Alex Imas and Phil Trammell – What remains scarce after AGI?
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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.
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.
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.
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.
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Chapters
8 chapters
1
What economic factors influence the future of AI and automation?
0:00–2:10
2
How do we define scarcity in a world dominated by AI?
2:10–2:38
3
What strategies exist for taxing and redistributing AI-generated wealth?
2:38–3:15
4
Why is demand collapse considered unlikely in the AI economy?
3:15–3:51
5
What challenges do human employees face in a machine economy?
3:51–4:12
6
How might intrinsic wealth accumulation impact human and AI interactions?
4:12–5:24
7
What should developing countries do to benefit from AI advancements?
5:24–7:44
8
How can countries outside the AI production chain participate in its gains?
7:44–1:16:07
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