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Chapter 1: What economic factors influence the future of AI and automation?
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 Epoch 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?
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.
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. You know, 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 a performance. But only humans have that preference. So there's this human economy where humans are doing services for each other. And part of their wealth is flowing to other humans.
But part of their wealth is also... They will want some of the automated goods this 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.
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Chapter 2: How do we define scarcity in a world dominated by AI?
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 like the human-only economy will become a smaller and smaller share?
I would like to pitch kind of a rephrasing of that question. So I think my view is that kind of forecasts 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.
Chapter 3: What strategies exist for taxing and redistributing AI-generated wealth?
The reason I think that, so there was this blog post by Andre Fredkin, Brian DeBerry, and then Andrew Coe that came out yesterday, actually, that looked at like kind of people's forecasts, economists' forecasts about the labor market. And what they found is that there's a ton of disagreement, like in every single direction.
So what they advocate for, and I think I'm in agreement here, is rather than thinking about individual forecasts, like what me and Phil are going to do, rather looking at kind of like basically generating prediction markets, where you get aggregate forecasts, where you get like kind of wisdom of the crowd effects.
And kind of the reason that I think this is because we have been famously terrible at forecasting.
Chapter 4: Why is demand collapse considered unlikely in the AI economy?
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. So David Ricardo is one of the classic economists, not neoclassical, classical economists. And he, when industrial revolution started happening, he 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 going to be really bad. Everybody's going to become unemployed and there's going to be political unrest and things like that.
Chapter 5: What challenges do human employees face in a machine economy?
And if you look at Ricardo's predictions, they're actually right. If you look at all those jobs that made money in Ricardo's time, they got automated. So if I was David Ricardo and I woke up and somebody told me all those jobs did get automated. And you asked me, Dave Ricardo, like, what do you think the prime age employment rate is in 2026?
Chapter 6: How might intrinsic wealth accumulation impact human and AI interactions?
I think he would be surprised if you told him it was the highest it's ever been other than 2000. 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 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 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 considered the fact that new jobs would be created. But it's kind of not obvious that, like, money would go to services. Like, why wouldn't they go to more automated goods 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 maybe a really useful tool that economists have is instead start with a premise. Like maybe we'll start it today. Look, labor share is zero. Like labor share has gone down. What could possibly explain this?
Chapter 7: What should developing countries do to benefit from AI advancements?
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? And if you don't take anything out of this conversation for me, we don't have any data. I've been kind of saying we need a Manhattan Project for data.
We don't have data on basically consumer demand elasticities. We don't know what they are. We're not really tracking what jobs are getting created or destroyed. like the O-Net database with all of the tasks and different jobs that's been rarely updated at super low quality. And so what I think is really useful is to think about like, what are the potential scenarios?
And we'll be talking about a lot of these scenarios, mapping them out and to say, what dimension of scarcity will generate that scenario. So if there's full employment, we could talk about the relational sector or something like that. If there's very labor share collapses, we can talk about other sorts of scenarios. And then that will tell us what data we should be collecting.
It's probably worth the defining labor share and capital share real quick.
So-
The whole economy, like the total sum of goods and services sold is either paid out to people in wages 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 get paid out and...
For many hundreds of years in the economy, 60 something percent of the economy or all the things that are sold in a given year basically gets paid out to humans and wages. And the other 30, 40 percent gets paid out to people who own machines and land and claims on companies and whatever. And the question is, well, right now, 60 percent is going to wages. Does that shrink as automation evolves?
Or as EIs get smarter and smarter and better and better.
And it's like, it really, this is a called or fact, like, right? So it's incredibly, we should stress this. It's incredibly surprising that it's over 60% after the industrial revolution, after all of the automation we've ever seen. The fact that it's almost like some people are worried it's an accounting error or something like that, that it's kept being, been so constant.
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Chapter 8: How can countries outside the AI production chain participate in its gains?
But, you know, depending on how you – there's been a lot of accounting changes in the last 30, 40 years. So, for example, Andy Atkinson has this paper showing that actually if you keep the accounting constant over the years, labor share hasn't even fallen ever.
But it's not that surprising, right? I mean, if – Phil, you made this point that if labor and capital are complements, you need both to do anything. It kind of makes sense that you'd kind of need to pay both of them to get something done.
You have had stuff be completely automated.
Although you had the post where you were pointing out that actually... Oh, yeah.
Well, I was going to say there's a sense in which nothing's yet been completely automated. If you look at the network adjusted factor shares of a good, which is to say you look down the supply chain and say, not just like the final step, how much of that is done by capital and labor, but what went into the machines that can automate that final step?
you'll find that labor's 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 50%, it's not 100%.
I do think there's this qualitative shift that I think we agree is coming, which is that there will be at least some goods whose network-adjusted capital share goes to one, right? Because the whole supply chain can be automated and there's no part in it that we care intrinsically about having a human do. So that'll be a, you know, that'll be a qualitative shift.
Interestingly, the implications of that shift for the overall capital share are ambiguous because if we, let's say that we've got the two sectors, the human intrinsic sector with the ballerinas and everything else, right? Right now, everything else has been scarce because of the lack of labor in it, right?
But if we fully automate the supply chains for everything else, right, and we satiate everything else really fast, then the quantity of everything that's not a ballerina, say, goes to infinity, but... or the marginal utility in that stuff goes to zero faster than the quantities rising.
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