Are you dubious of the trope that you know labor provides meaning? And if people don't have a clear, uh, sense for labor, then it will be very difficult for them to obtain alternative sources of meaning?

Humans have just adapted to so much: agricultural revolution, industrial revolution, the growth of states. Once in a while, like a communist or fascist regime will come around, or something like the idea that being free and having millions of dollars is the thing that finally gets us. I'm just suspicious of [Music].

Doresh, Noah, welcome. Our first podcast ever to get as a trio. Yes. Excited. I'm very excited. So, Doresh, it's almost, you, you came up with the scaling era. It's almost like you're a future historian. You're sort of telling the history as it's as it's being as it's being written. And so, it's, it's only appropriate to ask you, what is your definition of of AGI? And how has that evolved over time? Um, some people, super intelligence, you know, breaks down for us. I feel like I'm like five decades too young to be a historian. You got to be like in your 80s or something before I even, uh, um, but we're living in history right now.

So, the ultimate definition is: can do almost any job, like 98% of jobs, at least as well, fast, uh, cheaply as a human. I think the more, um, the definition that's often useful for near-term debates is: can automate 95% of white-collar work because there's a clear path to get to that. Whereas robotics, you know, there's like a, a long tail of things you have to do in the physical world, and robotics is slower. So, automated white-collar work, h, so it's, that's interesting because it's an economic definition. It's not sort of a definition about like how it thinks, how it reasons, etc. It's about like what it can do.

Yeah. I mean, we've, we've been surprised what capabilities have come first in AI. Is like they can reason already. Um, and why they seem to lack the economic value we would have assumed would correspond to that level of capability. Um, this thing can reason, but it's making OpenAI $10 billion a year, and McDonald's and Kohl's make more than $10 billion a year, right? Um, so clearly, there's more things relevant to automating entire jobs than we previously assumed. So then it's just useful to, like, who knows what all those things are, but once they can automate it, then it's AGI.

And so when Elia or Meta is using the word super intelligence, what, what do they mean? Do they mean the same, same thing or something totally different? Um, I'm not sure what they mean. Like, there's a spectrum between God and just something that thinks like a human but much faster. Um, yeah.

Do you have a sense of what that, you think they mean? God. I think probably they, they mean something they would worship as a god. Yeah.

And so when Tyler says, "We've achieved AGI," and you differ from him, where's the tangible difference there? Um, I'm just noticing that if there was a human who was working for me, they could do things for me that these models cannot do, right? And I'm not talking about something super advanced. I'm just saying, I'm, I have transcripts for my podcast. I want you to rewrite them the way a human would. And then I'll give you feedback about what you messed up. And I want you to integrate that feedback as you get better over time. You learn my preferences. You learn my content. And they actually don't, they can't, like, learn over the course of six months how to become a better editor for me or how to become a better transcriber for me. And since they, a human I hire would be able to do this, they can't. So therefore, it's not AGI.

Now, I have a question. I am a natural general intelligence. You are a natural general intelligence. But we cannot easily do each other's jobs, even though our jobs are fairly similar. Right. Um, put me in the Doresh podcast, and I could not interview people nearly so well. If you had to write Substack, you know, articles like several times a week, yes, on economics, you might not do as well. So then, um, but we are general intelligences, and we're not exactly substitutable. So why should we use substitutability as the criterion for AGI? I mean, what, what else is it that we want them to do?

I think with humans, we have more of a sense of like, there's some other human who theoretically could do what you would do. A model is an individual copy of a model might be say, fine-tuned to do a particular job, and it would be fair to say then, why expect this particular fine-tune to be able to do any job in the economy? But then there's the question of, well, there's many different models in the world, and each model might have many different fine-tunes or many different, um, instances. Any one of them should be able to do a particular white-collar job for it to count as AGI. I'm not, it's not that like every, any AGI should be able to do every single job. That like some artificial intelligence should be able to do this job for, like this model to count as AGI.

I see. Okay. But so let's take another similar example. Let's take Star Trek. Okay? You got Spock. He's very logical. He can do stuff that the, that, you know, Kirk and whoever can't do. But then those guys can do stuff that Spock can't do, like get in touch with their emotions, intuition, stuff like that. They're both general intelligences, but they're alien to each other. So, you know, AI feels alien to me. It sometimes, it, it talks just like us. It was built off of our thoughts, obviously, but then, um, you know, sometimes it talks just like us, and sometimes it's just like very alien. And so, but, but should we ever expect that to change such that it's, it's no longer an alien intelligence?

I think it'll continue to be alien, but I think eventually we will gain capabilities which are necessary to unlock, um, the trillions of dollars of economic value that are implied by automating human labor, which these models are clearly not generating right now. Um, so you could say, well, like, if I just put, if we substituted jobs right now immediately, there'd be a huge productivity dip, but over time we would learn to do, you know, start doing them better. I mean, maybe a better example is just that like you hire people to do things for you. I, I don't know if you actually hire people, but I assume. Okay. Okay. Like, why, why do you still have to do that rather than hiring an AI? And I have like many rules where it's like an AI might be generating hundreds of dollars of value for me a month, but like humans are generating thousands of dollars or tens of thousands of dollars of value for me a month. Um, like, why is that the case? And I think it's just like the cap, AI are lacking these capabilities. Humans have these capabilities.

And is the main thing missing in your view, sort of continual learning? The reason humans are so valuable is not just their raw intellect. It's not mainly their raw intellect, although that's important. It's their ability to build up context. It's to interrogate their own failures and pick up small efficiencies and improvements as you practice a task. Um, whereas with an AI model, it's understanding of your problem, your business will be expunged by the end of a session. Um, and you're just, you're starting off at the baseline of the model. And with a human, you got to train them over many months to make them useful employees.

What will need to happen for that to be changed? Like, what, what needs to change in order for us to develop, for AI to develop a capability? I mean, um, I, I probably wouldn't be a podcaster if I had the answer to that question. Um, uh, I, it just seems to me that like a lot of the modalities we have today to teach LLMs stuff do not constitute this kind of continual learning. For example, making the system prompt better is not the kind of continual learning that, or on-the-job training that my human employees experience. Or RL fine-tuning is not this. But I like what the solution to this looks like. It's precisely because I don't have an obvious solution that I think we're many years away.

Okay, so here's my question about replacing jobs. Uh, you know, it seems to me that it's partly about demand. So, for example, suppose that AI has already replaced my job, or, or can replace my job. So that, suppose that anyone who goes on to, you know, fires up ChatGPT or whatever they want, or whatever model, and says, "Search the web, find the most interesting topics that people are talking about economics, and write me an insightful post telling me some cool new thing I should think about." That, and they just do that every day, and then they get a better blog than no opinion. I don't know if that's happened yet. I, I mean, I've tried that, and I don't like it as much. But, but suppose that most people will, would like it as much, and so my job has been automated, and people just don't realize it, or people have this sort of like idea in their mind of like, "Well, is it really a human?" and blah, blah, blah. And then, as generational turnover happens, young people won't care about reading a human, they'll care about reading an AI. But in terms of functional capabilities, it's already there. But in terms of of demand, it's not there. Uh, how much of that could there be?

I expect there will be much less of that than people assume. If you just look at the example of Waymo versus Uber, I think you could previously could have had this thing about people will hesitate to take automated rides. And in fact, in the cities where it's been deployed, people like love this product, despite the fact that you had to wait 20 minutes, because the demand is so high. Um, and it's still like, it got some glitches to iron out, but just like the seamlessness of using machines to do things for you. The fact that it can be like personalized to you, it can happen immediately. Uh, I, I mean, it was like, okay, like one thing people would be like, "Okay, well, doctors and lawyers will set up guilds, and so you won't be able to consult." Um, I think there might be guilds in who can call themselves a doctor or a lawyer, but I just think if like, if genuinely ch as good medical advice as a real doctor, the experience of just talking to a shop rather than spending three hours in a waiting room is so much better that I, I think a lot of sectors of the economy look like this, where we like, we're assuming people will care about having a human, but in fact, they will not, if, if you assume that they will genuinely have the capabilities that the human brings to bear.

So, it's interesting, you know, AI is, is better for diagnosis on a lot of things than, than humans, right? But then, um, something about having humans to follow up with makes me also want to check with a human after I've gotten diagnoses from an AI on something. And so, it might, that might vary by job. Like cars may be one thing, and then, um, but, but maybe it is about capabilities. I, I can't say. I'm just saying like, I'm, I'm, I'm saying everybody seems to think that the, that AI is a perfect substitute for humans, and that's what it should be, and that's what it will be. And everyone seems to think of it in that case. However, every other tool that's ever been made, every other technological tool was a compliment to humans. It could do some things humans could do. Maybe even it, it could do anything humans could do, but at different relative costs, different relative prices. So that you'd have humans do something, and the tool do other things, and you'd have this complementarity between the two. And yet, when people talk about AI and think about AI, they essentially never seem to think in these terms. They always seem to think in terms of perfect substitutability. And so I'm trying to get to the bottom of like, why people insist on always thinking in terms of perfect substitutability when every other tool has been, you know, complimentary in the end.

Well, human labor is also, um, complimentary to other human labor, right? There's increasing returns to scale. Um, but that doesn't mean that there's like, uh, you know, Microsoft has to hire some number of software engineers, and like, it will care about the cost of what the software engineers cost. Like, it will go to markets where they're, they can get the highest performance for the relative value the software engineers are bringing in. I think it'll be a similar story with AI labor and human labor. And the AI labor just has a benefit of having extremely low subsistence wages. Is like the marginal cost of keeping an H100 running is much lower than the cost of keeping a human alive for a year.

No. Would you say you're AGI-pilled in the sense that Doresh described the term? And how do you understand, you, we've talked a little bit about AI's effect on labor. When you share your, why you're perhaps a little bullish that there will be a, you know, planning for for humans to do, and that'll be more complimentary. What is AGI-p?

We just believe in Doresh that it will automate a huge swath of the economy. I mean, labor. I'm, I am very unwilling to say like, here's something technology will never be able to do. I mean, that always seems like a bad bet. Here's two things people have been saying since the beginning of the industrial revolution, neither of which has ever remotely come close to being true, even in specific subdomains. Um, the first one is: here's a thing technology will never be able to do. And the second one is: human labor will be made obsolete. Those people have been saying those two things, and you can just go, you can read it. You can even, you know, ask AI to go search and find. I have done this and and and find you examples of people saying those two things. People have been saying those two things over and over and over and over and over, and it's never been true. That doesn't mean it could never be true. Sometimes something happens that never happened before, such as the industrial revolution itself. You know, you have this hockey stick where suddenly like, like, oh, we'll never get rich. We'll never get rich. Oh, we're rich. And so, you know, sometimes that happens. The unprecedented can happen. However, I'm always wary because I've seen it said so many times. And so, you know, within just the last 10 years or or or whatever, I've seen a couple predictions just spectacularly fail. So, for example, in in 2015, 10 years ago, I was sitting in the Bloomberg office in New York, and my colleague, um, was, was who I, I won't name, but he was, he was physically yelling at me that truck drivers were in trouble. And, you know, that truck drivers are all going to be put out of a job by self-driving trucks. And he just, he said, this is going to just devastate a sector of the economy. It's going to devastate the working class. It's going to devastate blue-collar labor, blah, blah, blah. And I, at the same time, I was reading like, I always read the sci-fi, you know, top stories of the year, whatever. And so there were two stories in the same year about truckers being mass unemployed by, you know, self-driving trucks. And then 10 years later, there's a trucker shortage, and the number of truckers we hire is higher than ever. I'm not saying truckers will never be automated. They may. However, I'm saying that was a spectacularly wrong prediction. And you also got Jeffrey Hinton's prediction that radiologists would be unemployed within a certain time frame. And by that time, radiologist wages were higher than ever, and employment was higher than ever. I'm not saying this can't happen. I'm not smugly sitting here and saying there's a law of the universe that says you'll never see this kind of mass unemployment, blah, blah, blah. There, there, I mean, there were encyclopedia sales people were mass unemployed by the internet. We've seen it happen in like, in real life. But these predictions keep coming wrong, and keep coming wrong. And I'm trying to figure out why. Why is that true? Why do they keep coming wrong? Is it simply that people overestimate progress in technical capabilities, or are there complementarities that people can't imagine from sort of like the one-to-one division of tasks or the standard mental division of tasks?

I, I think the, the problem has been that people underestimate how many things are truly needed to automate human labor. And so they think like, we've got reasoning, and now that we've got reasoning, we've, like, this is what it takes to take over a job. And I think in fact, there's much more to a job than is assumed. Um, that's why I, you know, wrote this blog post where I'm like, look, it's not a couple years away. It might be longer than that.

Then there's another question of like, by 2100, will there be, um, jobs that humans are doing? Um, like, if you just like zoom out long enough, will we ever be able to make machines that can think and do physical labor, um, at least as cheaply and as well as humans can? And fundamentally, the big advantage they have is like, we can keep building more of them, right? So we make as many of those machines as the cost of producing them, or sorry, the value they generate equals the cost of producing them. Um, and the cost will continue to go down, right? Yeah. And it will be lower than the cost of like keeping a human alive. So if like, even if a human could do the exact same labor, a human needs like a lot of stuff to stay alive, let alone, you know, to grow a human, everything. Um, an H100 costs $40,000 today. The, the yearly cost of running it is like thousands of dollars. We can just buy more H100s, right? Like, an, if currently we had the algorithm for AGI, we could run it on H100. Um, and yeah, so however big the demand is, the latent demand, right? Uh, that that's unlocked by by the more supply, we just increase the supply basically to to meet that demand.

So first, um, why don't you sort of, when, when AGI is here, what is the, what does the world look like? Because, you know, Sam Altman was reflecting on his podcast with, uh, with Jack Altman the other week. He was saying, you know, if you told me 10 years ago that we would have, um, you know, PhD-level, um, you know, AI, I would think the world looks, looks a lot different, but in fact, it, it doesn't look that different. And so, is there, is there a potential where we, uh, have, you know, much, much more increased capabilities, but actually the world doesn't, it's like the, you know, Peter Thiel called the 1973 test or something. It's like we have these phones, but the world just looks, looks the same. We just have phones in our pockets.

Yeah, I, I think if we have like chatbots that can answer hard math questions, I don't expect the world to look that different because the fraction of economic value that is generated by math is like extremely small. Um, I, but there's like other jobs that are much more mundane than quote-unquote PhD intelligence, which these chatbots just cannot do, right? A chatbot cannot edit videos for me. Um, and once those are automated, I actually expect a pretty crazy world because, uh, the big bottleneck to growth has been that human population can only increase at this slow clip. And in fact, you know, one of the reasons that growth has slowed since the 70s is that in developing countries, the population has, uh, plateaued. With AI, the like the capital and the labor are functionally equivalent, right? You can just like build more data centers or build more robot factories, and they can do real work, or they can build more robot factories, and so you can have this explosive dynamic. And once we get like that loop closed, I think it would just be like 20% growth plus.

Do you see that feasible, possible 20% growth? Tyler, I believe said 5%? Right? 5% more than the steady state, 5% more. And, and, and what's the argument? Just that, um, yeah, what is the argument for that? For Tyler's argument? Bottlenecks. I think the problem with the argument is that like, there's always bottlenecks, right? So you could have said before the industrial revolution, well, we will never 10x the rate of growth because there will be bottlenecks. And that doesn't tell you about like, you empirically have to just like look at the fraction of the economy that will be bottlenecked and like what is the fraction that's not, and then like actually derive the rate of growth. Um, the, the, the fact that there's bottlenecks doesn't tell you like, yeah.

Okay. Is he mostly referring to, uh, regulation? Yeah. And just that like, we live in a fallen world, and people will have to use the AIs, and there, yeah, things like that. Who'll be buying all the stuff?

So, so background in economics, GDP is what people are willing to pay for, right? Who will be buying all the stuff in a world where we get 20% growth? First of all, I don't know. You could have said, um, in 10,000 BC, like the economy is going to be a billion times bigger in, um, in 10,000 years. Like, what does it mean to produce a billion times more stuff than we're producing right now? Who is buying all this like stuff? It's just like, you can't like predict that in advance. In 1700s, I could tell you exactly who was buying stuff, which was everybody. You know, peasants. Like I could tell you like, you know, when, in fact, people wrote these these things in around 1900 about what the world would look like in a hundred years, you know, what, what we'll have. They didn't get exactly the right things right that we'll have, but they correctly identified that it would be regular consumers who would be buying all these things, regular people. And so that would, that came true, it was obvious.

But here's my point. Here's my point. Suppose that 99% of people do not have a job and are not getting paid an income, and all the money is going to sort of Sam Altman, Elon Musk, and like five other guys, okay? And, and they're captive AIs that they own because for some reason our property right system still exists. But okay, suppose that that's that's the future we're contemplating, right? And so 99% of people or more don't have any job. They don't have any income. They're out on the street. And yet you're saying 20% growth a year. That growth is defined by people consumers paying for things and saying, "Here is..."

I wouldn't define it just as people. I would just define it as like the, the, I mean, assume agents. Yeah. No, that doesn't, that doesn't count GDP. Only final good, only final good. Okay. So we're like launching the Dyson spheres. We're not allowed to count that because like the AIs are doing it. I mean, like I want to, I want to know what the solar system will look like. I don't care like what, like the semantics of that are. And I think the better way to capture what is physically happening is just like, you will, why will they do any of that? One argument is simply that if there's any agent, AI or human, who cares about colonizing the galaxy, um, even if like 99% of agents don't care about that, if like one agent cares, they can go do it. Colonizing the galaxy is a lot of growth because the galaxy is really big, right? So it's very easy for me to imagine if like Sam Altman decides to launch the probes, how like, you know, breaking down Mars and sending out the the virus probes, like, is like generates 20% growth.

I think what you're getting at here is that AI will have to have property rights. AI, AI agents will have to use control of resources. Even if, even I guess depends on what you mean by autonomous. Today we already have computer programs that have autonomous use of resources, right? Okay. But the program goes off and colonizes the solar system. It, it's not like a, a dude telling it, "Colonize the solar system now," and doing all this stuff. It's like the AI has made the decision to do it, and Sam Altman sitting back there saying, "Oh, well, you know, I'm just saying..." This is not a crux. Like, Sam Altman could say it, or the AI could say it. Like, if some agent cares about this, and they're not stopped from doing it, like, this is just like physically, you can easily see where the 20% growth is coming from.

Let me make this a little more concrete. Suppose that AI is going to produce a bounty of the things that humans desire, and that's going to be what growth is. How will it get to the humans if the humans don't have a job? And if the humans don't have a job, why will AI be? So, in other words, if there's no consumers to buy my cars, why am I building cars?

You, you might be assuming there's some UBI or some sort of. No, no, I don't need to assume that. Although, I mean, let's assume there's not that. Yes. I, I don't need to assume that. It seems like you're saying, look, if like 99% of consumers are no longer consumers, where's this economic activity coming from? Yeah. And I'm just saying, okay, if like one person cares about colonizing the galaxy, that's generating a lot of demand. It takes a lot of stuff to colonize the galaxy. So like, the, this world where like every, even if there's like not in a Gallerian world where everybody's like roughly contributing equivalent amounts of demand, um, the potential for one person alone to generate this demand is so high enough that like, so Sam Altman tells his infinite army of robots who got colonized the galaxy, we count that as consumption. We put a value on it, and that's GDP. Yeah. Or like it might be investment. Maybe he's like going to defer his cons. I'm trying to do one sees like after colonize the galaxy. Yeah. And I'm not saying this is the world I want. I'm just saying like, just like think about it physically. If you're colonizing the galaxy, which you can do potentially after AGI, I mean, I'm not saying like it'll happen tomorrow after AGI, right? But like, this is a thing that's physically possible. Is that growth like, like something's happening that's like explosive?

Maybe the, the thing is that, um, it's a very weird world. It doesn't look like the kind of economy we've ever had, right? And it's not, you know, we created the notion of GDP to represent, you know, people exchanging money for goods and services, people like basically exchanging their labor for goods, exchanging the value of their labor for goods and services. That's at a fundamental level, that's what GDP is. We're envisioning a radical shift of what GDP means to a sort of internal pricing that a few overlords set for the things that their AI agents want to do. And that's incredibly different than what we've called GDP in the past. Um, I mean, I think the economy will be, yeah, incredibly different from what it was was in the past. I, I want to say that I'm not saying this is like the, the modal world. There's a couple of reasons why this might not end up happening. One is, even if your labor is not worth that much, um, the property you own is potentially worth a lot, right? Like if you own the S&P 500 and there's been explosive growth, you're like a multi, multi-millionaire. Or the land you have is like worth a lot if like the AI can make such good use of that land to build the space probes, um, assuming the our system of property rights continues into this regime. Um, and second, so I mean, in many cases, it's hard to ascribe how much economic growth there has been over very long periods of time. For example, um, over 500 years, like if you're comparing the basket of goods that we can produce as an economy today versus like 500 years ago, it's like not clear how you compare. What you would, there's like we have antibiotics today. I wouldn't want to go back 500 years for any amount of money because they don't have antibiotics, and I might die, and it'll just suck. So there's actually like no amount of money, um, in like 1,500 bucks to live in 1500 that would rather have than live today. And so if we have those quality of goods for normal people, just like, you know, you can live forever, you have like euphoria, drugs, whatever. These are things we can imagine now. Hopefully, it'll be even more compelling than that. Then it's easy to imagine like, okay, it makes sense why this stuff is worth way more than the stuff that the world economy can produce for even for normal people today, right? Yeah.

And so I, I guess I'm just thinking about, um, this, this is a thing that economists really struggled with in the early 20th century. It's this idea that, you know, we had this capacity to expand production, expand production, expand production. And then the thing is that companies competed their profits to zero, and the profits crashed, and nobody wanted to expand production anymore because they weren't making any profit. We're seeing this happen again in China right now with overproduction. We're seeing BYD having to take loans from its suppliers just to stay financially afloat, even though it's the best car company in the world, because the Chinese government has paid a million other car companies to compete with BYD. And so you compete, you know, you overproduce. So you have this overproduction. The question is, so, so the solution was to expand consumption. This is the solution people are recommending for China now, to expand so that you can ref that will and and the profits from this go go go negative, that makes the GDP contribution go to zero, and basically OpenAI and Anthropic and XAI and whatever will just be sitting there saying, "Why am I, why am I doing this again? Why am I, no one's buying this [ __ ]?" You know? And so like, at that point, well, it seems like there will be corporate pressure on the government to do something to redistribute purchasing power so that they don't compete their profits to negative, and so they have some reason to, uh, create more economic activity so they can take a slice of it, which is essentially what happened in the early 20th century.

Yeah, I disagree with this. I, I think I'm not saying this will happen. I'm saying like, that would be the analogist. Um, I, I think this is like, I disagree. I, I would prefer it to be the case that the, even as a libertarian, I would prefer for significant amounts of redistribution in this world because like the libertarian argument doesn't make sense if there's like no way you could physically pick yourself up by the bootstraps, like your labor is not worth anything. Um, or your labor is worth less than subsistence, subsistence, you know, calories or whatever, which is a more relevant thing. Um, uh, but like, I don't think this is analogous to the situation in China. I think like what's happening in China is more due to the fact that you have the system of financial repression, which redistributes money, and also, um, currency manipulation, which basically redistributes ordinary people's money to these, uh, to basically producing one EV maker in every single province. So it is the market distortion that the government is creating that causes this overproduction. Uh, we can go into what like the analogous thing in the AI case looks like. But I think if there isn't some market distortion, I just think like people will use AI where it has the highest rate of return. Like, if it's not space colonization, there will be like, you know, longevity drugs or whatever. I'm just saying like, why would I, why would I, why would I invest all this money into AI producing stuff? Uh, why would I just invest the, the massive hundreds and billions of trillions and whatever of dollars into producing stuff for people who are all going to be out of a job and won't be able to buy this?

But again, I don't think you'll be producing it for them. I think you'd be producing it for whoever does have like some, there's stuff in the world, so somebody will have stuff. Maybe it's the AIs, maybe it's Sam Altman. Um, you're producing it for whoever has the capability to buy your stuff. Um, and will they want AI? And I'm just saying like, AI can do so many things, least of which is like colonizing the galaxy. People will, people willing to pay a lot of stuff, galaxy, right? I'm just trying to get this straight in my head of what this economy looks like. And I'm, I, I'm seeing a picture of the trillions of dollars needed to build out all these data centers will be done not for profit, not to make money for, you know, from a consumer economy, for the creators of the AI, but to satisfy the whims of a few robot lords to colonize the galaxy.

I think you're making two different points, and they're getting, um, they're getting wrapped into one. Um, I'm saying, yes, important word. Um, I, so there's one about like, do you expect it to be the case that, uh, the robot overlord world happens? And I'm saying, no, actually, like, even without redistri, first of all, I expect redistribution to happen. I hope it happens. But even if it doesn't, um, and I don't think it'll happen because people like corporations want the redistribution to happen. I think it's just like, it'll be good to happen for independent reasons, but I don't buy this argument that like the corporations will be like, "We need somebody to buy our AI, therefore we need to give the money to the ordinary consumers."

Do you believe broad-based asset ownership will create a whole lot of broad-based consumer demand, even in the absence of labor income? I'm honestly, I don't have like a super strong opinion, but I think that's like plausible. Um, uh, but independent of that, I'm like, okay, even if that demand doesn't exist, just like the things you can do, um, with a new frontier of technology, as long as one person wants it, there's like so much room to do things. You know, like space colonization is an obvious example. A lot of money, right? That like, there's like obvious demand for the things that AI will be able to produce, right? Like one of the things I can produce is colonize a galaxy, right? Exactly. So, but the question is like, who, you know, I can see a paperclip-maximizing, you know, autonomous intelligence is colonizing the galaxy. But in terms of, that's a lot of growth. That is. But in terms of, of U, and so by the way, I would like to say that I am a paperclip maximizer. I am the real paperclip maximizer. I want to maximize rabbits in the in the galaxy. I want to turn the entire galaxy into fluffy rabbits. That's my goal. And so my goal with AGI is to enlist the AGI to to help me in this fall, but then to align them towards rabbits. But anyway, get these down in front of the opening a board of directors. I know, I like objective, the social welfare function is sloofiness. Um, but I guess my, my point here is, um, as long as it's, as long as AI still doesn't have property rights, and it's, it's humans making all the economic decisions, be it Sam Altman and Elon Musk, or some, you know, you and me, um, then at that point, like, that really matters for what gets done. Because then if we're talking about the money needed to build all these massive data centers, which currently, it's a lot of money. It's a ton of money required to build these data centers, and that money need will not go away. We can't just like say, "Oh, cost goes to zero," because we can say unit cost goes to zero, but total cost doesn't go to zero, nor has it. It has, it has increased. Uh, the total spend on data centers has increased, and I think everyone expects it to increase for the foreseeable future. The question is, is that money being spent because AI companies expect to reap benefits from consumers like you and me? Or, or to what extent is it that? And to what extent is it Sam Altman feels like doing some crazy stuff, and Sam Altman's just godlike, richer than everybody else. And so Sam Altman is actually consuming when he builds those data centers. He is, he is building those data centers so that he can indulge his godlike whims. M. And so that, that's a, I mean, I think that more more plausible than either a single godlike person is able to direct the whole economy, or, um, like there's this broad-based consumer, every, every person example. These are extremes. I think more plausible is like AIs will be integrated through all the firms in the economy. Um, a firm can have property. Um, firms will be like largely run by AIs, even though there's nominally a human board of directors, and it might not even be nominal, right? Like maybe the AIs are aligned and genuinely give the board of directors an accurate summary of what's happening. But like, day-to-day, they're being run by AIs, and firms can have property rights, firms can demand things. So say all you have is a board of directors and AI. Yeah. Okay. I mean, in the ideal world. Okay. So then, then what we're basically looking at is capital, is the, the labor share of income goes to zero, or something approaching that, depends how you define the labor and and capital share of income, and capital income is not, is, is distributed highly unevenly. More distributed, much more unevenly than labor income, but it's still distributed reasonably broadly. Like I have capital income, you have capital income. So like, um, uh, so at that point, we have just an extremely unequal society where owners get everything, and then, you know, workers get nothing. And then so we have to figure out what to do about that.

Yeah, 100%. The hopeful situation here is the way our society currently treats retirees and old people who are not generating any economic value anymore. And if you just look at like the percent of your paycheck that's going basically being transferred to old people, it's like, I don't know, 25% or something. Um, and, uh, you're willing to do this because they have a lot of political power. They've used that political power in order to, uh, lock in these advantages. They're not like so overwhelming you're like, "I'm going to go to like Costa Rica instead." You're like, "Okay, I had to pay this money. I had to pay this concession. I'll do it." And hopefully humans can occupy that sort of like, um, can, can have be in a similar position to this massive AI economy that old people today have in, um, in today's economy.

All right. What do humans do? Uh, so let's say they get some, some money. They, they have enough to live. Uh, how do they spend their time? Is it art, religion, poetry, drugs? Broadcasting. It's the final job. Yeah. We're, we're ahead of ahead of the curve here. Or we're the last man of history.

Wait, so here's an idea. How about Sovereign Wealth Fund? Okay, sovereign wealth fund. We, uh, we tax Sam Altman and Elon Musk. We're using Sam as a metaphor here. He's a friend of the firm, you know. Yeah. Yeah. Yeah. We tax him. We tax Mark. And and so then we, we use their money. Only the friends of the show will be taxed. We, we use that money to buy, we use that money to buy like, um, shares in the things that those people have. So they get their money back because we're buying the shares back from them. Okay. Okay. So, it's okay. And then, and then we hire them. Yeah. Because then what we do is we hire a number of firms, including A6Z, and pay them 2 and 20, or whatever, to manage the investment of AI stuff on behalf of the, of the humans. But then the humans become broad-based sort of index fund shareholders, or or shareholders in whatever you guys choose to invest. And then you take a cut. And this could be the, the future economy. This is what my PhD advisor Miles Kimball has suggested. This is what the socialist Matt Bruenig has suggested. And this is what, uh, Alaska actually does with oil. Capitalists like it, socialists like it, Alaska likes it.

I, I think sovereign oil funds generally have a bad track record. There's some exceptions that have like managed to use their wealth, like Norway or Alaska, but there just like these political economy problems that come up when, uh, there's this tight connection between the investment, which should theoretically be just highest rate of return, and, um, uh, politicians. So, I, I don't have like, have a strong alternative. Ideally, you just let the market decide how the investment should happen, and then you can just take, take a tax. Um, but then exactly where does that tax happen? I, I haven't thought it through. But I, I wouldn't want the government influencing where that investment happens. But I want the government taking a significant share of the returns of that investment.

Yeah. Are you dubious of the trope that you know labor provides meaning? And if people don't have a clear, uh, sense for labor, then it will be very difficult for them to obtain alternative sources of meaning? Or is that kind of a, you know, a capitalist, uh, sort of stroke that that isn't necessarily true perspective?

I, I my suspicion is that humans have just adapted to so much like agricultural revolution, industrial revolution, the growth of states. The, um, you know, like once in a while, like a communist or fascist regime will come around, or something like the idea that, uh, being free and having millions of dollars is the thing that finally gets us. Yeah. Um, I'm just suspicious of, um, by the way, do we not disagree about the thing I'm saying? Like, once we get AGI, um, humans will not have high-paying jobs. Do we disagree about this?

Uh, I think humans may have high-paying jobs. Okay. Because of, uh, comparative advantage. You know, if there's some, the key here is if there's some AI-specific resource constraint that doesn't apply to humans, then, then comparative advantage law takes over, and then humans get high-paying jobs, even though AI would be better at any specific thing than a human, because there's some sort of aggregate constraint. The example I always use, of course, is Mark Andreessen, who is the fastest typist I have ever seen in my life, and yet does not do his own typing. And so, you know, because there's a, there's a Mark Andreessen-specific aggregate constraint on Mark Andreessen, there is only one of him. Um, and so then, um, so he hasn't taken all the the secretary's typist jobs, but, um, uh, because he has better things to do. And so if, if there's some sort of AI-specific resource constraint that hits, then humans could have. Now, I'm not saying there will be. Yeah. And I'm not saying there won't be. I'm saying I don't know if there is.

Yeah. The reason I find that implausible is that I, I think that is that will be true in the short term because, um, right now, there's 10 million H100 equivalents in the world. You know, in a couple years, there might be 100 million. The human, and, but like H100 has the same amount of flops as a human brain. So theoretically, they're like as good as a brain if you had the right algorithm. Um, uh, so there, there's like a lower population of AIs, even if you had AGI right now, than humans. But the key difference is that in the long run, you can just keep increasing the supply of compute or of robots. And so if it is the case, so if an H100 costs, um, a couple thousand dollars a year to run, um, and, you know, but this, the value of like an extra year of intellectual work is still like $100,000. So you're like, look, we, we've saturated all the H100s, and we still had to pay a human. We're going to pay a human $100,000 because like, there's still so much intellectual work to do in that world. The return on buying another H100, like an H100 costs $40,000, just like in a year that H100 will pay you over 200% return, right? So you'll just keep expanding that supply of compute until the until basically, you know, H100 plus depreciation plus running cost is the same as an extra year of labor. Um, and in that world, that's like much lower than human subsistence. So comparative advantage is totally consistent, okay, with like, uh, with human wages just being below subsistence. It, it is, but that comes from the common resource consumption. So, you know, if, um

If, basically, all of the human, all of the land, and energy that could be used to feed, and clothe, and shelter humans gets appropriated by H100s, then that is the case. However, if you pass a law that says this land is reserved for growing human food, and if you pass a law that says this land, you know, that we have a minimum, if, if we actually were to just pass a simple law saying that you have to use these resources, these resources reserved for humans.

But, but at that point, the comparative, comparative advantage at that point, like human labor has nothing to do with this. The only reason the system works is that you are basically transferring res. You've come up with a sort of like intricate way to transfer resources to humans. You're just like, "This resource is for you. You have this land," and therefore you can survive. And this is just like an inefficient way to allocate resources to humans. It's true that it is an inefficient way.

Um, so, but that has nothing to do with, like, I think people, you know, people will like hear this argumentative advantage and be like, "Oh, there's some intrinsic reason. Take UBI instead." Yeah. Okay. Um, um, yeah. Yeah. I mean, sure, it, it, but then again, we typically do not see the first-best, most efficient political solution implemented for things like redistribution. In real-world redistribution happens via things like the minimum wage, or, you know, like letting the AMA decide how many doctors there's going to be. So, so redistribution in the real world is not always the most efficient thing. So I'm just saying that like comparative advantage, uh, if you're talking about, will humans actually continue to get high-paid work, yes or no? It depends on political decisions that may depend on physical constraints that will happen. But the high-paid jobs are literally because, like you have said, that like there must be high-paid jobs, politically. Like, I understand you in this case, you've said it in an indirect way, but you still said it, right? That's right. You're absolutely right. Yeah. You're not wrong. Yeah. Or I guess it's like incredibly different from, um, what somebody might assume. Like, it just, it has almost nothing to do with the comparative advantage argument.

Okay, sure. But that's true of a lot of jobs that exist now. Like a lot of jobs that exist now, you know, like I'm not sure what, like university professors, like there's a lot of those jobs, or like, um, credit rating agencies, or, you know, there's a lot of things where, you know, probably we could wring out some significant TFP growth more or less by eliminating those things. But we don't, because our, our politics is a clueocracy. I think this is one of Tyler's points to make. Yeah.

I, I mean, I do think it's important to, um, uh, like point out in advance, like, basically, it would be better if we just bit the bullet about AGI, so that instead of doing redistribution by expanding Medicaid, and then, you know, Medicaid can't procure all the amazing services that AI will create. Um, it'd be better if we just like said, "Look, this is coming." And I'm not saying we should do a UBI today, but like the long, if all human wages go to zero or go below subsistence, then the only way to deal with that is through some kind of UBI, rather than, you know, if you happen to sue OpenAI, you get a trillion-dollar settlement. Otherwise, you're kind of screwed, right?

Some people said the bare case for UBI was something around like COVID, as an example. You gave people a bunch of money, and what do they go do? Go ride to the streets. I'm, I'm teasing, but, but like, are people going to use that money in, in an effective way? I mean, that was literally what happened. Yeah. So, um, yeah. Is UBI the form that you would think that, like, what is the most effective method? I, the reason I favor UBI is like this thing where, in a future world with explosive growth, we're going to see so many new kinds of goods and services that will be possible that are not available today. And so distributing just like a basket of goods is just inferior to saying, "Oh, if like we solve aging, here's like, here's some fraction of GDP. Go, like, go spend your tens of millions on, partly on buying this aging cure, whatever this new thing that AI enables, rather than, here's like, here's a food stamps equivalent of the AGI world that you can have access to."

Of course, I mean, this discussion may be academic, because I believe that, you know, you said that we got phones in the world, look the same. I mean, no, it doesn't. Phones have destroyed the human race. Like the fertility crash that's happening all around the world, right? Nobody has replacement-level fertility. Fertility is going far below replacement everywhere, uh, because of technology. And, uh, is that the phone or the pill or it? Well, no, it's a phone. I mean, well, no, the, the, the pill and other things like women's education, whatever, like lowered fertility like quite a bit, but some countries were still at replacement level, some were still around replacement level. The crash we've seen since everybody got phones is epic and is just unbounded. Like, uh, you know, the human race does not have a desire, a collective desire to perpetuate itself. Um, we can, you know, yes, we're going to get lonely, but we'll have company through AI and through the internet, social media, you know, until there's just a few of us and we dwindle and dwindle. Um, yeah, I mean, like technology has already destroyed the human race, and basically UBI is just like keeping us around on life support for a little while while we, while that plays out.

I, I have a take about like, I do think so far there's been a lot of negative effects from, you know, widespread TikTok use or whatever that, like, we're still, you know, like learning about. Um, I am somewhat optimistic that in the long run, there's some optimistic vision here that could work. Um, just because right now the ratio of, um, like it's impossible for Steven Spielberg to make every single TikTok, uh, and direct it in a sort of really compelling way that's like genuine content and not just video games at the bottom and some like, you know, music video at the top. Um, in the future, it might genuinely be possible to give every single person their own dedicated Steven Spielberg and create like incredibly compelling but long narrative arcs that include other people they know, etc. Oh, yeah. So, in the long run, I'm like, maybe this happens. I don't think TikTok is like the best possible medium. No. But I don't think, I also don't think TikTok is unique in destroying the human race. I think that, um, interacting online instead of interacting in person, that's that's how you make your money. That's that's the great, how do you make your money? Go ahead. I agree. We're all making, we're all making, destroying our species. But that's, but, but you don't think we're isolated to dating apps and sort of? No, I'm saying like, I'm saying like as long as you can get your, you know, why did humans perpetuate the human species? It was not because they wanted to see the human species perpetuated. It was because it's like, "Oop, I had sex and there came a baby." And that's that's done. We've severed that, and that's that's that is the end. We did not evolve to want our species to continue. Right.

But you're saying the reasons why we're not having babies is because we're, we can make friends on the internet. But is it that dating apps have created just a much more efficient market and thus there isn't fair but bun? I don't know. I mean, like, you know, people are having less sex. Uh, you know, if, if Elon gets his way, everybody will just sit there gooning to some sort of, um, Grok companion, the goon apocalypse. Uh, seem seems upon us. No, but, but like, is this available right now? What's the website? Oh, no. Um, um, anyway, this podcast got silly. But anyway, um, I guess the, the point is that, you know, the idea of a humanity that just keeps increasing in numbers and spreading out to the galaxy. I don't see a lot of evidence that that is in our future, and that we have to go to great lengths to make sure that that future is compatible with AGI, because I don't think it's happening in any case, AGI or none.

By the way, not to cope, uh, too hard, but in a world, in a world where AGI happens, um, how important is population? Like, how important is sort of increasing population? I mean, population has so far been the decisive, uh, factor in terms of which countries are powerful. And, like, the reason China, if the US was not involved, the reason China could take over Taiwan, uh, is just that there's 1.4 billion Chinese people, and there's 20 million Taiwanese people. Um, now, if in the future, your population is, uh, your effective labor supply is like largely AIS, then you just, like, this dynamic just means that like your inference capacity is literally your geopolitical power. Right. Correct.

I want to shift to, uh, short-term a bit. You've, you've had some people on the podcast. You have the AI 2027 folks who believe that AI is perhaps two, two years away. I think they updated to three years away. And then you've also had some folks on who who said it's not for 30-something years. Maybe you could steelman both, both arguments, and then share how where you net, net out.

Yeah. So, two years, if I'm steelmanning them, is that look, if you just look at the progress over the last few years, it's reasoning. This is like Aristotle, is like the thing that makes humans humans is reasoning. And we just, like, it was not that hard, right? Like, train on math and code problems, um, and have it like think for a second, and you get reasoning. Like, that's crazy. So what is the secret thing that we won't get? Right.

Um, can I ask a stupid question? Why was, uh, stuff like O3 type models, why are those called reasoning models, but like GPT-4o is not called reasoning? What, what are they doing different that's reasoning?

I, one, I think it's, like, GPT-3 can technically do a lot of things GPT-4 can, but it just does it way more. GPT-4 just does it way more reliably. And I think this is even more true of reasoning models relative to GPT-4o, where like 4o can solve math problems, and in fact, like modern-day 4o has been probably trained a lot on math and code, but the original GPT-4 just wasn't trained that much on math and code problems. So like it didn't have whatever meta, uh, meta circuits there exist for like, how do you backtrack? How do you be like, "Wait, but I'm on the wrong track. I got to go back. I got to like, I got to pursue the solution this way." Algorithmically, I have a, you know, okay idea of what a reasoning model does that the non-reasoning models don't. But what, in terms of how does that map to a thing that we call reasoning? What is the, what is the definition of what it means to reason that these people are using the operational definition here? Like, cuz I don't understand that myself.

I mean, 4o can't get a golden IMO. Okay. But, but I, I can reason, and I can't get a golden IMO. But I can reason. Yeah. Like, I can't get a gold either, but I don't think I can reason as well as a, uh, math Olympiad, at least in the relevant domain. I agree that reasoning is not just about mathematics, but, um, this is true of any word you come up with, like the zebra, like what is, you know, what about the thing that like is a mixture of a zebra and a giraffe and they have a baby? Is that a zebra still? Like, I agree there's edge cases to everything, but like, there's a general conceptual category of zebra, um, and I think there's like a general conceptual category of reasoning. Okay. I'm just wondering what it is. Like, what, what, like, when you have a, um,

No, I'm saying like, when you have a checkout clerk, right? That checkout clerk wouldn't, would look at an IMO problem and be like, "What?" But then like, you have a checkout clerk, and the checkout clerk, you're like, you know, okay, so you put the thing in the, you know, on this shelf, and therefore someone has looked for it and didn't find it. So something else must have happened. But I think a reason, I think a reasoning model will be more reliable and be better at solving that kind of problem than,

Okay. Okay. So you, you're steelmanning the AI 2027. Yes. So basically like, look, a lot of things we previously thought were hard have just been incredibly easy. So whatever additional bottlenecks you are anticipating, whether it's this continual learning, uh, on-the-job training thing, um, whether it's computer use, uh, this is just going to be the kind of thing where in advance, it's like, how would we solve this? And then deep learning just works so well that we, like, I don't know, try to like train it to do that, and then it'll work. Um,

The long timelines people will say, like, I don't know, there's a sort of longer argument. I don't know how much to bore you with this, but basically, the things we think of as very difficult and requiring intelligence have been some of the things that machines have gotten first. So just adding numbers together, we got in the 40s and 50s. Um, reasoning might be another one of those things where we think of it as the apogee of like human abilities, but in fact, it's only been recently optimized by evolution over the last few million years, whereas things like just moving about in the world and having common sense and so forth and having this long-term memory has, you know, evolution spent hundreds of millions, if not billions of years optimizing those kinds of things. Those might be much harder to build into these AI models.

I mean, the, the reasoning models still go off in these crazy hallucinations that they'll never like admit were were wrong, and we'll just like gaslight you infinitely on some crap it made up. Like they'll still like just knowing truth from falsehood. Yeah. You know, I, I've met a couple humans who don't seem to be able to know truth from falsehood. They're weird, you know. So, but O3 sometimes does this. I mean, I think it's a question. Do they hallucinate more than the average person? I think like, no less. They can hallucinate meaning like getting something wrong, and when they push them on it, they're like, "No, you know, whatever." And eventually they'll like, if there's, they're clearly wrong. I think like, I think they're actually more reliable than the average human. But so the thing about the average human is you can get the average human to not do that, right, with the right consequences. And maybe AI, we haven't found the right like reinforcement learning function or whatever to get them to not do,

Okay, now let's get to the view that it's, it's 30 years away, or the basically, what, what's that view? Oh, just this thing of like, um, reasoning is relatively easy in comparison to, forget about robotics, which is just like going to be, you know, evolution spent billions of years trying to get like, uh, robotics to work. Um, and but there's like other things involved with like tracking long-run state of, you know, a lion can, you know, follow a prey for a month or something, but these models can't do a job for a month. And these kinds of things are actually much more complicated than even reasoning. Um, yeah.

And, and where you've netted out is it's either going to happen in a few years or not for quite some time. Yeah. Uh, when you explain this to me, basically the progress in AI that we've seen over the last decade has been largely driven by stupendous increases in compute. So the compute used on training a frontier system has grown 4x a year for, I think, like the last decade. And that just like, you know, over four years, that's 160x, right? So that's like, you know, over the course of a decade, that's like hundreds of thousands of times more compute. Um, that, uh, that physically cannot continue. If you just like, okay, what would it mean? Right now, we're spending 1.2% of GDP or something on data centers. Um, not all of that is returning, of course, but what would it mean to continue this for another decade? Um, for maybe five more years, you could have, you could keep increasing the share of energy that we're spending on, uh, uh, training data centers, or the fraction of TSMC's leading-edge nodes, uh, wafers that we dedicate to making AI chips, or the even the fraction of GDP that we can dedicate to AI training. Um, but at some point, like you, you can't keep this like 4x trend going a year. And after that point, then it has to just like come from new ideas, like here's a new way we could train a model. And, and by the way, when I was writing that comparative advantage post, and I was thinking about AI-specific con, aggregate constraints, resource constraints, this is that's what I was thinking of, actually, that that expansion of compute has to slow down. But I don't know how much that matters.

Well, that's for training, and then, like, yeah, for the labor that will be like the inference will also use the same, has the same bucket of compute. Um, it is the case that for the amount of compute it costs to train a system, uh, if you, like, set up a cluster to train a system, um, you can usually run a hundred thousand copies of that model at typical token speeds on that same cluster. Um, uh, that's still obviously not like billions, but like, if we've got all this computing, these huge systems in the future, it would still allow us to sustain a population hundreds of millions, if not billions of AIs. Um, and, you know, at that point, maybe we obviously we'll still need, want more AIS, but what, what does a single AI mean in this instance? Oh, like a copy of a, like, you know, when you're talking to Claude, it's like a single instance. Okay. That's talking to you. Yeah. So, instances. Yeah. Yeah. Okay. So, what's going to determine whether it's in a, in a few years or? No. Oh, like basically, um, right now we're, we're basically, uh, riding the wave of this extra compute. That's why AI is getting better every year, mostly. Um, in terms of the contribution of new algorithms, it's a smaller fraction of the progress that's explained by that. So if this, we've just got this like rocket that's like, um, like how high will it take us, and does it get us to space or not? And if it doesn't, then like, you know, um, then we just have to rely on the algorithmic progress, which has been a slower. Yeah. I think it's, Yeah, but you think it might get us to space. Yeah. The, the, I think there's like a chance that like, oh, continual learning is also like, you know, I had this whole theory about like, oh, it's so hard, and how do you slot it in, and they're like, I [ __ ] trained it to do this, like, what are we talking about here? Yeah.

That that leads into another thing that I've thought about, which is how poor our track record for making predictions about the future of AI has has been. The first time you and I hung out, uh, I don't know if you remember this, was with Leopold. Yeah. Oh, really? Yeah. It was at your old house, and, and Leopold was just pronouncing a whole bunch of pronouncements, uh, from the couch, and, uh, he released this big situational awareness thing. And, I would say that that wasn't, how long ago was that? A year and a half. Yeah. Yeah. I would say that already most of the things he predicted have been invalidated or made irrelevant in the in the last year and a half, like especially in terms, like all the comp stuff about competition with China, you know, like it turns out filtration was able to get them a whole lot of things that he never predicted. It turns out that like so many of the things other than just the idea that AI would keep getting better, which he predicts, and a lot of people predict, but then I feel like a lot of the specific predictions about US capabilities and Chinese capabilities and what would be the bottlenecks and what would be the things that, you know, we had like, here's how we can compete with China has all been proven wrong since.

Um, I think this is actually an interesting trend in the history of science where like some of the scientists which were the who are the smartest in thinking about the progression of the atom bomb or progression of physics just had these like ideas about like we'll have a, the only way we can sustain this is we have a one-world government after, you know, I'm talking about after World War II, uh, there's no other way we can deal with this new technology. I do think relative to the technological predictions, Leo, you know, like I think the main way in which he's been wrong is that like, it didn't take some like breaking the breaking breaking the servers in order to learn how O3 or something works. It was just the physical, sorry, the, um, just public, just see you being able to use the model and learn what it knows, like just knowing a reasoning model works, and you can like use it, and you see like, oh, what is the latency, like how fast is this outputting tokens, that will teach you like how big is the model, like you learn a lot just from publicly using a model and like knowing a thing is possible. Um, he has been right in one big way, which is like, he he identified three key things that would be required to get us from GPT-4 to like a BBAI kind of thing, which was being able to think. So test-time compute, onboarding, which did you talk about test-time compute? Yeah. Yeah. It was like one of his, one of his three big unhobblings. Um, then like onboarding in terms of the workplace, and then I think the final one was, uh, computer use. And like, look, one out of three, like, and it was a big deal. Um, so yeah, I think I think you got some things right, some things wrong, but yeah.

And, and what's your take on the model of automating AI research as the path to AGI? The Meter Uplift paper, contrary to expectations, they found that whenever senior developers working in repositories that they understood well, uh, used AI, they were actually slowed down by 20%. Um, yeah, I did see that, yeah. Uh, whereas they themselves thought that they were sped up 20%. And right, um, and so there's a bunch of things I'm getting things done. This goes back to your theory about the phones are destroying us. Yeah. Um, that is an update towards the idea that AI is not on this trend to be this super useful assistant that's helping us already make the short process of training AI are much faster, and this will just like be this, um, feedback loop and exponential. Um, I have other independent reasons. I'm like, I don't know, I'm like 20% that like we'll have some sort of intelligence explosion.

One of the other labeled predictions was was nationalization. Um, is that something you could potentially foresee in the next few years? I don't think it's, uh, politically plausible. Um, especially given this administration, I don't think it's desirable. Um, first, I think it would like just drastically slow down AI progress because look, this is not 1945 America, and also building an atom bomb is like a way easier project than building AGI. But China's quasi-nationalized most of its, I mean, it, and China doesn't control BYD's day-to-day decisions about what to build. But then if China says, you know, do this, BYD does it, as does every Chinese company. I mean, that's kind of the relationship American companies have with the US government as well. You think so? I mean, somewhat. I also, the, the big difference is, what do we mean by nationalization? There's one thing which is like, there's a party cadre who is, uh, in your company. Exactly. There's another which is that, um, each province is like just pouring a bunch of money into building their own competitor to BYD, in this, you know, potentially wasteful way that like distributed competitive processes seems like the opposite of nationalization to me. Like when people imagine AGI nationalization, I don't think they're saying like Montana will have their AGI, and Wyoming will have their AGI, and they all compete against each other. I think they imagine that like all the labs will merge, which is actually the opposite of how China does industrial policy.

But then you do think that the American government basically, if it, if it says do this, then like XAI and and OpenAI will do it. Um, no, actually, I think in that way, obviously the Chinese system and the US is more different. Um, although it has been interesting to see that whenever, um, I don't know, we've, we've noticed the way that different lab leaders have changed their tweets in the in the aftermath of the election. I mean, also, yeah, more bullish open source, and didn't, uh, didn't Sam have a thing where, uh, I, I think previously he said that, uh, AI will take jobs, how do we deal with this? And then didn't he recently say something at a panel where like, I think President Trump is correct that AI will like, you know, create jobs or something where like, I don't think in the long run, you believe this. Um, but the reason why humans should be excited about even their jobs being taken is just they'll be so rich that why do they even need it? Yeah. Much richer than they are now. Right, modulo this redistribution slash not [ __ ] it over with some, you know, guild-like thing. Yeah.

The, you mentioned the atomic bomb, and we also mentioned off off camera that you don't think the nuke is a good comparison for what happens. How does it play out when a lab figures out AGI? What, what then then happens? Is there a huge advantage if one country has it first, or if one lab has it first? Do they, they dominate? What does it play? I, I think it's less like the nuclear bomb, where there's a self-contained technology that is so obviously, um, relevant to specifically this like offensive capability, and you can say like, there's nuclear power as well, but neither of those three, like nuclear power is just like this very self-contained thing, whereas I think intelligence is much more like the industrial revolution, where there's not like this one machine that is the industrial revolution. It is just this like broader process of growth and automation and, um, uh, and so forth. So I, but that, so Brad Dong's right, and Robert Gordon is wrong. If Robert Gordon said there's only like, it's four things. It's just four big things, really. And Brad Delong is like, "No, it's a process of discovering it." So anyway, interesting. And what were Rob's four things again? Oh, uh, um, I mean, electricity, test compute, uh, the internal combustion engine, um, uh, like steam power, and then like, um, what was the fourth one? Like maybe like, like plumbing, right? I think was the fourth one. Yeah. Or, or even in that case, maybe that actually is maybe that's closer to how I think about it, that then you needed so many complementary innovations in order. So internal combustion engines, I think invented in the 1870s. Drake finds the oil well in Pennsylvania in the 1850s. Obviously, it takes like a bunch of complementary innovations before like these two things can merge, before they're just like using the oil for the kerosene to light lamps, um, but regardless, so if it's this kind of process, it was the case that many countries achieved industrialization before other countries. And, you know, like China was dismembered and went through a terrible century because it, the Qing dynasty wasn't up to date on the industrialization stuff. And much smaller countries were able to dominate it. But that is not like, we developed the atom bomb first, and now we can just like, we have decisive advantage. Um, because it was us. If that had been Nazi Germany or the Soviet Union, it would have gone differently. Yeah.

How do you see the US-China competition playing out in terms of of AI? I genuinely don't know. Yeah, I don't know. I think it's comp, I think it's like possible that there could be some positive, some, uh, uh, like both. It's not like a nuclear weapon where like both countries can just, you know, adopt AI and, and there is this dynamic where if you have higher inference capacity, not only can you deploy AIS faster, and you have more economic value that's generated, but, um, you have, you can have a single copy, sorry, a single model, learn from the experience of all of its copies, and you can have this basically broadly deployed intelligence explosion. So, um, I think it really matters to get to that discontinuity first. Um, I don't have a sense of at what point, if ever, is it treated like the main geopolitical issue that countries are prioritizing. Um, I also, from the misalignment stuff, the main thing I worry about is the AI playing us off each other, rather than us playing the AIs off off each other. You mean AI? AI just like telling us all to hate each other, the way like Russian trolls currently tell us all to hate each other. More so like the way that the East India Company was able to play different provinces in India off of each other, and to ultimately, to the, once at some point you realize, okay, like they control India. Um, and so you could have a scenario like, okay, think about the Conquistadors, right? A couple hundred people show up to your border, and they take over an empire of 10 million people. And this happened not like once, it happened like two to three times. And like, okay, so why was this possible? Well, it's that the Aztecs, the Incas, um, weren't communicating with each other. Uh, like they didn't even know the other empire existed. Whereas Cortez learns from the subjugation of Cuba, and then he takes over the Aztecs. Uh, Pizarro learns from the subjugation of the Aztecs and takes over the Incas. Um, and so they're able to like just learn about like, okay, you take the emperor hostage, and then this is the strategy you employ, etc. It's interesting. The Aztecs and Incas never met each other, and that worked both times, sort of. Yeah. Like that's interesting that the these totally disconnected civilizations both had similar vulnerabilities. Yeah. I mean, it was like literally the exact same playbook. Um, the, the crucial thing that went wrong is that at this point in the 1500s, they, um, we don't, we actually don't have modern guns. We have arquebuses. But the main advantage that the Spanish had was they had horses. And then secondly, they had armor, and it was just incredibly, you'd have thousands of, uh, warriors. If you're fighting on an open plane, the horses with armor will just like trounce all of them. Um, eventually the Incas had this rebellion, and they learned they can like, you know, roll the rocks down hills, and the, the rebellion was moderately successful, even though it's eventually, well, we know who controlled, you know, we know what happened. Um, one could say that the Spanish on their side had guns, germs, and steel. Um, but so how could this have turned out differently if like, if the Aztecs had learned this and then had like told the Incas, I mean, they weren't in contact, but like, if there's some way for them to communicate, like here's how you take down a horse, you know? Um, I think the, what I would like to see happen between the US and China, basically, is like the equivalent of some red telephone during the Cold War, where, um, you can communicate, look, we notice this, especially when AI becomes more integrated with like the economy and government, etc., like we noticed this crazy attempt to, uh, do some sabotage, like be aware that this is a thing they can do, like train against it, etc. Right. AI is trying to trick you into doing this. Watch out. Yeah, exactly. Um, there would require a level of trust. I'm not sure it's plausible, but like, that's that's the optimal thing that would happen at the lab level.

Do you think it's a multipolar, you know, or or is there consolidation, and who's your bet to win? Um, I've been surprised. So you would expect over time as the cost of competing at the frontier has increased, you would expect there to be fewer players at the frontier. This is what we've seen in the semiconductor companies, right? That like, you know, um, it gets more expensive over time. There's fewer, like, there's now maybe one company that's at the frontier in terms of like global semiconductor manufacturing. Um, uh, we've seen the opposite trend in AI, where there's like more competitors today than there were a year ago, even though it's gotten more expensive. I don't know where the equilibrium here is. Um, because the cost of training these models is still much less than the value they generate. Um, so I think it'll like still make sense to 10x the amount of invest for somebody new to come into this field and 10x the amount of investment.

Do you have a take on where the equilibrium is? Oh, um, well, I mean, it has to do with entry barriers. Basically, it's all about entry barriers. It's the question of like, if I just decide to plunk down this amount of money. So if the only entry barrier is fixed costs, I'd say we have such a good system for like just loaning people money that that's not going to be that big a deal. But if there's entry barriers that have to do with like, if you make the best AI, it gets even better. So, you know, why why enter? That that's the big question. I don't actually know the answer to that question. Yeah.

There's a broad question we ask in general is like, what are the network effects here, right? Um, and what is the utility? And it seems often to be brand. Uh, yeah. Yeah. I, I mean, I'm not sure that's a network effect, but but brand, like, like everybody just sort of, you know, OpenAI, ChatGPT is the Kleenex, or, you know, of of AI. In that Kleenex is actually called a tissue, but we call it a Kleenex because there was a company called Kleenex. Where are you going with this? Are we making do anything? Oh, no. Well, um, no, I'm just saying it's, or, or what's, what's another example? Xerox. Yeah, you, you make, you Xerox this thing. Xerox is just one company that makes a copier, right? Not even the biggest, but like, but everybody knows that it's Xerox. And so like, ChatGPT gets massive rents from the fact that everyone just says like, I'll use AI. What's an AI? ChatGPT. I'll use it. And so like, brand is is the most important thing. But I think that's mostly due to the fact that so far this key capability of learning on the job has not been unlocked. And so for, um, I was saying that could be a technological network effect that could supersede the brand effect, possibly. Yeah. Yeah. And I think that that will have to be unlocked before most of the economic value of these models can be unlocked. And so by the point these labs are like worth hundred, they're already worth hundreds of billions, but by the point they're generating hundreds of billions of dollars a year, um, or maybe trillions of dollars a year, um, they will have had to come up with this thing, which will be a bigger advantage in my opinion than brand network effects.

Uh, is Zuck throwing away money, wasting it on hiring all the? No, I think it's, I mean, people have been saying like, look, the messaging could have been better, or whatever. I mean, I think it's just much better to have worse messaging or something, but then not sleepwalk towards losing. Um, also, if you just think about like, okay, if you pay an employee a hundred million, uh, and they're a great AI researcher, and they make your compute, uh, your training or your inference 1% more efficient. Zuck is spending on the order of like $80 billion a year on, uh, on compute. That's made 1% more efficient. That's easily worth $100 million. Like $100 million is below the break-even point for this extra researcher. So, um, the real question is like, why haven't we hit that, uh, that break-even point yet? And if we, as a pod, as podcasters, encourage one researcher to join Meta, I mean, what's the, how do you put a price on that? Yes.

Is there anything we have? You want any last words for the audience based, based on our conversation? I don't know. I read your stuff a bunch. It's great to actually just talk in person. Thanks, man. Yeah. I, um, I have to to come up with an English language book so I can do the podcast. I, I've written a, a Japanese language book published in Japan. But I have to write my English language, and I can do the Doresh podcast. One of my dreams. Amazing. Amazing. No, Doresh, thank you so much for coming on. It's been great. Awesome. Thanks, Eric. [Music]

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Dwarkesh Patel and Noah Smith on AGI and the Economy

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