kind of like, you know, there's the sense in which in America, you know, lots of people live in their kind of like own world.

3:46:39I mean, like we live in this kind of bizarre little like bubble in San Francisco. And people, you know, and, and, but I, I think that's important for this sort of evolution of idea of error correction and that sort of thing. You know, there's other ways in which the German system is more functional. Yeah. But it's interesting that there's a major mistakes, right? Like the sort of defense spending, right? And, you know, then, you know, Russia and Mezu Kreen and, and you're like, wow, what do we do? Right. No, that's a really good point, right? The main issues, there's everybody agrees, but exactly.

3:47:11Yeah. So I have consensus blob kind of thing. Right. And on the China point, you know, just having this experience of like reading German newspapers and I think how much, you know, how much more poorly I would understand the sort of German debate and sort of the sort of of state of mind from just kind of a far, I worry a lot about, you know, where I think it is interesting just how kind of impenetrable China is to me. It's a billion people, right? And like, you know, almost everything else is really global. I have a globalized internet and I kind of, I kind of, the sense what's happening in the UK.

3:47:39You know, I probably, even if I didn't read German newspapers, sort of what to have a sense of what's happening in Germany, but I really don't feel like I have a sense of what like, you know, what is the state of mind or the state of political debate, you know, of a sort of average Chinese person or like an average Chinese leader. And yeah, I think that that I find that distance kind of worrying and I, you know, and there's, and you know, there's some people who do this and they do really great work where they kind of go through the like party documents and the party speeches. And it seems to require kind of a lot of interpretive ability where there's like very specific words and Mandarin that like mean we'll have one constitution, not the other connotation.

3:48:14But yeah, I think it's sort of interesting, given how globalized everything is. I mean, now we have basically perfect translation machines and it's still so so impenetrable. That's really interesting. I've been, I should, I'm sort of ashamed almost that I haven't done this yet. Yeah, I think many months ago, I, when Alexi interviewed me on his YouTube channel, I said, I'm meaning to go to China to actually see for myself what's going on. And actually, I should. So by the way, if anybody listening has a lot of context on China, if I went to China, who could introduce me to people, please email me because I've got, you got to do some pods and you got to find some of the Chinese AI researchers, man.

3:48:47I know. I was thinking at some point, again, this is, I don't know if they can speak freely. But I was thinking of there's, so they had these papers and on the paper, they'll say who who's a co -author. Yeah, it's funny because while I was thinking of just emailing, cold emailing everybody, like, here's my calendar. Can you, let's, let's just talk. I just want to see what is the vibe. Even they don't tell me anything. I'm just like, what kind of person is this? How westernized are they? Yeah. But I was saying this, I just remembered that in fact, by dance, according to mutual friends we have at Google, they cold emailed every single person on the Gemini paper and said, if you come work for by dance, we'll make you an L .A .D.

3:49:27engineer. You'll report directly the CTO and, in fact, this actually got me to go, that's how the secrets go over, right? No, I'm actually figuring out, I meant to ask this earlier, but suppose they hired what, if there's only a hundred or so people, and maybe less, we're working on the key algorithm, we see parts. Yeah, yeah, yeah. If they hired one such person, yeah, is all the off -a -gon that these labs have? If this person was intentional about it, they could get a lot. I mean, they couldn't get the sort of, like, I mean, actually, you could probably just also actually treat the code. They could get a lot of the key ideas.

3:49:54Again, like, you know, up until recently stuff was published, but, you know, they could get a lot of the key ideas if they tried. If they like, you know, I think there's a lot of people who don't actually kind of like look around to see what the other teams are doing, but, you know, I think you kind of can. But yeah, I mean, they could. It's scary. Right. I think the project makes more sense there where you can just recruit a Manhattan project engineer and then just get it. And it's like these are secrets that can be used for, like, probably every training on the future that will be like, maybe are the key to the data wall that I'm like, they can't go on or they can't go on that are like, you know, they're going to be worth, you know, giving sort of like the multipliers on compute, you know, hundreds of billions, trillions of dollars, you know, and all that takes is, you know, China to offer $100 million to somebody.

3:50:32Right. Yeah, come work for us. Right. And then, and then, yeah, I mean, yeah, I mean, yeah, I'm really uncertain on how sort of seriously China is taking AGI right now. One anecdote that was related to me on the topic of anecdotes, the by another sort of like, you know, kind of researcher in the field was at some point, they were at a conference with somebody in Chinese AI researcher. And he was talking to him and he was like, I think it's really good that you're here. And like, you know, we got to have the international coordination and stuff. And apparently this guy said that I'm the kind of most senior most person that they're going to let leave the country to come to things like this.

3:51:05Mm. Wait, well, what's a, what's it take away? They're not letting really senior AGI's country. Interesting kind of classic, you know, Eastern block move. Yeah. I don't know if this is true, but it's what I hear is interesting. So I thought the point you made earlier about being exposed to German newspaper and also to, because earlier, you mentioned economics and law and national security. You have the variety and intellectual diet there has exposed you to thinking about the geopolitical question here in ways. Others talking about AI. I mean, this is the first episode I've done about this where we talked about things like this, which is now that I think about it, we are to give it that this is an obvious thing in retrospect.

3:51:44I should have been thinking about. Uh -huh. Anyways, so that's one thing we've been missing. Uh -huh. What are you missing? And national security, you're thinking about so you can't say national security. What what what like keep perspective? Are you probably under exposed to as a result? And China, I guess you mentioned. Yeah. So I think the China one is an important one. I mean, I think another one would be a sort of very Tyler Kownes take, which is like, you're not exposed to how well, like how will a normal person in America, you know, both like use AI, you know, probably not. Yeah. And and and that being kind of like bottlenecks of the fusion of these things.

3:52:17Yeah. I'm overrating the revenue because I'm kind of like, ah, you know, everyone has to stop it. But you know, kind of like, you know, Joe Schmoh engineer at a company, you know, like, ah, will they will they be able to integrate it? And then also the reaction to it, right? You know, I mean, I think this was a question again hours ago where it was about like, you know, won't people kind of rebel against this? Yeah. And they won't want to do the project. I don't know, maybe they will. Yeah. Here's a political reaction that I didn't anticipate. Yeah. So Tucker Carlslander is recently on the Chirogan episode.

3:52:47I already told you about this but I'm just going to tell the story again. So Tucker Carlsland is on Chirogan. Yeah. And they start talking about World War Two. And Tucker says, well, listen, I'm going to say something that my fellow conservatives won't like. But I think nuclear weapons are immoral. I think it was obviously immoral that we use them on Agasaki and Hiroshima. And then he says, in fact, nuclear weapons are always immoral. Except when we would use them on data centers. In fact, it would be immoral not to use them on data centers because look, we're people in Silicon Valley, these fucking nerds are making as is super intelligence.

3:53:26And they say that it could enslave humanity. We made machines to serve humanity, not to enslave humanity. And they're just going on and making these machines. And so we should of course be nuking the data centers. And that is definitely not a political reaction in 2024. I was expecting. I mean, who knows? Yeah, it's going to be crazy. It's going to be crazy. The thing we learned with COVID is that also the left right reactions that you would anticipate just based on hunches. It completely flipped. It's actually initially like kind of the right. It's like so contingent. And then and then and then the left was like this is racist.

3:54:05And then it flipped. The left was really into the coat. Yeah. And the whole thing also is just like so blunt and crude. And so I think I think probably in general, I think people are really under, you know, people like to make sort of complicated technocratic policy proposals. And I think especially if things go kind of fairly rapidly on the path to AI. You know, there might not actually be that much space for kind of like complicated kind of like, you know, clever proposals. It might just be kind of a bunch of crude or reaction. Yeah. Look, and then also when you mentioned the spies and the natural security getting involved and everything.

3:54:39And you can talk about that in the abstract. But now that we're living in San Francisco and we know many of the people who are doing the top AI research is also a little scary to think about people I personally know and friends with. It's not unfeasible if they have secrets that are that are head that are worth a hundred million dollars or something. Kidnapping, assassination, sabotage. Oh, they're family. Yeah, it's really bad. Yeah. And this is to the point on security, you know, like right now, it's just really formed. But no, at some point as it becomes like really serious, it's things, you know, you're going to want the security cards.

3:55:13Yeah. Yeah. So presumably you have thought about the fact that people in China will be listening to this and we've reading your series. Yeah. And somehow you made the trade off that it's better to let the whole world know. Yeah. And also including China. Yeah. And make them up to AGI, which is part of the thing you're worried about is China, we can come to AGI. Yeah. Then to stay silent. Yeah. And it's curious. Well, walk me through how you've thought about that trade off. Yeah, I actually look, I think this is a tough trade off. I thought about this a bunch, you know, I think, you know, I think people in the PRC will read this.

3:55:56I think, you know, I think there's some extent to which sort of cat is out of the bag. You know, this is like not, you know, AGI, being a thing people are thinking about very seriously, not new anymore. There's sort of, you know, a lot of these takes are kind of old. Or, you know, I've had, I had, you know, some reviews a year ago, might not have written it up a year and part because I think this cat wasn't out of the bag enough. You know, I think the other thing is I think to be able to manage this challenge, you know, I think much broader swath in society will need to wake up, right? And if we're going to get the project, you know, we actually need sort of like, you know, a broad bipartisan understanding, the challenge is facing us.

3:56:31And so, you know, I think it's a tough trade off, but I think this sort of need to wake up people in the United States in the sort of Western world and the Democratic coalition is ultimately imperative. And, you know, I think my hope is more people here will read it than the PRC. You know, and I think people sometimes underrate the importance of just kind of like writing it up, laying out the Shijie picture. And, you know, I think you have done actually a great service to the sort of mankind in some sense, by, you know, with your podcast. And, you know, I think it's overall been good. Okay. So by the way, you know, on the topic of, you know, Germany, we were talking at some point about kind of immigration story.

3:57:12Right. You have a kind of interesting story you haven't told. And I think you should tell. So a couple of years ago, I was in college and I was 20. Yeah, I was about to turn 21. Yeah. I think it was. So you're, yeah, you came from India when you were really young. Right. Yeah. So I don't know. I was eight or eight or nine. I lived in India. And then we moved around all over the place. Right. Because of the backlog for Indians. Yeah, green card backlog. Yeah. It's, um, we were, we've been in the queue for like decades. Even though you came at eight years, so long, then, you know, each one, yeah.

3:57:46And when you're 21, yeah, you get kicked off. Yeah. The queue. And you had to restart the process. I'm on my dad's, my dad's a doctor, and I'm like done as H1B as a dependent. But when you're 21, you get kicked off. Yeah. And so I'm 20. And I just like kind of dawns on me. This is my situation. Yeah. And you're completely screwed. Right. And so I also had experience that my dad, yeah, we've like moved all around the country. They have to prove that him as a doctor is like, you can't get native talent. Yeah. And you can't start up. Yeah. Yeah. So where can you not get like even getting the H1B for you would have been like, you know, 20 % lottery.

3:58:17Right. If you're lucky, you're in the same. And I'd approve that they can't get native talent, which means like for him and like, North, we live in North Dakota for three years, West Virginia for three years, Maryland, West Texas. Yeah. And so kind of dawn on me. This is my situation. I just turned 21. Yeah. I'll be like on this lottery, even if I get the lottery. Yeah. I'll be fucking code monkey for the rest of my life. Cause this thing isn't going to let up. Yeah. Can't do a startup. Exactly. And so at the same time, I had been reading for the last year, I've been super obsessed with program essays.

3:58:44I might my plan at the time was to make a startup or something. I was super excited about that. Yeah. And it just occurred to me that I couldn't do this. Yeah. That like this is just not in the cars for me. Yeah. And so I was kind of depressed about it. I remember I kind of just blew. I was in a days through finals. Cause I had just occurred to me. And I was really like anxious about it. Yeah. And I remember thinking to myself at the time, that if somehow I end up getting my green card with Brighter in 21, there's no fucking way I'm turning me coming to code monkey. Yeah. Because the thing that I've like this feeling of dread that I have is this realization that I'm just going to have to be a code monkey.

3:59:25And I realized that's my default path. Yeah. If I if I hadn't sort of made a proactive effort not to do that, I would graduate college as a computer science student and I would have just done that. And that's the thing I was super scared about. Yeah. So that was a important sort of, um, uh, realization for me. Anyway, so COVID happened because of that since there weren't, uh, foreigners coming, the backflop were fast. And by the skin of my teeth, like a few months before I turned 21, okay, extremely contingent reasons. I ended up getting a green card. Yeah. Because I got a green card. I could you know, the whole podcast, right?

3:59:57Exactly. It's like college. And it was like bumping around. Yeah. And I got was like, I got I graduated semester early. I'm going to like do this podcast. Yeah. What happens? And it was, it had an end to never green card. And the best case scenario. The fact, you know, and it only existed. Yeah. Yeah. It's actually because I think it's hard. It's probably it's, you know, what is the impact of like immigration reform? Right. What is the impact of clearing, you know, like whatever, 50 ,000 green cards in the backlog? And you're such like an amazing example of like, you know, all of this is only possible.

4:00:26And it's, yeah, it's, I mean, it's just a cartilage tragic. That's right. This is so dysfunctional. Yeah. Yeah. No, it's insane. I'm glad you didn't. I'm glad you kind of like, you know, tried the, you know, the, the, the unusual path. Well, yeah. But I could only do it. I'm obviously, I was extremely fortunate that I got the green card. I was like, I had a little bit of saved up money. I got a small grant on college, thanks to the fuse or fun to like do this for basically the equivalent of six months. And so it turned out really well. And then at each time. And I was like, oh, okay, podcasts.

4:01:01Come on. Like I wasted a few months on this. Let's now go do something real. Something big would happen. Yeah. I would, yeah. Yeah. But there would always be, I'd just like the moment I'm about to quit the podcast, something like Jeff Bezos will say that it's something that's my big Twitter. The really episodes gets like half a million views. You know, and then now this is my career. But it was sort of very looking back on it. It's incredibly contingent. Yeah. The things worked out the right way. Yeah. I mean, look, if the AGI stuff goes down, you know, it will be, uh, it'll be the most important kind of like, you know, source of, it'll be how maybe most of the people who kind of end up feeling need AI.

4:01:38We're sure to buy it. Yeah. Yeah. Also very much. You're very linked with the story in many ways. First, the, I got like a $20 ,000 grant from a future fund right out of college. And that's a stain to me for six months or how long it was. Yeah. And without that, I wouldn't. It's kind of crazy. Yeah. Yeah. And your underwear was it. Yeah. No, it's just tiny. But yeah. Yeah. It's, it goes to show kind of how small grants can go. Yeah. It's sort of the emergency ventures too. Yeah. Exactly. Well, the last year I've been in San Francisco, we've just been in close contact the entire time and just bouncing ideas back and forth.

4:02:17We're just basically the alpha I have. I think people would be surprised by how much I got from you, Shulto, Trenton, a couple of other hours. It's been, it's been an absolute pleasure. Yeah. I like it. I like it. It's been super fun. Yeah. Um, okay. So some random questions for you. Yeah. If you could convert to Mormonism. Yeah.

4:02:39And you well, okay. Okay. Uh, before I answer that question, one sort of observation about the Mormons. So actually, there's that there's an article that actually made a big impact on me because by my kick hop and at some point, you know, the Atlantic or whatever about the Mormons. And I think the thing he kind of, you know, and I think he even like interviewed me and so on. And I think the thing I thought was really interesting in this article was he kind of talked about how the experience of kind of growing up different, you know, growing up very unusual, especially if you grow up Mormon outside of Utah, you know, like the only person doesn't drink caffeine.

4:03:08You don't drink alcohol. You're kind of weird. Um, how that kind of got people prepared for being willing to be kind of outside of the norm later on. And like, you know, Romney, you know, was willing to kind of take stands alone, you know, in his party because he believed, you know, what he believed is true. And, I don't, I mean, probably not to the same way, but I feel a little bit like this from kind of having grown up in Germany, having, you know, and really not having like this German system and having been kind of an outsider or something. I think there's a certain amount in which kind of, yeah, growing up in an outsider gives you kind of an usual strength.

4:03:38Later on to be kind of like, yeah, willing to say what you think. And, um, yeah, so that is one thing I really appreciate about the Mormons, at least the ones that have, you know, grew up inside of Utah. I think, you know, the fertility rates, they're good, they're important. They're going down as well, right? Yeah, right. This, this, this is the thing that really clinched the kind of fertility client story. Yeah, even the Mormons, yeah, even the Mormons, right? You're like, oh, this is like a, you sort of good, good, sir, the Mormons are replaced, everybody. I don't know if it's good, but it's like, at least, you know, at least come on, you know, like it's these some people will maintain high, you know, but it's no, no, you know, even the Mormons and sort of basically one, one's the sort of, these religious subgroups have high fertility rates.

4:04:09Right. Once they kind of grow big enough, they become, they're too close in contact with sort of normal society and become normalized, the Mormon fertility rates dropped from, I remember the exact numbers, maybe like four to two, and in the course of 10, 20 years. Um, anyway, so it's like, you know, now people point to the, you know, Amish or whatever, but I'm just like, it's probably just not scalable. And if you grow big enough, then there's just like, you know, this sort of like, you know, this sort of like overwhelming force of modernity kind of gets you. Um, no, if I could convert to Mormonism, look, I think there's something, I don't believe it, right?

4:04:38If I believed it, I obviously would convert to Mormonism, right? Because it's, you got to, you got to, you got to, you can choose the world in which you do believe it. Um, I think there's something really valuable and kind of believing in something greater than yourself and believing and having a certain amount of faith. Um, you do, right? And, and, and, and, and, you know, I'm, you know, there's a, you know, feeling some sort of duty to the thing greater than yourself. Um, and you know, maybe my version of this is somewhat different, you know, I think I feel some sort of duty to like, I feel like there's some sort of historical weight on like, how this might play out.

4:05:13And I feel some sort of duty to like, make that go well, I feel some sort of duty to, you know, our country to the national security of the United States. And, um, um, you know, I think, I think that, I think that you can be a force for a lot of good. I, the, uh, uh, going back to the opening, I think, just, uh, uh, I, the, the thing that's especially impressive about that is, look, there's people who add the company who have through years and decades of building up savings from working in tech have probably tens of millions liquid more than that in terms of their equity. And the person, very many people were concerned about the clusters in the Middle East and, uh, the secrets leaking to China and all these things.

4:06:03But the person who actually made a hassle about it, and I think hassling people is so underrated. I think that I, I, I, one person who made a hassle about it is the 22 year old who has less than a year of the company who doesn't have savings built up, uh, who, who, who, who, who is a, like a solidified member of the, um, I think that's a sort of like me, and maybe, maybe it's me being naive and, you know, not having a knowing how big companies work and, you know, but like, I, there's a, you know, I think sometimes a bit of a speech geontologist, you know, I kind of believe in saying what you think.

4:06:34Yeah. Sometimes friends tell me I should be more of a people who, when they have the opportunity to talk to the person, we'll just bring up the thing. I've been with you in multiple contexts and I guess I shouldn't review who the person is or what the context was. But I've just been like, very impressed that the dinner begins and by the end, somebody who has a major voice in how things go, uh, is seriously thinking about a world view, they would have found incredibly alien before the dinner or something. Um, and I've been impressed that like just like, just give them the spiel and hassle them.

4:07:14Um, um, I mean, look, I just, I think, I think I feel this stuff pretty viscerally now. Yeah. I think there's a time, you know, there's a time when I thought about the stuff a lot, but it was kind of like econ models and like, you know, kind of like these sort of theoretical abstractions and, you know, you talk about human brain size or whatever. Right. And I think, you know, since, um, I think since at least last year, you know, I feel like, you know, I feel like I can see it. Yeah. And I just, I feel it. Um, and I think I can, like, you know, I can sort of see the cluster that AGI, I can see the kind of rough combination of algorithms and the people that be involved in how this is going to play out.

4:07:47Yeah. And, um, you know, I think, um, look, we'll see how it plays out. There's many ways this could be wrong. There's many ways it could go, but I think this could get very real. No. We should be talking about what you're up to next. Sure. Yeah. Okay. So you're starting investment for, yep, anchor investments from that Friedman, Daniel Gross, Patrick Lawson, John Collison. First of all, why is this thing to do? You believe the AGI is coming in a few years? Yeah. Well, well, well, well, why the investment firm? A good question, fair question. Okay. So I mean, a couple of things. One is just, you know, I think we talked about this earlier, but it's like the screen doesn't go blank, you know, when sort of AGI or spring intelligence happens.

4:08:26I think people really underrate the sort of, basically the sort of decade after it. You have the intelligence explosion. That's maybe the most sort of wild period. I think the decade after is also going to be wild. And you know, this combination of human institutions, but super intelligence, you have crazy kind of geopolitical things going on. You have the sort of broadening of this explosive growth. And basically, yeah, I think it's going to be a really important period. I think capital really matter, you know, eventually, you know, like, you know, going to go to the stars, you know, going to go to the galaxies.

4:08:51So anyway, so part of the answer is just like, look, I think don't do it done right. There's a lot of money to be made, you know, I think if AGI were priced in tomorrow, you could maybe make 100X, probably you can make even way more than that because of the sequencing. And, and, you know, capital matters. I think the other reason is just, you know, some amount of freedom and impendence. And I think, you know, I think there's some people who are very smart about the AI stuff and who are kind of, like, see it coming. But I think only us all of them, you know, are kind of, you know, constrained in various ways, right?

4:09:23They're in the labs, you know, they're in some, you know, some other position where they can't really talk about the stuff. And, you know, in some sense, I've really admired sort of the thing you have done, which is I think it's really important that there's sort of voices of reason on this stuff publicly, or people who are in positions to kind of advise important actors and so on. And so I think there's a, you know, basically the thing this investment firm will be will be kind of like, you know, a brain trust on AI. It's going to be all that situation awareness. We're going to have the best situational awareness in the business.

4:09:48You know, we're going to have way more situational business than any of the people who manage money in the, you know, New York. Definitely going to, you know, we're going to do great on investing. But it's the same sort of situational awareness that I think is going to be important for understanding what's happening, being a voice of reason publicly and and and sort of being able to be in a position to advise. Yeah. I, the book about Peter Teal, yeah, they had an interesting quote about his hedge fund. I think it got terrible returns. So this is an example. Right. That's the, that's the, that's the but they had an interesting quote that it's, it's that it's like basically a thing tank inside of a hedge fund.

4:10:28Yeah. And I'm trying to build right. Yeah. So presumably you've thought about the ways in which these kinds of things can blow. There's a really, there's a lot of interesting business history books about people who got the thesis right. Yeah. But timed it wrong. Yeah. Where they, they buy that internet's going to be a big deal. Yeah. They sell it the wrong time and buy the wrong time during the dot com boom. Yep. And so they miss out on the gains even though they're right about the, yeah. Anyways, yeah. Well, what is that trick to preventing that kind of thing? Yeah. I mean, look, obviously you can't, you know, not blowing up is sort of like, you know, task number one and two or whatever.

4:11:03I mean, you know, I think this investment firm is going to just be betting on AGI, you know, betting on AGI and super intelligence before the decade is out, taking that seriously, making the bets you would make. Yeah. If you took that seriously. So, you know, I think if that's wrong, you know, firm is not going to do that well. The thing you have to be resistant to is like you have to be able to resistance yet, you know, one or a couple or a few kind of individual calls, right? You know, it's like AI stagnates for a year because of the data wall or like, you know, you got, you got the call wrong on like when revenue would go up.

4:11:29And so that's pretty critical. You have to get timing, right? I do think in general that the sort of sequence of bets on the way to AGI is actually pretty critical and I think a thing people underrate. So, all right. I mean, yeah. So like, where does the story start? Right? So like, obviously, the sort of only bet over the last year was in video. And, you know, it's obvious now, very few people did it. This is sort of also, you know, a classic debate I and a friend had with another colleague of ours, where this colleague was really into TSM, you know, TSMC and he was just kind of like, well, you know, like these tabs are going to be so valuable.

4:12:02And also like, in video, there's just a lot of videos and credit grips, right? It's like maybe somebody else makes better GPUs. That was basically right. But sort of only in video had the AI beta, right? Because only in video was kind of like a large fraction AI, the next few doubling, which is like, meaningfully explode the revenue, whereas TSMC was, you know, a couple percent AI. So even though there's going to be a few doubling of AI, I'm not going to make that big of an impact. All right. So it's sort of like, the only place to find the AI beta basically was in video for a while. You know, now it's broadening, right?

4:12:29So now TSM is like, you know, 20 percent AI by like 27 or something is what they're saying. One more doubling, it'll be kind of like a large fraction of what they're doing. And, you know, there's a whole stack, you know, there's like a, you know, there's people making memory and co -hosts. And, you know, power, you know, utility companies are starting to get excited about AI. And they're like, oh, it'll, you know, power production in the United States will grow, you know, not 2 .5 percent, 5 percent of the next five years. And I'm like, no, it'll grow more. You know, at some point, you know, you know, like a Google or something becomes interesting.

4:13:01You know, people are excited about them with AI because it's like, oh, you know, AI revenue will be, you know, 10 billion or tens of billions. And I'm kind of like, ah, I don't really care about them before then. I care about it, you know, once it, you know, once you get the AI beta, right? And so at some point, you know, Google will get, you know, $100 billion of revenue for me. I probably their stock will explode. You know, they're going to become, you know, five trillion, $10 trillion company. Anyway, so the timing there is very important. You have to get the timing right. You have to get the sequins right.

4:13:24You know, at some point, actually, I think like, you know, there's going to be real tailwind equities from real interest rates, right? So basically in these sort of explosive growths worlds, you would expect real interest rates to go up a lot. Both on the sort of like, you know, basically both sides of the equation, right? On the supply side or on the sort of demand for money side, because, you know, people are going to be making these crazy investments. You know, initially in clusters and then in the robot factories or whatever, right? And so they're going to be borrowing like crazy. They want all this capital, higher I.

4:13:54And then on the sort of like consumer saving side, right? To like, you know, to give up all this capital, you know, sort of like, oily equations, standard sort of intro temporal transfer, you know, trade off of consumption. So standard. No. There's a standard. Also, you know, some of our friends have a paper on this, you know, basically if you expect, you know, if consumers expect real growth rates to be higher, you know, interest rates are going to be higher because they're less willing to give up consumption. You know, consumption in the, there was, they're, they're less willing to give up consumption day for consumption in the future.

4:14:24Anyway, so at some point real interest rates will go up if sort of data is greater than one that actually means equities, you know, higher growth rate expectations being equities go down because the sort of interest rate effect outweighs the growth rate effect. And so, you know, at some point there's like big, the big bond short, you got to get that right. You know, you got to get it right, that, you know, nationalization, you know, like, you got, you know, anyway, so there's this whole sequence of things, you got to get that right. And you know, no, no, no, no, no, no, no, no, yeah. And so you've, look, you've got to be really, really careful about your like overall expositioning, right?

4:14:51And because, you know, if you expect these kind of crazy events to play out, there's going to be crazy things you didn't see. You know, you do also want to make the sort of kind of bets that are tailored to your scenarios in the sense of like, you know, you want to find bets that are bets on the tails, right? You know, I don't think anyone is expecting, you know, interest rates to go above, you know, 10 % like real interest rates. But, you know, I think there's at least a serious chance of that, you know, before the decade is out. And so, you know, maybe there's some like cheap insurance you can buy on that.

4:15:16You know, that pays off. Very silly question. Yeah. In these worlds, yeah, our financial markets where you make these kinds of bets going to be respected and like, you know, like it's my fidelity account. I'm going to mean anything when we have 50 % economic growth. Like who's who's like, we got to respect his property rates. That's pretty deep into it. The bond for the sort of 50 and 50 % economic growth. That's pretty deep into it. I mean, again, there's this little sequence of things. But yeah, no, I think property rates will be extracted again in the sort of modal world, the project. Yeah.

4:15:44At some point, at some point, there's going to be figuring out the property rates for the galaxies. You know, that'll be interesting. That'll be interesting. So there's an interesting question about going back to your strategy about, well, the 30s will really matter a lot about how the rest of the future goes. Yeah. And you want to be in a position of influence by that point, because of capital. It's worth considering, as far as I know, there's probably a whole bunch of literature on this. I'm just riffing. But the landed gentry during the before the beginning of the Industrial Revolution, I'm not sure if they were able to leverage their position in a sort of georgist or pickety type sense in order to accrue the returns that were realized through the Industrial Revolution.

4:16:36And I don't know what happened. At some point, they were just weren't the landed gentry. But I'd be concerned that even if you make great investment calls, you'll be like the guy who owned a lot of land, farm land, before the Industrial Revolution, and the guy who's actually going to make a bunch of money is the one with the sea mentioned. Even he doesn't make that much money. Most of the benefits are widely diffused and so forth. I mean, I think that the analog is like you sell your land, you put it all in sort of the people who are building the new industry. I think that's sort of like real depreciating asset, for me, is human capital.

4:17:12Yeah, no, look, I'm serious. There's something about, I don't know, it was like the auditory of Columbia, the thing that made you special is your smart, but actually that might not matter in four years because it's actually automatable. Anyway, a friend joke that the investment firm is perfectly hedge for me. It's either like AGI this decade, and you're a human capital that's appreciated, but you've turned that into financial capital, or no AGI this decade, in which case, maybe the firm doesn't do that well, but you're still in your 20s and you're so smart. Excellent. And what's your story for why AGI hasn't been priced in?

4:17:49The story, financial markets are supposed to be very efficient and it's very hard to get an edge. Here, naively, you just say, well, I've looked at these scaling curves and they imply that we're going to be buying much more compute and energy than the analysts realize. Yeah. Sure, none of those analysts be broke by now. What's going on? Yeah. I mean, I used to be a true EMH guy. I was an economist. I think the thing I changed by mind on is that I think there can be groups of people, smart people who are, say they're in San Francisco, who do just have alpha over the rest of society and seeing the future.

4:18:31And so COVID, I think there's just honestly some group of people who just saw that. Called it completely correctly. And they showed at the market they did really well. A bunch of other things like that.

4:18:52Why is AGI not priced in? Why hasn't the government nationalized the labs yet? Society hasn't priced it in yet and it hasn't completely refused. Again, it might be wrong. I just think sort of not that many people take these ideas seriously. A couple of other ideas that I was playing around with with regards to reading and getting to change the talk about. But the system's competition. There's a very interesting, one of my favorite books about World Gorgeous is Victor Davis Hanson, a summary of everything. And he explains why the allies made better decisions than the Axis. Why did they? And so obviously there were some decisions that the Axis made.

4:19:43They were pretty like blitzkrieg whatever. Those sort of by accident though, what in what sense? That they just had the infrastructure left over. Well, no. I mean, the sort of I think, I mean, I think sort of my read of it is, it's blitzkrieg wasn't kind of some like a genius strategy. It was just kind of, there's like more like their hand was forced. I mean, this is sort of the very Adam Tuzian story of World War 2, right? But it was, you know, there's sort of this long war versus short war. I think it's actually kind of an important concept. I think sort of Germany realized that if they were in a long war, including the United States, you know, they would not be able to compete industrially.

4:20:13So their only path to victory was like make it a short war, right? And that that sort of worked much more spatically than they thought, right? And sort of take over France and take over much of Europe. And so then the decisions are invading the Soviet Union. It was it was it was it was it was about the Western front in some sense because it was like we've got to get the resources. No, we don't, we're actually we don't actually have a bunch of the stuff we need like, you know, oil and so on. You know, Auschwitz was actually just this giant chemical plant to make kind of like synthetic oil and a bunch of these things is the largest nested project in Nazi Germany.

4:20:45And so, you know, and sort of they thought, well, you know, we completely crushed them and ruled it one, you know, they'll be easy. You will invade them. We'll get the resources. And then we can fight on the Western front. And even during the sort of whole invasion of the Soviet Union, even though kind of like a large amount of the sort of, you know, the sort of deaths happened there, you know, like a large fraction of German industrial production was actually, you know, like planes and naval, you know, and so on. Those directed, you know, towards the Western front and towards the Western allies.

4:21:09Well, and then to the point that Hansen was making was, by the way, I think this concept of like long war and short war is kind of interesting and with respect to thinking about the China competition. Yeah. Which is like, you know, I worry a lot about kind of, you know, the kind of sort of American, like late in American industrial capacity, you know, like I think China builds like 200 times more ships than we do right now. You know, some crazy way. And so it's like maybe we have this sort of superiority, say in the non -AI world, we have the superiority in military material, kind of like winnish or at least, you know, kind of defend Taiwan in some sense.

4:21:42But like if it actually goes on, you know, it's like maybe China is much better able to mobilize, mobilize industrial resources in a way that like we just don't have the same ability anymore. I think this is also relevant to the AI thing in the sense of like if it comes down to sort of a game about building, right, including like maybe AGI takes the trillion dollar cluster, not the 100 billion dollar cluster, maybe or even maybe AGI takes the, you know, is on the 100 billion dollar cluster. But, you know, it really matters if you can run, you know, 10x, you can do one more order of magnitude of compute for your super intelligence or whatever that, you know, maybe right now they're behind, but they just have this sort of like raw light and industrial capacity to build us.

4:22:19Yeah. And that matters both in the run up to AGI and after, right, where it's like you have this super intelligence on your cluster. Now it's time to kind of like expand the explosive growth. And you know, like will we let the robo factories run wild? Like maybe not, but like maybe China will, we're like, you know, will we, will yeah, will we produce the, how many, how many of the drones will we produce? And I think, yeah, so there's some sort of like outbuilding and the industrial explosion that I work. You've got to be one of the few people in the world who is both concerned about alignment, but also wants to make sure that we'll let the robo factories proceed once you get the ASI to beat out China.

4:22:52Which is like, it's all part of the picture. Yeah, yeah. Yeah. And but by the way, speaking of the ASI's and the robot factories, one of the interesting things. Well, there's this question of what you do with industrial scale intelligence. Obviously, it's not chatbots. Yeah. But it's a, I think it's very hard to predict in this. Yeah.

4:23:18But the history of oil is very interesting, where in the, I think it's in the 1860s that we figure out how to refine oil some a geologist. And so then standard oil, I guess, there's a huge boom. It changes American politics. Yeah. Entire legislators are getting bought out by oil interest. Yeah. Then presidents are going to be elected based on the division. Then it's about oil and breaking that him up and everything. Uh -huh. And all of this has happened. Yeah. The world isn't ever illusionized before the car has been invented. Uh -huh. And I, so when the light bubble was invented, I think it was like 50 years after oil refining had been discovered.

4:23:58As majority of standard oil history is before the car is invented. The caracene land. Exactly. So it's just used for lighting. And then they thought oil would just no longer be relevant. Yeah. Yeah. So there was a concern that standard oil would go to bankrupt when, um, when the label was invented. Yeah. And but then there's sort of, do you realize that there's immense amount of compressed energy here? Yeah. You're going to have billions of gallons of this stuff a year. Yeah. And it's hard to, I was sort of predicting advance what you can do with that. Yep. And then later on in terms of transportation, cars, uh, with, uh, yeah.

4:24:35That's, that's, that's what it would be used for. And anyways, with intelligence, maybe one answer is the intelligence explosion. Right. But even after that, yeah. So you have all these ASIs and you have enough compute, especially the compute they'll build to run hundreds of millions of GPUs. Well, hum. Yeah. But what are we doing with that? Yeah. And it's very hard to predict an advance. I think we'll be very interesting to figure out what the Jupiter brain will be doing. So look, there's situational awareness. Yeah. Um, where things stand now. Uh, and we've gotten a good dose of that. The, uh, obviously, a lot of the things we're talking about now, you couldn't have prejudged many years back in the past.

4:25:15Right. And part of your role, it implies that things will accelerate because of AI getting the process. Yeah. But many other things that we are that are unpredictable, fundamentally, yeah. Basically, how people will react, how the political system will react, how foreign adversaries will react. Yeah. That those things will become evident over time. Yep. So the situational awareness is not just knowing what the picture stands now, but being in a position to react appropriately to new information, to change a world view as a result, to change the recommendations as a result. Yep. What is the appropriate way to think about situational awareness is a continuous process, rather than as a one time thing you realized?

4:25:58Yeah. No, I think this is great. Look, I think there's, there's a sort of mental flexibility in willing to change your mind. That's really important. I actually think this is sort of like how a lot of brains have been broken in the AGI debate. And I'm sort of the doomers who actually, you know, I think we're really prescient on AGI, thinking about the stuff, you know, like a decade ago, but, you know, they haven't actually updated on the empirical realities of deep warning. They're sort of like the proposals are really kind of naive and unworkable. This really makes sense. You know, there's people coming with sort of predefined ideology.

4:26:24There's kind of like the E -axle a little bit, you know, like they like to ship post about technology, but they're not actually thinking through like, you know, I mean, either they're sort of stagnationists who think the stuff is only going to be, you know, a chatbot. And so, of course, it isn't risky, or they're just not thinking through the kind of like actually immense national security implications and how that's going to grow. And, you know, I actually think there's kind of a risk in kind of like having written the stuff down and like put it online and, you know, there's a, there's a, I think this sometimes happens to people who just sort of calcification of the worldview because now they publicly articulated this position.

4:26:53And, you know, maybe there's some evidence against it, but they're clinging to it. And so I actually, you know, I want to give the big disclaimer on like, you know, I think it's really valuable to paint this sort of very concrete and visceral picture. I think this is currently my best guess on how this decade will go. I think if it goes anywhere like this, it will be wild. But, you know, given given the map of the case of progress, we're going to keep getting a lot more information. And, you know, I think it's important to sort of keep your head on straight about that. You know, I feel like the most important thing here is that, you know, and this relates to some of the stuff we've talked about, and you know, sort of the world being surprisingly small and so on.

4:27:35You know, I feel like I used to have this rule of view of like, look, there's important things happening in the world, but there's like people who are taking care of it, you know, and there's like the people in government and there's again, even like AI labs have idealized and people are on it, you know, surely there must be on it, right? And I think just some of this personal experience, even seeing how kind of COVID went, you know, people aren't necessarily, there's not some, not as that somebody else is just kind of on it and making sure this goes well, however it goes. You know, the thing that I think will really matter is that there are sort of good people who take this stuff as seriously as it deserves and who are willing to kind of take the implication seriously, who are willing to, you know, have situational awareness or willing to change their minds or willing to sort of steer the picture in the face.

4:28:21And, you know, I'm counting on those good people. All right, that's a great place to close Leopold. Thanks so much, Tarkash. Yeah, this is excellent. Hey everybody, I hope you enjoyed that episode with Leopold. There's actually one more riff about German history that he had after a break and it was pretty interesting, so I didn't want to cut it out. So I've just included it after this outro. You can advertise on the show now. So if you're interested, you can reach out at the forum in the description below. Other than that, the most hopeful thing you can do is just share the episode if you enjoyed it.

4:28:57Send it to group chats, Twitter, wherever else. You think people who might like this episode might congregate. And other than that, I guess here's this riff on Frederick the Great. See you on the next one. I mean, I think the actual funny thing is, you know, a lot of this sort of German history stuff we've talked about is sort of like not actually stuff I learned in Germany. It's sort of like stuff that I learned after. And there's actually, you know, funny thing where I kind of would go back to Germany over Christmas or whatever. And I tell them to see you know. So many understand the street names.

4:29:23You know, it's like, you know, Gnais now in Scharnhorst, and then all these like, Prussian military reformers. And you're like finally understood, you know, Sansa C. And you know, like it was Frederick, you know, Frederick the Great is this really interesting figure. Where he's this sort of in some sense kind of like gay lover of arts, right? Where he, he, he, you know, he hates speaking German. He only wants to speak French. You know, he like plays the flute. He composes. He has all the sort of great, you know, artists of his day, you know, over at Sansa C.

4:29:56And he actually had this sort of like really tough upbringing where his father was this sort of like really stir and sort of Prussian military man. And he had had a Frederick the Great as a sort of a 17 year old or whatever. He basically had a male lover. And what his father did was imprison his son and then I think hang his male lover in front of him. And again, his father was this kind of very strong Prussian guy. He used this kind of gay, you know, but then later on, Frederick the Great turns out to be this like, you know, one of the most kind of like, you know, successful kind of Prussian conquerors, right?

4:30:34Like he gets sideleaz -y. Yeah, he wins the seven years war. You know, also, you know, amazing military strategist, you know, amazing military strategy at the time consisted of like he was able to like flank the army. And that was crazy, you know, and that was brilliant. And then, and then they like almost lose the seven years war at the very end, you know, the sort of, the, the, the Russian saw our changes. And he's like, ah, I'm actually kind of a Prussia Stan. You know, I think I'm like, I'm into the stuff. And then he lets, you know, let's spread the great loose and how let's, let's, let's, let's let the army be okay.

4:31:01And anyway, sort of like a, yeah, kind of bizarre, interesting figure in German history.