Dwarkesh Podcast - Leopold Aschenbrenner — 2027 AGI, China/US super-intelligence race, & the return of history

Episode Date: June 4, 2024

Chatted with my friend Leopold Aschenbrenner on the trillion dollar nationalized cluster, CCP espionage at AI labs, how unhobblings and scaling can lead to 2027 AGI, dangers of outsourcing clusters to Middle East, leaving OpenAI, and situational awareness.Watch on YouTube. Listen on Apple Podcasts, Spotify, or any other podcast platform. Read the full transcript here.Follow me on Twitter for updates on future episodes. Follow Leopold on Twitter.Timestamps(00:00:00) – The trillion-dollar cluster and unhobbling(00:20:31) – AI 2028: The return of history(00:40:26) – Espionage & American AI superiority(01:08:20) – Geopolitical implications of AI(01:31:23) – State-led vs. private-led AI(02:12:23) – Becoming Valedictorian of Columbia at 19(02:30:35) – What happened at OpenAI(02:45:11) – Accelerating AI research progress(03:25:58) – Alignment(03:41:26) – On Germany, and understanding foreign perspectives(03:57:04) – Dwarkesh’s immigration story and path to the podcast(04:07:58) – Launching an AGI hedge fund(04:19:14) – Lessons from WWII(04:29:08) – Coda: Frederick the Great Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe

Okay, today I'm chatting with my friend Leopold Aschenbrenner. He grew up in Germany, graduated valedictorian of Columbia when he was 19. And then he had a very interesting gap year, which we'll talk about. And then he was on the OpenAI super alignment team, may it rest in peace. And now he, with some anchor investments from Patrick and John Collison and Daniel Gross and Nat Friedman, is launching an investment firm. So Leopold, I know you're off to this. slow start, but life is long and I wouldn't worry about it too much. You'll make up for it in due

time. But thanks for coming on the podcast. Thank you. You know, I first discovered your podcast when your best episode had, you know, like a couple hundred views. And so it's just been, it's been amazing to follow your trajectory. And it's a delight to be on. Yeah, yeah. Well, I think in the show with her in Trenton episode, I mentioned that a lot of the things I've learned about AI, I've learned from talking with them. And the third part of this triumph, probably the most significant in terms of the things that I've learned about AI has been you. We'll go out of the stuff on the record now. Great. Okay, first thing not to get on record, tell me about the trillion dollar cluster. But by the way, I should mention, so the context of this podcast is today,

there's, you're releasing a series called situational awareness. We're going to get into it. First question about that is tell me by the trillion dollar cluster. Yeah. So, you know, unlike basically most things that have come out of Silicon Valley recently, you know, AI is kind of this industrial process. you know, the next model doesn't just require, you know, some code. It's building a giant new cluster. You know, now it's building giant new power plants. You know, pretty soon it's going to be building giant new fabs. And, you know, since chat chvety, this kind of extraordinary sort of techno capital acceleration has been set into motion.

I mean, basically, you know, exactly a year ago today, you know, Nvidia had their first kind of blockbuster earnings call, right? Where it like went out 25% after hours and everyone was like, oh my God, AI, it's a thing. You know, I mean, I think within a year, you know, you know, and, you know, Nvidia, NVIDIA data center revenue has gone from like, you know, a few billion a quarter to like, you know, 20, 25 billion a quarter now and, you know, continue to go up, like, you know, big tech, capex is skyrocketing. And, you know, it's funny because it's both, there's this sort of this kind of crazy scramble

going on, but in some sense it's just the sort of continuation of straight lines on a graph, right? There's this kind of like long run trend, basically almost a decade of sort of training compute of the sort of largest AI systems growing by about, you know, half an order of magnitude, you know, zero point five booms a year. And you can just kind of play that forward, right? So, you know, GPD4, you know, reported to have finished pre-training in 2022, you know, the sort of cluster size there was rumored to be about, you know,

25,000 H-100s, you know, sorry, A-100s on semi-analysis. You know, that's roughly, you know, if you do the math on that, it's maybe like a $500 million cluster. You know, it's very roughly 10 megawatts. And, you know, just play that forward, half a room a year, right? So then 2024, that's a cluster that's a cluster that's, you know, 100 megawatts, that's like 100,000 H-100 equivalents.

You know, that's, you know, costs in the billions, you know, play it forward, you know, two more years, 2026. That's a cluster that's a gigawatt. You know, that's, you know, sort of a large nuclear reactor size. It's like the power of the Hoover Dam. You know, that costs tens of billions of dollars. That's like a million H-100 equivalents. You know, 2028, that's a cluster that's 10 gigawatts, right? That's more power than kind of like most U.S. states.

That's, you know, like 10 million H-100's equivalents, you know, costs hundreds of billions of dollars. And then, 2030, a trillion dollar cluster, 100 gigawatts, over 20% of U.S. electricity production, you know, 100 million H-100 equivalents. And that's just the training cluster, right? That's like the one largest training cluster. And then there's more inference GPUs as well, right? Most of, you know, once there's products, most of them are going to be inference GPUs. And so, you know, U.S. power production has barely grown for, like, you know, decades.

And now we're really in for a ride. So, I mean, when I had Zuck on the podcast, He was claiming not a plateau for a say, but that AI progress would be bottlenecked by specifically this constraint on energy. And specifically like, oh, gigawatt data centers are going to build another three gorgeous dam or something. I know that there's companies, according to public reports, who are planning things on the scale of a gigawatt data center. 10 gigawatt data center, who's going to be able to build that? I mean, 100 gigawatt center, like a state, or are you going to pump that into one physical data center? How is this going to be possible?

Yeah. What is Zuck missing? I mean, you know, I don't know. I think to 10 gigawatts, you know, like six month ago, you know, 10 gigawatts was the taco town. I mean, I feel like now, you know, people have moved on, you know, 10 gigarwats is happening. I mean, I don't know, there's the information report on opening eye and Microsoft planning a $100 billion.

So, you know, you got to, you know, if that's that the gigawatt or is that the 10 gigawatt? I mean, I don't know. But, you know, if you try to like map out, you know, how expensive with the 10 gigawatt cluster be, you know, that's maybe a couple hundred billion. So it's sort of on that scale. and they're planning it. They're working on it. You know, so the, you know, it's not just sort of my crazy take.

I mean, AMD, I think, forecasted a $400 billion AI accelerator market by 27. You know, I think it's, you know, and AI accelerators are only part of the expenditures. It's sort of, you know, I think sort of a trillion dollars of sort of like total AI investment by 2027 is sort of like, we're very much in track on. I think the trillion dollar cluster is going to take a bit more sort of acceleration. But, you know, we saw how much sort of chat GPT unleashed, right? And so like every generation, you know, the models are going to be kind of crazy and people that's going to shift the Overton window. And then, and then, you know, obviously the revenue comes in, right?

So these are forward-looking investments. The question is, do they pay off? Right. And so if we sort of estimated the, you know, the GPD4 cluster at around $500 million, by the way, that's sort of a common mistake people make is they say, you know, people say like $100 million for $1. But that's just the rental price, right? They're like, ah, you rent the cluster for three months.

But it's, you know, if you're building the biggest cluster, you got to like, you got to build the whole cluster. You got to pay for the whole cluster. You can't just rent it for three months. But I mean, really, you know, once you're trying to get into the sort of hundreds of billion, eventually you've got to get to like $100 billion a year revenue. I mean, I think this is where it gets really interesting for the big tech companies, right? Because, like, their revenues are in order, you know, hundreds of billions, right?

So it's like $10 billion fine, you know, and it'll pay off the, you know, 2024-sized training cluster. But, you know, really, one sort of big tech, it'll be gangbusters is $100 billion a year. And so the question is sort of how feasible is $100 billion a year from AI revenue. And, you know, it's a lot more than right now. But I think, you know, if you sort of believe in the trajectory of the AI system, as I do, and which we'll probably talk about, it's not that crazy, right? So there's, I think there's like 300 million, you know, is Microsoft Office subscribers, right? And so they

have co-pilot now, and I don't know what they're selling it for, but, you know, suppose you sold some sort of AI add-on for $100 a month. And you sold that to, you know, a third of Microsoft officer subscribers subscribe to that. That'd be $100 billion right there. You know, $100 a month is, you know, a lot. It's a lot. It's a lot. For a third of our office subscribers? Yeah, but it's, you know, for the average knowledge worker, it's like a few hours of productivity a month. And it's, you know, kind of like, you have to be expecting pretty lame AI progress to not hit, like, you know, some few hours of productivity a month of, yeah. Okay, sure. So let's assume all this. What happens in the next few years in terms of what is the

one gigawatt training, the AI that's trained on the one gigawatt data center? What can it do the one on the 10 gigawatt data center? Just map out the next few years of AI progress for me. Yeah, I think probably the sort of 10 gigawattish range is sort of my best guess for when you get the sort of true AI. I mean, yeah, I think it's sort of like, one. Gigawatt Data Center. And again, I think actually compute is overrated, and we're going to talk about that. But we'll talk about compute right now. So, you know, I think so 25, 26, we're going to get models that are, you know, basically smarter than most college graduates. I think sort of the practice, a lot of the economic usefulness, I think, really depends on sort of, you know, sort of on hobbling.

Basically, it's, you know, the models are kind of, you know, they're smart, but they're limited, right? There's this chatbot, you know, and things like being able to use a computer, things like being able to do kind of like a genetic long horizon tasks. Yeah. And then I think by 27, 28, you know, if you know, if you're extrapolate the trends and, you know, we'll talk about that more later, and I talk about it in the series, I think we hit, you know, basically, you know, like as smart as the smartest experts, I think the on hobbling trajectory kind of points to, you know, looks much more like an agent than a chat bot and much more almost like basically like a drop in a remote worker, right?

So it's not like, I think basically, I mean, I think this is the sort of question on the economic returns. I think a lot of the intermediate AI systems could be really useful, but, you know, it actually just takes a lot of schlep to integrate them, right? Like GPD4, you know, whatever, 4.5, you know, probably there's a lot you could do with them in a business use case. But, you really got to change your workflows to make them useful. And it's just like there's a lot of, you know, it's a very Tyler Cowanest take. It just takes a long time to diffuse. Yeah.

It's like, you know, we're an SF and so we missed that or whatever. But I think in some sense, you know, the way a lot of these systems want to be integrated is you kind of get this sort of sonic boom where it's, you know, the sort of intermediate systems could have done it, but it would take a schlap. And before you do the schlap to integrate them, you get much more powerful systems, much more powerful systems that are sort of unhobled. And so they're this agent. and there's a drop in a remote worker.

And then you're kind of interacting with them like a coworker, right? You can take do Zoom calls with them and you're slacking them. And you're like, ah, can you do this project? And then they go off and they go away for a week and write a first draft and get feedback on them and, you know, run tests on their code. And then they come back and you see it and you tell them a little bit more things. And, you know, and that'll be much easier to integrate. And so, you know, it might be that actually you need a bit of overkill

to make the sort of transition easy and to really harvest the games. What do you mean by the overkill? Overkill on the model capabilities? Yeah, yeah. So basically the intermediate models could do it, but it would take a lot of slap. I see. And so then, you know, actually it's just the drop in remote worker kind of AGI that can automate, you know, cognitive task that actually just ends up kind of like, you know,

basically it's you're like, you know, the intermediate models would have made the software engineer more productive, but, you know, will the software engineer adopted? And then, you know, 27 model is, well, you know, you just don't need the software engineer. You can literally interact with it like a software engineer and it'll do the work of a software engineer. So the last episode I did was with John Schulman. Yeah. And I was asking about basically this. And one of the questions I asked is,

we have these models that have been coming out in the last year. And none of them seem to have significantly surpassed GPD4, and certainly not in the agentic way in which they are interacting with as a coworker. You know, they'll brag that they got a few extra points on MMLU or something. And even GPD40, it's cool that they can talk like Scarlett Johansson or something, but like... And honestly, I'm going to use that. Oh, I guess not anymore.

Not anywhere. Okay, but the whole coworker thing. So this is going to be a run-on question, but you can address it in any order. But it makes sense to me why they'd be good at answering questions. They have a bunch of data about how to complete Wikipedia text or whatever. Where is the equivalent training data that enables it to understand what to make sense, what's going on in the Zoom call, how does this connect with what they were talking about in the Slack? What is the cohesive project that they're going after based on all this context that I have? Where is that turning data coming from?

Yeah. So I think a really key question for sort of AI progress in the next few years is sort of how hard is it to do sort of unlock the test time compute overhang? So, you know, right now, GPD4 answers a question and, you know, it kind of can do a few hundred tokens of kind of chain of thought. And that's already a huge improvement, right? Sort of like, this is a big unhobbling before, you know, answer a math question. It's just shotgun. done. And, you know, if you try to kind of like answer math question by saying the first thing that came to mind, you know, you wouldn't be very good. So, you know, GP4 thinks for a few hundred tokens. And, you know, if I thought for a few hundred, you know, if I think at like 100 tokens a minute and I thought for a hundred tokens a minute, you know, if I thought for like 100 tokens a minute, you know, it's like, you know, it's equivalent to me thinking for three minutes or whatever, right? You know, suppose GPD4 could think for millions of tokens, right? That's sort of plus four room. plus four orders of magnitude on test time compute, just like on one problem.

It can't do it right now. It kind of gets stuck, right? Like write some code, even if, you know, you can do a little bit of iterative debugging, but eventually just kind of like it can't, it kind of gets stuck in something. It can't correct its errors and so on. And, you know, in the sense, there's this big overhang, right? And like other areas of ML, you know, there's this great paper on AlphaGo, right, where you can trade off train time and test time compute.

And if you can use, you know, four ooms more test time compute, that's almost like, you know, a three and a half oom bigger model. Just because, again, like, you can, you know, if 100 tokens of minimum, a few million tokens, that's a few months of sort of working time. There's a lot more you can do in a few months of working time than and then right now. So the question is, how hard is it to unlock that? And I think the sort of short timelines AI world is if it's not that hard. And the reason that might not be that hard is that, you know, there's only really a few extra tokens you need to learn, right? You need to kind of learn error correction tokens, the tokens where you're like, ah, I think I made a mistake. Let me think about that again. You need to learn the kind of planning tokens. That's kind of like, I'm going to start by making a plan. And here's my plan of attack and then I'm going to write a draft and I'm going to like now I'm going to critique my draft. I'm going to think about it. And so it's not things that models can do right now, but the question is how hard is that? And in some sense also there's sort of two paths to agents, right?

You know, when Cholto was on your podcast, you know, he talked about kind of scaling leading to more nines of reliability. And so that's one path. I think the other path is a sort of like unhobling path where you, it needs to learn this kind of like system two process. And if it can learn this sort of system two process, it can just. use kind of millions of tokens and think for them and be cohesive and be coherent. You know, one analogy, so when you drive, here's an analogy, when you drive, right? Okay, you're driving. And most of the time you're kind of on autopilot, right? You're just kind of driving and you're doing well. And then sometimes you hit like a weird construction zone or a weird intersection,

you know, and then I sometimes are like, you know, my passenger seat, my girlfriend, I'm kind of like, ah, be quiet for a moment. I need to like figure out what's going on, right, right? And that's sort of like, you know, you go from autopilot to like the system two is jumping in. And you're thinking about how to do it. And so the scaling, scaling is improving that system one autopilot. And I think it's sort of, it's the brute force way to get to kind of agents. You just improve that system. But if you can get that system two working, then, you know, I think you could like quite quickly jump, you know, to sort of this like more agentified, you know, test time compute overhang is unlocked. What's the reason to think that this is an easy win in the sense that,

oh, you just get the, there's like some loss function that easily enables, you to train it to enable the system two thinking. Yeah. There's not a lot of animals that have system two thinking. You know, it like took a long time for evolution to give us system to thinking. Yeah. That free training, like, listen, I get it. You got like trillions of tokens of internet techs.

I get that like, yeah, you like match that and you get all these, all this free training capabilities. What's the reason to think that this is an easy and hobbling? Yeah. So, okay, a bunch of things. So I, first of all, free training is magical, right? And it's, and it's, and it gave us this huge advantage. for models of general intelligence because, you know, you could just predict the next token.

But predicting next token, I mean, this is sort of a common misconception. But what it does is lets this model learn these incredibly rich representations, right? Like these sort of representation learning properties are the magic of deep learning. You have these models. And instead of learning just kind of like, you know, whatever, statistical artifacts or whatever, it learns sort of these models of the world. You know, that's also why they can kind of like generalize, right? Because it learned the right representations.

And so, you know, you pre-trained these models and you have this sort of like raw bundle of capabilities that's really useful. And sort of this almost unformed raw mass. And sort of the unhobbling we've done over sort of like GPD2 to GP4 was you kind of took this sort of like raw mass and then you like Arleigh shaft it into a really good chat bot. And that was a huge win, right? Like, you know, going going, you know, in the original I think it's struck GPT paper,

you know, Rlychf versus non-Rly HF model. It's like 100x model size win on sort of human preference rating. You know, it started to be able to do like simple chain of thought and so on. But you still have a disadvantage of all these kind of like raw capabilities. and I think there's still like a huge amount that you're not doing with them. And by the way, I think this sort of this pre-training advantage

is also sort of the difference to robotics, right? Where I think robotics, you know, you know, I think people used to say it was a hardware problem, but I think the hardware stuff is getting solved. But the thing we have right now is you don't have this sort of huge advantage of being able to brute-strap yourself

with pre-training. You don't have all this sort of unsupervised learning you can do. You have to start right away with the sort of RL, self-play and so on. All right. So now the question is, why might some of this on hobbling and RL

and so on, work. And again, there's sort of this advantage of bootstrapping. So, you know, your photo bio is being pre-trained, right? But you're actually not being pre-trained anymore. You're not being pre-trained in like grade school and high school. At some point, you transitioned to be able, being able to like learn by yourself, right? You weren't able to do that in elementary school. I don't know, middle school, probably, high school is maybe when sort of started, you need some guidance. You know, college, you know, if you're smart, you can kind of teach yourself. and sort of models are just starting to enter that regime, right?

And so it's sort of like it's a little bit, probably a little bit more scaling. And then you've got to figure out what goes on top. And it won't be trivial, right? So a lot of, a lot of deep learning is sort of like, you know, it sort of seems very obvious in retrospect. And there's sort of some obvious cluster of ideas, right?

There's sort of some kind of like thing that seems a little dumb, but there just kind of works. But there's a lot of details you have to get right. So I'm not saying this, you know, we're going to get this, you know, next month or whatever. I think it's going to take a while to really figure out the details. A while for you is like half a year or something. I think, I don't know.

That makes a month, six months. Between six months and three years, you know. But, you know, I think it's possible. And I think there's, you know, I think, and this is, I think is also very related to the sort of issue of the data wall. But, I mean, I think the, you know, one intuition on the sort of like learning, learning by yourself, right, is sort of pre-training is kind of the words are flying by. Yeah. Right. You know, and, and, and, or it's like, you know, the teacher is lecturing to you.

And the models, you know, the words are flying by, you know, they're taking, they're just getting a little bit from it. But that's sort of not what you do when you learn from yourself, right? When you learn by yourself, you know, so you're reading a dense math textbook. You're not just kind of like skimming through it once. You wouldn't learn that much from it. I mean, some word cells just skimmed through, you know, reread and reread the math textbook and then they memorize. Yeah, yeah.

You know, like, if you just repeated the data, then they memorize. What you do is you kind of like, you read a page, kind of think about it. You have some internal monologue going on. You have a conversation with a study buddy. You try a practice problem. You know, you fail a bunch of times. And at some point it clicks. Then you're like, this made sense.

Then you read a few more pages. And so we've kind of bootstrapped our way to being able to do that now with models or like just starting to be able to do that. And then the question is, you know, being able to like read it, think about it, you know, try problems. And the question is, you know, all this sort of self-play synthetic data RL is kind of like making that thing work. Yep. So basically translate translated translating like in context, right? Like right now there's like in context learning, right? Super sample efficient.

There's that, you know, in the Gemini paper, right? It just like learns language in context. And then you're pre-training, not at all sample efficient. But what humans do is they kind of like, they do in context learning, you read a book, you think about it until eventually it clicks. But then you somehow distill that back into the weights. And in some sense, that's sort of like what RL is trying to do. And like when RL is super finicky, but when RRL works, RL is kind of magical because it's sort of the best possible data for the model. It's like when you try a practice problem and then you fail and at some point you kind of figure it out in a way that makes sense to you,

but sort of like the best possible data for you because like the way you would have solved the problem and that's sort of that's what RL is rather than just you know you kind of reads how somebody else solved the problem and doesn't you know initially click yeah by the way if that takes sounds familiar because it was like part of the question I asked John Shulman

that goes to illustrate the thing I said in the intro where like a bunch of the things I've learned about AI just like we do these dinners before the interviews and me Shulte and a couple of them like what should I ask John Shulman what should I ask Dario Okay, suppose this is the way things go and we get these unhobblings.

Yeah. Well, and the scaling, right? So it's like you have this baseline, just enormous force of scaling, right? Where it's like GPD2 to GPD4, you know, GP2, it could kind of like, it was amazing, right? It could string together plausible sentences. But, you know, it could barely do anything. It's kind of like preschooler. And then GPD4 is, you know, it's writing code.

It like, you know, can do hard math. It's sort of like smart high school. And so this big jump and, you know, and sort of the essay series I go through and kind of count the orders magnitude of compute scale up with algorithmic progress. And so sort of scaling alone, you know, sort of by 27, 28 is going to do another kind of preschool to high school jump on top of GPD4. And so that will already be just like at a per token level, just incredibly smart. They'll get you some more reliability.

And then you add these on hobblings that make it look much less like a chat bot, more like this agent, like a drop in remote worker. And, you know, that's when things really get going. Okay. Yeah. I want to ask most questions about this. Let's zoom out. Okay.

So suppose you're right about this. Yeah. And I guess you, this is because of the 2027 cluster, you've got 10 gigawatt, 2027, 10 gigawatts. 28 is the 10 gigawatts. Okay. Maybe it'll be pulled forward. Okay, sure. Something.

Yeah. And so I guess that's like 5.5 level by 2020. Like whatever that's called, right? What does the world look like at that point? You have these remote workers who can replace people. What is the reaction to that in terms of? of the economy, politics, geopolitics. Yeah, so, you know, I think 2023 was kind of a really interesting year to experience

as somebody who was like, you know, really following the eye stuff where, you know, before that What were you doing in 2023? I mean, Open AI. Oh, yeah, yeah, yeah. And, you know, it kind of went, you know, I mean, I was, I was been thinking about this and, you know, like talking to a lot of people, you know, in the years before, and it was this kind of weird thing, you know, you almost didn't want to talk about AI or AI and it was kind of a dirty word, right?

And then in 2023, you know, people saw Chat GPT for the first time and saw GP4 and it just like exploded, right? It triggered this kind of like, you know, you know, a huge sort of capital expenditures from all these firms and, you know, the explosion and 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 oven. And I sort of expect sort of every generation these kind of like G forces to intensify, right? It's like people see the models. There's like, you know, people haven't counted them. So they're going to be surprised and it'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, end of 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 big tech, you know, like their stocks, you know, that's going to explode.

And, 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 is a 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 where it's strained 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. And 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 rubber company is like, well, Oh, GPD 4-0 is going to change our business.

I'm so bearish on the rapper companies, right? Because they're the ones that are going to be, like, the rapper companies are betting on stagnation, right? The rapper companies are betting, like, you have these intermediate models and it takes so much left 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 unhavel, we're going to get the drop in remote worker.

And then, you know, your stuff is not going to matter. Okay, sure, sure. So that's done. Now, who, so the SF is paying attention now, or this crowd here is paying attention. Who is going to be paying attention in 2020? 26, 2027. And presumably these are years in which hundreds of billions of cap-ex 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 happens? Yeah. Like, 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, well, cheaply-oh, oh my God, like MMLEU score on that. What are you doing about this comrade?

Yeah. And so what happens when the, like, where the GPD, 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 superintelligence is going to be absolutely decisive for national power, right? And this is where, you know, the sort of intelligence explosion stuff comes in, which, you know, we should also talk about later. You know, it's sort of like, you know, you have AGI, you have the sort of drop in remote worker that can replace, you know, you or me. At least that's sort of remote jobs, you know, kind of jobs. And then, you know, I think fairly quickly, you know, I mean, by default, 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 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. And 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, at this point, you know, you're going to have GPU fleets in the tens of millions for inference, you know, or more. And you're going to be able to run like 100 million human equivalence of these sort of automated AI researchers. And if you can do that, you know, you can maybe do, you know, a decade's worth of sort of ML research progress in a year. You know, you get some sort of 10x speed up. And 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 know, superbly competent, all the things, you know, they're going to figure out robotics.

We talked about it being a software problem. Well, you know, you have a billion of super smart, smarter than the smartest human researchers, AI researchers on 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 in say, like, military competition, right? You know, if you look at like Gulf War I, right, GoFer 1, you know, like the Western Coalition Forces, you know, they had a, you know, like 100 to 1 kill ratio, right? And that was like, they had better sensors on their tanks, you know, and they had, they had better, you know, 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 explosion as well, you have the robots, you're just making lots of material. You know, I think that could compress, I mean, basically compressed kind of like a century worth of technological progress since the last than a decade. And that means that, you know, a couple years could mean a sort of Gulf War I style, like, you know, advantage in military affairs. And, you know, including, like, you know, a decisive advantage that even like preempts nukes, right? Suppose, like, you know, how do you find the stealth and nuclear submarines? Like, right now, that's a problem of, like,

you have sensors, you have the software, like, attack 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. And anyway, so I think enormously disabilizing, 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? Like, they, you know, their, you know, power in China, you know, like the electric grid, you know, they added a U.S. is, you know, a complete, like, they added as much power in the last decade as, like, sort of entire U.S. electric good.

So, like, the 100 gigawat cluster, at least 100 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. 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. And 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 kind of like you know it's not like you know on and off it's a little bit more gradual but it's sort of like it's an explosion that starts narrowly it can do cognitive jobs you know the highest RI use for cognitive jobs make the eye better like solve robotics you know and as as you know 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 AirPods, you know, and the AI is instructing them, right? Because, you know, 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. This process expands. Metas, radars are a compliment to their llama. Well, you know, it's like, whatever, like, you know, the fabs in the U.S. the constrained to skilled workers, right? You have, you have, even if you don't have robots yet, you have the cognitive superintelligence and, you know, can kind of make them all into skilled workers immediately. But that's, you know, it's a very period. You know, robots will come soon.

Sure. Okay. Okay. So suppose 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. And 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 bureaucrats, some 60-year-old guy, he looks at this and he's like, oh, it's like, co-pilot has gotten better now. Why are they now? I mean, this is much more than co-fellate has gotten better now. I mean, at this point, like, yeah, so they're, because to shift the production. of an entire country to dislocate energy that is otherwise being used for consumer goods or something and to make that all feed into the data centers.

What part of this whole story is you realize the superintelligence is coming soon, right? And 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 midgame, basically, and where you have a few more

2023s, and that just starts updating more and more people. And, you know, I think, you know, the trend lines, you know, will become clear. You know, I think, I think you will see some amount of the sort of COVID dynamic, right? You know, like COVID was like, you know, February, February of 2020, you know, it's like, honestly feels a lot like today, you know, where it's like, you know, it feels like this utterly crazy thing is happening, you know, as impending, is coming. You kind of see the exponential and yet most of the world just doesn't realize right the mayor of new york is like go out to the shows and this is just you know like asian racism or whatever you know and and um but you know at some point the exponential like you know at some point people saw it and then you know like just

kind of crazy radical reactions came right okay so by the way what were you doing during covid or when like february okay uh like freshman sophomore or what junior mm yeah but still like a boy like 17 year old junior or something. And then you bought, like, did you short the market or something? Yeah, yeah, yeah, yeah. Did you, did you sell at the right time? Yeah. Okay.

Yeah, so there will be like a March 2020 moment that 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 Manhattan Project for America here. I wonder what 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 energy 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 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 the. this really depends on sort of how much people are feeling it, how much people are seeing it. You 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 hegemony and nothing matters. But, you know, the sort of like extremely intense and these extraordinary things happening in the world

and like intense international competition is like very much the historical norm. Like in some sense, it's like, you know, sort of, there's a sort of 20 years. a very unique period. But like, you know, the history of the world is like, you know, you know, like in World War II, right, it was like 50% of GDP went to, you know, like, you know, war-prudine production, the U.S. borrowed over 60% of GDP, you know, and in, you know, I think Germany and Japan over 100% World War I, you know, UK, Japan, sorry, UK, France, Germany, all borrowed over 100% of GDP. And, you know, I think the sort of much more was on the line, right? Like, you know, and, you know, people talk about World War I being so 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. And, 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 is 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 sort of historical mission and rejuvenation of the Chinese 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? Like, 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 war 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. 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, had the pleasure to live in America, let's say, 80 years, including dictatorships, including all kind of, obviously war, famine, whatever. I was reading the Gula Archipelago, and one of the chapters begins with,

So Janice and saying, if you would have told a Russian citizen under the Tsars 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. But 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, imagine you have 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 surveillance of everybody.

You know, you can perfectly figure out who's the dissenter, 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. And 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, solves, you know, a lot of debates and a lot of people had really strong convictions, but, you know, all of those have been overturned by time because there has 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, and you supercharged that with super intelligence, I think there's a way in which that could just be, like, locked in and trying for, you know, a long time. And I think the possibilities are pretty terrifying. your point about, you know, your point about, you know, history and, 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 it's, you know, it's the reason I exist, right? And then, you know, growing up in Berlin and, you know, former wall, you know, my great grandmother, who is, you know, still alive, it was very important in my life. You know, she 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, where, you know, they as kids were, you know, 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, 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 the Iron Curtain and then was put in a Stasi prison for a while. You know, and then finally, you know, when she's almost 60, you know, it was 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, 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 connotations 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 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, are 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, can't you like convince them about the dangerous super intelligence or something? Will they be in charge, though? I mean, some sense, this is, I mean, this is also the case, you know, you know, in the U.S. or whatever. This is sort of like rapidly depreciating influence of

the lab employees. Like right now, the sort of AI, like, lab employees have so much power, right? Over this, you know, like, but they're going to get automated and then, yeah, I mean, you saw this November event, so much power, right? But 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, there's sort of, this is the sort of some of these classic scenes from, you know, 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. I actually, yeah, I think, I actually think it's good for like lab employees to be aware of this. It's like, you have a lot of power now, but, you know, maybe not for that long. And, you know, use it wisely. Yeah, I do, I do think they would benefit from some more, you know, organs of representative democracy. What do you mean to that? Oh, I mean, you know, in the, sort of the open-AI board events, you know, employee at power was exercised in a very sort of direct democracy way. And I feel like, that's how some of how that went about, you know, I think I really highlighted the benefits of representative democracy and having some deliberative organs.

Interesting. Yeah. Well, let's go back to the 100 billion revenue, whatever, and so these companies... A trillion-dollar cluster. Yeah, the companies are deploying, we're trying to build clusters that are this big. Yeah. Where are they building it? Because if you say it's the amount of energy that would require it for a small or medium-sized

U.S. state, is it then Colorado gets no power and it's happening in the United States, or is it happening somewhere else? Oh, I mean, I think that, I mean, in some sense, this is the thing that I always find funny 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 gigawad. And, you know, we're going to replace it with, with the data center because that's important. I 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 seems like in practice what it's what is 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 dedicating all of the power to the AGI. Anyway, so right now it's building new power. 10 gigawatt, I think quite doable. You know, it's like a few percent of like U.S. natural gas production. when you have the 10 gigawatt training cluster, you have a lot more inference. So that starts getting more. I think 100 gigawatt, that starts getting pretty wild. You know, that's, you know, again, it's like over 20% of U.S. electricity production.

I think it's pretty doable, especially if you're willing to go for like natural gas. But 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 U.S.? I mean, look, I think there's some people who are. are, you know, trying to build clusters elsewhere. And, 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 the Manhattan Project in the UAE, right? And I think, I think basically, like, putting the clusters, you know, I think you can put them in the U.S., you can put them in sort of, like, allied democracies. But I think once you put them in kind of, like, you know, dictatorships, authoritarian dictatorships, you kind of create this, you know, irreversible security risk, right? So, I mean, one, cluster is there, much easier for them. exaltrate the weights. You know, they can like literally steal the AGI, the superintelligence. It's like

they got a copy of the, you know, of the atomic bomb, you know, and they just got a direct replica of that. And it makes it 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 is a, you know, chat, TVT, big tech product clusters. But I think the cluster is being planned now, you know, three to five years out, like, it will be the like AGI superintelligence clusters. And so anyway, so like, when things get hot, you know, they might just seize the compute. And I don't know, suppose we put like,

you know, 25% of the compute capacity in these sort of Middle Eastern dictatorships, 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 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. You can do a lot with that amount of compute. And then look, 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 seat at the AGI table.

And, you know, I don't know why we're giving authoritarian dictatorships to see at 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 I'm going to do. Okay, okay. I guess there's reports, I think Microsoft or? Yeah, yeah, yeah, yeah.

Which we'll get into. Yeah. So the UIE 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 100 million AI researchers or you don't? I mean, you can do a lot with, you know, 33 million extremely smart scientists. And, you know, 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? And 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 build these crazy new WMDs that will be possible superintelligence. And now you've just kind of like proliferated the stuff. And it'll be really powerful. And also, I mean, I think, you know, 3X on compute isn't actually that much. And so the, you know, the, you know, I think a thing I worry a lot about is I think everything, I think that riskiest situation is if we're in some sort of like really tight neck feverish international struggle, right? 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, is a little bit more like, you know, the U.S.,

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 tails. You got to go, go, go. You got to maximum speed. You have no wiggle room. You're worried that at any time they can overtake you. I mean, they can also just try to outbuild you, right? Like, they can 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 kind of like whatever unreasonable regulations we have. Or you're just in this really tight race. And I think is that sort of like, if you're in this really tight race, this 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 and then the Middle East realize this. What is the,

is it just that it's impossible to do this in America? And if you want American companies to do this at all, then you do it in Middle East or not at all. And then you're just like, I'm trying to build the three gorgeous damn cluster. I mean, there's a few reasons. One of them is just like people aren't thinking about this is the EGI superintelligence cluster.

They're just like, ah, you know, like cool clusters for my, you know, for my chat. But so they're building. And the plans right now are clusters, which are ones that are like, because if you're doing ones for inference, for some people you could like spread them out across the country or something, but the ones they're building, they realize we're going to do one training run in this thing we're building. I 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 intending compute. Yeah. Sorry, they might say it's inference compute and actually it's useful for training.

Because of synthetic data and things like that? Yeah, the future of trend. You know, like RL looks a lot like in France, for example, right? Or you just kind of like end up connecting them, you know, in time. You know, it's like you have this like a lot raw material. You know, it's like, you know, it's placing your uranium refinement facilities there. Sure. Anyway, so a few reasons, right.

One is just like they don't think about this is the age of a cluster. Another is 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 U.S. And, you know, I think we actually face a sort of real system. competition here because, again, some people think there's only autocracies that can do this, that can kind of like top down, mobilize the sort of industrial capacity of 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. But, you know, during the Cold War, like, we really, there was this sort of

intense system competition, right? Like east-west Germany was this, right? Like, West Germany, kind of like liberal democratic capitalism versus kind of, you know, communist state planned. And, you know, now it's obvious that the sort of, you know, the free world would win. But, you know, even as late as like 61, you know, Paul Samuelson was predicting that the Soviet Union 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 shippost about loving America by day, but then in private, they're betting against America. They'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 bet is I think this stuff is just really possible in the U.S.

And so to make it possible in the U.S., there's some amount that we have to get our act together, right? So I think there's basically two paths doing it in the U.S. 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, Mercell Shale. 10-gog cluster is super easy. 100 gigawatt cluster also pretty doable.

You 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 over 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. So not just government, it's actually the private companies themselves, right? The Microsoft, the Amazon's and so on. 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.

The other path is like, you know, you can do this sort of green energy megaprojects, 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 deregulatory push, right? So like you can't have permitting. take a decade, right? So you've got to reform FERC. You got to like have, you know, blanket NEPA exemptions for this stuff. You know, there's like inane 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. You 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? ideally 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 of this, by the way, before the conversation I was reading, there's a good book about World War II Industrial Mobilization in the United States called Freedom's Forge. And I guess when we think back on that period, especially if you're from, if you read the Patrick Koss and Fast

and the progress study stuff, it's like, you had state capacity back then and people just got shit done, but now it's a cluster fuck. Wasn't it all the case? No, so it was really interesting. So you have people who are from the Detroit auto industry side, like Knudsen who are running mobilization for the United States.

And they were extremely competent. Yeah. But then at the same time, you had labor organization and agitation, which is actually very analogous to the climate pledges and climate change concern we have today. Yeah. Where they would have these strikes while literally into 19, 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 for, 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 paying labor the money deserves. And so what climate changes today, like you think, ah, fuck, America's fucked. Like, we're not going to able to build this shit. Like, if you look at NEPA or something.

But I didn't realize how debilitating labor was in, like, World War II. It wasn't just that, right? It was just, you know, before at the, you know, 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 had gone, even in peacetime. 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. Everything 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 seven nanomut trips 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 seven nanometer chips. And there's a lot of latent industrial capacity in China.

And they are able to, like, you know, build a lot of power fast. And maybe that isn't activated for AI yet. But at some point, you know, the same way, the United States and like, you know, a lot of people in the U.S. 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, 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. And so they understand that we're going to be in 2020, building this and gigawatt data centers. And at this point, the people who can keep up are big tech, just potentially at, like, the edge of their capabilities. Yeah. Then sovereign wealth fund fund of things. 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 this landscape? Do they not want the leverage of 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? It'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 of 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. They're going to pour money into AI regardless. And if we don't work with them, you know, they'll just support China. And look, I mean, I think there's some merit to the argument 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. should be the sort of narrow coalition of democracies. That's sort of the coalition that's developing AGI. And then there should be a broader coalition where we kind of go to other countries, including dictatorships, and we're willing to offer them, you know, we're willing to offer them some of the benefits of the AI, some of the sharing. 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 by default, 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 coursey at 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. You know, I think the other, yeah.

Who, like, 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't. 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 U.S. doesn't work with them, they'll go to China, is, you know,

I've heard from multiple people, and this wasn't, you know, for my 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 to fund and sell AI by starting a bidding war. between the governments of the United States, China and Russia. And 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 well, you know, if 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're 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 what would you have at the former? We're less accountability. And like, you know, the Microsoft board. It's only, it's only the dictator. Yeah, yeah, yeah, yeah. Okay, 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.

Yeah. And they're like, fuck, we want us to have GP-5, where we're going to be off-building superintelligence. This item's per 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 just think. I think the UAE on its own is not competitive, right? It's like, I mean, they're already export controlled.

Like, you know, we're not, you know, there's like you're not actually supposed to ship Nvidia 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 this almost industrial process of you put in the compute and then you put in the algorithms, you add that up and you get AGA on the other end.

Yes. 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 disposed. Well, well, if they can steal the algorithms and if they can steal the way it's. And that's 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 right now, how easy would it be for an actor to steal the things that are like,

not the things that are released about. Scarlett Johansson's voice, but the RL things are talking about, the N hobblings. I mean, extremely easy, right? You know, DeepMind even like, you know, they don't make a claim that it's hard, right? DeepMind put out there like whatever, frontier safety, something, and they like lay out security levels and they, you know, security level zero to four and four is the one resistant to state actors and they say we're at level zero, right? And then, you know, I mean, just recently there's 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, you know, copy the code and put it into Apple Notes and then exported as PDF. And that got past their monitoring, right? And, you know, Google is the best security of any of the AI labs, probably, because they have the, you know, the Google infrastructure. I mean, I think, I don't know, roughly I would think of this as like, you know, security of a startup, right? And, like, what does security of a startup look like, right? You know, it's not that good. It's easy to steal.

So even if that's the case. Yeah. A lot of your post is making the argument that, you know, why are we going to get the intelligence explosion because if we have somebody with the intuition of an Alec Radford to be 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 we're, that's not going to be just in the code, right? And also because of expert controls, these countries are going to have slightly different hardware. If you're going to have different tradeoffs and probably

rewrite things to be able to be compatible with that, including all these things? Is it just a matter of getting the right pin drive and you plug it into the gigawatt data center next to the Three Gorge's Dam and then you're off to the races? I mean, look, there's a few different things, right? So one, one threat model is just stealing the weights themselves. And the weights one is sort of particularly insane, right? Because they can just, like, steal the literal, like, end product, right? Just like make a replica of the atomic bomb and then they're just like ready to go.

And, you know, I think that one just is, you know, extremely important around the time we have AGI and superintelligence, right? Because it's, you know, China can build a big cluster. By default, we'd have a big lead, right? Because we have the better scientists. But we make the superintelligence. They just steal it. They're off to the races.

Wights are a little bit less important right now. Because, you know, who cares if they steal the GPD4 weights, right? Like, whatever. And so, you know, we still have to get started on weight security now because, you know, look, if we think AGI every 27, you know, this stuff is going to take a while. And it, you know, it's not just going to be like, oh, we, we do some access control. It's going to, you know, if you actually want to be resistant to sort of Chinese espionage, It needs to be much more intense.

The thing, though, that I think, you know, people aren't paying enough attention to is the secrets, as you say. And, you know, I think this is, you know, the compute stuff is sexy. You know, we talk about it. But, you know, I think that, you know, I think people underrate the secrets because they're, you know, I think they're, you know, the half in order of magnitude a year just by default, sort of algorithmic progress, that's huge. You know, if we have a few year lead by, by default, you know, that's 10, 30x, 100x, x, per year 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 alpha go step two, right? Alpha go step one is learns from human imitation. Alpha go step two is the sort of self-play URL. 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're stuck. If they can't steal it, they're off to the basis. 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 don't you just have to hire Alec Bradford? Like, what are you copying down? Well, I think

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. You 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 or 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 are we even

why do we think that getting state level security and these startups will prevent China from catching up? If it's just like oh we know some sort of self-play URL we're required to get past a data wall and if it's as easy as you say in this some fundamental sense I mean again but it's going to be solved by 20207 you say like right it's like not that hard. I just think, you know, the U.S. and the sort of, I mean, all the leading AI labs in the United States, and they have this huge lead. I mean, by default, you know, China actually has some good LLMs. Why do they have good LLMs? They're just using the sort of open source code, right? You know, Lama or whatever. And so the, I think people really underrate the sort of both the sort of divergence on algorithmic progress and the lead the U.S. would have by default. Because by, you know, all this stuff was published. And so, you know, all this 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 now like Bradford, you know, 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. I think that is a little bit more tacit knowledge.

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 out, but not how to get the RL thing working. I mean, look, I don't know. Germany during World War II, they went down the wrong path. They did heavy water, and that was wrong. And there's an amazing anecdote

in the making of the atomic bomb on this, right? So Secreti 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. And so an atomic bomb was possible, and he went around, and he was like,

ah, this is going to be of enormous strategic importance, military importance. And a lot of people didn't believe it, or they were kind of like, well, maybe this is possible. but, you know, I'm going to act as it's not possible, and, you know, science should be open and all these things. And anyway, and so in 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 Fermi made some new measurements on graphite, and 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 erupted in another guy, Pegram. And then Fermi didn't publish it. And, you know, that was just in time. Because Fermi not publishing it meant that the Nazis didn't figure out graphite would work. They went down this path of heavy water.

And that was the wrong path. That was why this is a key reason why this 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 past the data wall? What's the next paradigm? Or are we not? So, and the reason this would matter is if there's, like, being one year ahead would be a huge advantage. In the world where it's like, you deploy AI over time and they're just like, oh, they're going to catch up anyway. I mean, I interviewed Richard Rhodes, the guy who wrote making an atomic bomb. Yeah. And one of the anecdotes he had was, When so they realized America had the bomb, obviously, we dropped it in Japan. And Baria goes, the guy who ran the NKBD, which is a famously ruthless guy, just evil. And he goes to, I forgot the name, but the guy, the Soviet scientist was running their version of the Manhattan 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 Baria says, no, you will get us the American bomb or your family will be camped us. But 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 would suggest that often in history, this is something that's not just true the Manhattan Project, but there's this pattern of parallel invention where because the tech tree implies that the certain thing is next, in this case, a self-play, RL, 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 gap and who gets it first.

Was it like famously a bunch of people were invented something like the light bulb around the same time 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, it will make. 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 lock down the labs, we have, we have much better scientists. We're way ahead. It would be two years.

But even, I think even, I think whether you, I think, yeah, I think even six months a year would make huge difference. And this gets back to the sort of intelligence explosion dynamics. Like a year might be the difference between, you know, essentially. system that's sort of like human level and a system that is like vastly superhuman, right? Might be like five ooms, you know, I mean, 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 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. And that was 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 super intelligence, 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 Gulf War I, right? 20, 30 years of technological lead, totally decisive.

You know, I think it really matters. The other reason it really matters is, you know, suppose they steal the weight. Suppose they still the algorithms and, you know, they're close on our tails. suppose we still pull out ahead, right? We're a little bit faster, you know, we're three months ahead. I think the sort of like world in which we're really neck and neck, you know, you only have a three month lead are incredibly dangerous, right? And we're in this feverish struggle where like if they get ahead, they get to dominate, you know,

sort of maybe they'd get a decisive advantage. They're building clusters like crazy. They're willing to throw all caution of 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 attraction, like keeps changing, you know, every few weeks and it's like,

no, completely unstable volatile situation. That is incredibly dangerous. So I think, I think, you know, 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 the 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. We're feeling uneasy about how it's going.

And so I think in some sense that, like, one of the most important, you know, we're important inputs to 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 yeah that almost nobody at least nobody I talked to yeah thinks about the geopolitical implications of AI yeah and I think I have some object level disagreements I will get into but or 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 if people realize that this is where intelligence is headed, it's not just going to be like the same world where like what model are redeploying tomorrow and what is the latest. Like, people on Twitter are like, oh, the GPT4 is going to shake your expectations or whatever. You know, COVID is really interesting because before a year or something, when March 2020 hit, we, it became clear to the world, like president, CEOs, media, average person.

There's other things happening in the world right now, but the main thing we as a world are dealing with right now is COVID. Soon on AGI. Yeah. Okay. And then so... This is the quiet period. You know, if you want to go on 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, you know, I know when I'm like you know off doing work or whatever she's like I'm not spending time with her she's like you know she threatens to replace me with like you know GPD6 or whatever and I'm like you know GV6 will also be too busy for doing AI research. Okay anyway so what's the answer to the question of why you why aren't other 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 a lot of people thought this kind of at the beginning of COVID. They shut down their office. It was a month or whatever.

I think the thing I just really didn't price in was the societal reaction, right? And within weeks, you know, Congress spent over 10% of GDP on like COVID measures, right? The entire country was shut down. That was crazy. And so, I don't 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 a sort of way in which being kind of in the trenches actually kind of, I think,

gives you a less clear picture of the trend lines. 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. You 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. 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, the surveillance company had had software that could just zero-click hack any iPhone, right? They just put in your number and then it's just like straight download of everything, right?

Like the United States infiltrated and air-gapped atomic weapons program, right? Wild, you know, like- Are you about sex stuff? 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. maybe will send special forces, right, to like, you know, 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. There's a good book, you know, along the lines of the Gulag or could develop out, you know, the inside the aquarium, which is by a Soviet GRU defector. GRIU was like military intelligence. I'll recommend this book to me. And, you know, I think reading that is just kind of like, shock to have intense sort of state-level espionages.

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. I mean, yeah, maybe one anecdote, you know, so when, so this spot, 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 you're sent abroad, you kind of had to pass a test to show that you can do this. And 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. But of course, for whomever you accruited, the penalty for giving away sort of secret information

was death. And so to graduate from the Soviet spy, this GRU spy academy, you had to condemn a countryman to death. States do this stuff. I started reading the book on Reh as a Side in the series. Yeah. And I was actually wondering, the fact that you use this anecdote. Yeah.

And then you're like, enough, a book recommended by Ilya, is this some sort of, is this some sort of Easter egg? We'll leave that for an exercise for the reader. Okay, so. The beatings will continue until them are all improved. So, suppose that we live in the world in which these secrets are locked down, but China so realizes that this progress is happening in America. in that world, especially if they realize, and I guess it's a very interesting open question.

I mean, the secret probably won't be locked down. Okay, but suppose... We're 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 they won't be locked down, but I think it's just, it's not happening.

Hmm. But, and so tomorrow, the lab leaders get the message. How hard, like, what do they how to do? They get the more security guards. They, like, air gap the... Well, you know, you know, they... What did they do? So, again, I think basically it's, you know, I think people, there's kind of like

two reactions there, which is like it's, you know, we're already secure, you know, 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 pill is 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? Because 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, 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.

And that sort of like, that's like, that's the like list of details of like really how to make. You're going to worry about that pretty soon. You're going to worry about that pretty soon. Yeah, yeah, yeah. Well, anyway, and so all the alpha could be 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 kind of like, you know, good private sector security hedge funds, you know, the way Google treats, you know, customer data or whatever. That'd be good right now. The issue is, you know, basically the C. will also get more AGI filled.

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 the sort of insane capabilities 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 on 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 skep, like, your cluster needs to be air-gapped and basically be a military base. It'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 basically have security guards. You know, it's, you know, you can't use any kind of like, you know, other dependencies. It's all got to be like intensely vetted. You know, all your hardware has to be intensely vetted.

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 the thing private companies can do. But, 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, you know, it's basically there's just a lot of stuff that only kind of, you know, the people behind the security currencies know and only they deal with. And so, you know, I think to actually kind of resist the sort of full force of espionage, you're going to 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 going to always be behind the curve. And I think, you know, maybe unless we get the sort of government project. Okay, so going back to the naive perspective of of we're very much coming at this from there's going to be a race and the CCP we must win um and listen i understand like bad people are in charge of the chinese government like the 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 our super intelligent jupiter brain uh descendants won't know which other like

china will be something like distant memory that they have america too So shouldn't it be a more the initial approach, just come to them like, listen, this is super intelligence. This is something like we come from a cooperative perspective. 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, in some ideal world, you know, we, you know, it's just all, you know, Mary go around and cooperation. But again, it's sort of,

I think, I think people wake up to AGI. 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 juniors, 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 you know, basically breakout, breakout doesn't matter that much, right? Suppose the other guy now try to make 20,000 nukes. Well, it's like, who cares, right? You know, like, it's still

mutually a 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 it had been zero nukes, then it's just like one rogue stake makes one nuke. The whole thing is destabilized. Breakout is very easy. You know, your, your adversary state starts making nukes. And so, basically, basically, you know, it's Basically, when you're going to sort of like very low levels of arms or when you're going to kind of in your sort of very dynamic technological situation, arms control is really tough because breakout is easy. You know, there's, I mean, there's some other sort of stories about this in sort of like 1920s, 1930s.

You know, it's like, you know, all the European states have done disarmament 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 others didn't really have one. and that really destabilized things. And so I think the issue with EGI and superintelligence 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 develop some crazy new WMD or because you have some like, you know, super hacking ability that lets you, you kind of, you know, completely deactivate the sort of enemy arsenal.

That means like 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 cusp of aGI or whatever there's going to be such an enormous incentive to kind of race ahead to break out and we're just going to do the intelligence explosion if we can get three months ahead we win um i think that makes it basically i think any sort of arms control agreement that comes at a 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 uh-huh but But if some country tries to break out and starts developing nuclear weapons, the six months for whatever that you would get is enough to get international consensus and invade the country and prevent them from getting nukes.

And I thought that was sort of, that's not a stable equilibrium. It just seems really tough. Yeah. But so on this, right? So, like, maybe it's a bit easier because you have a GI. 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 in so many different places. A data center, you're 100 gigawatt data center,

we can blow that shit up if you're like, we're concerned, right? Like just some cruise missile or something. Yeah. It's like very vulnerable to sabotage. I mean, that gets to the sort of, I mean, that gets to the sort of insane vulnerability, the volatility of this period post-superintelligence, right? Because basically, I think, so you have the intelligence explosion. You have these, like, 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 kind of, you haven't covered the desert in, like, robot factories yet. And that is the sort of crazy moment where, you know, say the United States is ahead, the CCP is somewhat behind. There's actually an enormous incentive for first strike, right? Because if they can take out your data center, they, you know, they know you're about to have just this command and decisive lead. they know if we can just take out this data center, then we can stop it. And they might get desperate.

And, you know, 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 we're protecting data centers with like the threat of nuclear retaliation. It's like maybe sounds kind of crazy, though, you know. This is the inverse of the LEAS or are we going to take the data centers anywhere or obviously. Nuclear deterrence for data centers.

I mean, this is, you know, Berlin, you know, in the late 50s, early 60s. Yeah. 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?

Like 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 superintelligence, you have two years, you have the robots, you're able to get that 30-year lead. look then you're in this like go 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 uh you know these like a few hundred kind of spaniards were able to like topple the aztec empire you know a couple i think a

other empires as well you 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 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 haven't 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 some, you know, you have sort of like some crazy new bioweapons. Maybe, maybe there's even some way to like pretty easily get a paradigm that like deactivates enemy nukes. Anyway, so I think this 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 a position where it is clear that the United States that a sort of coalition of Democratic allies will win. It's clear the United States, it would be clear 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. And then when it is clear that the Democratic coalition is well ahead, then you go to China and then you offer them a deal. 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 breakneck race

right at the end, and where things could really go awry. And so then we offer them a deal. I think there's an incentive to come to the table. I think there's a more stable arrangement you can do. It'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 use super intelligence against you.

You can do what you want. You're going to get your like, you're going to get your slice of the galaxy. 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 AIs 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.

We're good. And sort of it's like a new world order that's US led, that's sort of democratic led, but that respects China, let's them do what they want. Okay, there's so much too. There's so much there. First on the Galaxy's thing. thing. I think it's just a funny anecdotes. I kind of want to tell it. And this, we're at an event. And I'm respecting Chatham House rules here. I'm not revealing anything about it. But we're talking to

somebody, or Leopold was talking to somebody influential. And 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. And he goes, the guy goes, I honestly don't know if he meant galaxies like the brand of private plane galaxy or the physical galaxies. And there was an actual debate. Like he went away to the restroom and there's an actual debate among people who are very influential about he can't amend galaxies. And the other people who knew you better, be like, no, he means galaxies. I mean the galaxy. I mean the galaxies. I mean, I think it'll be interesting. I mean, I think there's a, I mean, there's two ways to buy the galaxies. One is like at some point, you know, it's like post super

intelligence. You know, there's some crazy. I love, okay, so what happens is, He's out of my ass off. I'm not even saying, people were like, having 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 private plane in the galaxy versus the actual galaxy. But do you want to buy the property rights with the galaxy or actually just send out

the probes right now? Exactly. Exactly. Oh, 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 credible, promised.

You will get some part of galaxies, whether they care about that. I mean, you have AI to help 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 the fever struggle. greatest peril mankind will I've ever seen. So, but given the fact that 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 had to actually get the galaxies. 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. Yep. Why aren't we just going to invade, listen, we don't want, like worst case scenario

is they win the superintelligence, 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, I mean, look, I mean, talked 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. There's also maybe ways they can do it without sort of attribution, right? Like you pay-Suxnet. Stuxnet.

Yeah, I mean, this is, I mean, this is part of, we'll talk about this later, but, you know, I think, look, I think we need to be working on the Stuxnet for the Chinese project. But the, but by the audience. Taiwan, I mean, Taiwan, the Taiwan thing, the, you know, I talk about, you know, EGI by, you know, 27 or whatever. Do you know about the like terrible 20s? No. Okay, well, I mean, sort of in the sort of Taiwan watcher circles, people often talk about like the late 2020s is 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 has 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. And, you know, that's 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 cut off of the chips. And so then it doesn't mean they get the chips, but it just they, you know, it's just, 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, And, you know, and Berlin was already, I mean, almost multiple times, it was caused nuclear war. So, God help us all.

Well, the Groves had a plan after the war that the plan was that the America would go around the world and getting the rights to every single uranium deposit. Because they didn't realize how much uranium there was in the world. And they thought this was the thing that was feasible. Not realizing, of course, that there was, like, huge deposits in the Soviet Union itself. Right, right. Okay. East Germany, too.

There's 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, and I'm not sure I buy yet, is that the United States, this is our leverage, this is our data center. 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. Why will it be the case that 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 sort of normative positive claims. I think the main thing I'm trying to say is, you know, you know, look, we're at these SF parties or whatever, and 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 looks like the national security state is going to get involved. And, you know, I think there's a lot of ways. this could look like, right? Is it like nationalization? Is it a public-private partnership? Is it a kind of defense-contractor-like relationship? Is it a sort of government project that so except all the people? And so there's a spectrum there. But I think people are just vastly underrating the chances of this more or less looking like a government project.

And look, I mean, look, if, you know, it's sort of like, you know, 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 100 billion, they're like, Sorry, you have a billion super intelligent scientists that 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 really think that will be like a private company and the government would be like, oh my God, what is going on?

You know, like, yeah. Suppose there's no China. Suppose there's people like Iran, North Korea who theoretically at some point will be able to do super intelligence, 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. You know, you've got to have Russia-proof security, right? It's like, you can't, you can't just have Russia steal all your stuff. And, 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 drone storm, you know, and so on. And so, I mean, 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. 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. There's like this enormous destabilization still. That said, look, I agree with you. If, you know, if, you know, by somehow things had shaking out differently and like, you know, EGI would have been in 2005, you know, sort of like unparalleled, you know, American degemony.

I think there would have been more scope for, less government involvement. But again, as we were talking about earlier, I think that would have been sort of this very unique moment in history. And I think basically almost all other moments in history, there would have been the sort of great power competitor. So, okay, so let's get into this debate. So my position here is if you look at the people who are involved

in the Manhattan Project itself, 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 regret their participation because of the project or because of the technology itself. I think people will regret it, but I think it's about the nature of the technology and it's not about the project. I think they also probably had a sense that different decisions would have been made if it

wasn't some concerted 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, in Japan you might not develop so that's a technology part but also like but you wouldn't actually 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 the technology and that will unfold regardless you know but I think this underrates the power of muddling you imagine you go through like the 20th century in like you know a decade you know it's just the sort of the sort of yes great technological

part just actually so let's actually run to that example yeah there was some reason that the 20th century would be run through in one decade. Do you think the cause of that should have been, should have been like the technologies that happened through the 20th century shouldn't have been privatized, that it should have been a more sort of concerted a 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 developed the military angle.

with it. And then, you know, it was like, you know, then the government worked with private companies. There's a sort of like real like flourishing of nuclear energy until, 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 War II. It was the B29 bomber, right? Because they needed the bomber that had long enough range to reach Japan, um, to destroy their cities. Um, and then, you know, Boeing made some Boeing made that be, Boeing made the B-47, made the B-52, you know, the plane the U.S. military uses today. And then they used that technology later on to build the 707 and sort of the...

But what is later on me in this context? Because in the other... Like, I get what it means after a war to privatize. But if you have, the government has ASI. Maybe just let me back up and explain my concern. Yeah. So you have the only institution in our society, which has a monopoly on violence.

And then we're going to give the... Give it some, in a way that's not broadly deployed, access to the ASI. Yeah. The counterfactual. And this maybe sounds silly, but listen, we're going to go through higher and higher levels of intelligence. Yeah. Private companies will be required by regulation to increase their security.

Yeah. But they'll still be private companies and they'll deploy this. And they're going to release the AGI. Now McDonald's and J.P. Morgan and some random startup are now more effective organizations because they have a bunch of AGI workers. And 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 we're trying to, why do we want to win against China? We want them against China because we don't want a top-down authoritarian system to win.

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, why do we, so let's like run our cards with privatization. That's the way we get to the classic liberal market-based system we want for the ESIs. Yeah. 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 there's no alternative comes from. And then let's look at 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. And, you know, like, you know, some giddy 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, $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 mistral, they just kind of like leave deep mind and, you know, take all the secrets with them and they just kind of replicate it. But that's not going to continue being the case. And so, you know, the sort of like open source alternative. I mean, also people say stuff like, you know, 1026 flaps, it'll be in my phone. You know, it's, no, it won't.

You know, it's like Moore's Law is really slow. I mean, AI chips are getting better, but like, you know, the $100 billion computer will not cost, you know, like $1,000, you know, within your lifetime or whatever, aside from me. So anyway, 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, you know, enormous power that sort of superintelligence will have and that the government will have. I think it's pretty plausible that the alternative world is that, like, one AI company has. that power. And it's basically, if we're talking about lead, you know, it's like what?

I don't know. Open AI has a six-month 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, and this is not necessarily. Like, any other thing that's privatized, we don't account on that being benevolent.

We just, look, to think of, for example, somebody who manufactures industrial fertilizer. Yes. Right. This, the person with this factory, if they went back to, an ancient civilization. They could blow up Rome. They could probably blow up Washington, D.C. And I think in their series,

you talk about Tyler Cowan's phrase of muddling 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 gather things are proceeding really fast. But we have a lot of historical evidence that 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 have their own little nuclear arsenal. No, no, it's like, it's institutions, it's laws, it's, it's courts.

And so, so anyway, 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? Like Lanfrieden 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 a, 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 offense defense 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 this sort of enormous array of destructive technology and the sort of like enormous amount of like, you know, basically military advance. I mean, you would have gone from, you know, kind of like, you know, bayonets and horses to kind of like tank armies and fighter jets in like a couple 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 is there's going to be this initial, just incredibly volatile, 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. If 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 offense defense balance, right? Like, I think bioweapons initially are a huge issue, right? like an attacker can just create like a thousand different synthetic, you know, viruses and spread them. And it's like going to be really hard for you to kind of like make a defense against each. But 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 offense defense balance. Or you do the thing, you know, you do with planes where it's there'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, you know, you let this sort of civilian, civilian uses. So I'm skeptical of this because, well, And then, sorry, I mean, the other important thing is, so I talked about the 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.

And I think it's like, I think it is unprecedented because it's like the industrial fertilizer guy cannot overthrow the U.S. government. I think it's quite plausible that like the AI company was super intelligence can overthrow the U.S. But there'll be multiple companies, right? And I buy that one of them could be ahead. So it's not obvious that it'll be multiple. I think it's, again, if there's like a six-month lead, maybe, maybe there's two or three. I agree.

But if there's two or three, then what you have is just like a 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. And, and they're both developing their nuclear arsenals and the robot. It's just like, also like, come on, the government is not going to let these people, you know, are they going to let, like, you know, is Dario going to be the one developing the kind of like, 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. They'll be two or three, and it'll be China and Russia and North Korea because in the private, in the private lab world,

there is no way they'll have security that is good enough. I think we're also assuming that somehow if you nationalize it, like the security just, especially in the world where this stuff is priced in by the CCP, that now you've like got it nailed down. And I'm not sure why we would expect that to be the case. But on this. The government's the only one who does the stuff. So if it's not Sam or Dario, who's, we don't want to trust them to be a benevolent dictator or whatever.

Well, we're just corporate governance. So, 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 modal president in 2020, 2025, but Donald Trump will be the person that you don't trust Sam or Dario to have these capabilities. And, okay, I agree that, like, I'm worried if Sam or Dario have a one-year lead on ASI in that world, then I'm, like, concerned about this being privatized. But in that exact same world, I'm very concerned about Donald Trump having the capability.

And potentially, if we're living a world where the takeoff is slower than you anticipate, In that world, I'm like very much I want the private company. So like in no part of this matrix, is it obviously true that the government-led project is better than the private project. Let's talk about the government project a little bit and checks and balances. In some sense, I think my argument is a sort of Berkian argument,

which is like American checks and balances have held for, you know, over 200 years and through crazy technological revolutions. You know, the U.S. military could kill like every civilian in the United States. But you're going to make that argument. The private public balance of power has held for hundreds of years.

Corporate, but yeah, why has it held? because the government has the biggest guns. And has 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 versus the track record of the private company checks and balances?

Well, the eye lab, you know, like first stress test, you know, went really badly, you know, that didn't really work, you know? I mean, even worse in the sort of private company world. So it's both like, it is not just the two, 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 superintelligence to do whatever they want. And this won't be true 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. They 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 go, let's talk about the government checks and balances a little bit. So, you know, what are, what are checks and balances? 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 sort of modeled on the Quebec agreement, right? This was

like Churchill and Roosevelt. They kind of agreed secretly, we'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, bringing in the UK, they have deep mine, bringing in the kind of like Southeast Asian states who have the chip supply chain, bringing some more of 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 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 similar to 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. We're in fact going to help you and share the benefits and, you know, sort of kind of like this new sort of post-superintelligence, world order. All right. U.S. checks and balances, right? So obviously Congress is going to have to be involved, right? Appropriate 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 have withheld its test of time in a really sort of powerful way. You know, eventually, you know, this is why, honestly, alignment is important is like, you know, the AI is, you know, You program the AIs 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 AIs. So what's wrong with this argument where you say, listen, maybe you have a point in the world where we have extremely fast takeoff. It's like one year from AGI to ASI. Yeah. And then you have the like years after of ASI where you have this like extraordinary explanation. Sure. Okay. So maybe you have a point.

Yeah. We don't know. You have these arguments. We'll like get into the weeds on them. about why that's a more likely world, but maybe that's not the world we live in. 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 don't know. So, when you nationalize, 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, okay, I'll just respond to that first. I mean, I don't expect us to nationalization. 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 know, you're going to be trying to, like,

do this crash program to 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, the whole historically institutions have held up well. First of all, they've actually almost broken a bunch of times. It's like, this is, this is, this is, this is the argument that some people who are saying that we shouldn't be that concerned about nuclear war, where it's like, listen, we have the nuke for 80 years. 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 it 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 the lot of effort. And I think the base rate, like, you can talk about America. I think America is very exceptional, not just in terms of dictatorship, but in terms of of every other country in history

has had a complete drawdown of wealth because of war, revolution, and something. America's very unique in not having that. And the historical base rate, we're talking about great power competition. I think that has a really big, that's something we haven't been thinking by the last 80 years, but it's really big.

Dictatorship is also something that is just the default state of mankind. And I think relying on institutions which in an ASI world, like there's 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 AKA AR-15s and I there's like things that I can make it harder you crush the hour 15s

no I think it actually pretty hard the reason it was Vietnam and Afghanistan are pretty hard country yeah yeah I agree but like I'm they could I agree similar with ASI um yeah I think it's just like easier if you have what you were talking about with there are institutions there are legal restraints there are courts there are checks and balances the crazy bet is the bet which like private company CEOs. The same thing, by the way, isn't the same thing true of nukes where we have these institutional agreements

about non-polar probation and whatever, and we're still very concerned about that being broken and somebody getting nukes and like you should stay up at night worrying about that. But ASI is going to be a really precarious situation as well. And like given how precarious nukes are,

we've done pretty well. And so what does privatization in this world even mean? I think the other thing is like, what happens after? I mean, the other thing, you know, because we're talking about like 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 increase bioweapons, ones that are like targeted, you know, can kill everybody but the Han Chinese or, you know, that, 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. 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. And 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 suck.

It's not what I want to use ASI for. We'll 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, you know, North Korea and Russia and China. And 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, then I'm very worried where it's like, this is part of the U.S. military, where if it's more like, listen, you got to talk to Jake Sullivan before you like run the next training one. It's like Lockheed Martin Skunkwark's part of the U.S. 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 the, what is the scenario? What is the alternative? 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 the next training cluster. Yeah.

But there's many companies who are still going for it. Yeah. And the government will be intimately involved in the security. Yeah. The, but the, like, three different companies are turning about. Is Dukes-Snet attack? Yeah. What do you, what do you, is launching?

Launching. Okay. So Darry is activating the Chinese data centers. I think this is similar to the story you can tell about there's a lot of companies, like literally big tech right now. Yeah. I think Sacha, if you wanted to, he probably could get his engineers like, what are the zero days in Windows and the companies and like, well, how do we get infiltrate the president's computer

so that like we can shut down? No, no, no. But like right now I'm saying Sacha could do that, right? Because he knows it be 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 out, pull off a a coup, whatever.

But like, I think there's like multiple AI companies. Okay. Okay. Okay. Fine, fine, fine. I agree. I'm just saying like something closer to. So what's wrong with the scenario where 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 AI is to be broadly deployed. I mean, first of all, again.

Even if it's a government project. Yeah, I mean, look, I think, first of all, like, I think the meadows of the world, you know, open sourcing their eyes, you know, that are two years behind or whatever. Yeah, super valuable role. They'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 two year old AI's, it's fine, or it's like there's like there's some restrictions on the most extreme dual use capabilities. Like, you know, you don't let private companies sell kind of crazy weapons. And that's great. And that will help with a diffusion.

And, 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 like nuclear energy, you know, like all the civilian applications will have their day. I 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 like. Yeah, there'll be still be Google clusters. And so Google, because they got the contract from the government, they'll be the ones that control the ASI. But like, why are they trading with anybody else? Why is like, why is there some random startup? It'll be 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 government or like their DPA for all their compute goes to the government. And but it's like it's very natural. So it's like sort of how the UlyS. SAC is work. After you get the ASI and then we're building the robot armies and building fusion reactors or whatever, that the that's, only the government will get to build rogue armies.

Yeah, now I'm worried. Or like the fusion reactors and stuff. It's what we do with news. It's the same situation we have today. Because if you already have the real armies and everything, like the existing society doesn't have some leverage where it makes sense with the government to. Yeah. Again, in 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? Government has the biggest guns. And the way we regulated is institutions, constitutions, legal restraints. Okay, so tell me what privatization looks like at the ESI world afterwards. Afterwards. Like the Boeing example, right? It's like, you have this government. Who gets it?

Like, Google and Microsoft. And who are they selling it to? Like, they already have the robot factory. It's like, why are they selling it to us? Like, they already have the, they don't need like our, this chum change in the ASI world. Because we didn't get like the A, the ASI broadly deployed throughout throughout this takeoff. So we don't have the robot. We don't have like the fusion reactors and whatever advanced decades of advanced science that

you were talking about. So like it just what would have they trading with us for? Trading with whom for? That everybody who was not part of the project. They got that technology that's decades ahead. Yeah. I mean, look, that's a whole other issue of like, well, how does like economic distribution work or whatever?

I don't know. That'll be rough. Yeah. I think I'm just saying I don't. Basically, I'm kind of like, I don't see the alternative. The alternative is you like overturn a 500 year civilization achievement of Lundfleeton. you basically instantly leak the stuff to the CCP, and either you barely scrape out ahead,

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. The alternative is, you know,

alternative is like you aren't actually bundling your efforts to kind of like win the race against the authoritarian powers. 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. But 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 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 the 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 War II.

It's like, yeah, you know, nuclear energy had it stay. But 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 nukes, you had to do it right. And then the civilian applications have the day. I think of closer analogy to what this is because nuclear, I agree that nuclear energy is a thing that happens later on it. It's like dual use in that way. But it's something that happened like literally a decade after nuclear weapons were developed.

Yeah. Whereas with AI, like immediately all the applications are unlocked. And it's closer to literally, I mean, this is analogy people like explicit in the context of AGI is like, assume your society had 100 million more John Wynneumann. Yeah. And I don't think like if that was literally what happened. If tomorrow you just have 100 million more of them, the approach should have been, well, some of them will convert to ISIS and we need to like be really careful about that. And then like, oh, you know, like what if a bunch of them are born in China? and then we like if we get to nationalize the John Monouin's.

I'm like, though I think it'll be generally a good thing and I'd be concerned about one power getting like all the John Monuments. I mean, I think the issue is the sort of like bottling up in the sort of intensely short period of time, like 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 Minowments.

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, 1 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. Yeah. Because if the goal is we need to beat China, part of that is protecting. I mean, that's one of the goals, right? Yeah, yeah. I agree. Well, one of the goals is read China.

And also just like manage this incredibly crazy scary period. Right. Right. So part of that is making sure we're not leaking algorithmic secrets to them. Yep. Part of that is all. cluster. I mean, building the trillion dollar cluster, right?

Yeah, but like, in your whole point, the Microsoft can release corporate bonds that are... I think Microsoft can do the like hundreds of billions of dollar cluster. I think the trillion dollar cluster is closer to a national effort. I thought that your earlier point was that American capital markets are deep and so forth. They're pretty good. I mean, I think the trillion, I think it's possible. It's possible. But it's going to be like, you know...

By the way, this point, we have a. AGI that's drabably accelerating productivity. I think the trillion dollar cluster is going to be planned before, before the AGI. I think it's sort of like you get the AGI on the like 10 gigawatt cluster, like intelligent, 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 cluster is almost finished. And then you're like and then you do your super intelligence on 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 on inference clusters everywhere. And this isn't resolved. Like I think private, in this world, they think private companies have the capital and can raise the capital do it. The thing you will need the government force to do it fast. I was just about to ask, like, wouldn't it be the, like, we know private companies are on track to be able to do this and be China

if they're unhindered by climate pledges or whatever. Well, that's part of what I'm saying. So if that's the case, and if it really matters that we be China, there's all kinds of practical difficulties of like, will the AI researchers actually join the AI effort? If they do, there's going to be three different teams,

at least, who are currently doing pre-training on different, different companies. Now who decides, at some point you're going to have the, like, YOLO, the hyperparameters of the trillion dollar cluster. Who decides that?

Just like merging extremely complicated research and development processes across very different organizations. Yeah. This is somehow supposed to speed up America against the Chinese. Like, why don't we just let it? 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. I mean, 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, it wasn't the smoothest of all processes, but, you know, define 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, maybe it was laid, but then we had 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 will give you. Well, no, it's also taking, making like the big contractor commitments or whatever. But I agree, but it was like fundamentally, it was like a private sector led effort.

Yeah. That was the only part of COVID that worked. I mean, I think, again, I think the project will look closer to Operation Warps speed. And it's not even, I mean, I think you'll have all the companies involved in the government project. I'm not that sold that merging is that difficult. You know, you have one, you know, you select one code base and, you know, you run free training on like GPUs with, you know, one code base. And then you do the sort of secondary RL step on the, you know, the other code base with TPUs. I don't know.

I think it's fine. Um, I mean, to the topic of like, will people sign up for it? They wouldn't sign up for it today. I think this would be kind of crazy to people. But also, you know, this is part of the, like, secrets thing. You know, people gather at parties or whatever. You know, 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 you're building is the national security of the United States for like whether, you know, like the free world will have another century ahead of it.

Like this is, this 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, oh, 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 stuff was really contentious initially. 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 will come around. On the, like, whether it'll be competent. I 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 they think you're pessimistic about it and like we don't have a plan for it we need to do it very soon because a GI is upon us yeah then fuck the only capable competent technical institutions capable of making AI right now are private companies let's let's go play that leading role it'll be a sort of a partnership basically but you know the other thing is like you know again we talked about World War II. And, you know, American Unpreparedness, the being of World War II is complete, you know, complete shambles, right? And so there was a sort of like very company, you know, 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 Operation Warp Speed, public private partnership, something like that, you know, is sort of what I imagine it would look like. Yeah. I mean, the recruiting the talent is an interesting question because the same sort of thing were 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 think this is generally a thing with war where... I mean, I think they're also wrong to regret it, but... Yeah, I mean, 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. Then because of the race that was set up, you immediately go to the H-bomb. I mean, I think my view is, again, 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's this, you know, world war. And then obviously there was the, you know, Cold War right after. Of course, like, you know, the military 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, you know, the military angle of this in which that doesn't happen, we're just like, ah, we're all not going to build nukes. And also just like, nukes went really well. I think that could have gone terribly, right? You know, like, again, I mean, this sort of, I think this is like not physically possible with nukes, the 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 U.S. leading on nukes and then sort of like building this new world order that was kind of U.S. led or at least sort of like a few great powers and a nonproliferation regime for nukes, a partnership and a deal that's like, look, no military sort of application of nuclear technology, but we're going to help you with the civilian technology. We're going to enforce safety norms on the rest of the world. That worked. It worked. And it could have gone so much worse. I'm zooming on.

And I'm not sure in Nagasaki, you know, they were, 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 was just like, you know, the fire bombing. Yeah. The thing, I think the thing that really changed the game was like the super, you know, the, the, the, the, the, the, the, the, the, the, the, the, the, the, the, the, the, the, the, the, the, a whole new level. I think part of me thinks when you say we will tell the people that for the free role to survive, we need to pursue this project. It sounds similar to World War II is, so World War II is a sad story, obviously the fact that it happened, but also like the victory is sad

in the sense that Britain goes in to 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, we're protecting the free world, that's why I got to 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 Britain going into World War II had about trying to protect Poland. Look, I mean, I think there's going to be a lot of unfortunate things that happen. 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 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. There's another path, right? So it's like, if you just have the break net grace, both internationally, because you're just instantly leaking all this stuff, including the weights, and just, you know, the commercial race, you know, Demas 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 write some guidelines. But 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, it's like, is it safe to do the next oom?

You know, and it's like, ah, you know, like, we're like three ooms 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. You know, some of our like automated AI researchers that are doing alignment are saying it's fine, but we don't quite trust them in this test, you know, the like the eyes started doing naughty things and, ah, but 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, or we, you know, they're about to, like, deploy the Romero Army. Like, what do we do? I think it's this, I think it is this crazy situation. And, you know, basically, you, you, you were relying much more on kind of like a sane chain of command than you are on sort of some, like, you know, deliberative regulatory scheme.

I 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 all claim. they're going to do safety, but I think it's really rough when you're in the commercial race, and they're startups. You know, and startups, startups are 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 the 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. And 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 the sort of misuse and then it would have to be nationalized. 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 it seems to be you. 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 Mon Bailey almost in the sense of Well, look, here's what I think about RSP type stuff or sort of safety regulation that's happening now.

I 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, you know, the RSP thing is like preserving the optionality. Let's see how the stuff goes.

But like, we need to be prepared. Like if the red lights start flashing, if we're getting the automated eye researcher, then it's like, 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 prior on a pursuing in a market-based way, unless you're, right about what the explosion looks like of the intelligence explosion. And so like 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 of that. I can, yeah, I am somewhat of the way there. Okay. I hope it goes well. It's going to be, ah, very stressful. And again, right now is the chill time. enjoy your vacation while at last. It's funny to look out over, just like, this is San Francisco. Yeah, yeah, yeah, yeah. And opening eyes right there, you know, anthropics 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 years. And that power is depreciating. Yeah. Who is you guys? Like, you know, people at labs. Yeah, yeah, yeah. But it is a sort of crazy world. And you're talking about, like, you know, I feel like you talk about like, oh,

maybe it'll nationalize this soon. It's like, you know, almost no. Nobody really like feels it, sees what's happening. And it's, I think this is the thing that I find stressful about all this 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, like a few hundred guys like paying attention. And 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 they think nationalization is. Yeah. And they said, oh, you know, like it's really hard to nationalize stuff. in the long time since you've done it.

There'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 constraints at a defense production act or whatever that won't be nationalized. There's the Supreme Court to overturn that. And they're like, yeah, I guess that would be nationalized.

That's the short summary of my post or my view on the project. Okay, so, but before we go further on the AI stuff, let's just back up. Okay. You, we began the conversation. I think people would be confused. You graduated by Victorian of Columbia when you were 19. Uh-huh.

So you got to college when you were 15. Right. And you were in Germany, then you got to college of 15. Yeah. How the fuck did that happen? I really wanted out of Germany. Mm-hmm. 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. And, you know, I mean, in what sense? It's just like, no peers that are? Yeah, look, I mean, it wasn't, yeah, it was, you know, there's, I mean, there's also just a sense in which sort of like, there's this particular sort of German cultural sense. I think in the U.S., 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 Poppy syndrome of us, right?

Where it's, 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 resent you for it and they're, like, trying to crush you. one. 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. Um, 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 U.S. college was this

kind of like radical act. And like, you know, it doesn't seem radical at 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 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 where, you know, it's where, where all the stuff is. And people don't do it. And so, yeah, anyway.

So, you know, I skipped a few grades. And, you know, I think at the time it seemed very normal to me to kind of like go to college and in 15 and come to America. I think, 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 my mother is going to do this plan. And as you get to call, you were like presumably the only 15 year old. Yeah, yeah.

As it 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. Yeah. So again, it's like now I understand why my mother's worried. And, you know, I didn't, you know, 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.

also great because I, you know, I actually really like college, right? And 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, and, you know, like the core curriculum and, you know, like the core works of political philosophy and literature and, and, um, you did about econ and I mean, my majors were math and statistics and economics, um, but, you know, Columbia has 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. Um, was there one professor or class that stood out that way? I mean, a few, there's like a class by Richard Betts, um, on, uh, war piece and strategy. Um, Adam 2 is obviously fantastic. Um, uh, and, you know, has written very riveting books. Yeah, yeah, yeah. You should have them on the podcast, by the way. How you tried. Okay. Yeah. I tried. I think you tried for me.

Yeah, you got to give it on the pod, man. Oh, it'd be so good. Okay, so then in a couple of years, we were talking to Tyler Cowan recently, and he said that when the way he first encountered you was you wrote this paper on economic growth and existential risk. And he said, when I found read it, I couldn't believe that a 17-year-old had written it. I thought if this was an MIT dissertation, I'd be impressed. So you were like, how did you go from? I guess we've been junior then.

You're writing, you're writing, you know, pretty novel economic papers. Why, right did you get interested in this kind of thing? And 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 it feels very natural to me. It's like I get excited about a thing. I read about it.

I immerse myself. I think I can, you know, I can learn information very quickly and understand it. I mean, I think to the paper, I mean, I think one actual, at least for the way I work, I feel like moments of peak productivity matter much more than sort of average productivity. I think there's some jobs, you know, like CEO or something, you know, like average productivity really matters. But I think there's sort of, I often feel like I have periods of like, you know, there's some, there's a couple months where there's sort of nephrovescence 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 similar. And it's just like you write it. And it's like, it's really flowing. And 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. There's one of our following shot-of-house 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. Like the call option on your productivity is the most important thing. And you get it by just increasing the volatility through bipolar. 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 move. Like, if you wanted, you know, you could kind of got a slow start on M.O. Right? You could have, you wasted all these years on econ. There's an alternative world or you're like on the super alignment team at 17 instead of 21 or whatever it was. Oh, no. 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. and like thinking about the feedback loops and equilibrium arms control dynamics. And, you know, it's, I think it is a sort of a way of thinking that I find very useful. And, you know, like what, you know, Dario and I see scaling early in some sense, that is a sort of very economic way of. And also the sort of physics, kind of 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, you know, I thought a lot of the sort of like,

core ideas of economics. I thought were just beautiful. And, 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. I 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, where you do the work, but you do the work to kind of uncover insights that weren't obvious to you before.

Once you've done the work, it's like some sort of mechanism falls out of it that like makes a lot of crisp intuitive sense that like explains some facts about the world that you can then use in arguments. I think, you know, I think, you know, like a lot of econ 101 like this and is it's great. A lot of econ in the, you know, in the 50s and the 60s, you know, was like this. And, you know, Chad Jones' papers are often like this. I really like Chad Jones's papers for this. You know, I think, you know, why did I ultimately not pursue?

econ academia was a 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. Really? I didn't realize that. Well, yeah, and it was, it was, it was good because, um, you 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 last one more time. Wait, Tyler introduced you the Twitter weirdos? A little bit. Yeah. Or just kind of like the sort of brought, you know, I, year old economist to introduce you 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, in a U.S. elite institution and sort of got, got some vibe of like sort of, you know, meritocratic elite, you know, U.S. society. And then sort of, yeah, basically this sort of like, there was a sort of trajectory then to being like, look, I, you know, find the true American spirit. I got to come out here. But anyway, the other reason I didn't become economist was because, or at least econ academia was so 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, you know, like, what are econ papers these days? You know, it's like, you know, it's like 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. I think it's important work.

But I think it is not uncoverment 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 Y happens. You have no idea what that had, 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, but then there's sort of this crisp intuition. Yeah, yeah. 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, this paper and things.

But the valedictorian, that's getting straight A's or whatever, is very much average productivity phenomenon, right? So. There's one award for the highest GPA, which I won. But the valedictorian is like, among the people, which have the highest GPA and then, like, selected by faculty. Okay. Yeah.

So it's just not, it's not just peak productivity. It's just, it's, it's, it's, it's, I generally just love this stuff. You know, I just, I was curious and I thought it was really interesting. and I love learning about it. And I love kind of like, it made sense to me. And, you know, it was very natural. And, 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 class 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 pre-productivity come when I, you know, I'm just really excited and engaged and, and, and love it. And, you know, I, you know, if you take the like courses, you know, that's what you got in college. Yeah, yeah. It's the Bruce Banner Code and Avengers. You know, I'm always angry.

I'm always excited. I'm always curious. That's why I'm always deep creativity. 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 ahead in college than me, or at least maybe two years ahead. 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. Yeah. About when I had a couple of videos and they had a couple hundred views or something. Yeah. It's a small world. Yeah. I mean, this is the crazy thing about the AI world, right? It's kind of like it's the same few people at the kind of SF parties and they're the ones, you know, running the models at DeepMind and, you know, open AI and anthropic and, and, you know, I mean,

I think some other friends of ours have mentioned this were 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, before the 20s or really 20s. The, I mean, look, I actually think, you know, and why is it a small world? 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, this is a thing I sort of took away from the sort of Germany experience where it was, I mean, look, I, it was crushing. I really didn't like it. And it was, it was, it was such an unusual move to kind of skip grades and such an unusual move to come to the

United States. And, you know, a lot of these things I did were kind of unusual moves. And, you know, there's some amount where like just like just trying to do it and then it was fine and it worked that kind of reinforced like, you know, you don't, you don't just have to kind of conform to what the overturn window is. You can just kind of try to do the thing, the thing that seems right to you. And like, you know, most people can be wrong. I don't know, things like that. And I think that was kind of a valuable kind of like early experience that was sort of formative. Okay. So after college, what did you do to do?

I did econ research for a little bit in Oxford and stuff. And then then I worked at Future Fund. Yeah. Okay. So tell me about it. Yeah. Yeah. Future Fund was a, you know, it was a foundation that was, you know, funded by San Bank and

Freed. I mean, we were our own thing. You know, we were based in the Bay. You know, at the time, this was in sort of early 22. it was this just like incredibly exciting opportunity, right? It was basically like a startup, you know, foundation, which is like, you know, it doesn't come along 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. That would be able to have, you know, this like great impact, you know, the causes we focused on where, you know, biosecurity, you know, AI, you know, finding exceptional talent and putting them to work on hard problems.

And, you know, like, a lot of the stuff we did, I was, I was really excited about, you know, like academics who would, you know, usually take six months would 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. I think on the future fund that I think is a context for people who might not realize. Yeah. not only were you guys planning on deploying billions of dollars, but it was a team of four people. Yeah, 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, yeah, in future fund, you know, the, yeah, I mean, so that was sort of the heyday, right? And then obviously, you know, when, when in sort of, you know, November of 22, you know, was kind of revealed that Sam was this, you know, giant fraud. and from one day to the next, you know, the whole thing collapsed. That was just really tough. I mean, you know, obviously it was devastating. I mean, it was devastating, obviously, for the people at their money on FTX, you know,

closer to home, you know, all these grantees, you know, we wanted to help them, and we thought they were doing amazing projects. And so, but instead of helping them, we ended up saddling them with, like, a giant problem. You know, personally, it was, you know, it was a startup, right? And so I, you know, I had worked 70-hour weeks every week for, you know, basically a year on this to kind of build this up. we're a tiny team. And then from one day to the next, it was all gone. And not just gone, it was associated with this giant fraud.

And so, you know, that was incredibly tough. Mm-hmm. Yeah. And were there any signs early on that SBF was? Yeah. And, like, obviously, I didn't know he was a fraud. And the whole, you know, I would have never worked there again, you know. And, you know, we weren't, you know, we were a separate thing.

We weren't with the working with the business. I mean, I do think there are some takeaways for me. I think one takeaway was, you know, I think there's a, 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. And, you know, I didn't know Sam McMahon-Food was a fraud, but I knew SBF, and I knew he was extremely risk-taking, right?

I knew he was narcissistic. 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 were some biosecurity grants he really liked because they were 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.

And so I knew his character. And I think, you know, I feel like one takeaway for me was, was, you know, like, I think it's really worth paying attention to people's character, including like people you work for and successful CEOs. And, you know, that can save you a lot of pain down the line. Okay. So after that, FDX implodes and you're out. And then you got into, you, you went to Open AI, 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. So, I mean, what was the goal of the super alignment team? You know, the alignment team at Open AI, you know, other labs sort of like several years ago, kind of had done sort of basic research and they developed RELHF, reinforcement learning from human feedback. And that was sort of, you know, ended up being really successful technique for controlling sort of current generation of AI models.

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. RlyHF relies on sort of of human raters who kind of thumbs up, thumbs down, you know, like the model said something, it looks fine, looks good to me. At some point, you know, the superhuman models, the super intelligence, it's going to write, 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 add side constraints? You know, the reason I joined was

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. And maybe we should talk about that a bit more later.

But so... It was so solvable. You solved it in a year. It's all over now. Anyway, so look, opening, I wanted to do this really ambitious effort on on alignment and you know, Elliot 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 sort of on 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. And I was just really into like, let's just try to solve it. And let's do the ambitious effort. You know, do the, you know, Operation WarpSeed for solving alignment. And it seemed like an amazing opportunity to do so. Okay. And now, basically the team doesn't exist. I think the head of it has left, both heads of head up left, Yon and Ilya, that's been the news of the last week. What happened? Why did the thing break down? I think opening I 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...

No, I mean, look, obviously sort of after the November board events, you know, there were personnel changes. I think Ilya leaving was just incredibly tragic for opening your 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 superalignment 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, you know, some amount of, you know, not keeping that and deciding to go in a different direction.

Okay. So now Jan has left, Elia has left. So this team itself has dissolved. But you were the sort of first person who left or, you were the sort of first person who left or, was forced to leave, the information reported that you were fired for leaking. What happened? Is this accurate? Yeah. Look, why don't I tell you what they claim I leaked 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

You know sometime last year I had written a sort of brainstorming document on preparedness on safety and security measures we need in the future on the path to AI. And I shared that with three external researchers for feedback. So that's it. That's the leak. You know, I think for context, it was totally normal at opening eye at the time to share sort of safety ideas with external researchers for feedback. 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. The internal version had a reference to a future cluster, but I redacted that for the external copy.

you know, there's a link in there to some slides of mine, internal slides. But, you know, that was a dead link to the external people I shared it with. You know, the slides weren't shared with them. And so, obviously, 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 27, 28, and that's setting timelines for preparedness. You know, I wrote this doc, you know, a couple months after the story.

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 the sort of thing. Sam says publicly all the time. I think sort of John said it on a podcast a couple weeks ago. Anyway, so that's it. That's it? So that seems pretty thin for if the cause was leaking, that seems pretty thin. Was there anything else to it? Yeah, I mean, so that was the leaking claim. I mean, say a bit more about sort of what happened in the firing. Yeah. So one thing was last year I had written a memo, internal memo, about opening eye security. I thought it was egregiously insufficient.

I thought it wasn't sufficient to protect the theft of model weights or key algorithmic secrets from foreign actors. So I wrote this memo. I shared it with a few colleagues, a couple members of leadership, who sort of mostly said it was helpful. But then, a couple weeks later, a sort of major security incident occurred. And that prompted me to share the memo with a couple members of the law. the board. 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. You know, apparently the board had hassled leadership about security. And then I got sort of an official

HR warning for this memo, you know, for sharing it with the board. The HR person told me it was racist to worry about CCPS, espionage. And they said it was sort of unconstructive. And, you know, look, I think I probably wasn't at my most diplomatic. You know, I definitely could have been more politically savvy. But, you know, I thought it was a really, really important issue. And, you know, the security incident had been really worried. 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 is a major reason for my being fired. You know, I think it was something like, you know, the reason that this is a firing and not a warning is because of the security memo.

But you're sharing it with the board? The warning I'd gotten for the security memo. 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 of 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 on, you know, what I was up to during the opening I board events, you know, things like that. And, 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 at my, you know, time at opening out, you know, messages, docs. And that's when they found, you know, the leak. Yeah. And so anyway, so the main claim they made was this leaking allegation. You know, that's what they told employees. the security memo. There's a couple other allegations they threw in. One thing they said was that I was unforthcoming

during the investigation because I didn't initially remember who I had shared the doc with, the sort of preparedness brainstorming doc, only that I had sort of spoken to some external researchers about these ideas. And, you know, look, the doc was over six months old. You know, I'd spent the day on it. You know, it was a Google doc.

I shared with my opening email. It wasn't a screenshot or anything I was trying to hide. It simply didn't stick because it was such a non-issue. And then they also claim that I was engaging on policy in a way that they didn't like. And so what they cited there was that I had spoken to a couple external researchers,

somebody got a think tank, about my view that AGI would become a government project, you know, as we discussed. In fact, I was speaking to lots of people in the field about that at the time. I thought it was a really important thing to think about. 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 relayed this and, you know, they cited that. 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. So anyway, so that's what they allege. That's what happened. You know, I've spoken to kind of a few dozen former colleagues about this, you know, since, I think the sort of universal reaction is kind of like, you know, that's insane.

I was sort of surprised as well. you know, I had been promoted just a few months before. I think, you know, I think Ilya's comment for the promotion case at the time was something like, you know, Leopold's amazing. We're lucky to have him. But look, I mean, I think the thing I understand, and I think in some sense is reasonable is like, you know, I think I ruffled some feathers and, you know, I think I was probably kind of annoying at times. You know, it's like, security stuff and I kind of like repeatedly raised that and maybe not always in the most diplomatic way. you know I didn't sign the employee letter during the board events you know despite pressure to do so

and you were what one of like eight people or something I'd like yeah I guess the I think the sort of two senior most people didn't sign were Andre and yeah I knew both since left 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 it was appropriate for the board to resign I think they kind of like lost too much credibility and trust with the employees. 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 to have an independent board.

Anyway, you know, it's other things. You know, in sort of other discussions, I pressed leadership for sort of opening eye to abide by its public commitments. 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. So, you know, look, you know, it's a free country, right? That's what I love about this country.

You know, we talked about it. And so, you know, they have no obligation to keep me on staff. 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. You know, we don't trust you enough to sort of tow the company line anymore. You know, thank you so much for your work at Open AI, but I think it's time to part ways. I think that would have made sense.

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

When you left, now there's been reporting about an NDA that former employees have to sign in order to have access to their vested equity. Did you sign such NDA? No. My situation was a little different, and that it was sort of, I was basically right before my cliff. But then, you know, they still offered me the equity. But I didn't want to sign a non-disparagement. You know, freedom is priceless. And how much was, how much was the equity? Like, close to a million dollars. So it was definitely a thing you were, you and others were

aware of that this is like a choice that opening I is explicitly offering you. Yeah. And presumably the person on opening eyes 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 opening I that you're giving on this podcast right now. Look, I don't know what the whole situation is. I certainly think sort of vested equity is pretty rough if you're conditioning that onto an NDA. It might be a somewhat different situation if it's a sort of severance agreement.

Right. But an opening I employee who had signed it presumably could not give the podcast that you're giving today. Quite plausibly not. Yeah. 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 and 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, or you're supposed to have an adversarial relationship with the board, where to give the board some information, which is relevant.

to whether Open AI is fulfilling its mission and whether it can do that in a better way is part of the leak as if the board is that is supposed to ensure that Open AI is following his mission is some sort of external actor. That seems pretty... I mean, I think, I mean, to be clear, the leak allegation was just that sort of document

I'd share feedback. This is just sort of a separate thing that they cited and they said, I wouldn't have been fired if not for the security memo. They said you wouldn't have been fired about. They said 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 rehired a CEO, and now he's on the board. Now, Ilya and Jan, who are the heads of the super alignment team, and Ilya, who is a co-founder of Open AI, obviously the most significant in terms of stature,

a member of Open AI from a research detective. They've left. It seems like, especially with regards to super alignment stuff, and just generally with Open AI, a lot of the sort of personnel drama has happened over the last few months. What's going on? Yeah, there's a lot of drama.

Yeah, so why is there so much drama? You know, I think there would be a lot less drama if all opening I claim to be with sort of building chat GPT or building business software. I think what a lot of the drama comes from is, you know, opening AI really believes they're building AGI, right?

And it's not just, you know, a claim that you make for marketing purposes, is, you know, whatever. You know, there's this report that Sam is raising, you know, $7 trillion for chips. And it's like, that stuff only makes sense if you really believe in AGI. And 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, this is 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 AGI infrastructure or does it, you know, a Middle Eastern dictator control the core AGI 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, right? 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 super alignment. But that's not really the issue. The issue is that this commitment was made.

And it was used to recruit people and, you know, 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. And but 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 another example of this is, you know, when I raised these issues about security, you know, 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, the sort of unreliability that causes a bunch of the drama. So let's zoom out, talk about the big part of the story, and also a big motivation of the way in which we must proceed with regards to geopolitics and everything, is that once you have the AGI,

pretty soon after you proceed to ASI, superintelligence, because you have these AGIs, which can function as researchers into further AI progress, and within a matter of years, maybe less, you go to something that is like super intelligence. And then from there, then you can do up, according to your story,

do all this research and development into robotics and pocket nukes and whatever other crazy shit. Yeah. But at a high stuff. Okay, but there's, I'm skeptical of this story for many 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 Hollis and others have made this point that 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 AI researchers themselves. Okay, great. So this is getting into some good stuff. I have classic disagreement. with Patrick and others.

Okay. So, you know, obviously inputs matter, right? So it's like, United States produces a lot more scientific and technological progress than, you know, Liechtenstein, right? Or Switzerland. And even if I made, you know,

Patrick Hollison, dictator of like Liechtenstein or Switzerland, and Patrick Hollison was able to implement his, like, you know, utopia of ideal institutions, keeping the talentful fix. He's not able to, like, do some crazy high school immigration thing

or like, you know, whatever, some, like, crazy genetic breeding scheme or whatever he wants to do. keeping the talentful fixed, but amazing institutions. I claim that still, even if you made Patrick Collison dictator Switzerland, maybe you get some factor, but Switzerland is not going to be able to outcompete the United States

in scientific and technological bars. Obviously, magnitude's matter. Okay. No, I actually, 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 Skunkworks or something because a couple hundred researchers.

Open AI, right? A couple hundred researchers. They do make... Highly selected, though. right you know it's like it's like saying you know that's part of that's part of why Patrick allison as a dictator is 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 know that's that's the talent pool it's like you have the you know 300 best AI researchers in the world but but there's

there has been it's not a case that from 100 years to now there haven't been population has increased massively a lot of the world in fact you would expect the density of talent to have increased in the sense that malnutrition and other kinds of debility of poverty whatever that have debilitated past talent at the same sort of level is no longer dilapilitated in the same way. 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 that much harder to find. You know, why would you have needed this 10x increase in research effort? 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 deep learning researcher's dream, right? What is this log, log, log, plot? On the X axis, you have log cumulative research effort.

On the y-axis, you have log GDP or looms 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 gigawatt of solar. And it's extremely natural for that to be a straight line. You know, this is sort of a classic. Yeah, it's a classic. And, 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. And so, you know, in some sense, I think there's a natural story. Now, one objection to 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, equilibriates. And to there I say, you know, it's just, it's an equilibrium. It's an adagis equilibrium, right? So it's like, you know, isn't it a coincidence that supply equals demand, you know, and the market clears, right? And that's, and the same thing here, right?

So it's, you know, ideas getting, how much ideas have gotten harder to find is a function of how much progress you've made. 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 in the sort of volume of 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. So I think there's some process like this.

I think it's totally plausible that, you know, institutions have gotten worse by some factor. Obviously, there's some sort of exponent of diminishing returns on more people, right? So, like, serial time is better than just parallel. But still, I think it's like clearly inputs matter. Yeah, I agree. But if the coefficient of how fast it diminish as you grow the input is high enough, then in the abstract, the fact that inputs matter isn't 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. Open AI has a staff of at most low hundreds who are directly involved in the algorithm of progress in future models. If 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 higher, much better AI's for Open AI basically.

Then it's not clear why OpenEI 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 story there is there's transaction costs to managing all these people that don't just go away if you have a bunch of AI's that they, 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

open AI go on a recruiting binge of every single genius in the world. Okay, great. 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, just 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 is 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 of the world. I think my response to your thing is like, you know, almost all of these 150 IQ people, you know, if you just hire them tomorrow, they wouldn't be good AI researchers. They wouldn't be an Alec Radford. But they're willing to make investments that take gears to pan out of the, the, 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 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 Anthropic co-founders. But like if you talk to, I had Daria on the podcast, I'm like, they have this very careful policy of like, we're not going to just hire arbitrarily. We're going to be extremely selective. Yeah. 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, I mean, you couldn't train them all. It'd be really hard to train them all. You know, you wouldn't be doing any eye research. Like, you know, there's huge costs to 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 the AIs will have. So it's like, you know, training, right?

It's like, what does it take to be an Alec Radford? You know, we need to be in a really good engineer, right? The AIs, 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 a good engineer, but have really good research intuitions and, like, really understand deep learning. And this is stuff that, you know, I like Radford or, you know, somebody like him has acquired over years of research over just like being deeply immersed in deep learning, having tried lots of things himself and failed. They are going to be able to read every research paper I've written, every experiment ever run at the lab,

you know, like gain the intuitions from all of this. They're going to be able to learn in parallel from all of each other's experiments, you know, experiences. You know, I don't know, what else? You know, it's like, what does it take to be in Alec Redford? Well, 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 AI case, you just make replicas, right? There's a like motivation aspect for it, right?

So it's like, you know, Alec, you know, if I could just like duplicate Alec Radford. And before I run every experiment, I have him spend, like, you know, a decade's worth of human time, like double checking the code and thinking really careful, be carefully about it. I mean, first of all, I don't have that many, like, Radford's, and, you know, he wouldn't care, and he would not be motivated. But, you know, the AI is, it can just be like, look, I have 100 million of you guys. I'm just going to put you on, just, like, really making sure this code is correct. There are no bugs. This experiment is thought through. Every hyperparameter is correct.

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 100 million copies. You know, so there's tradeoffs you can make between serial speed and in parallel. So you might make the tradeoff is, look, we're going to run them at, you know, 10x, 100x serial speed. It's going to result in fewer tokens overall because of sort of inherent tradeoffs. But, you know, then we have, I don't know what the numbers would be. But then we have, you know, 100,000 of them running at 100x human speed and thinking. And, you know, there's other things you can do on coordination.

You know, they can kind of like share latent space. It tend to each other's context. 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, I run the math in my series, and it's basically, you know, 27, 28, you have this automated AI researcher. You're going to be able to generate an entire internet's worth of tokens every single day. So 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 we may. So yeah, you're going to generate all these tokens. Are you generating as much codified knowledge as humanity has been able to generate in the initial creation of internet? Internet tokens are usually final output, right? Right. A lot of these tokens, if we talked about the unhobbling, right? And I think of a kind of like, you know, a GPDN token is sort of like one token of my internal monologue. Yeah.

And so that's how I do this math on human equivalents. 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, why haven't we seen the huge revenues from people often ask this question, that if you took GP4 back 10 years and you show people with this, they think this is going to automate, this is already automated half the jobs. And so there's a sort of modus ponens, modus to toll in zero where part of the explanation is like, oh, it's like just on the verge, you need to do these unhoplings.

And part of that is 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 hoblings that you don't realize that are hidden behind the scenes. I think the same will be true of the AGI as that you have running as AI researchers. I think a lot of things. I basically agree, right?

I think my story here is like, you know, I talk about, I think there's going to be some long tale, right? And so, you know, maybe it's like, you know, 26, 27, you're like the proto auto 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 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 these math competitions. And so you have the initial thing in 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. By 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 than that thing, or maybe it's two years. 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 in a bunch of other fields. But, you know, AI researchers at AI labs really know the job of an AI researcher. And it's in some sense, it's sort of there's lots of clear metrics.

It'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 fog, right? It's like most companies are badly managed. It's really hard to do the stuff. Yeah. For AIs, the sort of like, we talk about AGI, but it'll be some bespoke. set of abilities, some of which will be higher than humans,

and which will be at human level. And so it'll be some bundle and we'll need to figure out how to put these bundles together with their human overseers, 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, technological revolution in history has been very piecemeal, 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 the steam engines. You use the steam engines to run these railroad.

That helps us get more coal out. And there's sort of like factorial store you can tell where in like a 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 after electricity to change from the pulleys and water wheel base 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 EA researchers to work together. Okay, 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 explode 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 it 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 expands to sort of broader areas.

This is part of why I think it's like initially this sort of AI research explosion, right? It's like AI research doesn't run into these real world bottlenecks. It doesn't require, you know, like, plow a field or dig up coal. It's just, you're just doing AI research. The other thing, you know, the other thing about it, like, in your model, the AI research, it's not complicated, like, about flipping a burger. It's just AI research. I mean, this is because people make these arguments like, oh, you know,

AGI, I won't do anything because it can't flip a burger. I'm like, yeah, we won't be able to flip a burger, but it's going to be able to do algorithmic progress, you know? And then, and then when it does algorithmic progress, it'll figure out how to flip a burger, you know? and then we'll have the furqable up there, you know, robot. You know, look, the, sorry, the other thing is about, you know, again, these are the sort of quantities are lower bound, right? So it's like, this is just like, we can definitely run 100 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 preschooler and the high schooler, right? So I think pretty quickly, you know, there's probably some like simple algorithmic changes you find. You know, instead of one Alec Radford, you have 100. You know, you don't even need 100 million. 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. They'll pretty quickly become kind of qualitatively superhuman. You know, it's sort of like, you 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. And 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 a thing that's possible. I'm just like, how is this happening in a year? Like you, okay, first of all. So I think the story is sort of like basic, I think it's a little bit more continuous. You know, like I talked about, you know, 25, 26, you're basically going to have models as good as a college graduate. And, you know, I don't, I don't know where the unhobling 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 unhoblings 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 an 100% automated Alec Radford. Once you have that, you know, things really take off, I think. Yeah. Okay. So let's go back to the un-hoplings. 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 E.I. Parker is going to take longer than you are projecting. Yeah. I mean, I think there's two kind of key things. There's the on hobbling 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 this stuff has been about, you know, crazy eye progress, I think the data wall is actually sort of underrated. I think there's like a real scenario where we're just stagnant. 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, you know, 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 add a normalization layer, you know, you fix the scaling laws. You know, and these already have been huge things, let 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. Lama 3 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, Boas Barak and some others. that does scaling laws for this. And they're basically like, yeah, you can repeat it sometime. After 16 times of reputation, it's just like returns basically go to zero. You're just completely screwed. And so, I don't know, say you can get another 10x on data from, say like, Lama 3 and GP4,

you know, Lama 3 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 100x better model than GPD4, which is like, you know, 100x effective compute from GB4 is, you know, not that much. You know, if you do half an order of magnitude a year of compute, half an order of magnitude a year of, of algorithmic progress, you know, that's kind of like two years from GDP4. So, you know, DPP4 finished pre-treading in 22, you know, 24. So 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 happen to have now. And for decades, the internet, other things, we've been rapidly increasing the stock data that humanity has. 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 ooms higher, then it progress would have

been slightly faster. Yeah. 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 GPD3 and then we'd have to kick us

on the start of oral explosion. Yeah. But we would have still figured it out. The sort of the, we didn't just like gluck out on the amount of data we happen to have in the world. I mean, three ooms is pretty rough, right?

Like three ooms, if less data, means like six-ooms smaller, six-ooms, like less compute model and Chichil scaling laws. You know, that's basically capping out at like GP2, but 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 the 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'd only gotten the preschooler model, it can't learn from itself. You know, if you'd only gotten the elementary schooler model, can't learn from itself. And, you know, maybe GP4, you know, smart high school is really where it starts.

Ideally, you have a somewhat better model, but then it really is able to kind of like learn from itself or learn by itself. So, yeah, I think there's interesting. I mean, I think maybe one UM, less data. I would be like more ify, but maybe still doable. Yeah, I think it would feel chiller if we had, you know, like one or two. It would be an interesting exercise to get probably distributions of H.E.I. contingent done across like the ooms of data.

Yeah. Okay. The thing that makes me skeptical of this story is that the things, it totally makes sense by pre-training works so well. Yeah. These other things, there are stories of in principle why they ought to work. Like, 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 and maybe there's some key thing we're missing. 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 than humans are suggest that there's a lot of room for improvement. 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'm just like, there's a lot of like, first principles argument stack on top of each other where you get these unhoplings and then you get to AGI. Then you, because of these reasons where you can stack all these things on top of each other,

you get to ASI. And I'm worried that there's too many steps of this. Yeah. Sort of first reasonable thinking. I mean, we'll see, right? I mean, on the, 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. And there's a lot of details to get right. So it might take some time, but it's now what 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 horizon than just a couple of? couple hundred tokens, right? That was unlocked by chain of thought. And on that point in particular, the many people who have longer timelines have come on the podcast have made the point that the way to train this Long Horizon RL, it's not, I mean, earlier 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 hours of their 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 co-worker, you know, in your first five minutes, like a new smart high school intern, first five minutes, not useful at all. A month in, you know, much more useful, right? Because they've like looked at the mono repo and understand how the code works and they've 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. Yeah. Yeah. Right. But on the production of a million tokens, yeah. there's no public evidence that there's some easy loss function where you can...

GP4 has gotten a lot better since... It's actually... So the GP4 gains since launch, I think, are a huge indicator that there's like... You know, so you talked about this with John Shimon on the podcast. John said this was mostly post-training gains. You know, if you look at the sort of LM-Sysisc scores, you know, it's like 100 ELO or something. It's like a bigger gap than between Claude 3 Opus and Claude 3 Haiku. And the price difference between those is 60x.

But it's not more agentic. it's like better in the same chat about way. Like, you know, it went from like, you know, 40% to 70% math. The crux is like whether like be able to like. No, but I think I think it indicates that clearly there's stuff to be done on hobbling. I think, yeah, I think the, I think the interesting question is like this time a year from now, you know, is there a model that is able to think for like, you know, a few thousand tokens

coherently, cohesively, agentically. And I think probably there's, you know, again, this is what I'd feel better if we had an oom or two 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 GP4 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. And then, yeah, and does, I don't know if it'll work,

but it's a key question. Oh, yeah, I think it's a good place to sort of close that part where we know what the cruxes and what the progress, what evidence that would look like. On the AGI to super intelligence, maybe it's the case that the games are really easy right now and you can just sort of let loose an Alec Ratford, give him a compute budget, and he comes out the other end with something that is an additive, like change as part of the code, this is compute multiplier, changes to the part. What other parts of the world, maybe there's an interesting way to ask this.

How many other domains in the world are like this, where? where you think you could get the equivalent of in one year, you just throw enough intelligence across multiple instances. Yeah. 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's a million instances of GPD6,

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, you know, I think, you know, a decade's worth of progress in a year or something. I think that's a reasonable prompt. So I think this is where, you know, basically the sort of automated AI research 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 Alec Radford's. You come out at the other end. You've done another five ooms. You have a thing that is like vastly smarter. Not only is it vastly smarter, you like, you know, you've been able to make it good at everything else, right? You're like, you're solving robotics. the robots are important, right?

Because, like, for a lot of other things, you do actually need to, like, try things in the physical world. I 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, like, last Nvidia GTC and it was all about the, like, digital twins and just, like, having all your manufacturing processes in simulation. I don't know. Like, again, if you have these, like, you know, super intelligent, like, cognitive workers, like, can they just, like, make simulations of everything, you know, kind of off-flood style? And then, you know, make a lot of progress in simulation possible. But I also just think you're going to get the robots.

again, I agree about like there are a lot of real world bottlenecks, right? And so, you know, I don't know, it's quite possible that we're going to have, you know, crazy drone forms, but also, you know, like lawyers and doctors still need to be humans because of like, you know, regulation. But, you know, I think, you know, you kind of start narrowly, you broaden, and then the worlds in which you kind of let them loose, which again, because of I think these competitive pressures, we will have to let them loose in some degree on, you know, various national security applications. I think quite rapid progress is possible. The other thing, though, is it's sort of, you know,

basically in this sort of explosion after, there's kind of two components. There's the A, right, 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're superbly competent and everything.

You 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, let them loose, which is like, now you can just build, you know, you can cover Nevada. And, like, you know, you start with one robot factory as producing more robots. And basically this, 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 the rapidly increased inputs. Yeah. And that does have the effect of being geopolitically game-changing where you, it is remarkable. Like you go to Shanghai over a six course of decades. I mean, they throw up these crazy cities in a decade. Right, right.

And that's, I mean, the closest thing to like people talk about 30% growth race or whatever for on the AI. Yeah, yeah, so it's totally possible. Yeah. 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. Your goods have gotten cheaper. Yeah. They can manufacture more things. But,

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 of 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 GP4 to AGI story, we're talking about an order of magnitude of effective compute increase a year, right? Half an order of magnitude of compute, half an order of magnitude of algorithm of progress. That 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 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 magnetages really matter. They also really matter on the sort of intelligence explosion, right? So like just the automated AI research part. So you know, one story you could tell there is like, well, ideas get harder to find,

algorithmic progress is going to get harder. Yeah, right now you have the easy wins, but in like four or five years, there'll be fewer easy wins. And so the sort of automated eye researchers 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 just assumption economics where you assume it's just enough. 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 were saying by the time you get to 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. 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.

We're not just getting a 10x. You're getting, you know, a million X or 100,000 X. There's just the magnitudes really matter. And the magnitude is just basically, you know, But 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 going to, it's just it's, it's, it's way faster. And eventually it's going to overtake, right? And even if you have, you can almost sort of just do the simple revenue extrapolation, right? If 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 going to transition. the sort of whole economy from as it broadens from the sort of you know two percent 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 change you know stories of right there's this sort of like you know there's a sort of long run hyperbolic trend you know it manifested in the sort of like sort of change in growth mode in the austral you know revolution but there's just this long and hyperbolic trend and you know now you have this 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 went ahead of 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 is really interesting it shows that I mean from the perspective of somebody in the middle ages or before yeah 2% is equivalent to the sort of 10% yeah

I guess you're projecting even higher for the AI economy but yeah I mean it's fine I think again and it's all this stuff you know I have a lot of uncertainty right so a lot of the time I'm trying to kind of tell the modal story I think it's important to be kind of concrete and visceral about it. And I, you know, I have, 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 fucking crazy. But, but, you know, exactly what, you know, where the bottlenecks are and so on. I think that will be kind of like. So let's talk through the numbers here. You hundreds of millions of AI researchers. So right now, GPD 40 turbo is like 15 bucks for a million tokens outputted and a human things, 150 tokens a minute,

or something. And 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. Now, cheaper than a human worker. Cheaper than a human worker. But it can't do the job. That's right. That's right. But by the time you're talking about models that are trained on the 10 gigawatt cluster, then you have something that is four orders of magnitude, more expensive, 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.

Is 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 sort of 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? 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. The, the question is like per token pricing, right? And so like, GPD3 when it launched was like actually more expensive than GPD4 now. And so over just like, you know, fast increases in capability gains, inference costs remain constant.

That's sort of wild. I think it's worth appreciating. And I think it gestures at sort of an underlying pace of algorithmic progress. I think there's a sort of like more theoretically grounded way to why inference cost would stay constant. And it's the following story, right? So on Chichilla scaling laws, right, you know, half of the additional compute you allocate to bigger models and 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 a year of algorithmic progress, you're also kind of like, you're saving half an order of magnitude a 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 or also inference efficiencies, you know, 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, I think it's not crazy baseline assumption that actually these models, the frontier models are not necessarily going to get more expensive

per token. Oh, really? Yeah. Like, okay, that's, that's wild. We'll see, we'll see. I mean, the other thing, 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, it happens. But, okay, so part of the intelligence explosion is that each of them has to run experiments that are GPD4-sized. Uh-huh. And the result of, so that takes up a bunch of compute.

Yes. Then you're to consolidate the results of the experiments and what is the synthesized way. I mean, you have a much bigger influence treat anyway than your training. Sure. Okay. But I think the experiment compute is a constraint. Yeah. Okay.

Going back to maybe a sort of bigger fundamental thing we're talking about here. we're projecting in a series you say 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 algorithms. 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 450s 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 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. But that seems like an odd way to denominate when you're going to get to the moon. 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 like, you know, the amount of jet fuel. I think the, I think in AI, you know, I mean, first of all, the scaling laws, you know, they've just helped, right? And so a friend of mine pointed 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 nine to 10 pedophop days and then, you know, concatenate additional compute to from there to kind of GP4, you assume some algorithmic progress. You know, it's like the scaling laws have held, you know, like probably over 15 ooms,

you know, I know it was rough calculates, probably maybe even more held for a lot of ooms. They held for the specific loss function, which they're trained on, which is a training next token. Whereas the progress you are forecasting, which we're required for further progress. Yes. 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.

And I'm not sure whether the, you can extrapolate that same scaling curve to tell us whether these hobblings will also, like, 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 things here, right? Okay. So the, on the effect of compute scaling, the, you know, in some sense, I think it's like people center the scaling laws because they're easy to explain and the sort of like why, why is scaling matter. The scaling laws like came way after people, at least, you know, like Dario, I realized that scaling mattered.

And 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 podcast. It's just like, you know, Ilya was like models. They just want to learn. You know, you make them bigger, they learn more. 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? If you just put them on the effective compute graph, these unhoblings 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? Um, like, say MLPR on this benchmark or whatever, right? And so, you know, like, you know, we mentioned the sort of, you know, the LMSS differences, you know, RLHF, you know, again, as good as 100x more chain of thought, right? Chain of just going from this prompting change, a simple algorithmic change can be like 10x effective compute increases on like math benchmarks. I think this is like, you know, I think this is useful to illustrate that on hobblings are large. Um, but I think they're like, I kind of think of them

is like slightly separate things. And 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 the bottom of the human range to the top of the human range on like 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. And then you're getting real close. I'm reminded of... Again, the story might be wrong, but I think it is strikingly plausible. I agree. And so I'm just... And in fact, 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 and 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 only takes one or two, three point five to four jumps per token. Like one or two of those jobs for token, plus uses test time compute. And 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 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. In fact, before World War II, then these are just aberrations, whatever. and basically as soon as it happens, Ukraine, Gaza, the, everything is like, so impending ASI and crazy global conflict. Right, right.

Asi and crazy new W&D. I think this is a sort of thing that happens in history where you see a straight line and you're like, oh my gosh. And then just like as soon as you make that prediction. Yeah. Who is that famous author? So, yeah. Again, people are predicting deep learning will hold a wall every year.

Right. Maybe one year they're right. But it's like gone a long way. And it hasn't hit a wall. Sure. We 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 let's just run with it. Yeah. 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 dumer scheme to get the point zero and percent of probability distribution where things don't go off the rails. It's more about just controlling the systems, making sure they do what we intend them to do.

So 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 them making the CCP bots 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 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?

How 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 dual use, right? Like, Rly HF, you know, it's like, alignment team developed.

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, 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 USAI,

so like follow the constitution and like disobey, you know, unlawful orders and, you know, like respect separation of powers and checks and balances. And 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 have to start with this a little bit. Okay.

So fundamentally there's many different ways of future could go. Yeah. There's one path in which the L.Azer type, crazy AIs with the nanobots, take the future, and to turn everything into great goo or paperclips. And the more you solve alignment, the more that path of the decision tree is circumscribed. And then so the more you solve alignment, the more it is just different humans and the visions they have. And of course, we know from history that things don't turn out the way you expect, so it's not like

you can decide the future. But it will appear. It's part of the beauty of it, right? You want these mechanisms, the error correction, moralism. 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. And so the more you solve alignment and the more you circumscribe the different futures that are the results of AI will, the more that accentuates the conflict between humans and

their visions of the future. And so in the world where alignment is solved 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. Yeah. I mean, by removing the worlds in which the AIs take over, then like, you know, the remaining world to the ones where it's like, the human. 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 superintelligences and they have the rules of their own, but they're in some sort of law and market-based order. I worry about if you have things that are conscious and should be treated with rights. If you read about what alignment 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 tasked with watching each

other. You have brainwashing. You have red teaming where you have the spy stuff you were talking about, where you try to convince somebody you're on like a defect. and you see if they defect with you. Yeah. And if they do, then you realize they're an enemy. And then you... And listen, maybe I'm stretching the analogy too far. Yeah.

But the way, like, the ease of those sorts of these alignment techniques actually map on to something you could have read about during, like, Miles Cultural Revolution is a little bit troubling. Yeah. I mean, look, I think Sentient AI is 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, a technical 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. Yeah. You know, Supreme Court justices. And it's amazing. They're actually quite high quality. Yeah.

Right. And they're 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 wonderful, you know, these wonderful. they have different persuasions, but they have sort of, I think, very sincere kind of debates about what is the meaning of the Constitution, what is the best actuation of these principles.

You know, I guess, by the way, recommendations, sort of SCOTUS oral arguments is like the best podcast, you know, when I run out of high quality content on the Internet. 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 edit to that. But anyway, you want them to like, you know, for example, for the checks and balances, they really love the Constitution and they believe in it and they take it really seriously.

And 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 a Supreme Court justice does or like in the way that like a federal reserve, you know, an official takes their job really seriously. 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 AIs. Right. And so it's, it's really important that, like, each political 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, super intelligence. And, and, 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 know, maybe the AI 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. Not GPD4, you know, like. Oh, sorry, sorry, so GP5, yeah. Yeah, yeah, yeah, yeah. And there's ones where it takes much longer. There's ones where it's something that's a couple years.

In a modal world, yeah. So GPD4 seems pretty aligned in the sense that I don't expect to go off the rails. Yeah. Maybe with scaffolding things might change. Yeah, exactly. Exactly. So, and maybe you will keep turn at, there's cranks, you keep going up, and one of the cranks gets 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? This is the thing to worry about.

Yeah. Is there anything qualitatively that you expect to change with regards to the alignment perspective? Yeah, yeah, yeah. 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 quality of changes that happen between now, and kind of like somewhat superhuman systems, kind of like early on the intelligence explosion, and then important quality of changes that happen from like early in intelligence explosion to kind of like true super intelligence and all its power and might.

And let's talk about both of those. And so, okay, so the first part of the problem is one, you know, we're going to have to solve ourselves, right? We're going to have to align the like initial AI and the intelligence explosion, you know, the sort of automated out of Bradford. I think there's kind of like, I mean, two important things that change from GPD4, 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 a 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? They have long, you know, they're somehow they're able to act over long horizons, right? But you need that,

right? That's the sort of prerequisite to be able to do the sort of automated AI 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 representation, as representations of doing bad things, you know, but, but there's, there's, it's, it's, it's, it's not like, you know, scheming against you or whatever. Um, I think this sort of misalignment can arise once you're doing more kind of long horizon training, right? And so you're training, you know, again, two simplified example, but to kind of illustrate, you know, you're training in AI to make money. And, um, 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 deceive or seek power, simply because those are successful strategies in the world world, right? So maybe, you know, RLs 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. So basically, I think there's 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 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 RLHF, right? You're kind of like, yeah, it has this goal of like, you know, make money or whatever, but you're watching what it's doing. It 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 anti-reinforce that. The sort of critical issue that comes in is that these AI systems are getting superhuman, right? And they're going to be able to do things that are too complex for humans to evaluate it, 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 billions,

billions, trillions of lines of complicated code. You know, they might be doing all sorts of stuff. You 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 or like exultrating 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 sort of post RLHF. But I think it's a solvable problem and it's like you know there's various things I'm bullish on I think there's like ways in which deep learning has shaped out favorably The second part of the problem is you're going from your like initial systems and the intelligence explosion to like super intelligence And you know it's like many ooms ends up being like by the end of it you have a thing that's

Fastly smarter than humans I think the intelligence explosion is really scary from an alignment point of view Because basically if you have this rapid 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 you know, failure would be bad, but it's not catastrophic to like, you know, saying a bad word. It's like, you know, it's something goes awry. To like, you know, failure is like, you know, it extraded 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. There's something that potentially has a very sort of alien and different architecture, right, after having gone through another decade of a maladvances. I think one example there that's very salient to me is, legible and faithful chain of thought. Right? So 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. It learns to 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 mechanistic interpretability. You can just like literally you read what they're thinking, which is great. You know, it's like huge advantage, right? However, it's 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 to us like, you know, an expert in the field might be to like an elementary school or middle schooler. And so, you know, I think it's this sort of incredibly sort of like, I'm a

hairy period for alignment. Thing you do have is you have the automated AI researchers, right? And so you can use the automated AI researchers to also do alignment. And so in this world, why are we optimistic that the project is being run by people who are thinking? I think so here's here's something to think about. Okay. The open AI starts 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 still hear, here's the thing. No, no, even the people who are there. Even, like, the current leadership is, like, exactly these things. You can find them in your views and their blog posts talking about.

And what happens is when, as you were talking about, when some sort of trivial, and Yon talked about it, this is not just you. Yon talked about his tweet thread. When there is some tradeoff that has to be made with, we need to do this flashy release this week and not next week because whatever Google I.O. is the next week. So we need to get a, and then the trade-off is made in favor of 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, is it naturally thinking in this way about how I'm worried the chain of thought is unfaithful and how do we think about the features that are represented here,

why should it be optimistic that a project run by people like that will be thought about these kinds of considerations? I mean, they might not be. You know, I agree. I think, all, a few thoughts, right? First of all, I think the private world, even if they sort of nominally care, is extremely tough for alignment. A couple reasons.

One, you just have the race between the sort of commercial labs, right? And it's like, you don't have any head room 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 automate 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 age, China has your secrets, they're right on your tails, you're in this fever struggle, no room at all for maneuver.

So, like, the way, it's, like, absolutely essential to get alignment right, and you get it during this intelligence explosion to get 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 spot if you don't have this clearly. So I think the sort of private world is kind of rough there. Unlike whether people will take it seriously, you know, I don't know. I have some faith in sort of sort of normal mechanisms of a liberal society.

Sure. Sure. If 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, if evidence is ambiguous, then that's the world in which you really want the safety margins. And that's also the world's much kind of like running the intelligence explosion is sort of like, you know, running a war, right? It's like, ah, the evidence is in big U.S. We have to make these really tough tradeoffs. And you like, you better have a really good chain of command for that. And it's not just like, you know, yolowing yet.

Ah, 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 made a really interesting point many hours ago at this point. Oh, no.

We should saw it after. after the marathon. The fact that throughout history, World War II, is not unique, at least when you think in proportion to the size of the population. Yeah. But these other sorts of catastrophes where a significant portion 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 disclosure, and they're going to get into the AI table. We were talking about Iran and North Korea and Russia. We didn't talk about Germany, right? Well, because they're allies. Yeah, yeah. But so what happened?

I mean, World War II and now it didn't like come back over the seven years of war or something, right? Yeah, 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 underrating a little bit. I think it's probably still one of the, you know, top five most important countries in the world.

You know, I mean, Europe overall, you know, it still has, I mean, it's a GDP that's, like, close to the United States, the size of the GDP, you know, and there's things actually that Germany is kind of good at, right? Like, state capacity, right? Like, you know, the, you know, the roads are good and they're clean and they're well maintained 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 Germany, right? So in the U.S., it's a little bit, like, there's a bit more of sort of wild west feeling to the United States, right? And it includes the kind of,

like, you know, political candidates that are sort of, you know, there's much broader spectrum and, you know, much, you know, like both in Obama and Trump as somebody, you just wouldn't see in the sort of much more confined kind of German political debate. You know, I wrote this blog post at some point, your political stupor about this. But anyway, and so there's this sort of punctilious 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 a sort of very constrained view of the world in some sense. You know, and that includes kind of, you know, I think afterward or two, 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.

Why is that the logic? Excellence isn't cherished. You know, there's, yeah. Why is that the logical, intellectual, um, think to rebel against if what, if you're trying to overcorrect from the Nazis. Yeah. Was it because the Nazis were very much into elitism? What was, I don't understand why that's a logical sort of, uh, kind of reaction.

I know. Maybe it was sort of a counter reaction against the sort of like whole like Aryan race and sort of that sort of thing. I mean, I also just think there was a certain amount in what a amount, certain, I mean, look at sort of world or one, end of order one, versus. end of World War II for Germany, right? And sort of, you know, a common narrative is that the piece of Versailles, you know, was too strict on Germany. But, you know, the peace imposed after World II was, like, much more strict, right? It was a complete, you know, I mean, the whole country was

destroyed. You know, it was, you know, in all the, most of the major cities, you know, over half of the housing stock had been destroyed, right? Like, you know, in some birth cohorts, you know, like 40% of the men had died. Half the population displaced. Oh, yeah. I mean, almost 20 million people right, displaced, right? Huge, crazy, right? You know, like... And the borders are way smaller than the Versailles borders. Yeah, exactly. And, and sort of complete imposition of a new political system and, and, you know, on both sides, you know, and, um, yeah, so it was, um, but in some sense, that worked out better than the post-World War I piece, um, where then there was this kind of

resurgence of German nationalism and, you know, in some sense, the thing that has been a pattern. So 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. 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 was this book about verdant by a Chinese economist called China's World View. Yeah.

And overall, I wasn't 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 selects for, you're not going to get some Marjorie Taylor Green or somebody running. Don't get that in Germany either.

Right. Yeah. But he explicitly made the point in the book that also means we're never going to get a Henry Kissinger or Barack Obama. Right. In China, we're going to get like, by the time they end up in charge of the Paula Buren

on the Polybura, there'll be like some 60-year-old bureaucrat who's never like ruffled any feathers. Yeah, yeah, yeah. I mean, I think there's something really important about the sort of like very raucous political debate 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. I mean, like, we live in this kind of bizarre little like bubble in San Francisco and people, you know, and and, but I think that's important for the 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 major mistakes, right? Like the sort of defense spending, right? And, you know, then, you know, Russia invades Ukraine. And, and you're like, wow, what did we do? Right. No, that's a really good point, right? The main issues, there's everybody agrees.

There's like no debate about it. Exactly. Yeah. So a consensus blob kind of thing. Right. 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 state of mind from just kind of afar, I worry a lot about, you know,

where I think it is interesting just how kind of impenetrable. world, China is to me. It's a billion people, right? And like, you know, almost everything else is really globalized. You have a globalized internet. And I kind of, I kind of have a sense of what's happening in the UK. You know, I probably, even if I didn't read German newspapers, just sort of would 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? What is the state of political debate, you know, of a sort of average Chinese person or like an average Chinese elite? And yeah, I think that that I find that distance kind of worrying. And I, you know, and there's, you know, and, you know,

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 in Mandarin that like mean we'll have one connotation not the other connotation. But yeah, I think it's sort of interesting given how globalized everything is. And like, 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, 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. You got to do some pods and you got to find some of the Chinese AI researchers, man. I know. I was thinking at some point, again, this is the fact that I have been freely, but, you know, 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'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 just talk. I just want to see what is the vibe? Even if they don't tell me anything.

I'm just like, what kind of person is this? Are they? How westernized are they? Yeah. But as I was saying this, I just remembered that, in fact, Bight Dance on, 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 Bid Dance, we'll make you an LID engineer. You would report directly to CTO.

And in fact, this actually, I'm going to go. That's how the secrets go over, right? Right. No, I meant to ask this earlier, but suppose they hired what, if there's only 100 or so people, or maybe less, we're working on the key algorithmic secrets. Yeah, yeah, yeah, yeah. If they hired one such person, yeah. Is all the alpha gone 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 extiltrate the code. They could get a lot of the key ideas. Again, 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 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't just recruit a Manhattan project engineer and then just get. And it's like, these are secrets that can be used for like probably every training around in the future. That'll be like maybe are the key to the data wall that are 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, given sort of like the multipliers on compute, you know, hundreds of billions, trillions of dollars. you know and all it takes is you know China to offer 100 million dollars to somebody and be like ah can work for us right and then and then yeah I mean yeah I'm I'm really uncertain on how

sort of seriously China is taking AGI right now one one anecdote that was really 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 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 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. Wait, well, what's a what's a takeaway? As in they're not letting really senior average, which is leaving the country.

Interesting. Kind of classic, you know, Eastern Bloc move. Yeah. I don't know if this is true, but it's what I heard. That's interesting. So I thought the point you made earlier about being exposed to German newspapers and also to because earlier, you were interested in economics and of law and security. You have the variety and intellectual diet there has exposed you to thinking about the geopolitical question here in ways. Others talking about, yeah, I mean, this is the first episode I've done about

this where we've talked about things like this, which is now that I think about it, weird, this is an obvious thing in retrospect. I should have been thinking about. Anyways, so that's one thing we've been missing. What are you missing? And national security you're thinking about, so you can't say national security. What, like, keep perspective, are you probably under-exposed to us? 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 Cowanest take, which is like, you're not exposed to how, like, how will a normal person in America, like, you know,

both like use AI, you know, probably not, you know, and that being kind of like bottlenecks to the fusion of these things. And I'm overrating the revenue because I'm kind of like, ah, you know, everyone has staff is adopting it. But, you know, kind of like, you know, Joe Schmo engineer at a company, you know, like, ah, 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 um um 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 maybe they will um yeah here's a political reaction that i didn't anticipate yeah so tucker carlson was recently on the joe rogan episode i already told you about this but i'm just going to tell the story again so tucker carlinson is on joe rogan 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 Nagasaki 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, these people in Silicon Valley, these fucking nerds are making superintelligence. And 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, man. 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 multiple times. Initially, like, kind of the right is like, you know. It's like so contingent. And then and then and then and the left was like, this is racist. And then it flipped, you know, the left was really into the code.

Yeah. Yeah. And the whole thing also is just like so blunt and crude. And so I think I think probably in general, you know, I think people are really under, you know, people like to make sort of complicated technocratic AI policy proposals. And I think especially if things go kind of fairly rapidly on the last day GI. 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 cruder reactions. Yeah. Look, and then also

when you mention the spies and the national security getting involved and everything and 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 EI research is also a little scary to think about people I personally know and friends with, it's not unfeasible if they have secrets in their head that are worth $100 billion or something, kidnapping, assassination, sabotage. Oh, their family. Yeah, it's really bad. I mean, this is to the point on security, you know, like right now it's just really foreign, but, you know, at some point, as it becomes like really serious, it's things, you know, you're going to want the security cards. Yeah. Yeah. Yeah. So presumably you have thought about the fact that people in China

will be listening to this and we'll be 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 and make them up to AGI, which is part of the thing you're worried about, is China-weaking-up to AGI. Yeah. Then to stay silent.

Yeah. I'm just curious, 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 people in the PRC, we'll read this. I 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, he's not new anymore. There's sort of, you know, a lot of these takes are kind of old.

Or, you know, I've had, you know, similar views a year ago. It might not have written it up a year ago, in 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 we actually need sort of like, you know, a broad bipartisan understanding, the challenge is facing us. And so, you know, I think it's a tough tradeoff, but I think the 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 strategic picture. And, you know, I think you have done actually. a great service to sort of mankind in some sense by you know with your podcast um and um you know i think it's overall been good okay so by the way you know on the topic of you know germany um yeah you know we

were talking at some point about kind of immigration story right i feel like you have a kind of interesting yeah yeah yeah you haven't told and i think you should tell so um a couple years ago i was in college yeah and i was 20 yeah i was about to turn 21 yeah i think it was yeah you're yeah you came from India when you were really young. Right. Yeah. So until I was eight or I was eight or nine, I lived in India. Yeah. And then we moved around all over the place. Yeah. Because of the backlog for Indians. Yeah. The green card backlog. Yeah. It's, um, we were, we've been in the queue for like decades. Even though you came at eight. You're still on the, you know, H1B. Yeah. And when you're 21, you get kicked off. Yeah. The queue and you had to restart the process. I'm on my dad's,

my dad's, my dad's a doctor and I'm on his age one B as a dependent. But when you're 21, you get kicked off. Yeah. And so I'm 20. and I just kind of dawns on me that this is my situation. Yeah. And you're completely screwed. Right. And so I also had the 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 a startup. Yeah. You just like, where can you not get native? And like even getting the H-1B for you would have been like, you know, 20% lottery. So if you're lucky, you're in this. And they had to prove that they can't get native talent, which means like for him.

And we live in North Dakota for three years, West Virginia for three years, Maryland, West Texas. Yeah. And so it kind of dawned me, this is my situation. As I turn 21, I'll be like on this lottery. Even if I get the lottery, I'll be a fucking code monkey for the rest of my life. Because 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 Paul Graham essays. Uh-huh. 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, I was in a daze through finals because I had like, I had just occurred to me and I was really like anxious about it. Yeah. And I remember thinking to myself at the time.

Yeah. That if somehow, yeah. I end up getting my green card before I turned 21. Yeah. There's no fucking way I'm turning, becoming a code monkey. Yeah. Because the thing that I've, like this feeling of dread that I have, yeah. Yeah.

Is this realization that I'm just going to have to be a code monkey. Yeah. And I realized that's my default path. Yeah. If I hadn't sort of made a proactive effort not to do that, I would have graduated 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 an important sort of realization for me. Anyway, so COVID happened because of that, since there weren't foreigners coming, the backlog cleared fast. And by the skin of my teeth, like a few months before I turned 21. So crazy. Extremely contingent reasons. So crazy.

I ended up getting a green card. Yeah. because I got a green card I could you know the whole podcast right exactly right I graduated college and I was like bumming around and I got was like I graduated semester early I'm going to like do this podcast yeah what happens and it was it hadn't it didn't have a green card it's such a magnificent cultural artifact you know and it only existed yeah it's actually because it's 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. And it's, yeah, it's, I mean, it's just incredibly tragic that this is so dysfunctional. Yeah, yeah. Yeah. No, it's, yeah, it's insane. I mean, I'm glad you did it. I'm glad you kind of like, you know, tried the, you know, the, the, the, the, uh, the unusual path. Well, yeah, but I could only do it, obviously I was extremely fortunate that I got the green card. I was like, um, I had a little bit of saved up money. I got a small grant out of college. Thanks to the, Fuse are fine 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, podcast, come on. Like I wasted a few months on this. Let's now go do something real. Something big would happen. Yeah. I would, yeah. And you kept with it. Huh?

You kept with it. Yeah, yeah. But there would always be just like the moment I'm about to quit the podcast. I'm being like Jeff Bezos will say that is something nice about me on Twitter. The early episodes gets like a half a million views. And then now this is my career. but it was sort of very looking back on it, incredibly contingent, that things worked out the right way.

Yeah. I mean, look, if the AGI stuff goes down, you know, it will 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 the AGI. Yeah, 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 sustained to me for six months or however long it was. Yeah.

And without that, I wouldn't- It was kind of crazy. Yeah, 10 grand or where was it? No, it's tiny, but it goes to show kind of how far small grants can go. Yeah. It's sort of the emergent ventures too. Exactly. The immersion ventures.

Yeah. And the last year I've been in San Francisco. Yeah. We've just been in close contact the entire time and just bouncing ideas back and forth. We're just basically the alpha I have, I think people would be surprised. by how much I got from you, Sholto, Trent, and a couple others. It's been an absolute pleasure. Yeah, likewise.

Likewise. It's been super fun. Yeah. Okay, so some random questions for you. Yeah. If you could convert to Mormonism. Yeah. And you could really believe it.

Yeah. Would you do it? Would you push the button? Well, okay. Before I answer that question, one sort of observation about the Mormons. So actually, there's an article that actually made a big impact on me. I think it was by me kick-hop and at some point, you know, on the Atlantic or whatever, about the Mormons. And I think the thing he kind of, you know, and I think he even interviewed Mitt Romney in it 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, you don't drink alcohol, you're kind of weird. How that kind of got people prepared for being willing to be kind of outside of the norm later on. And like, you know, Mitt Romney, you know, was willing to kind of take stands alone, you know, in his party because he believes. you know, what he believed is true. And I don't know, I mean, probably not to the same way, but I feel a little bit like this from kind of having grown up in Germany and having, you know, and really not having like this sort of 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 an outsider gives you kind of unusual strength later on to be kind of like, you know, willing to say what you think. And anyway, so that is one thing I really appreciate about the Mormons, at least the ones that, you know, grow up outside of Utah. The other thing, you know, the fertility rates, they're good, they're important. So, you know, they're going down as well, right? Right, this is the thing that really clinched the kind of fertility decline story.

Yeah, even the Mormons. Yeah, even the Mormons, right? You're like, oh, this is like a sort of good story. The Mormons will replace everybody. Well, no, I don't know if it's good, but it's like at least, you know, at least come on. Like at least some people will maintain high, you know, but it's no, no, you know, even the Mormons. And sort of basically, once the sort of these religious subgroups have high fertility rates,

right. Once they kind of grow big enough, they become, you know, they're too close in contact with sort of normal society and become normalized. Mormon fertility rates dropped from, I remember the exact numbers, maybe like four to two in the course of 10, 20 years. 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, the sort of like, you know, the sort of like overwhelming force of modernity kind of gets you. Yeah. No, if I could convert to Mormonism. Look, I think there's

something, I don't believe it, right? If I believed it, I obviously would convert to Mormonism, right? Because it's, you got to convert. But you can choose the world in which you do believe it. I think there's something really valuable and kind of believing in something greater than yourself and believing, having a certain amount of faith. You do, right? That's what I see is. Yeah, yeah. And, and, and, you know, there's a, you know, feeling some sort of duty to the thing

greater than yourself. 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. And 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, you know, I think that, I think that can be a force for a lot of good.

I'm going back to the opening, I think, just to be. The thing that's especially impressive about that is, look, there's people at 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, pretty many people were concerned about the clusters and the Middle East and the secrets leaking to China and all these things, by the person who actually made a hassle about it. And I think hassling people is so underrated.

I think that 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. Who isn't like a solidified member of the, I think that's a sort of like. Maybe it's me being naive and not having up knowing how big companies work and, you know. But like 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. Yeah. Sometimes friends tell me I should be more of a speech consequentialist.

No, I think I really think the amount of people who, when they have the opportunity to talk to the person, will 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 very impressed that the dinner begins, and by the end,

somebody who has a major voice and how things go is seriously thinking about a worldview they would have found incredibly alien before the dinner or something. And I've been impressed that just give them the spiel and hassle them.

I mean, look, I just, I think I feel this stuff pretty viscerally now. You know, 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, I think since at least last year, you know, I feel like, you know, I feel like, you know, and I just, I feel it.

And I think I can like, you know, I can sort of see the cluster that AGI is going to be trained on. And I can see the kind of rough combination of algorithms and the people that be involved and how this is going to play out. And, you know, I think, 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. Yeah. Should we talk about what you're up to next? Sure. Yeah. Okay. So you're starting an investment firm. Yep. Inker investments from Nat 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.

Harris. Why the investment firm? 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 superintelligence happens. I 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. But I think the decade after is also going to be wild. And, you know, this combination of human institutions but superintelligence, 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 will really matter. You know, eventually, you know, like, you know, going to go to the stars, you know, going to go to the galaxies. So anyway, so part of the answer is just like, look, I think 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 100 X. Probably you can make even way more than that because of the sequencing. And, you know, capital matters.

I think the other reason is just, you know, some amount of freedom and independent. And I think, you know, I think there's some people who are very smart about the say I stuff and who are kind of like see it coming. But I think almost all of them, you know, are kind of, you know, constrained in various ways, right? They're in the labs, you know, they're in 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've 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. be, will be kind of like, you know, a brain trust on AI. It's going to be all that situational awareness. We're not the best situational awareness in the business. You know, we're going to

have way more situational business than any of the people who manage money in New York. Yeah. We're definitely going to, you know, we're going to do great on investing. But it's the same sort of a situational awareness that I think is going to be important for understanding what's happening, being a voice of reason publicly and, and sort of being able to be in a position to advise. Yeah. I, I, there, the book about Peter Thiel. Yeah. They had, an interesting quote about this hedge fund. I think it got terrible returns. So this isn't the example. Right, right. That's the sort of bare case. Right. It's like too theoretical. Sure, yeah. But they had an interesting quote that it's, that it's like basically a think tank inside of a hedge fund. Yeah.

That's what 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 very, there's a lot of interesting business history books about people who got the pieces right, but timed it wrong, where 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, 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. I mean, you know, I think this investment firm is going to just be betting on AGI, you know, betting on AGI and superintelligence before the decade is out. taking that seriously, making the bets you would make, you know, 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, 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 the call wrong on like when revenue would go up. And so anyway, 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, classic debate I and a friend had with another colleague of ours, where this colleague was really into TSM, you know, TSM, and he was just kind of like, well, you know, like these fabs are going to be so valuable. And also like in video, there's just a lot of videocrystic risk, right? It's like maybe somebody else makes better GPUs. And that was basically right. but sort of only Nvidia had the AI beta, right?

Because only Nvidia was kind of like large fraction AI. The next few doublings would just like meaningfully explode their revenue, whereas TSMC was, you know, a couple percent AI. So, you know, even though there's going to be a few doublings of AI, not going to make that big of an impact. All right. So it's sort of like the only place to find AI beta basically was Invidia for a while. You know, now it's broadening, right?

So now TSM is like, you know, 20% AI by like 27 or something is what they're saying. When we're doubling, it'll be kind of like a large fraction of what they're doing. There's a whole stack. You know, there's like, you know, there's people making memory and co-os 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%, 5% 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.

And, you 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 kind of like, ah, I don't really care about them before then. I care about it, you know, once 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 from AI. Probably their stock will explode. You know, they're going to become, you know, $5 trillion, $10 trillion company. Anyway, so the timing there is very important. You have to get the timing right.

You have to get the sequence right. You know, at some point, actually, I think, like, you know, there's going to be real tailwind to equities from real interest rates, right? So basically in these sort of explosive growth 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 roboph factories or whatever, right?

And so they're going to be borrowing like crazy. They want all this capital, higher ROI. And 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 oil equation, standard sort of intratemporal transfer, you know, tradeoff of consumption. It's standard. Very standard.

Some of our friends have a paper on this. Basically, if you expect, if consumers expect real growth rates to be higher, interest rates are going to be higher because they're less willing to give up consumption. Consumption in the, they're less willing to give up consumption day for consumption in the future. Anyway, so at some point, real interest rates will go up. If sort of ATA is greater than one, that actually means equities, higher growth rate expectations mean 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, the big bond short. You got to get that right. You got to get it right that, you know, nationalization, you know, like, you got, yeah. So there's this whole sequence of things. You got to get that right. And the unknown unknowns. Unknown unknowns. Yeah. And so you've, look, you've got to be really, really careful about your like overall like, you're like, you know, if you expect these kind of crazy events to play out, there's going to be crazy things you didn't receive. 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. You know, that pays off. Very silly question. Yeah. In these worlds, yeah. Are financial markets where you make these kinds of vets going to be respected and, like, you know, like, it's my fidelity account going to mean anything when we have the 50% economic growth like who's who's like we got to respect his property rights into it the bond short the sort of 50 second hour growth that's pretty deep into it i mean again there's this whole sequence of thing

but yeah no i think property rates will be instructed again in the sort of modal world the project yeah at some point at some point there's going to be figuring out the property rights for the galaxies you know that'll be interesting that will be interesting so there's an interesting question about yeah 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, but 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 return. that were realized through the Industrial Revolution. Yeah. And I don't know what happened.

At some point, they were just weren't the land of 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, farmland before the Industrial Revolution. And the guy who's actually going to make a bunch of money is the one of the steam engine. Even he doesn't make that much money. Most of the benefits are widely diffused and so forth. I mean, I think the analog is like you sell your land. you put it all in sort of the, you know, the people who are building the new industry.

I think the, I mean, I think that sort of like real depreciating asset, you know, for me is human capital, right? Yeah, no, look, I'm serious, right? It's like, you know, there's something about like, you know, I don't know, I was like, valedictorian of Columbia, you know, the thing that made you special is you're smart. Right. But actually, like, you know, that might not matter in like four years, you know, because it's actually automatable. Right. And so anyway, a friend joke that the sort of investment firm is perfectly hedged for me.

It's like, you know, either like AGI this decade. And yeah, your human capital is depreciated, but you've turned that into financial capital. Or, you know, like no AGI this decade, in which case, maybe the firm doesn't do that well. But, you know, you're still in your 20s and you're still smart. Excellent. And what's your story for why AGI hasn't been priced in? The story, financial markets are supposed to be very efficient, is very hard to get an edge. Yeah.

Here, naively, you just say, well, I've looked at the scaling curves and they imply that we're going to be buying much more computed energy than the analysts realize. Shouldn't 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, you know.

Yeah. I think the thing I, you know, change my mind on is that I think there can be kind of groups of people, smart people, you know, who are, you know, stay there in San Francisco, who do just have off over the rest of society. and kind of seeing the future. And so, like COVID, right? Like, I think there's just honestly kind of similar group of people

who just saw that and called it completely correctly. And, you know, they shorted the market. They did really well. You know, a bunch of other sort of things like that. So, you know, why is AGI not priced in? You know, it's sort of, you know, why hasn't the government nationalized the labs yet, right? It's like, you know, this, you know,

society hasn't priced at it. in yet and sort of it hasn't completely diffused and you know again it might be wrong right but um um i just think sort of you know not that many people take these ideas seriously yeah yeah yeah yeah a couple of other sort of ideas that i was playing around with with regards to we didn't get a chance to talk about but the the systems competition yeah there's a very interesting um the one of my favorite books about world which is a victor davis hanson uh-huh 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 of the Axis made. They were pretty like Blitzkriek, whatever. That was sort of by accident, though. Well, in what sense? That they just had the infrastructure left over. Well, no, I mean, the sort of, I think, I mean, I don't, I mean, I think sort of my read of it is Blitz Creek wasn't kind of some, like, a genius strategy.

It was just kind of, it was like more like their hand was forced. I mean, this is sort of the very Adam-Tusian story of World War II, 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. So their only path to victory was like make it a short war, right? And that sort of worked much more spectacularly than they thought, right? And sort of take over France and take over much of Europe. And so then, you know, the decision to invade the Soviet Union, it was a, you know,

it was, look, it was, it was about the Western Front in some sense, because it was like, we've got to get the resources. Yeah. You know, 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 on a bunch of these things. It's the largest industrial project of Nazi Germany. And so, you know, and sort of they thought, well, you know, we completely crushed them in Rural War I. You know, they'll be easy. We'll 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 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 that was directed, you know, towards the Western Front and towards, you know, the Western allies. Well, and then so the point that Hanson 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, which is like, you know, I worry a lot about kind of, you know, America, decline of sort of American, like latent 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-AIAid worlds, we have the superiority in military and material, kind of like win a short war, at least, you know, kind of defend Taiwan in some sense. But 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 that 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 cluster, maybe, or even maybe AGI takes the, you know, is on the $100 billion 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 latent industrial capacity to outbuild us. Yeah, yeah. And that matters both in the run-up to AGI and after, right, where it's like you have the 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, well, yeah, will we produce the, how many of the drones will we produce? And I think, yeah, so there's some sort of like outbuilding in 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 robot factories proceed once you get the AASI to beat out China. Like it all, it's all, it's all a matter.

It's all part of the picture. Yeah, yeah, yeah. And by the way, speaking of the AASIs and the robot factories, one of the interesting things. Robo armies too. Yeah, one of the interesting things, there's this question of what you do with industrial-scale intelligence, and obviously it's not chat bots.

Yeah. But it's a, I think it's very hard to predict. Yeah, yeah. But the history of, oil is very interesting. We're in the, I think it's in the 1860s that we figure out how to refine oil some geologists. And so Ben Standard Oil gets started. There's this huge boom. It changes American politics. Entire legislators are getting bought out by oil interest and presidents regarding elected based on the divisions about oil and breaking the aim up and everything. And all of this

has happened. The world has never illusionized before the car has been invented. And so when the light bulb was invented, I think it was like 50 years after oil refining had been discovered, as the majority of Standard Oil's history is before the cars invented. The kerosene lamps are. Exactly. So it's just used for lighting. Then they thought oil would just no longer be relevant.

Yeah, yeah. So there was a concern that standard oil would go to brain corrupt when the light bulb was invented. Yeah. And but then there's sort of, you realize that there's immense amount to compress energy here. Yeah.

You're going to have billions of gallons of this stuff a year. Yeah. And it's hard to sort of predict in advance what you can do with that. Yep. Yep. That's true. And then later on it turns out, oh, transportation, cars with, that's what it would be used for. Anyways, with intelligence, maybe one answer is the intelligence explosion.

Right. But even after that, so you have all these ASIs and you have enough compute, especially the compute they'll build. Yeah. To run. Hundreds of millions of GPUs will hum. Yeah. But what are we doing with that?

And it's very hard to predict in advance. I think it's very interesting to figure out what the Jupiter brains will be doing. So look, there's situational awareness of where things stand now. And we've gotten a good dose of that. Obviously, a lot of the things we're talking about now, you couldn't have prejudged many years back in the past. Right. And part of your worldview implies that things will accelerate. because of AI getting the process,

many other things that are that are unpredictable fundamentally, basically how people will react, how the political system react, how foreign adversaries will react. Those things will become evident over time. So the sexual awareness is not just knowing where the picture stands now, but being in a position to react appropriately to new information, to change a worldview as a result,

to change your recommendations as a result. Yep. what is the appropriate way to think about situational awareness as a continuous process rather than as a one-time thing you realized? Yep. No, I think this is great. Look, I think there's a sort of mental flexibility and 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. The Dumers who actually, you know, I think we're really prescient on AGI. I'm thinking about the stuff, you know, like a decade ago. But, you know, they haven't actually updated on the empirical realities of deep learning. They're sort of like, their proposals are really kind of naive and unworkable. It doesn't really make sense. You know, there's people who come in with sort of a predefined ideology. There's kind of like, you know, the Eax a little bit, you know, like they like to shippost

about technology, but they're not actually thinking through. Like, you know, I mean, either the sort of stagnationists who think the stuff is only going to be, you know, a chat bot. 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 go. 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, I think this sometimes happens to people as a sort of calcification of the worldview, because now they've publicly articulated this position. And, 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 a 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 the rapid pace of progress, you know, given the map of pace 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. You know, I feel like I used to have this world 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, they have idealized. and people are on it. You know, surely they 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 somebody else who's 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, who have a situation, awareness, are willing to change their minds, are willing to sort of stare the picture in the face. And, 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 an 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 form in the description below. Other than that, the most hopeful thing you can do is just share the episode if you enjoyed it. Send 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 the 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 like suddenly understand the street names.

You know, it's like, you know, Gniz now and Scharnhorst. And there are all these like Prussian military reformers. And you're like finally understood, you know, Sansa C. And you're like, it was for 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 hates speaking German. He only wants to speak French.

He plays the flute. He composes. He has all the sort of great artists of his day over at Sanssouci. And he actually had this sort of like really tough upbringing where his father was this sort of like really stern and sort of Prussian military man. And he had had a Frederick the Great as 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 impression guy he was this kind of gay lover of arts 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 like he gets Silesia

he wins the seven years war you know also you know amazing military strategists 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. And at the very end, you know, the sort of the, the Russian Tsar changes. And he's like, ah, I'm actually kind of a Prussia stand. You know, I think I'm like, I'm into the stuff.

And then he lets, you know, let's Frederick the Great Lewis and let's let's let's let's their army be okay. And anyway, sort of like a, yeah, kind of bizarre, interesting figure in German history.