Dwarkesh Podcast - Alex Imas and Phil Trammell – What remains scarce after AGI?

Episode Date: June 4, 2026

Economics of AGI episode w Alex Imas and Phil Trammell.There’s a bunch of important questions about how we deal with AI that only economics can answer.What is the optimal way to tax and redistribute the wealth that will be generated? How should countries not in the AI supply chain index into the gains? Is there any world where inequality doesn’t explode?It might seem like these questions have obvious answers, but the first thing economics teaches you is that your intuitions can often be entirely wrong.It was very helpful to chat through these things with Alex and Phil.Watch on YouTube; read the transcript.SponsorsJane Street invests heavily in turning smart people into exceptional researchers and engineers. In addition to their apprenticeship model, Jane Street runs lectures and bootcamps in their in-office classrooms -- managers clear their teams’ schedules to encourage attendance. If you’d like to work at a place that takes learning this seriously, Jane Street is hiring. Check out their open roles at janestreet.com/dwarkeshGoogle’s Gemini Omni has incredible video editing capabilities -- you can upload a video and have Omni change the background, adjust lighting, or add specific elements. But Omni is also a preview of how future frontier models will be trained -- fully multimodal on both input and output. You can try it yourself in the Gemini app at gemini.google or in Flow at flow.googleCursor used targeted RL with textual feedback to help train their Composer 2.5 model. One of their researchers, Sasha Rush, gave me an impromptu blackboard lecture to explain how this form of on-policy self-distillation works -- I posted the full thing on X. If you want to try Composer 2.5, go to cursor.com/dwarkeshTimestamps(00:00:00) – Will capital share increase?(00:19:36) – Messy Middle scenario(00:25:57) – How to tax and redistribute AI wealth(00:30:02) – Why demand collapse is unlikely(00:39:26) – Human employees would be hard to integrate into the machine economy(00:43:08) – What if some humans (or AIs) value wealth accumulation intrinsically?(01:01:28) – What should developing countries do? Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe

Today, I'm chatting with Alex Emas, who is director of AGI economics at Google Deep Mind and Professor of Economics at University of Chicago, and Phil Trammell, who is head of economics at EFAC and research scholar at Stanford. In general, in this interview, what I want to understand is what economics tells us about what we can expect in a world with more and more automation, more and more advanced AI, what that tells us about what will happen to wages, to labor share, what the best way to tax and redistribute the wealth that we've generated as a result of AGI will be, and what kinds of things will be scarce,

because what a scarce kind of tells you where the value will accrue. So I want to start there. What are some plausible candidates of what will be scarce? Something like the relational sector, which is what I defined as, you know, basically services and goods, where the fact that the human was in the loop was actually part of the value of that product.

So because humans are naturally scarce, if we have automation where a lot of other things stop being scarce, we will still have scarcity in things that humans are kind of involved in and in the loop for. I'm curious to understand whether humans doing services for other humans can never be a big part of the economy. And here's maybe one intuition pump. So in a world where AI can physically do anything humans can do, there's this whole machine economy where they're like building factories and doing research and coming up with new ideas. and humans may or may not be involved in the physical production of those things,

but probably not, given that in the ultimate limit, if robotics is solved, if you don't care about humans being involved in that process, why would humans be involved in that process? But then there's these other things which you point out where we actually maybe in some cases do want the ballerina or the barista or whatever to be a human that's part of the value of going to a cafe or our performance.

But only humans have that preference. So there's this human economy where humans are doing services, for each other, and part of their wallet is flowing to other humans. But part of their wealth is also, like, they will want some of the automated goods that's like machine-only economy is creating. And so part of that wealth is flowing out. And so if you just think of this as, like, this is not a closed-loop,

but a lot of things of the machine-only economy are a closed-loop, because the machines don't care about, like, getting the human barista to make them a coffee. And so within that model, isn't it intrinsic that, like, the human-only economy will become a smaller and smaller share? I would like to pitch kind of a rephrasing of that question. So I think my view is that kind of forecast that economists like us would make are not necessarily as individual forecasts like me and me and Phil are talking right now are not necessarily very useful. The reason I think that, so there was this blog post by Andre Fredkin, Brian DeBerry,

and Andrew Coe that came out yesterday actually that looked at like kind of people's forecasts, economists forecasts about the labor market. And what they found is that there's a ton of disagreement like in every single direction. So what they advocate for, and I think I'm an agreement here, is rather than thinking about individual forecasts, like what me and Phil are going to do, rather looking at kind of like basically generating prediction markets, where you get aggregate forecasts, where you get like kind of wisdom of the crowd effects.

And kind of the reason that I think this is because we have been famously terrible at forecasting. And so let's take, let's go all the way back to 1820. This sort of debate that we've been having actually is like 200 years old. So David Ricardo is one of the classic economists, not neoclassical, classical, classical economists. And he, when Industrial Revolution started happening, he wrote a bunch of stuff saying, like, look, this is going to be great for everybody.

Prices are going to come down. But then he turned around and he's like, wait, I can actually see all of these jobs that are creating value. They're going to be automated by these machines. This is going to be really bad, that everybody's going to become unemployed, and there's going to be political unrest and things like that. And if you look at Ricardo's predictions,

they're actually right. If you look at all those jobs that made money in Ricardo's time, they got automated. So if I was David Ricardo and I woke up and somebody told me all those jobs did get automated, and you asked me, David Ricardo, like, what do you think the prime age employment rate is in 2026? I think he would be surprised if you told him it was the highest that's ever been other than 2000. We have the highest number of employed people that could potentially be employed since 2000. and that was like the peak, and now it's like the second peak, basically. So what David Ricardo ended up missing is the fact that, you know,

essentially you have these economics of structural change where basically everything that got automated became cheap, people had more money to spend on things, and then they started spending money on services. And, you know, this is kind of like the lump of labor fallacy. That's what they call it. David Ricardo didn't think, hey, I should have, you know, consider the fact that new jobs would be created. But it's kind of not obvious that like money would go to services.

Like why wouldn't they go to more automated goods and something like that? And I'm not saying that like, I'm not using this anecdote as to say like this is what's going to happen now. We're going to have full employment. I'm using that anecdote as to say it's really hard to make predictions. And what I think maybe a really useful tool that economists have is instead start with a premise. Like maybe we'll start it today. Look, labor share is zero. Like labor share has gone down.

What could possibly explain this? Let's write down an economic model of what happened. Phil will talk about this later today. Or you can start write down a model to say, hey, what if labor share just stays the same? What can make that happen? And here's my main, if you don't take anything out of this conversation for me, we don't have any data. I've been kind of saying we need a Manhattan project for data. We don't have data on basically consumer demand elicities.

We don't know what they are. We don't know, we're not really tracking what jobs are getting created or destroyed, like the own net database with all of the tasks and different jobs that's been rarely updated. It's super low quality. And so what I think is really useful is to think about like what are the potential scenarios and we'll be talking about a lot of these scenarios, mapping them out and to say what dimension of scarcity will generate that scenario. So if there's full employment, we could talk about the relational sector or something like that.

If there's, you know, very labor share collapses, we can talk about other sorts of scenarios. And then that will tell us what data we should be collecting. It's probably worth the defining labor share and capital share real quick. So the whole economy, like the total sum of goods and services sold is either paid out to people in wages. Yeah. Or it's paid out to capital, which is to say that there's like rents on buildings and then there's shareholders of companies that we get paid out. And for many to hundreds of years in the economy, 60 something percent of the economy, or all the things that are sold in a given year, basically gets paid out to humans and wages.

And the other 30, 40 percent gets paid out to people who own machines and land and claims on companies and whatever. And the question is, well, right now, 60 percent is going to wages. Does that shrink as automation or as EIs gets smarter and smarter and better and better? And it's like it really, this is a call-door fact. like, right? So it's incredibly, we should stress this, it's incredibly surprising that it's over 60% after the industrial revolution, after all of the automation we've ever seen. The fact that it's almost like some people are worried it's an accounting error or something like that, that it's kept been so constant. And the fact that it's like been over 60%. And, you know, there's, there's even a

controversy right now. So some might say like, you know, labor share has been falling in the last 20, 30 years. But, you know, depending on how you, there's been a lot of accounting changes in the last 30, 40 years. So, for example, Andy Atkinson has this paper showing that actually if you keep the accounting constant over the years, labor share hasn't even fallen ever. But it's not that's not that surprising, right? I mean, Phil, you made this point that if labor and capital are compliments, you need both to do anything, it kind of makes sense that you kind of need to pay both of them to get something done. You have had stuff can be completely automated. Although, you had the post where you were pointing out that actually...

Oh, yeah, well, I was going to say there's a sense in which nothing's yet been completely automated. If you look at the network-adjusted factor shares of a good, which is to say you look down the supply chain and say, not just like the final step, how much of that is done by capital and labor, but what went into the machines that can automate that final step, you'll find that labor is adding a lot of belly down the supply chain. So like, you know, computer and electronic products in the U.S. have a very stable capital share, network adjusted capital share of around 50%. It's not 100%. I do think there's this qualitative shift that we, I think we agree, is coming, which is that there will be at least some goods whose network adjusted capital share goes to one, right? Because the whole supply chain can be automated and there's no part in it that we care intrinsically about having a human do. So that'll be a, you know, that'll be a qualitative shift. Interestingly, the implications of that shift for the overall capital share are ambiguous.

Because if we, let's say that we've got the two sectors, the human intrinsic sector with the ballerinas and everything else, right? Right now, everything else has been scarce because of the lack of labor in it, right? But if we fully automate the supply chains for everything else, right, and we satiate and everything, else really fast, then the quantity of everything that's not a ballerina, say, goes to infinity, but the marginal utility and that stuff goes to zero faster than the quantity is rising. I also kind of want to move, if you don't mind, move away from the ballerina example. Because I think, like, the kind of point that I was trying to make in my post, again, and the point of the post was like to work backwards from a particular scenario, was that kind

of the ballerina and the kind of performer, that's the wrong reference class. right now we have a lot of jobs where you have different tasks. So this is the task-based model of jobs where you have like a lot of different tasks. So like a doctor, what is their job? They're filling out insurance documents. They're, you know, going and like calling different pharmaceutical companies. And one of their tasks is to actually see the patient and talk to them. But that's like actually not the main part of the job.

So you could have a job and a service or a good be a product of different types of tasks. automate a ton of those tasks. And if the consumer is willing to pay more for a product or service where every single task is to automate versus every single thing except for that one part where the doctor's actually delivering the diagnosis, providing support and things like that, we would call that job a part of the relational sector, right? Because a human is, people are willing to pay more for the human to stay in the loop in the job.

Right. Right. So I think we don't have data. to say, like, here are relational jobs here or not, because you literally need to collect data of the following sort. Do a conjoint analysis of, like, here's my willingness to pay for this service, this good, here's the counterfactual where everything is pursued to spy machine. Here's the counterfactual where this one task is not produced.

What is your willingness to pay? What is your elasticity for that, for the human to not be in the loop? And, like, literally, if I don't have that data, what prediction am I going to make in this story? Right. Right. But I guess isn't there another point, which is that there's a lot of fully automated goods that don't even exist yet. And you can't collect any data right now about, say, how much people will want to keep

buying more and more of some drug that makes you healthier. Absolutely. That's fully produced by the AIs. And that's kind of Phil's point. That's right. And you can make it is that, look, you know, you could have an increase in variety in capital where you don't get the satiation, right? So you're increasing variety.

So you're not hitting that really diminishing. marginal utility point where you're, you know, you're basically most of your income is going to the human sector. If that increasing variety is fast enough and there is no such increasing variety in the human sector, then you can get all of the relational that you want, but it doesn't matter for labor share. It goes to zero. Phil, I liked your analogy to some Mongolian economy sitting in 1400 thinking about what will be scarce and the limits of that kind of analysis. I think you should talk to that. Sure, yeah. So if you just looked at the goods available to, you know, a Mongolian of the distant past, no expert on this society, but I know that they didn't have

nearly the variety that we have now. And they looked at the jobs that were sort of intrinsically human, like being being a singer, say. And they looked at the things that were not intrinsically human, like, you know, the transportation services provided by their horses or the different kinds of food they had. If they just kind of held the varieties fixed in both categories and asked what will happen once we have a lot more automation, they might have said, well, we'll just satiate in, you know, horse-like transportation and in yogurt and in yurts. Those shares will all go to zero and we'll be less spending all of our money on singers.

But of course, that's not what's happened because as we've accumulated more wealth and, you know, more advanced machines and so on, we've expanded the range of things other than singers to spend our money on. And the share spent on singers that stayed sort of negligible. So likewise, that's sort of my central prediction about how future unfolds, though it could go either way. I was going to make a point and I realize it's a fallacy, but the reason it's a fallacy is interesting. So I was going to say, I mean, it's just hard to imagine a world. where there's trillions upon trillions of robots, but there's only some billion on humans. And then, like, the cumulative amount we're spending on robots

and, like, building more robots and whatever is less than what we're spending to, like, pay, you know, Magnus Carlson. Or financial advisors or doctors or tutors. Or podcasters. But then I realize that's a fallacy. The number of transistors in the world has, like, literally, certainly trillion X, maybe quadrillion X or something. And your colleague, Chad Jones, has a very interesting result

about how the share of the economy that is going towards paying for computing, basically, like paying for the transistors, has to be decreasing. The point that you made is that one way to think about Moore's law, you know, what sets price? Well, the prices of a supply, demand. And so not only are we producing more transistors more cheaply, but also we're like the value of the marginal transistor is decreasing, right? So more, as you were saying, another way of

saying Moore's law is, you should say. Oh, yeah, I like the, yeah, the pessimistic framing of Moore's law is every 18 months, the value of computation has. Yeah. Right? Like, we're just running out of uses for computation so fast that it's sustaining Moore's law. And this is in fact, like, literally relevant to a conversation about AI where maybe for the first time this is no longer true. Right. So the famous fact here is that in H-100 costs more to rent now than it did three years ago, even though we have much superior technology and we have much more compute in the world because

as models get smarter, the opportunity cost of compute gets higher. But this is Phil's point about increasing variety. Right. Right. What we have done is increased, increase the types of things that people demand from capital. Now all of a sudden, you have a new variety that you could be using capital for, and all of a sudden, you jump back up. Yeah, you could imagine we just never satiate demand for compute. And as long as that stays the case, then the share of the economy that is going towards compute would keep increasing. And that's the big question, right? It's like, that is the ultimate question that we need to be kind of looking at is like what number. of new uses are we finding for that commute, where you have the demand for these uses. So

what I kind of want to emphasize is that a lot of models in economics, especially in the space that we're talking about, take demand is almost kind of exogenous. And they don't unpack, like, what is the, like, the psychology of what people actually want. And so what got me kind of also thinking about this, the idea of the relational sector's work that I was doing on the fact that there does seem to be this value, this intrinsic value that is, it's not just because it's scarce, it's because there's some intrinsic preference that people have for like empathy and connection and, you know, getting, interacting with another person. So like one of the experiments that we ran was like, there's an art print, right? And we actually have an incentive compatible way of like,

basically saying, like, how much are you willing to pay for this art? People are actually paying a real money for it. And then we say, like, look, there's only one, one of those art prints, and it's either made, and these are between subject conditions by AI or by a person. So with one, you get the effect that the person produced ARPrint is valued much, much higher than the AI version. And then what we do is to say there's, in a set of other conditions, there's 500 of these being produced. So for the human-made one, the price goes down a lot because it's no longer seen as like you're not like making a connection with this one artist. versus with AI, there's no difference. AI is already viewed as like a commodity.

And we need to do a lot more research on this, but it seems like that's kind of like the key difference between something like, let's say, a horse, right? There's no, a horse was an input into an output where you can replace the horse with something else. You only care about the output. The only way this relational story works, and this is what we need more data on,

is if it's not, a human is not a horse in the sense that it is providing value, from the output, where if you replace the human, the value of the output decreases. And if that's not strong enough, and if it doesn't hold for enough sectors, if it doesn't hold for enough jobs, then this kind of story doesn't work anymore. There aren't that many institutions that have thought as hard as Jane Street and how to turn smart people into some of the most competent researchers and engineers in the world. This relies in part on an apprenticeship model, where new hires are paired with senior mentors.

But Jane Street also runs a bunch of classroom-style lectures and hands-on boot camps. These courses cover a range of topics, and they go pretty deep. There's one lecture that focuses on reverse engineering systems with tools like S-Trace and GDB, and another that teaches you how to profile code down to the cache hierarchy level. Importantly, Jane Street designs these courses not just to teach the relevant object-level skills, but also to impart the relevant tacit knowledge. For example, their week-long neural net bootcamp starts with general theory, but then quickly progresses to how to apply neural networks to train.

And here they cover the specific obstacles that Jane Streeters tend to encounter and the workarounds they've come up with to get around them. Jane Street takes this sort of learning incredibly seriously. Every office has dedicated classrooms face and courses are prioritized as part of regular work. If you'd like to work at a place like this, Jane Street is hiring. You can check out their open roles at jane street.com slash Dorcasch. There's one possibility which Molly Kinder has written something about this messy middle scenario, and there's that possibility. made me think about whether it might be better to have, at least as far as wealth distribution and redistribution goes,

it might be better to have much faster AI takeoff. And I want to ask you whether the following possibility is at all likely, or there's any set of assumptions that this can make it so, which is that AI makes it possible to automate jobs such that, like many people are losing their jobs, but it doesn't create enough wealth while the process of automation is happening

to pay off basically the people who are getting laid off. There's like a Pareto improvement. Everybody's getting better as a result of AI automation. And of course, there's a trivial sense in which that must be true because whatever money you're saving, whatever money the company is saving by not paying the humans instead of just paying the AIs, those resources still exist in the economy

and they can just be paid off to people. But there's going to be some allocative inefficiency if the government doesn't know exactly who got laid off because of AI. There's some political problem of like, If the meta worker gets laid off first and they're making 200K a year, is there a politically sustainable situation where you give them a 200K check a year when there's many people who are working who are making much less? So do you at all find this in a narrow plausible where AI is actually automating a bunch of things,

but there isn't enough wealth creation as there is automation? I think it's... Is that plausible? Possible? To me, it does seem like a pretty narrow window. My guess is that if we have the technology, to automate so many jobs that it becomes like a new kind of political problem, then the pie will also be growing really fast.

Well, unless in all of those professions that it's automating, it's just a hair more productive. So like the cost of all the capital to replace all the software engineers. It's just, you know, a hair less than the cost of what we've made the software engineer. And why is it implausible that it's just like a company can save money by laying off a bunch of software engineers? But, and in the long run, there's a Jevon's Paradox thing. and, you know, we can't participate in advance what we do with more software and surely there's going to be more uses.

But in the short run, the fact is just that a lot of people are laid off and they still need to figure out how they can use a million X more JavaScript tokens. I think the thing that is in either, like, you know, Phil and I have been like writing about these things and we have mathematical models in the back of these things. We don't have any political economy in any of our models. Andy Hall wrote a really nice blog post about the politics of AGI. And he made a really interesting observation. If there's a 2% increase in unemployment, the political wins completely change. Like unemployment, it has a huge effect on what happens politically. So, you know, to Mali's excellent essay, by the way, I think in some ways, like one of the worst scenarios is a drip scenario because of the political economy piece, right?

Because, like, you know, people, essentially what you might see is like people not really being unemployed in mass, but kind of like moving in. into sectors that pay them less money, kind of basically getting what happened with phone operators in the mid-century of the, between 1920 and 1940, phone operators were completely automated, right? But it took 20 years, even though it's a technology existed. And therefore, there was this drip. It wasn't like this giant sector just disappeared. Yeah.

And when it ended up happening, there's a really nice QG paper on this, basically showing that they got reabsorbed into the economy. but at lower salaries and they were mostly underemployed. And I think that's the scenario that Molly was writing about this, like, kind of messy middle where, like, things aren't a disaster. Because we saw with COVID, like the fiscal response can move quickly if there's an emergency. And an emergency is a quick uptick in unemployment, which could even look like two or three percent. That's like a national, that becomes a national emergency if it becomes fast. The concern is that suppose what?

Whatever you're saving on those white-collar workers, if that's not growing the economy, but it's just creating some, you know, saved resources that can be allocated elsewhere, is that enough to do a broad-based redistribution scheme? Because then you have, like, the money you've saved up a couple of people. Yeah. And unless you can figure out exactly how to get to them specifically,

you got the problem of, can I do like, can I do a UBI off the money I've saved by laying up? Look, the pie did not grow that much. Yeah. You're just basically saying, you're just, basically displacing a bunch of people, but that actually didn't grow the technological frontier of what the economy could produce. And so then there's a question of like, well, maybe every time, I don't know if this is a case,

maybe every time this has happened in history, the technological frontier has expanded a bunch. And so I think that's the case. I think simply in history, the technological frontier has expanded. So it's kind of, and I think Philip made the same point. Like, it's hard to imagine that sort of scenario where you are getting like intelligence that's kind of just enough to replace the software engineer, but still costs a lot of money. Like it's just a hair less expensive than the software engineer.

So you're not getting this abundance effect. Right. And so where is the redistribution going to happen because the pie didn't grow? Yeah, yeah. Okay, so this is very helpful. So there's many different things out to be true for the scenario to come to pass, each of which seem unlikely. One, it has to be the case that it is possible to automate entire white collar

jobs, but only in a piecemeal way. That is to say that you can only automate its software engineers, but that same program can't also automate an accountant and an analyst and whatever, where I think at least my model of intelligence is such that both of like the breadth of tasks that acquires to do something like software engineering and what intelligence is, is such that, you know, if you can really just lay off all the software engineers, you've got enough in the bucket there that you could like automate all kinds of white color work. So, yeah, you're saving, there's huge amounts of potential savings that have happened as a result of these layoffs. And also that AI is going to be cheaper than human labor.

And if both of those things are true, this messy middle scenario where we literally don't have the wealth to go around seems unlikely. And the question is, like, what is the best way to tax it and redistribute it? Yeah, I have some thoughts. I think it's just really important to outline the costs and benefits. Like, it's also important to know that they're, so first, there's differential complexity and, like, implementing these things. Two, they differ in the timeline of being actually helpful. So something like universal basic capital, that's not going to generate returns for something that happens in six months.

So you probably are going to end up with a layer of things. So like, for example, like a negative income tax. Like you implement it and the day it turns into law that you already have this sort of insurance that like, you know, there's a floor for which, you know, everybody, everybody gets a certain amount of money. And then, you know, if you earn more money, you get tax more and things like that. And but, you know, there's positives and negatives to negative income tax. With UBI, for example, I worry a lot about like the political economy implications.

Like, for example, like if people are just kind of dependent on a check, it really matters who's in power. Like right now we're endowed with labor that can turn into income. when that is no longer the case, and we are now at the mercy of the elected official for, like, basic needs. Right. So that, to me, feels like a power sharing arrangement that's really dangerous. But wouldn't that be true of any sort of government redistribution program? So something like University Basic Capital, where you have like an ownership share and you have property rights for capital, then you just, you're just, you're a normal shareholder. You're just a normal person.

But this goes back to the question of indexing, because if indexing is hard, then universal basic capital is. And that's the problem of University of Basic Capital is targeting. Right. Right. What do you target to put into people's portfolios? Like what if Anthropics goes to zero, but some random robotics company takes all the surplus? Exactly. Exactly.

So that's the risk of universal basic capital. With the negative income tax, you have the same sort of issues with UBI where, like, you know, somebody comes into power and says, like, this is, we're not going to do that anymore. And people can't work. And then, you know, you have the issue of the floor being open. One concern with the wealth tax is that, you know, there's no political, politically sustainable equilibrium at like 0.5% well tax. And, you know,

I mean, this happened with the income tax, of course, right? You start slow, it's like for war or something, and then it slowly and slowly escalates until the marginal tax rate in the U.S. is probably on the order of income tax rate is like 40% or something. And in certain states, upwards of 50%. With a capital tax, is there a reason

to worry, would that distort investment because people would just be like, why would I invest in anthropic or intel? The government is going to take larger and larger shares of it and dilute my share. Well, hold on. So I think it's worth separating, like, how the revenue is raised, like what's taxed and then how it's distributed. It could be that the government hands out shares of anthropic to everyone by broad-based tax and then buying anthropic. Yeah, okay, fair. Which would probably be the right thing to do. I mean, hopefully

some, like, populist proposal doesn't interfere with that and, like, expropriate some, like, particular company that everyone happens to know about. Yeah. But how, So you're suggesting there could be a tax that is some sort of optimal tax. We're taxing externalities or we're taxing land or we're, I guess we probably need to tax something other than just those two things. But that tax pays. Or consumption. Okay. So a consumption tax, like a European value-outed tax type thing that allows the government to go buy a bunch of stocks and then it just distributes those stocks to everybody.

That's David Otters. Yeah. Yeah, I mean, that's not going to be that different from just, like, redistributing the stocks, but it'll be a little different. Yeah. That's what social security. That was the proposal for Social Security, by the way. That was privatizing Social Security, right? So it's like, you turn like this sort of weird, like, not weird, but it's been working.

It's worked so far. But, you know, there's questions for how long it's going to keep working. Like, basically, privatizing Social Security was giving everybody a basket of stocks. Right. All right. I'm curious to understand people talk about whether there's a lot of. white color apocalypse already. Is there any evidence that suggests that there is mass automation or unemployment as a

result of AI already? I think there's a lot of people are looking at it. So this is an area where there's like a lot of eyes and a lot of data being produced. So the budget lab over Yale is doing really good analysis on this. They just recently released the report. And I think like you really have to squint to see anything happening. Like, basically, if you want to take kind of like an approach across the entire economy and even looking at like software engineering, like the most exposed sort of sectors, there's just like

not really anything going on. There might be a little bit of a signal about like junior developers getting jobs less than before. But that's like a less than before rather than a level shift is then there's actually an increased demand for senior manager, for senior software engineers, if anything. And so if you look at trend, it's kind of like for junior managers, it's a bit below trend. So as you're saying, the growth is slower than before. Yes.

But there is still growth even on entry-level software engineers. Yeah, exactly. And what do you think is going on with the anecdotal evidence of graduating college students saying that they're finding it harder to find CS jobs or something? I think that's anecdotal evidence. You think it's always been hard to get jobs for some people. And now it's getting turned into an AI narrative. Same with the layoffs where it's probably just normal layoff. they turned it into an AI layoff.

Yeah, I mean, you have to be careful with all of this. I think, like, there are these, like, you know, there are these, like, coordinate, public coordination devices for, like, let's say we get into a narrative where, like, if you're a firm and you're not laying people off, then you're seen as, like, not adopting AI enough. So, like, then you're going to just get a cascade effect. Right. A firm's, like, just needing to keep up with the Joneses in terms of, like, starting to lay people off.

And that's kind of, like, that's super worrying where, like, actually the firm might be worse off. Right. after the layoffs than before the layoffs, but it's just doing the layoffs to have the perception that, look, look, we're not behind the times.

We're using AI, like you have the, you probably heard these anecdotal stories of like these token counters that, like, you have to maximize tokens and things like that. So again, like right now we have, we don't really have any evidence of a white collar bloodbath.

And is that surprising at all? I feel, given the fact, all these things they can do is just like, this is the oldest time. If you automate some compliment task, the overall bucket of things that the human labor, which complements the automation will increase in value.

So this is one of the statistics that's really important for that argument is elasticity of demand. Yeah. So like the you take the O-ring model of jobs. So like, again, jobs is a series of tasks. Let's say the AI automates like nine out of 10, nine out of 10 tasks. One task is not automated. If that person can now kind of focus in on that task, and the, the, the, the,

job will become more productive. If that translates into a price effect where the product is actually cheaper, if the demand responds enough where there's, it's being bought more, it's being used more, the service is being used more, that could actually lead to more hiring. Right. And a lot of people on the internet have been like kind of making that argument kind of very generally saying, like, look, we're seeing, if anything in the data, we're seeing an uptick in software engineering. Right. Yeah, yeah. Which suggests that at least for now, given the way the jobs work, it might be But I think this elastic of demand argument

is incredibly important both for a lot of arguments that people make or just a lot of labels that people use without understanding what the underlying causation is.

So people often talk about Jevin's Paradox. This is this idea that as something gets cheaper, you will want so much more of it that the total amount you spend on the thing increases. And so famously this happened

to Cole in Britain 200 odd years ago. But really this only happens if there's the demand for something is highly elastic. There's many things for which there is not super elastic demand. If oil, for example, gets super cheap, it's not like magically, right? Yeah, exactly. Magically, there's going to be so many more cars that now we're going to be using way more oil than before.

At least not in the short run. Exactly. So long run elasticity is higher than short run elasticity. But even in the long run, so agriculture famously is an example where we can produce weight. more food if we dedicated the same portion of the economy that we dedicated to agriculture. We're already producing more food regardless, but we could produce even more food if the same portion of the economy that was producing food a hundred years ago was currently producing food. But, you know, you eat enough and then you're done. And so the claim with software is that

it is not some inherent property of markets that as it gets cheaper, you'll just keep wanting more of it. It is the thing about software is this is a particular kind of good, whereas it gets cheaper, we'll want more and more. But it is also highly relevant and you wrote an essay about this. A lot of this podcast is me summarizing your essays back to you. That there's this very viral scenario planning about the future by Satrini where they're predicting as a result of automation, as a result of very powerful AI, there will be a recession because white collar workers will get automated. There are salaries, which we're, you know, paying for a bunch of things will no longer be available, and so there will be a slump. Do you want to recapitulate why this might be implausible?

Well, I mean, so part of it is plausible, part of it's not not plausible. So like the part that's kind of like within the, this is something that we started the conversation with is the idea that there could be unemployment, a lot of unemployment, if the speed of automation is quick and things like that, people could get laid off and they may not find work very, uh, very quickly. So that part of the Centrini essay about the unemployment, you know, we, We can quibble about that, but that's not the issue. The issue is that they talked about negative economic growth. Right.

And so what I did in the piece that actually Phil and I had a back and forth on was to say, like, let's start with the proposition that there's negative economic growth. What conditions do you need on the economy to get negative economic growth? And it turns out the conditions are pretty improbable. So one thing that you need is like for the holders of capital, like rich people basically, like basically what you have in those certain. of scenarios, like you have a reallocation of wealth and income from, like, lower income people who are working who are using their label towards capital owners. So what you need is that basically

demand to be bounded, like a hard bound, not even like a soft sort of like diminishing sensitivity. You need for them to eventually say, I've had enough. I don't want to spend any more money. And for that money to not enter his investment. Right. Right. And then you can get negative growth, which is like... And the crucial thing is even if we don't want more. shit. The world in which there's a singularity and we don't want to invest more money is crazy, right? We're not like, let's build more data centers, let's build more fabs. Even though we have AGI, we're not like investing in more data centers to run the AGI. And that's like driving more economic growth. Yeah. And so I sent the essay to Phil and Phil actually wrote back being like,

this is pretty dumb. Yeah. Like my asset saying like you're trying to say that there's going to be negative economic growth, but these are very implausible conditions. And I was like, actually, that's the point of the essay, that these are very implausible economic conditions. So that's where I think, like, scenario planning really shines is you have the Centrini essay, which I think is like, I think it was great that it's written because I kind of started a conversation. But it's just like, you need, it's so intuitive this idea that like, look, if there's demand collapse, we can get the economy to shrink.

But it's actually, you could get that with a depression, right? Where in the depression, the technological frontier didn't expand. Right. Here, the technological frontier is expanding. You actually have abundance and for abundance to generate negative economic growth. That's really hard to get it. Right, exactly. Google recently announced Gemini Omni and its video editing capabilities are incredible.

You can upload a video and then tell Omni to do things like change the background or adjust the lighting or add or remove elements, all while keeping everything else consistent. But Omni isn't just a video editor. I got a chance to sit down with the research and product team behind Omni, and I learned that it's a preview of how future frontier models will be trained. It can take in any kind of input, whether that's text or audio or video. And while it doesn't currently do so, architecturally it's capable of just as seamlessly outputting images or text. So it's really a bet on the multimodal data transfer hypothesis.

The model becomes better at predicting one data type by seeing the others. For example, Omni is really good at accurately rendering text on video, even though Google didn't specifically target that capability in this model. And Omni is the next step towards more accurate world models. Because in order to predict the next frame of a video, you have to have a deep understanding of physics and spatial dynamics. As Omni progresses, it'll be interesting to see whether it can close a sim to real gap. Because it's much harder to collect data in the real world than it is in simulation,

robotics progress has lagged other applications of AI. But if you have really good video models that can simulate reality, maybe that stops being the case. In the meantime, if you want to try Omni, you can check it out in the Gemini app at Gemini. Google, or use it in Google's AI Creative Studio Flow at Flow.com. We're talking a second thing about why there isn't more automation as a result of LLMs. And one plausible mechanism could be that, as you're saying with the O-Ring,

so O-Ring theory refers to this fact that the Challenger Shuttle blew up because there's one component that malfunctioned and it destroyed the whole thing. And maybe that's a more general model of how goods are producing the economy, that you've got to make sure everything is reliable and works well. and you can't automate entire job to an AI right now, even though it might be able to perform it at some probability. You need extreme reliability in order for it to not destroy the finished good. I think this is, yeah, so this might explain why there's less automation now

than there otherwise could be, but I think it works in the other direction once AIs get advanced enough that integrating humans into the production flow of future goods, even beyond the, even beyond the arguments about how humans will be more expensive, or dumber or whatever. Even beyond that, just there will be whole production flows that are organized for AI labor where they're talking and neuralese. They're thinking many thousands of times faster. So even if there's some comparative advantage where it makes sense to hire a human, there will be like transaction cost and worries of a reliability that will actually make it hard to integrate humans into future production flows. Yeah, that seems right to me. In particular,

I just want to distinguish between the point that if you automate like nine tenths of a job, then people might kind of shift over to the last tenth, but like there might be ten times more work demanded of them from the model of O-ring automation from like Gans and Goldfarb recently, which was that if you can only automate nine-tenths of the job, but you can do it to a lower standard of quality than the human could do it. You might not want to automate even those nine-tenths. And that's the thing that could totally port over to like symmetrically, it could be a reason why we don't use a human for one bed of the job anymore because a human just can't perform it to the level of quality that the AI can perform the other parts

of the job or the level of speed or whatever. And they end up pulling down the quality or speed of the finished product. By the way, the model you're talking about seems extremely plausible to me of why more lawyers or accountants or whatever are not automated. Like there are cases or even software engineers where there's a pretty good probability that the thing worked as you expect, but the thing you're paying a lawyer for is like, no, really, my company's not going to go under because...

You're also paying for a lot of, like, regulation type stuff, right? So, like, with lawyers particularly, you need some entity to back up the product. You need kind of like an ownership of the product. You need somebody to be able to fire or hire, like licensing issues. There's a lot of, like, sort of, like, regulatory layers that are, like, also going to be keeping, even if there's no relational element, human in the loop, that have nothing to do with the ability of the human to actually perform the service. Yeah. Yeah, I mean, you know, all of these frictions on the political type decisions that we are accustomed to only trusting human,

you know, only having humans for, like legislation and being a judge, being a jury, or all the licensing that keeps certain professions human, that all strikes me as transitional, right? I mean, what we expect to come from a human and, like, how we organize our politics, that's changed so many times throughout history, right? From little undergatherer bands to empires to whatnot. And, yeah, once an AI-run political system is much more efficient than the alternatives, then those will probably tend to out compete the others. And, you know.

So speaking of which, we've been talking about what preferences humans currently have and what impact that has on what kinds of goods will be scarce in the future. But, of course, we'll have different kinds of entities in the future. AIs, right? There's a time when there were no humans on Earth, but evolution selected for agents that have specific drives and preferences, because those tend to survive the most, and those preferences now basically determine how $100 trillion world economy what it produces. And so why not expect the same thing of AIs in the future? This is not even a world with catastrophic misalignment,

that is to say they just kill everybody. But there will be evolution of, even if not individual AIs, then firms which have AIs as part of them. And what will that evolution favor? Where it will favor probably firms or agents that grow, right? There's like a selection argument that things which grow will be more prevalent. And maybe just based on that, you can make some predictions

about what their preferences will be. But is the kind of entity which prefers to have human intrinsic goods going to be the kind of entity that accumulates resources the most? Probably not, right? It probably saves more. It has unsatisfiable demand for things like whatever the relevant resource happens to be. Comput is an obvious one. And can we use that to make some prediction about the non-human preferences that will be guiding the future? Yeah.

So I think if there's like an AI that has its own welfare and it's fully autonomous. and it's like making its own decisions and that are wealthy relevant. To be honest, I have absolutely no prior that they would at all prefer to like deal with humans. There's like no reason.

But let me take like a quick, the other side of that argument. Will humans' preferences to be interacting with one another and to trust and empathize and all of these sorts of like things with other humans versus a simulated AI? I think it's a really important question

whether those will change. Right? So I've heard a lot of arguments saying, like, look, you know, right now we're just not used to the technology. And at some point, like what you're thinking of relational or something like that, people are just going to see like an AI therapist as a superior product. And they're not going to need the sort of like empathy or whatever that the human is providing.

I think this is actually a really complicated question. Here's one argument for why it's not going to go away. and that that has to do with evolution. So let's say there's two types of people. One person doesn't really have this preference. They can just interact with other AI, whatever, can simulate it better. The other one has almost like a moral emotion, like from using Jonathan Heights framework,

moral emotion against, like offloading those sorts of social interactions to an AI. Which of those two people are going to reproduce, find a mate, all of these sorts of things? I think the answer is kind of clear, right? it's the second one that has the preference for other people. It doesn't how the reproduction is happening. Fair. But if we're in the world where like reproduction is still happening the way that it's

happening, I think, and this is a big question. I'm not even like, I'm not making a prediction. Again, I'm just saying, like, if we're thinking, you had David Reich on the show, like his point on the last podcast was that, you know, we're buzzing with natural selection. Right. So even if, like, you get some sort of indifference now, you might get selection to point into like an even stronger preference for other humans. Here's one way to think about it.

How is the wealth of the richest people in the world instantiated? Of course, they can, as you were, we were hiring a call earlier and making the point that their consumption is more geared towards relational goods, like Mark Zuckerberg is hiring MMA instructors and dancers for his wife's birthday and so forth. But most of his wealth is just stock and meta, and he as a controlling shareholder, could say, hey, meta, just give me all this income, or turn all this wealth into dividend income. And I will just spend that on consumption. But instead, he rather would have his wealth compound and meta to build more data centers, basically.

So you don't even have to change humans for this to be the case. It is just the case that the humans which are wealthiest and are growing wealthier are because they're wealthist compounding. Just have this like almost Nicalandian preference for like accelerating capital. And that does seem to suggest that, yeah, is that an important determinant of what kinds of things are produced in the future? Yeah, I could kind of just say, like, there's two ways you could get the two kinds of people, one of whom prefers a human therapist and one of whom is fine interacting with the AI. If they both satiate equally quickly in capital, right? But the one who likes the human therapist just also likes having some human intrinsic services, then the marginal value, like how the marginal value of capital is,

capital in the future, compared to the marginal value capital today for each of them, if they start out equally rich, should be basically the same. I mean, there could be interactions and whatnot, but basically that should be the same. If what's driving the difference is that one person just doesn't satiate in capital because they're engaged by the prospect of, you know, exploring the universe and turning their head into a galaxy brain or whatever, and the other one satiates. Then the person who doesn't satiate in capital is going to have, if they're being rationaled, they're going to have a higher savings rate. Yeah.

Okay, so in the long run, they're going to have most of the well. And the overall capital share will basically be the capital share of that person spending, which is going to be one. It's important that this is, we're not talking about a hypothetical future. Yeah, yeah. Like, Elon Musk is talking about mass drivers on the moon. Right. And he's like, by far the wealthiest person in the world.

I mean, obviously currently his investments are going towards humans as well as machines. But I don't think he cares particularly that is like future researchers and engineers are humans versus the ethics. And he managed to reproduce fast as well. Yes. So anyway, so I just think it's worth drawing that distinction. Yeah, there are currently some rich people that don't seem to satiate quickly in capital. And so maybe in the long run they'll save the most. Right.

Yeah. That does seem sort of right to me. And I would just also say, even if they do, we. produce more slowly, like biologically, that might just not matter that much than long, right? If they can live forever and, you know. The living forever is key. Yeah. Great.

So I think, I think, again, like, and we're scenario building here, right? So I think if you could live forever, like, a lot of stuff changes for my story as well. I think it's, to your point about, you know, rich people just consuming, not consuming a lot investing, I think this will all depend on the returns to capital. Right. So like right now, the returns to data centers are super hot. But if we get into a situation where people are satiated with capital, then the returns to accumulating capital are going to be lower. And so then these rich people are going to be consuming more. Right. So because the incentive to invest is smaller. So basically, you kind of think about this in general equilibrium. The general

equilibrium of this sort of process. Like, we have gotten tremendously more richer since, you know, 1820. We've gotten many more people are investing. But you're still getting a consumption response, which keeps, you know, people employed in labor share high. And that's because... Wait, hold on. Not necessarily.

I think you're probably making the same point. But, I mean, they could just... It could be that their investment has to be chytrated through actual laborers, but to go, like, do things for their investment to work, which, like, would not... In the future, only the consumption is human-mediated, right? Because the investment can just be done by the robots. But if the returns are, if you, so we're in this scenario with, like, how you can keep high labor share, right? Let's take that scenario.

In the scenario with high labor share, for whatever reason, the returns to capital are going to be lower. Yeah, that's right. And I mean, to the earlier thing where we're in the messy middle, we're saying why this is implausible, I feel like we can do a similar thing here. We're for our returns to capital to be lower. the growth rate has to have be lower, right? I mean, it certainly has to be lower than what we're expecting through the period of transformative AI.

You know, if there's explosive growth. Yeah, yes and no. I mean, so the capital stock could grow quickly, but the price of capital goods relative to consumption goods could be falling faster than the capital stock is growing. Oh, interesting. Yeah. It's the difference between like the potential frontier of technology

and like what the realized prices of these things because you have relative prices. That's really important. So you're saying, I could be putting my money towards, you know, earning 30% interest in investing in data centers, or whatever, there will be something in the future, if growth rate is high, that earns high returns. Or I could, as a result of all the technological breakthroughs or some cool product that I really want to buy right now, and both of those will be compelling options.

Yeah, it doesn't have to be a new product. It could be a human intrinsic product. Right. Although, if it's a human interest in the product, we would want to have it much more in the future than we want it now because the sort of the thing it compares against is... So we might want it the same as we want it now in the sense that like the marginal utility in a ballerina performance.

It's exactly the same as now, right? But the marginal utility in a robot, and it might just be a lot lower than now, right? So in units of robots, we want it a lot more than we want it now. Right, right? So would the interest rate be 30%? It depends what you mean by the, interest rate, okay? It might be that every robot now can turn into, you know,

a hundred robots next year, right? So in units of robots, the interest rate is 10,000 percent. Right. But if the price of robots is falling really fast. Prices adjust. I mean, that's the whole, I think that's the whole point. Yeah, but here prices are adjusting this interesting way that too many macro models don't allow for, right? So what's happening is what would be called investment-specific technical change, where, yeah, the price of capital is like, falling relative to the price of consumption, instead of like the standard, doing the standard macro thing of saying there's just output.

It's like chimera of a thing called output, which is one for one can be allocated to capital or consumption, right? That's not going to be true in this world. Every unit of capital next year is giving up way less consumption than each unit of capital this year. Because like the just one robot now turns into many robots next year, but the number of Balabian is the same. And again, we're going to go back to the increasing varieties. where like if all of those extra robots next year are actually different varieties of robots and I'm not getting satiated on those robots, then it's a very different story. Yeah, right.

But now we're talking about the consumption world, whereas for the investment side of things, there could be just some greedy titan of industry who keeps wanting more and more robots. And that alone would be enough to increase the marginal value of robots. and therefore decreased labor share? Yes. Yeah, okay. But why are we not expecting greedy titans of industry to keep existing? I mean, greedy Titans of Industry historically have, like, built libraries and...

But that's because they die. And they're like... Oh, they all die. Everybody dies. Well, we'll see. But I mean, like, conditional on people dying, I think, like, you know, his... Like, again, you had a guest on the show who said, like, you know, to understand the future, you should think about the past. And I think like I, I, you could have new types of titans being, right, being born,

where their entire reason for accumulating wealth is just to accumulate wealth. Yeah. But a lot of the time, you know, at least historically, I'm just talking about historically, the wealth accumulation process is part of a large social, sort of like social interaction amongst peers, amongst the community where you want to be admired in some way or something like that. So people end up,

like the stylized fact of titans of industry is like you accumulate the capital and then you like buy a bunch of stuff. Yeah. I mean, I guess it is sort of a historical question, but it does seem to me in a lot of cases what is happening is that as a near the end of their life,

they either handed off to their children who are worse stewards of capital than they are and they don't even manage to grow their wealth at the rate the economy grows, much less faster than the economy grows, which their parents were doing. And also, they're like, well, I care less about my children having it than me sort of playing this game of accumulating wealth, and so I'm just going to give it to some trust. And if people are living longer, or if they can figure out some way in which to align their trust to this wealth accumulation process, it just feels like the evolution here

is so strong where you just need a couple of agents that think this way for this to be the dominant thing, determining the preferences of the whole economy, because this part is growing much faster than the other parts of the economy. I think you just, like, the part about satiation and diminishing marginal utilities, it keeps coming up, but I think it's really, really important. Like, you know, if a person has an intrinsic preference for accumulation, right, that's just like that's what they want. I think your story is totally right. But that's just like not how usually preferences work. Right. Like you have enough, whatever. You hedonics. in your life. And then like the social status, all of the sort of, you know, Rousseau wrote about this.

St. Augustine wrote about this. This is like a kind of like a basic part of preferences. Now, two, you guys are arguing about something else where like you could have such high concentration that you could just have a couple of exceptions to the rule and that's going to be enough. And I have nothing to say about that. Yeah. Yeah. I mean, I think the claims a little stronger, You're not just like you could have some exceptions, but that it seems that historically, and today we see the exceptions, and they just haven't really taken over the economy historically because they've been these dissipation shocks, as they're called. So they've, like, given it to their kids. They've spotted it or they put it in foundations, which, which vented it. I mean, it's not really a shock, but, I mean, is people went, people might have liked to, you know, fill the universe with monuments to themselves and sort of whatever.

live forever very wealthy. It's like a weird preference, but it's not a hypothetical preference. I think that's the thing. But who knows what's going on in their heads? I think even without though, like the kind of intrinsic preference for accumulation, there are some instrumental reasons why some people might value accumulation, which is also worth bringing up. So there's a desire for,

political or philosophical or religious influence, right? So people get to sort of an arms race over like what, you know, what society looks like and what people believe. And then similarly, but differently, because it's not an arms race. There's just a total, totally utilitarian philanthropy, right? So when I think about why it might be good to have a lot of wealth in the future as a good classically utilitarian, to me, the values, or at least one way you could have a kind of almost unsatiating utility function and having wealth in the future is to create new happy beings, right? They just add to the total welfare of the world. You know, I mean, this idea goes, at least as far back as, like, Bostrom's astronomical waste point that we could, like,

put Dyson's fears around the stars and turn all the energy into really happy simulations and whatnot. I think the particular greediness of this optimizer doesn't matter what they're greedy for. I think forgetting about utilitarian philosophy or whatever, like just a pure von Neumann pro, has, I don't know what the, is this an accurate way to say yet? They just have high marginal value for like the random solar system they'll occupy because that turns into like more solar systems. It turns into more solar systems. But like a Von Monormon-Norpe is a thing that can exist, right?

And that's like a very greedy optimizer. Yeah. I mean, if we're talking about like whether they'll dominate the economy, maybe this is a technicality. But, you know, we only count final consumption goods and investment goods as GDP, right? If there's just this phenomenon. How does the Von Neumann probe show up in GDP? Yeah, exactly, right?

So if it's like, if we recognize it as a person that like owns itself and it's like sort of, you know, optimizing on the margin between like spending a bit more on a baby von Neumann probe that colonizes another star system or like a ballerina or something and it's just like, it doesn't value the ballerina very much. But it's, yeah. Yeah, when we're talking about like AI beings or like, like it just, it just completely depends on how we're doing the accounting there. Right.

Yeah. But it's just like, what does the world look like in a world where like von Neumann Probes are possible? Is it possible? labor share is high. Anyway. Yeah, I think it's possible with the labor share is high the way we usually account it.

One of the biggest problems in R.L right now is credit assignment because you have these extremely long ruleouts and you need to know why they succeeded or failed. One of Cursor's researchers, Sasha Rush, gave me a blackboard lecture on how they use targeted URL with textual feedback to deal with this problem

and train Composer 2.5. I filmed on my iPhone, so apologies for the camera work. So we generated this output. Yeah. It's just a sequence of tokens. We're going to send those sequence of tokens to this model that's going to read it. Yeah. And then it's going to isolate a specific, say, turn that it says is problematic. Yeah.

Then we're just going to do text manipulation. We're just going to take that trajectory, and we're literally just going to, like, smash in some extra tokens. After Cursor injects these Hint tokens, they run another forward pass. The trajectory itself doesn't change. But the Hint causes the model to assign lower probability to the error tokens. cursor then trains the original model to match those probabilities, basically teaching it to downweight these specific mistakes. There's a lot more nuance that we couldn't include in this mineral.

If you want to watch the full thing, I posted it on my Twitter. And if you want to try out Composer 2.5, head to cursor.com slash dwarfish. Do economists have any advice or countries which are not in the AI production chain? If you're not either producing the AI models, you're not producing the hardware that goes into AI models, if you're not Korea making HBM or Taiwan making with the FAS or not the Netherlands with ASML. Like, what is India or Nigeria? What should they be doing right now? If you're talking to Modi right now, what do you say?

I think the biggest lack of resources that we have allocated in the economic profession is thinking about middle income developing countries in the age of AI. And I mean, this is my fault. You know, this is something I fault myself with as well. there's not enough people thinking about this question. Like there are scenarios where, you know, you get like AI technology, you know, being allocated and dissipating to Nigeria and developing countries and things like that. And like that leveling the playing field, like essentially like giving them like a level up as far as capabilities. But there's another world where like because they don't have enough resources, they're not making it.

They're not training the models. They don't have the hardware where they just completely get left behind. And because of, you know, automation, we can produce commodities in developed countries now. Right. Then we don't even have, you know, the consumer market. And then that world looks pretty, pretty bad. Yeah. This seems to me like an extension of the messy middle case, right?

One of the ways in which the messy middle might only be bad in a narrow range of scenarios isn't just that, like, it would be easy to redistribute because it would be bigger. But because the interest. rate would be way higher and or sort of equivalently, the price of everything except the human intrinsic goods would be falling really rapidly, sort of two sides of the same point. A little bit of savings would turn into a lot of consumption next year, right? So things have to go really wrong for us to like just get over the threshold of, you know, capital being productive enough to automate lots of work, but not be productive enough that

the interest rate is high and or the price of capital produced goods is falling a lot, okay? So even without redistribution, a little bit of savings will save a lot of people. Sorry, you're saying if the developing countries have some savings in the developed world, that will be enough to produce a lot of surplus that they can. They will now be able to consume a lot using their savings. But I mean, the messy middle could be like wider in this case. I mean, they're starting from such a lower level in terms of like how much they've saved and how much it's like actually indexed to the global economy.

Right, yeah. And I think it's important for them to get on it now. and I don't have strong feelings about whether it should take the form of like sovereign wealth funds that invest in the right supply chains or or just, you know, subsidies to their own citizens to buy a little bit. This is actually, I think, a crucial point. We were talking earlier about why the Rockefellers or whatever the world, why their descendants don't control everything, if our argument about the selection of these kind of greedy optimizers hold. And one argument is just that it's like very hard to index the economy. And maybe they would have just decided to have their airs index the economy and have it grow at the rate of. have their wealth growth,

the rate of academic growth, and they would be, you know, trillionaires. Their ears would be trillionaires by now. But it just has, before index funds existed, it's just very hard to just get, get a represent, it's just a very small fraction in the economy going back 100 years,

accounts for a majority of the value created now. And if you miss those particular things, you would have basically, your wealth would have just kind of stagnated. And maybe there was a brief golden window from the creation of index funds up until, I don't know, five years ago where actually you could index the economy and you could have your wealth

grow at the rate of the economy grows. But now that we're in this world with very concentrated returns, especially to private companies, which is capital that is, as we were making a point in our blockposts, the average person has disproportionately less access to, as opposed to, most of their capital is like having a random house, at least in the U.S. Or part of a house. Yeah, which is, as we're saying, is sort of unique. a capital that is uniquely ill-suited to be complementary to the production of AI or the serving of AI or to robots. Or the kinds of goods that the rich will bid up the prices up.

Exactly, right? Because what is the value of a house currently? It is really the land is close to other humans and modular relational stuff. That is just not going to be the main factor of production. And this is the way Georgian tax would not raise enough money for the sort of programs that. that we were described. Right. But stepping back,

this point I was trying to make is if it gets harder to index the economy now, and that it's supposed to be the main way in which both one and normal people are supposed to modulate some sort of

use universal banking income. In the developed world. In the developed world, are supposed to have some leverage on, or have some purchase on the wealth from AI. And it's also the way that developing countries

are supposed to have some purchase on the wealth gains from AI. But it's very hard, I don't know, is like a, Is Nigeria own a lot of SK-Hinex and, like, anthropic? I'm guessing not, right? It's not enough for them to just own the S&P 500.

So actually, this brings up a really important point. Like, is AI going to be like electricity or social media? Right. If it's... So think about ComEd or ComEdison, whatever the electricity provider here is. It's a monopoly. It provides a resource that everybody uses. But do we think about electricity as like generating...

creating concentration of power, and as ComEd, like, having, like, this huge amount of political power, social power or something like that? No, because with electricity, a lot of the downstream benefits actually came to, like, the users of the electricity rather than the actual entity producing the electricity. On the other hand, with social media, it was the opposite case, right? Social media, you know, it was everywhere. Everybody uses social media, but the rents went to the platform. But that's a really interesting point. The more. more you think, I don't endorse this take yet, I'm going to talk out loud, the more you think AGI is going to be, our economy is going to be run on AGI, the way our economy currently runs

on electricity. That is a broad fundamental transformation of the entire economy. The more it looks like electricity, and the more it's like every company in the S&P of the future. Exactly. If it's going to make it to the S&P 500, it is because it has leveraged AI. Exactly. And then you're indexed again. Yeah, exactly. But then again, I guess it is totally, if you just look at how concentrated the S&P is over time, you know, just like these big tech companies, much more so. I guess this goes to a fundamental point that it's hard to reason about, about how much of the gains from AI these individual private companies will be able to control. And I think like the open model thing is going to be a big point here, right? So like if we're indeed like we're in a world where it's like the open miles are models are six months behind the frontier, nine months.

then, you know, we'll hit AGI, we'll hit whatever. And, like, in six months, like, everybody has access to this resource. And this goes to show you that every question is connected to every other, because then that question about whether there's runaway gains connects to questions about recursional improvement. And even if not recursional improvement, then continual learning, which, or online learning, which lets a model learn on the jobs. If it's deployed, it gets to learn more.

And these are just sort of, like, technical questions or forecasting technical questions, which then impact, I guess, whether Uganda will have any purchase. on the returns of AGI. But it sounds like your answer really, the reason I'm emphasizing the question is I think both for the messy middle and for developing countries, a recommendation that is often made naively is you've got to do some kind of retraining,

you've got to do some kind of like jobs program, or you've got to have them build data centers in our country. And I think you guys are suggesting something closer to just buy the index of AGI. That's like probably much more cleaner and much more likely to succeed, strategy. It's really good.

These are the two scenarios, right? So I think there is a world where it is concentrated. In which case, it's going to be really hard to index AGI. Yeah. There is another world where it is not, it's electricity, then, like, basically every company has access to AGI. So you just buy, you just buy the index. So, like, you know, Nigeria just needs to buy the index.

Right. And Nigeria has access to AGI. Yeah. Right. Like, because of the open models. Yeah. So just to get back to the question of like about whether to go with retraining or just trying to index, I would prioritize trying to index, but just given how fast AI could, you know, hit the world.

But I definitely wouldn't just rely on that because like it could the sort of messy middle type cases or just the long timelines cases on which like you, we don't get anything like AGI all that soon. we'll still, you'll just be like leaving a lot of value on the table if you could have like retrained to be a bit better, you know, like educated to how, you know, how to use the latest wave of computing. And yeah, so I don't, I don't think there's that much of a, an either war there. I mean, maybe the reason to be pessimistic about this is because one of the reasons the country's poor is that is a bad education system and to becoming the best in the world are retraining people at using AI. It doesn't seem like a particularly promising, particularly promising strategy for those, that, that for, that for, that for, that for. country. Although there are cases where, like, in developing countries, you had this, like, leapfrogging effect. With, like, for example, like mobile banking or something like that, it's much more prevalent than, like, Nigeria than it is in Germany or something like that.

Like, everybody is doing mobile banking. They have it on their phones. They're constantly doing this sort of thing. So, I mean, I, again, I'm not putting probabilities on this, but, like, with a transformative technology like AI, you could get leapfrogging. Yeah. Where, you know, you skip the step in the middle and you can get like really astronomical growth. Maybe. Just about the ease of indexing. Can I just quickly say?

I think it's definitely something to worry about a bit and keep an eye on. But as discussed in our own essay and as other people have pointed out, it's already not that hard to index. So it's not, there's been a bit of an increase in the privatization of returns. But it's still like, you know, well under 20% of the total market tax. of non-tiny companies in the U.S. is private. And, you know, everyone thinks about open AI and anthropic. And then if that's where all the wealth will accrue, then yeah, like all these questions about whether open models will stay only a little bit behind.

You know, those are important. But, you know, even they look like they're going public before too long, probably. And the frictions that have been keeping companies from going public might themselves be alleviated by AI a lot, right? Just all the disclosure requirements and whatnot. They want to get access to more potential investors too. And if I had to guess, I would guess that the kind of long kind of general trend of just like lowering those frictions and making it easier for more and more people to index more

and more will continue despite the recent bump in the other direction. This actually makes me hope even more so than before that the labs do get commoditized. or at the very least they go public as soon as possible, but hopefully they just get totally commoditized because I think AI will be much more popular and more importantly will be much more likely to lead to broad increases in prosperity if the gains are just not particularly...

It is as hard to capture the gains of AI as it's to capture the gains of electrification. Yeah, exactly. So I think like everybody, there's no anti-electricity people out there, right? I mean, electricity doesn't take your job, but... well.

It took some people's jobs. Yeah. Yeah. And I think it's, I, you know, this is maybe a tangential to the conversation. I think like there's, there's like a really, narratives matter. And there's this like really negative narrative around AI right now. But that's because people are not putting out the positive narrative or because, and there's a reason. It's, it's more difficult to imagine something that doesn't exist that's a good thing than losing something that

exists. Right, yeah. Right. So it's very easy for somebody to go on a podcast than to say, like, these jobs that you like, they're going away, than to somebody to spin up like a utopia, which doesn't exist yet. I hope this isn't too out of left field, but I think I would be remiss if I didn't point out one big cost of having commoditized frontier AI models, which is the, the tech race dynamic, right? That, like, for safety purposes, you might want fewer frontier companies so that each one has a buffer in case they want to slow things down to make things safer. And the way this relates to our point before about the kind of widespread access of the returns, is that I think there's a lot less of a trade-off there than some people imagine where some people

think either Frontier AI gets commoditized and we all enjoy the benefits, but there might be some risk because the market's really competitive and cutthroat. or things are safer because it's a big gap between the leader and the laggard, but that means that the leaders get fantastically wealthy. No, like you could just have a relatively big gap, but it's a public company ownership and it's widely distributed. Yeah, yeah, yeah. More recently, I have been thinking that the risk of commodification,

which is that it sort of diffuses the, it diffuses the ability to use AI to harmful ends is worth the benefit that I just feel I worry that not only having these concentrated labs makes it so that the sort of surplus isn't as widely distributed through society, but also it creates a very tangible, clear political target for the government to, I mean, we saw this with the Defense Production Act threat against Anthropic. If there wasn't one lab that is, or a couple of labs that are clearly ahead of others, this kind of threat would be much harder to make. Thank you guys for doing this.

Yeah, thank you. Thank you. I feel like there's a lot of unresolved questions, but it is helpful to know what the relevant, at least like what is the first branch along all these important dimensions. Great. Thank you. Okay. Six.