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

Economics of AGI episode w Alex Imas https://www.aleximas.com/ and Phil Trammell https://philiptrammell.com/. 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 https://youtu.be/Jj-kBHzUohs; read the transcript https://www.dwarkesh.com/p/alex-imas-phil-trammell. Sponsors Jane Street https://janestreet.com/dwarkesh 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/dwarkesh https://janestreet.com/dwarkesh Google’s Gemini Omni https://gemini.google 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 https://gemini.google or in Flow at flow.google https://flow.google Cursor https://cursor.com/dwarkesh 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/dwarkesh https://cursor.com/dwarkesh Timestamps (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? This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.dwarkesh.com https://www.dwarkesh.com?utm_medium=podcast&utm_campaign=CTA_1

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Dwarkesh Patel?

  Today, I'm chatting with Alex Emas, who is Director of AGI Economics at Google DeepMind and Professor of Economics at University of Chicago, and Phil Trammell, who is Head of Economics at EPOC 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 will be generated as a result of AGI will be, and what kinds of things will be scarce? Because what is 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?

• 0:44Speaker 01

  Something like the relational sector, which is what I defined as, 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.

• 1:05Dwarkesh Patel?

  I'm curious to understand whether humans doing services for other humans can ever 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 that 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.

• 1:50Dwarkesh Patel?

  But only humans have that preference. So there's this human economy where humans are doing services for each other, and part of their wealth is flowing to other humans. But part of their wealth is also like, they will want some of the automated goods that the 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 in the machine-only economy are a closed loop, because the machines don't care about getting the human barista to make them a coffee. And so within that model, isn't it intrinsic that the human-only economy will become a smaller and smaller share?

• 2:24Speaker 01

  I would like to pitch kind of a... Refrasing of that question.

• 2:27Speaker 02

  Sure.

• 2:27Speaker 01

  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 Phil are talking right now are not necessarily very useful.

• 2:38Speaker 02

  Hmm.

• 2:38Speaker 01

  The reason I think that so there was this blog post by Andre Fredkin, Brian DeBarry, and then Andrew Ko 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 in agreement here, is rather than thinking about individual forecasts like what me and Phil are gonna do, rather than 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 go all the way back to 1820. This sort of debate that we've been having actually is like 200 years old.

• 3:22Speaker 01

  So David Ricardo is one of the classic economists, not neoclassical, classical economists. And he, when industrial revolutions started happening, he was, wrote a bunch of stuff saying like, look, this is gonna be great for everybody, prices are gonna 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 gonna be automated by these machines. This is gonna be really bad, that everybody's gonna become unemployed, and there's gonna 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,

• 4:06Speaker 01

  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 it's ever been other than 2,000. We have the highest number of employed people that could potentially be employed since 2000, 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, considered the fact that new jobs would be created.

• 4:52Speaker 01

  But it's kind of not obvious that like money would go to services. Like, why wouldn't they go to more automated goods or 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 may be 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 to write down a model to say, hey, what if labor share just stays the same? What can make that happen?

• 5:36Speaker 01

  And here's my, if you don't take anything out of this conversation from 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 elasticities. 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 ONET 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.

• 6:19Speaker 01

  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.

• 6:27Dwarkesh Patel?

  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.

• 6:38Speaker 01

  Yeah.

• 6:39Dwarkesh Patel?

  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 would get paid out. And for many hundreds of years in the economy, Sixty something percent of the economy for all the things that are sold in a given year basically gets paid out to humans and wages and the other thirty forty percent gets paid out to people who own machines and land and claims on companies and whatever. And the question is, well, right now, if sixty percent is going to wages, does that shrink as automation or as EIs get smarter and smarter and better and better?

• 7:14Speaker 01

  And it's like it really, this is a call to our fact, like, right? So it's incredibly, we should stress this, it's incredibly surprising that it's over sixty percent 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 being, been so constant. And the fact that it's like been over sixty percent 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 twenty thirty years, but you know, depending on how you, there's been a lot of accounting changes in the last thirty forty 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.

• 7:59Dwarkesh Patel?

  But it's not, it's not that surprising, right? I mean, if, Phil, you made this point that if labor and capital are complements, you need both to do anything, it would kind of make sense that you'd kind of need to pay both of them to get something done.

• 8:11Speaker 01

  You have had stuff can be completely automated.

• 8:13Dwarkesh Patel?

  Although you had the post where you were pointing out that actually, sorry.

• 8:17Speaker 03

  Oh, yeah, well, I was gonna 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 US have a very stable capital share, network adjusted capital share of around fifty percent, it's not a hundred percent. 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?

• 8:59Speaker 03

  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, 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,

• 9:40Speaker 03

  say, goes to infinity, but are the marginal utility in that stuff goes to zero faster than the quantity is rising.

• 9:48Speaker 01

  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.

• 10:27Speaker 01

  So you could have, you could have a job and a service or a good be a product of different types of tasks. And you can 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 automated, 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.

• 11:02Speaker 03

  Right.

• 11:02Speaker 01

  Right? So I think we don't have data to say like, here are relational jobs, here are 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 by machine. Here's the counterfactual where this one task is not pursued. 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 gonna make in this in this story, right?

• 11:35Dwarkesh Patel?

  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.

• 11:49Speaker 01

  Absolutely.

• 11:49Dwarkesh Patel?

  That's fully produced by the AIs.

• 11:50Speaker 01

  And that that's kind of Phil's point.

• 11:52Dwarkesh Patel?

  That's right.

• 11:53Speaker 01

  And you you can you can make it is that, look, you know, you could have an increase in variety and 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 to the 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 laborship. It goes to zero.

• 12:25Dwarkesh Patel?

  Phil, I liked your analogy to some Mongolian economist sitting around in fourteen hundred, thinking about what will be scarce and the limits of that kind of analysis. Do do I think you should talk to that?

• 12:35Speaker 03

  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

• 12:49Speaker 03

  they looked at the jobs that were sort of intrinsically human, like being being a singer, say. And they looked at the the things that were not intrinsically human, like, you know, the transportation services provided by their horses or the the different kinds of food they had. If they just kind of held the varieties fixed on 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 left spending all of our money on on singers.

• 13:30Speaker 03

  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 has stayed sort of negligible.

• 13:47Speaker 03

  So likewise, that's sort of my central prediction about how the future unfolds, though it could go either way.

• 13:52Dwarkesh Patel?

  I I was gonna make a point, and I realize it's a fallacy, but the reason it's a fallacy is interesting. So I was gonna say.

• 13:59Dwarkesh Patel?

  I mean, it's just hard to imagine a world where there's trillions upon trillions of robots, but there's only some billion odd 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 Carlsen and-

• 14:16Speaker 01

  Or financial advisors, or doctors, or tutors.

• 14:19Dwarkesh Patel?

  Or podcasters, or whatever.

• 14:20Speaker 01

  Or podcasters.

• 14:21Dwarkesh Patel?

  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 been decreasing. The point that you made is that, one way to think about Moore's Law, you know, what sets price? Well, the price is of a supply and demand. And so, not only are we producing more transistors more cheaply,

• 15:03Dwarkesh Patel?

  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-

• 15:11Speaker 03

  Oh yeah, I like the, yeah, the pessimistic framing of Moore's Law is every 18 months, the value of computation halves.

• 15:18Dwarkesh Patel?

  Yeah.

• 15:18Speaker 03

  Right? Like, we're just running out of uses for computation so fast that it's sustaining Moore's Law.

• 15:23Dwarkesh Patel?

  And this is in fact, like, literally relevant to a conversation about AI where-

• 15:27Speaker 03

  Yeah.

• 15:29Dwarkesh Patel?

  Maybe for the first time, this is no longer true.

• 15:32Speaker 03

  Right.

• 15:32Dwarkesh Patel?

  So, the famous fact here is that an H100 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.

• 15:50Speaker 01

  But this is Phil's point about increasing variety.

• 15:53Dwarkesh Patel?

  Right.

• 15:54Speaker 01

  What we have done is increased, increased 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, you, you jump back up.

• 16:06Dwarkesh Patel?

  Yeah, you could imagine we just never satiate demand for compute. And as long as that stays the case- Um, then the share of the economy that is going towards compute would keep increasing.

• 16:15Speaker 01

  And that 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, what number of new uses are we figuring 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

• 17:00Speaker 01

  for like empathy and connection and, you know, getting, getting, uh, uh, interacting with another person. So like, one of the experiments that we ran was like, there, 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 print? People are actually paying real money for it. And then we say like, look, there's only one, one of those art prints. Uh, and it's either made, and these are between subject conditions by, by AI or by a person. So with one, you get the effect that the person produced art print is valued much, much higher than, 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.

• 17:42Speaker 01

  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.

• 17:51Dwarkesh Patel?

  Right.

• 17:52Speaker 01

  And, you know, we need to do a lot more research on this, but it seems like that's kind of like the, the, the key difference between, you know, something like, let's say a horse, right? There's no, a horse was an input into a, 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, 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, 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, um, then this kind of story doesn't work anymore.

• 18:32Dwarkesh Patel?

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• 19:15Dwarkesh Patel?

  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 classroom space, 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 janestreet.com/thorcash. There's one possibility, which Molly Kinder has written something about this messy middle scenario. And 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.

• 19:55Dwarkesh Patel?

  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. So 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 allocated inefficiency,

• 20:38Dwarkesh Patel?

  like, 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 inero plausible where AI is actually automating a bunch of things, but there isn't enough wealth creation as there is automation?

• 21:05Speaker 03

  I think it's

• 21:06Dwarkesh Patel?

  Is that plausible?

• 21:07Speaker 03

  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.

• 21:19Dwarkesh Patel?

  Well,

• 21:19Speaker 03

  Unless in all of those professions, that's automating. It's just a hair more productive. So like the costs of all the capital to to replace all the software engineers is just, you know, a hair less than the cost of what we think is off.

• 21:33Dwarkesh Patel?

  And why is it implausible that it just like a company can save money by laying off a bunch of software engineers? But and in the long run, there's a Sheven's paradox thing and, you know, we can't just paint 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.

• 21:54Speaker 01

  I think the thing that like 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 winds completely change. Like unemployment, it has a huge effect on what happens politically. So, you know, to Molly'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 what

• 22:36Speaker 01

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

• 23:00Dwarkesh Patel?

  Yeah.

• 23:01Speaker 01

  And what ended up happening, there's a really nice QG paper on this, basically showing that they got reabsorbed into the economy.

• 23:09Speaker 01

  But at lower salaries and they were mostly underemployed. And I think that's the scenario that Molly was was writing about this like kind of messy middle where like things aren't a disaster because we saw with COVID like the fiscal fiscal response can move quickly if there's an emergency.

• 23:25Dwarkesh Patel?

  Right.

• 23:26Speaker 01

  And an emergency is a quick uptick in unemployment, which could even look like two or 3%. That's like a national that becomes a national emergency if it becomes fast.

• 23:35Dwarkesh Patel?

  The concern is that suppose 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 you have like the money you've saved off a couple of people.

• 23:56Speaker 01

  Yeah.

• 23:56Dwarkesh Patel?

  And unless you can figure out exactly how to get to them specifically, you have the problem of can I do like, can I do a UBI off the money I save?

• 24:04Speaker 01

  Yeah, so you're basically saying like, look, the pie did not grow that much.

• 24:07Dwarkesh Patel?

  Yeah.

• 24:07Speaker 01

  You're just basically you're just basically displacing a bunch of people, but that actually didn't grow the technological frontier of what the economy can produce.

• 24:16Dwarkesh Patel?

  And so then there's a question of like, well, maybe every time, I don't know if this is the case, maybe every time this has happened in history, the technological frontier has expanded a bunch and so.

• 24:23Speaker 01

  I think that's the case. I think simply in in in history, the technological frontier has expanded.

• 24:28Dwarkesh Patel?

  Yeah.

• 24:28Speaker 01

  So it's it's it's it's kind of, and I 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 less less expensive than the software engineer. So you're not getting this abundance effect.

• 24:50Dwarkesh Patel?

  Right.

• 24:51Speaker 01

  And so you're where is the redistribution going to happen because the pie didn't grow?

• 24:54Dwarkesh Patel?

  Yeah, yeah, okay, so the the this is very helpful. So there's a many different things ought to be true for this 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 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 requires to do something like software engineering and what intelligence is is such that, you know, if you can really just like lay off all the software engineers, you've got enough in the bucket there that you could like automate all kinds of white collar work.

• 25:37Dwarkesh Patel?

  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?

• 25:56Speaker 01

  Yeah, I have some thoughts. I think, I think it's just really important to outline the costs and benefits. Like, it's also important to know that there, so first, there's differential complexity in like implementing these things. Two, they differ in the timeline of like being actually helpful. So like something like universal basic capital, that's not like, 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 it, the day it turns into law, that is already, 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

• 26:40Speaker 01

  know, if you earn more money, you get taxed more and things like that. And, but, you know, there's positives and negatives to negative income tax with UBI, the, for example, the, 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, that could turn into income, when that is no longer the case, and we are now at the mercy of the, of the elected official for like basic needs, right? So that, to me, feels like a power sharing arrangement that's really dangerous.

• 27:21Dwarkesh Patel?

  But wouldn't that be true of any sort of government redistribution program?

• 27:24Speaker 01

  So something like universal basic capital, where you have like an ownership share, and you have property rights for capital? Then you're just, you're just, you're just

• 27:31Dwarkesh Patel?

  You're a normal shareholder.

• 27:33Speaker 01

  You're just a normal person.

• 27:33Dwarkesh Patel?

  And, but this goes back to the question of indexing, because if indexing is hard, then universal basic capital is hard.

• 27:38Speaker 01

  And that's the, that, that's the problem of universal basic capital is targeting.

• 27:41Dwarkesh Patel?

  Right.

• 27:41Speaker 01

  Right. What do you target to put into people's portfolios?

• 27:44Dwarkesh Patel?

  Like what if Anthropic goes to zero, but some random robotics company takes all the surplus?

• 27:47Speaker 01

  Exactly. So that's the risk of universal basic capital. With a negative income tax, you have the same sort of issues that with UBI, where, 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, you have the issue of the floor being

• 28:02Dwarkesh Patel?

  Right. One concern with the wealth tax is that, you know, you, there's no political, politically sustainable equilibrium at like 0.5% wealth tax. And, you know, I mean, this happened with the income tax, of course, right? It starts low, it's like for war or something, and then it slowly and slowly escalates until the marginal tax rate in the US is probably on the order of, income tax rate is like 40% or something. And in certain states upwards of 50%.

• 28:28Dwarkesh Patel?

  With a capital tax, is there a reason to worry? Would, would that distort investment? Because people would just be like, why would I invest in Anthropic or Intel? The government's just going to take larger and larger shares of it and dilute my share?

• 28:40Speaker 03

  Well, 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, by broad-based tax and then buying Anthropic.

• 28:54Dwarkesh Patel?

  Yeah.

• 28:56Speaker 03

  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.

• 29:06Dwarkesh Patel?

  Yeah. But how, so you're, you're suggesting there could be a tax that is some sort of optimal tax. It's, 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

• 29:19Speaker 03

  Or consumption, yeah.

• 29:21Dwarkesh Patel?

  Huh?

• 29:21Speaker 03

  Or consumption.

• 29:21Dwarkesh Patel?

  Okay. So a consumption tax. Like a European value added tax type thing that allows the government to go buy a bunch of stocks. And then they just distribute those stocks to everybody.

• 29:34Speaker 01

  That's David Otters.

• 29:35Dwarkesh Patel?

  Yeah, yeah, I mean, that's not gonna be that different from just like redistributing the stocks, but it'll be a little different. Yeah.

• 29:41Speaker 01

  That's what social, that was the proposal for social security, by the way. That was privatizing social security, right? So it's like you have, 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 gonna keep working. Like basically privatizing social security was giving everybody a basket of stocks.

• 30:00Dwarkesh Patel?

  Right. All right. I'm curious to understand. People talked about whether there's a white collar apocalypse already. Is there any evidence?

• 30:11Dwarkesh Patel?

  That suggests that there is mass automation or unemployment as a result of AI already?

• 30:17Speaker 01

  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. Uh, so the budget lab over Yale is doing really good analysis on this. They just recently released a report. And I think like, you really have to squint to see anything happening. Like basically, if you want to take kind of like, uh, an approach across the entire economy and looking at, 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. And that, but that's like a less than before, rather than a level shift. As then there's actually an increased demand for senior manager, for senior software engineers, if anything.

• 31:03Speaker 01

  And so if you look at trend, it's kind of like for junior managers, it's a bit below trend.

• 31:08Dwarkesh Patel?

  So as in you're saying the growth is slower than before.

• 31:10Speaker 01

  Yes.

• 31:11Dwarkesh Patel?

  But there is still growth even on entry level software engineers.

• 31:14Speaker 01

  Yeah, exactly.

• 31:14Dwarkesh Patel?

  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?

• 31:22Speaker 01

  I think that's anecdotal evidence.

• 31:24Dwarkesh Patel?

  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 a normal layoff and they turned it into an AI layoff?

• 31:33Speaker 01

  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 gonna just get a cascade effect.

• 31:51Dwarkesh Patel?

  Right.

• 31:51Speaker 01

  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 actually worse off.

• 32:01Dwarkesh Patel?

  Right.

• 32:01Speaker 01

  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 where, where, you know, using AI, like you have the, you've 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.

• 32:23Dwarkesh Patel?

  And is that surprising at all? I feel, given the fact, all these things they have to do, it was just like, this is a story as old as time. If you automate some complimentary task, the overall bucket of things that the, the human labor which complements the automation.

• 32:37Speaker 01

  Yeah.

• 32:38Dwarkesh Patel?

  Will increase in value.

• 32:39Speaker 01

  So this is, this is one of the statistics that's really important for that argument is elasticity of demand.

• 32:44Dwarkesh Patel?

  Yeah.

• 32:44Speaker 01

  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, 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 now there's, it's being bought more, it's being used more, the service is being used more, that could actually lead to more hire.

• 33:16Dwarkesh Patel?

  Right.

• 33:16Speaker 01

  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.

• 33:25Dwarkesh Patel?

  Right, yeah, yeah.

• 33:26Speaker 01

  Which suggests that, at least for now, given the way the jobs work, it might be elastic.

• 33:31Dwarkesh Patel?

  But I think this elasticity 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 Jevon's paradox. Yeah. 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 coal 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 where there is not super elastic demand. If oil, for example, gets super cheap, it's not like magically. Or insulin, right? Yeah, exactly.

• 34:14Dwarkesh Patel?

  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 the long run elasticity is higher than the short run elasticity. But even the long run, so agriculture famously is the example where we can produce way 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 100 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 a it is not some inherent property of markets that as it gets cheaper, you will just keep wanting more of it. Absolutely not. It is the claim about software is this is a particular kind of good

• 34:57Dwarkesh Patel?

  where as it gets cheaper, we'll want more and more of it. It is also highly relevant, and you ordered an essay about this. A lot of 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 Satriani, 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. Their salaries, which were, 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?

• 35:31Speaker 01

  Well, I mean, so part of it is plausible, part of it's 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 can quibble about that, but that's not the issue. The issue is that they talked about negative economic growth.

• 36:00Dwarkesh Patel?

  Right.

• 36:01Speaker 01

  And so what I did in the, in the piece that actually Phil and I had a back and forth on, was to say like, let's start with the, 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, the holders of capital, like rich people, basically like, basically what you have in this, in those sorts 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 tech 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.

• 36:46Speaker 01

  And for that money to not enter as investment.

• 36:49Dwarkesh Patel?

  Right.

• 36:50Speaker 01

  Right. Which is like, and then you can get negative growth, which is like.

• 36:53Dwarkesh Patel?

  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? It's crazy, right? Where 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.

• 37:10Speaker 01

  Yeah.

• 37:10Dwarkesh Patel?

  And that's like driving more economic growth.

• 37:12Speaker 01

  Yeah. And so I, I've sent the essay to Phil and Phil actually wrote back being like, this is pretty dumb.

• 37:17Dwarkesh Patel?

  Yeah.

• 37:17Speaker 01

  Like my essay saying like, you're, you're trying to say that there's going to be negative economic growth, but they're, these are very implausible conditions. And I was like, actually, that's the point of the essay. 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, I think it was great that it's written because it kind of started a conversation, but it's just like. 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.

• 37:54Dwarkesh Patel?

  Right.

• 37:54Speaker 01

  Here, the technological frontier is expanding. You actually have abundance, and for abundance, to generate negative economic growth. That's really hard to get.

• 38:03Dwarkesh Patel?

  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.

• 38:45Dwarkesh Patel?

  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.google. We're talking a second about why there isn't more automation as a result of LLMs.

• 39:30Dwarkesh Patel?

  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 produced in the economy, that you've got to make sure everything is reliable and works. Well, and you can't automate an 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, this might explain why there's a lot 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

• 40:15Dwarkesh Patel?

  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 in neural ease, 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 costs and worries of a reliability that will actually make it hard to integrate humans into future production flows.

• 40:41Speaker 03

  Yeah, that, that seems right to me. In particular, I just want to distinguish between the, 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 10 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 can 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 half of the job anymore, because a human

• 41:24Speaker 03

  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.

• 41:34Dwarkesh Patel?

  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 and, or even software engineers where there's a pretty good probability that the thing worked as you expect, but the, the thing you're paying the lawyer for is like, no, really, my company's not going to go under because.

• 41:52Speaker 01

  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 like the ability of the human to actually perform the service.

• 42:23Speaker 03

  Yeah, yeah, I mean, you know, all of these frictions on the political type decisions that we are accustomed to only trusting humans, 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 undergather bands to empires or whatnot. And yeah, once an AI-run political system is much more efficient than the alternatives, then those will probably tend to outcompete the others and, you know.

• 43:07Dwarkesh Patel?

  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 was 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 a hundred trillion dollar world economy, what it produces. And so why not expect the same thing of AIs in the future? This is not, this is not even a world with catastrophic misalignment. That is to say, they just kill everybody.

• 43:47Dwarkesh Patel?

  But there will, 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? Well, it will favor probably firms or agents that grow, right? There's just 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? Probably like saves more, it like has unsatisfiable demand for things like.

• 44:26Dwarkesh Patel?

  Whatever the relevant resource happens to be computed is an obvious one. And can we use that to make some prediction about what the non-human preferences that will be guiding the future?

• 44:34Speaker 01

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

• 45:17Speaker 01

  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.

• 45:26Dwarkesh Patel?

  Right.

• 45:27Speaker 01

  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. Um, 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 like a moral emotion like from the using Jonathan Haidt's 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.

• 46:10Dwarkesh Patel?

  It depends on how the reproduction is happening.

• 46:12Speaker 01

  Fair, but if we're if we're in, you know, 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 know, you had David Reich on the show, like his point on the last podcast was that, you know, we're buzzing with natural selection.

• 46:32Dwarkesh Patel?

  Right.

• 46:32Speaker 01

  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.

• 46:40Dwarkesh Patel?

  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 having 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

• 47:25Dwarkesh Patel?

  because their wealth is compounding. It just have this like, almost Nick Landian 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?

• 47:41Speaker 03

  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 in the future compares to the marginal value of 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

• 48:23Speaker 03

  because they're, 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.

• 48:31Dwarkesh Patel?

  Yeah.

• 48:32Speaker 03

  Then the person who doesn't satiate in capital is going to have, if they're being rational, they're going to have a higher savings rate.

• 48:37Dwarkesh Patel?

  Yeah.

• 48:38Speaker 03

  Okay, so in the long run, they're going to have most of the wealth. And the overall capital share will basically be the capital share of that person spending, which is going to be.

• 48:49Dwarkesh Patel?

  It's important that this is, we're not talking about a hypothetical future.

• 48:52Speaker 03

  Yeah.

• 48:52Dwarkesh Patel?

  Elon Musk is talking about mass drivers on the moon.

• 48:55Speaker 03

  Right.

• 48:55Dwarkesh Patel?

  And he's like, by far the wealthiest person in the world. I mean, obviously currently his. His investments are going towards humans as well as machines. But I don't think he cares particularly that his like future researchers and engineers are humans versus AI.

• 49:08Speaker 03

  And he manages to reproduce fast as well.

• 49:11Dwarkesh Patel?

  Yes.

• 49:13Speaker 03

  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, uh, and so maybe in the long run, they'll save the most.

• 49:25Dwarkesh Patel?

  Right.

• 49:25Speaker 03

  Yeah.

• 49:28Speaker 03

  That does seem sort of right to me.

• 49:31Speaker 03

  And I would just also say, even if they do reproduce more slowly, like biologically, that might just not matter that much in the long run. Right. If, if, uh, they can live forever and, you know.

• 49:40Speaker 01

  The, the living forever is key.

• 49:42Speaker 03

  Yeah.

• 49:42Speaker 01

  Right. So I think, I think, again, like, and we're, we're, we're scenario building here, right? So I think, um, if you could live forever, like a lot of stuff changes for my story as well. I think, I think it's, um, uh, to your point about, you know, rich people just consuming, not consuming a lot and 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. Right. But if we get into a situation where people are satiated with capital, um, 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 they were, the incentive to invest is smaller. So basically you kind of think about this in general equilibrium.

• 50:27Speaker 03

  Yeah.

• 50:28Speaker 01

  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 and labor share high. And that's because.

• 50:46Dwarkesh Patel?

  Not necessarily. I think you're probably making the same point, but it, I mean, they could just, it could be that their investment has to be chittrated through actual laborers. But to go like do things where their investment will work, which like would not, in the future, only the consumption is human mediated. Right. Cause the investment can just be done by the robots.

• 51:04Speaker 01

  But if the returns are, if you, so we're in the scenario with, with like how you can keep high labor. Let's take that scenario. In the scenario with high labor share, for whatever reason, the returns to capital are going to be lower.

• 51:17Dwarkesh Patel?

  Yeah, that's right. And I mean, to the earlier thing where the messy middle, where you're saying, why this is implausible. I feel like we can do a similar thing here. Wherefore 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.

• 51:37Speaker 03

  Yeah, yes and no. I mean, so the 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.

• 51:45Dwarkesh Patel?

  Oh, interesting.

• 51:46Speaker 01

  Yeah, it's the it's the difference between like the the the potential frontier of technology and like what the realized prices of these things. Because you have relative prices that are important.

• 51:55Dwarkesh Patel?

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

• 52:19Speaker 03

  Yeah, it doesn't have to be a new product. It could be a human intrinsic product.

• 52:23Dwarkesh Patel?

  Right, although. If it's a human intrinsic 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.

• 52:33Speaker 03

  So we might want it the same as we want it now in the sense that like the marginal utility in a ball or a performance is exactly the same as now, right? But the margin utility in a robot might just be a lot lower than now, right? So in in units of robots, we want it a lot more than we want it now.

• 52:47Dwarkesh Patel?

  Right.

• 52:48Speaker 03

  Right. So would the interest rate be 30%? It depends what you mean by the real interest rate, okay? It might be that every robot now can turn into, you know, 100 robots next year, right? So in units of robots, the interest rate is 10,000%.

• 53:03Dwarkesh Patel?

  Right.

• 53:03Speaker 03

  But if the price of robots is falling really fast.

• 53:05Speaker 01

  Prices adjust. I mean, that's the whole. I think that's the whole point, is that.

• 53:09Speaker 03

  Yeah, but though but here prices are adjusting in this interesting way that too many macro models don't allow for, right? So what what a. 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.

• 53:28Speaker 01

  Yeah.

• 53:29Speaker 03

  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.

• 53:35Speaker 01

  Yeah.

• 53:36Speaker 03

  Every unit of capital next year is giving up way less consumption than each unit of capital this year. Cause like the, just one robot now turns into many robots next year, but, but the number of ballerinas is the same.

• 53:49Speaker 01

  And again, we're going to go back to the increasing varieties thing. 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, then it's a very different, different story.

• 54:04Speaker 03

  Yeah, right. But now we're talking about the consumption world, whereas for the investment side of things,

• 54:13Speaker 03

  there could be just some greedy titan of industry who keeps wanting more and more robots. And that alone would be enough.

• 54:20Speaker 01

  It would.

• 54:20Speaker 03

  To increase the marginal value of robots and therefore decrease labor share?

• 54:25Speaker 01

  Yes.

• 54:26Speaker 03

  Yeah, okay. But why are we not expecting greedy titans of industry to keep existing?

• 54:30Speaker 01

  I mean, greedy titans of industry historically have like built libraries and.

• 54:35Speaker 03

  But that's because they die and they're like.

• 54:37Speaker 01

  They all die. Everybody dies.

• 54:39Speaker 03

  Well, we'll see.

• 54:42Speaker 01

  But I mean, like conditional on people dying, I think like, you know, his, like, again, you, 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.

• 54:59Speaker 03

  Right.

• 55:00Speaker 01

  Being born who's, where their entire reason for accumulating wealth is just to accumulate wealth.

• 55:06Speaker 03

  Yeah.

• 55:07Speaker 01

  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 then the people end up, like, the stylized fact of titans of his, of industry, is like, you accumulate the capital and then you, like, buy a bunch of stuff.

• 55:33Dwarkesh Patel?

  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 they near the end of their life, they either hand it 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 fast than the economy grows, which their parents are 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 gonna 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,

• 56:18Dwarkesh Patel?

  determining the preferences of the whole economy, because this part is growing much faster than the other parts of the economy.

• 56:22Speaker 01

  I think you just, like, the, 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,

• 56:35Dwarkesh Patel?

  Right.

• 56:36Speaker 01

  that's just like, that's what they want, I think your story is totally right. But, but that's just like, not how usually preferences work.

• 56:43Dwarkesh Patel?

  Right.

• 56:43Speaker 01

  Like, you have enough, whatever, hedonics in your life, and then, then, like, the social status, all of this, or, you know, Rousseau wrote about this, Saint Augustine wrote about this. This is like a, kind of like a basic part of preferences. Now, two, you, you guys are arguing about something else, where, like, you, you could have such high concentration, that you could just have a couple of exceptions to the rule, and that's gonna be enough. And I, I have nothing to say about that.

• 57:09Dwarkesh Patel?

  Yeah.

• 57:10Speaker 03

  Yeah, I mean, I, I think the, the claim's a little stronger, 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, there've been these dissipation shocks, as they're called, so they've, like, given it to their kids. They've squandered it, or they put it in foundations, which, Which meant it. I mean, it's not really a shock, but I mean, is

• 57:34Dwarkesh Patel?

  Right.

• 57:34Speaker 03

  People went, people might have liked to, you know, fill the universe with monuments to themselves and sort of whatever. Live forever, very wealthy. And it's like a weird preference, but it's not a hypothetical preference. I think that's, that's the thing.

• 57:49Dwarkesh Patel?

  Yeah.

• 57:49Speaker 03

  But who knows what's going on in their heads. I think

• 57:54Speaker 03

  even without though, like the kind of intrinsic preference for accumulation, there are some instrumental reasons why people, some people might value accumulation, which is also worth bringing up. So there's

• 58:10Speaker 03

  desire for a political or philosophical or religious influence, right? So people get into 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, total 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 classical utilitarian, to me, the value is at least one way you could have a kind of almost unsatiating utility function in having wealth in the future is to create new happy beings, right? They just add to the total welfare of the world.

• 58:51Speaker 03

  You know, I mean, this, this idea goes at least as far back as like Bostrom's astronomical waste point that we could like put Dyson spheres around the stars and turn all the energy into really happy simulations and whatnot.

• 59:02Dwarkesh Patel?

  I think the particular greediness of this optimizer doesn't matter what they're greedy for. I think you're forgetting about utilitarian philosophy or whatever, like just a pure von Neumann probe has, I don't know what the, is this inaccurate to say it? 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 Neumann probe is a thing that will can exist, right? And that's like a very greedy optimizer.

• 59:27Speaker 03

  Yeah. I mean, if we're talking about like whether they'll dominate the economy, maybe this is a technicality, but

• 59:34Speaker 03

  you know, we, we only count.

• 59:38Speaker 03

  Final consumption goods and investment goods is GDP, right? If there's this phenomenon.

• 59:42Dwarkesh Patel?

  How does a von Neumann probe show up in GDP?

• 59:44Speaker 03

  Well, 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.

• 1:00:00Speaker 01

  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.

• 1:00:06Dwarkesh Patel?

  Right, yeah. But it 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?

• 1:00:13Speaker 03

  Yeah, I think it's possible the labor share is high the way we usually account it.

• 1:00:17Dwarkesh Patel?

  One of the biggest problems in RL 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 RL with textual feedback to deal with this problem and train Composer 2.5. I filmed on my iPhone, so apologies for the camera work.

• 1:00:37Speaker 02

  So we've generated this output.

• 1:00:38Dwarkesh Patel?

  Yeah.

• 1:00:39Speaker 02

  It's just a sequence of tokens. We're going to send that sequence of tokens to this model that's going to read it.

• 1:00:44Dwarkesh Patel?

  Yeah.

• 1:00:46Speaker 02

  And then it's going to isolate a specific, say, term that it says is problematic.

• 1:00:51Dwarkesh Patel?

  Yep.

• 1:00:51Speaker 02

  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.

• 1:01:00Dwarkesh Patel?

  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 midroll. 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/dwarke.

• 1:01:28Dwarkesh Patel?

  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?

• 1:01:52Speaker 01

  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 a 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.

• 1:02:36Speaker 01

  And because of, you know, automation, we can produce commodities in developed countries now.

• 1:02:43Dwarkesh Patel?

  Right.

• 1:02:43Speaker 01

  Then we don't even have, you know, the consumer market. And then that that world looks pretty, pretty bad.

• 1:02:49Dwarkesh Patel?

  Yeah.

• 1:02:49Speaker 03

  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 the power 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 would be falling really rapidly. It's 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 that the interest rate is high

• 1:03:33Speaker 03

  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.

• 1:03:39Dwarkesh Patel?

  And sorry, you're saying that the developing countries have some savings?

• 1:03:41Speaker 01

  Yeah, yeah.

• 1:03:42Dwarkesh Patel?

  In the developed world, that will be enough to produce a lot of surplus that they can then?

• 1:03:46Speaker 01

  They will now be able to consume a lot using their savings.

• 1:03:48Speaker 03

  So, but I mean, the messy middle could be like wider in this case. And they're starting from such a lower level in terms of like how much?

• 1:03:57Dwarkesh Patel?

  They have saved.

• 1:03:57Speaker 03

  And how much it's like actually indexed to the global economy.

• 1:04:00Dwarkesh Patel?

  Right, yeah.

• 1:04:02Speaker 03

  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

• 1:04:09Dwarkesh Patel?

  Right.

• 1:04:10Speaker 03

  the right supply chains or just subsidies to their own citizens to buy a little bit of it.

• 1:04:16Dwarkesh Patel?

  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 heirs index the economy and have it grow at the rate of economic, have their wealth grow at the rate of economic growth, and they would be trillionaires, their heirs would be trillionaires by now. But it just is, before index funds existed, it's just very hard to just get, get a represent, it's just a very small fraction of 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

• 1:05:00Dwarkesh Patel?

  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 blog posts, the average person has disproportionately less access to, as opposed to, you know, most of their capital is like having a random house, at least in the US.

• 1:05:28Speaker 03

  Or a part of a house.

• 1:05:29Dwarkesh Patel?

  Yeah, which is, as we were 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.

• 1:05:43Speaker 03

  Or the kinds of goods that the rich will bid up the prices of.

• 1:05:45Dwarkesh Patel?

  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.

• 1:05:57Speaker 01

  And this is why Georgian tax would not raise enough money for the sort of programs that we were discussing.

• 1:06:04Dwarkesh Patel?

  Right. But stepping back, the 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 modular some sort of use in universal banking income.

• 1:06:17Speaker 03

  In the developed world.

• 1:06:18Dwarkesh Patel?

  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 Nigeria own a lot of SK Hynix and like Anthropic? I'm guessing not, right? It's not enough for them to just own the S and P 500.

• 1:06:39Speaker 01

  So actually, this brings up a really important point. Like, is AI going to be like electricity or social media?

• 1:06:46Dwarkesh Patel?

  Right.

• 1:06:47Speaker 01

  If it's, so think about ComEd or ComEdison, whatever, 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 is ComEd like having like this huge amount of political power, social power, or something like that? No, because a lot with electricity, a lot of the downstream benefits actually came to like the users of the electricity rather than the, 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.

• 1:07:29Dwarkesh Patel?

  But that's a really interesting point. The 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, there's 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 and P of the future.

• 1:07:53Speaker 01

  Exactly.

• 1:07:54Dwarkesh Patel?

  If it's gonna make it to the S and P 500, it is because it has leveraged AI.

• 1:07:57Speaker 01

  Exactly. And then you're indexed again.

• 1:07:59Dwarkesh Patel?

  Yeah, exactly.

• 1:08:03Dwarkesh Patel?

  But then again, I guess it is totally, if you just look at how concentrated the S and P is over time, you know, just like these big tech companies, much more so. I guess this is 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.

• 1:08:19Speaker 01

  And I think like the open model thing is going to be a big, big point here, right? So like if, if we're indeed like, we're in a, we're in a world where it's like the open miles or models are six months behind the frontier, nine months, then, you know, we'll hit AGI, we'll hit whatever. Like in six months, like, everybody has access to this, to this resource.

• 1:08:39Dwarkesh Patel?

  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 recursive sub improvement. And even have recursive sub 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 question, or forecasting technical questions, which then impact, I guess, whether Uganda will have any purchase on.

• 1:09:03Speaker 03

  Yeah.

• 1:09:03Dwarkesh Patel?

  The returns of AGI.

• 1:09:06Dwarkesh Patel?

  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 gotta do some kind of retraining, you gotta do some kind of like, jobs program, or you gotta 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 a probably much more cleaner and much more likely to succeed, strategy.

• 1:09:33Speaker 01

  It's really, these are the two scenarios, right? So I think there is a world where it is concentrated.

• 1:09:40Dwarkesh Patel?

  Yeah.

• 1:09:40Speaker 01

  In which case, it's gonna be really hard to index AGI.

• 1:09:43Dwarkesh Patel?

  Yeah.

• 1:09:44Speaker 01

  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 use buy the index, so like, you know, Nigeria just needs to buy the index.

• 1:09:55Dwarkesh Patel?

  Right.

• 1:09:56Speaker 01

  And Nigeria has access to AGI.

• 1:09:58Dwarkesh Patel?

  Yeah.

• 1:09:59Speaker 01

  Right, like, because of the open models.

• 1:10:01Speaker 03

  Yeah, so just to, um, 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, um, I definitely wouldn't just rely on that, because like, it could, the, the sort of, um, messy middle type cases, or the just the long timelines cases, on which like, you, we don't get anything like AGI all that soon.

• 1:10:32Speaker 03

  We'll still, it'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, I don't think there's that much of a, an either or there like.

• 1:10:45Dwarkesh Patel?

  I mean, maybe the reason to be pessimistic about this is because one of the reasons the country is poor is that it's a bad education system. And so becoming the best in the world are retraining people at using AI. It doesn't seem like a particularly promising, particularly promising strategy for this, that, that for country.

• 1:11:02Speaker 01

  Although there, there are cases where like in, in develop, developing countries, you, you, you had this like leapfrogging effect. With like, for example, like mobile banking or something like that. It's more, it's much more prevalent than like Nigeria than it is in, in Germany. Or something like that, like there, everybody is doing mobile banking. They have, they have it on their phones, they're constantly doing this sort of thing. So, I mean, I, I, again, I'm not putting probabilities on this, but like with a transformative technology like AI. You could get leapfrogging.

• 1:11:31Dwarkesh Patel?

  Yeah.

• 1:11:32Speaker 01

  Where, you know, you skip the step in the middle and you can get like really astronomical growth.

• 1:11:38Dwarkesh Patel?

  Maybe.

• 1:11:38Speaker 03

  Just about the ease of indexing, can I just quickly say, I, I think it's definitely something to worry about a bit and keep an eye on. But as discussed in, in our own essay and as other people pointed out, it's already not that hard to index. So it's not, 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 cap of.

• 1:12:03Speaker 03

  Non, non tiny companies in, in the US is, is private. And, you know, everyone thinks about OpenAI and Anthropic. And then if, 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, 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.

• 1:12:45Speaker 03

  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.

• 1:12:53Dwarkesh Patel?

  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 is to capture the gains of electrification.

• 1:13:22Speaker 01

  Yeah, exactly. So I think like everybody, there's no anti-electricity people out there, right?

• 1:13:26Dwarkesh Patel?

  I mean, electricity doesn't take your job, but.

• 1:13:28Speaker 01

  Well.

• 1:13:29Dwarkesh Patel?

  It took some people's jobs.

• 1:13:31Speaker 01

  Yeah, yeah. And I think it's, you know, this is maybe 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.

• 1:13:59Dwarkesh Patel?

  Right, yeah.

• 1:13:59Speaker 01

  Right? So it's very easy for somebody to go on a podcast and to say like, these jobs that you like, they're going away than to somebody to spin up like a, an utopia which doesn't exist yet.

• 1:14:10Dwarkesh Patel?

  Right.

• 1:14:11Speaker 03

  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, you know, of the returns is that I think there's a lot less of a trade off there than some people imagine where, you know, some people think either frontier AI gets commoditized and we all enjoy the benefits, but there might be some risk because like it's, the market's really competitive and cutthroat or.

• 1:14:58Speaker 03

  Things are safer because there'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.

• 1:15:12Dwarkesh Patel?

  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.

• 1:15:55Speaker 03

  Yeah, thank you.

• 1:15:56Dwarkesh Patel?

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

• 1:16:05Speaker 03

  Great, thank you.

• 1:16:06Dwarkesh Patel?

  Okay.