Episode summary
The better AI gets, the smaller its share of the economy might get – Alex Imas and Phil Trammell
Dwarkesh Podcast
· · 1 hr 16 min · Hosts: Dwarkesh Patel · Guests: Alex Imas, Phil Trammell
Summary
The conversation argues that the main question after AGI is not simply whether jobs disappear, but what remains scarce and therefore valuable. The host and the guests focus on two broad possibilities: a machine economy where most production is automated, and a relational sector where humans still pay for services that require a real person in the loop. They stress that labor share could stay high for a long time, but it could also fall sharply if whole supply chains become fully automatable and consumers keep finding new uses for capital-intensive goods like compute and robots.
A major theme is that standard intuitions are unreliable, so the guests prefer scenario-building and data collection over bold forecasts. They argue that economists lack good data on demand elasticities, task-level automation, and willingness to pay for human involvement, and that many apparent trends may be misread as AI effects before the evidence is clear. They also suggest that even when automation causes job losses, it may simultaneously expand the technological frontier and create enough wealth to make broad redistribution feasible.
The discussion then turns to policy. The guests compare negative income taxes, UBI, universal basic capital, wealth taxes, and consumption taxes, emphasizing political durability, targeting problems, and implementation speed. They also worry about a “messy middle” where automation displaces workers faster than growth or redistribution can compensate, especially if unemployment rises just enough to shift politics. For developing countries outside the AI supply chain, they say the risk is being left behind unless they index into the gains early.
Key takeaways
- The host and guests think the key AGI question is not only employment, but whether human-intrinsic goods, compute, or some other category becomes the scarce bottleneck.
- Phil Trammell argues that labor share could still stay high, but it could also go to zero if capital keeps finding new uses and human-in-the-loop goods are not demanded enough.
- Alex Imas says economists have too little data on task-level automation, demand elasticities, and willingness to pay for human participation, so scenario analysis is more useful than point forecasts.
- The guests think a “messy middle” with widespread layoffs but not much new wealth is possible, but they treat it as a narrow and politically unstable window.
- On redistribution, they compare UBI, negative income taxes, universal basic capital, and consumption taxes, and stress that targeting and political durability are the hardest problems.
- They say middle-income countries outside the AI supply chain could either be leveled up by diffusion or left behind if they fail to index into AI-driven gains early.
Notable quotes
“What is scarce tells you where the value will accrue”
“We don't have any data. I've been saying we need a Manhattan Project for data”
“The whole supply chain can be automated, and there's no part in it that we care intrinsically about having a human do”
“The thing about software is this is a particular kind of good where as it gets cheaper, we'll want more and more of it”
“I think the biggest lack of resources that we have allocated in the economics profession is thinking about middle-income developing countries in the age of AI”
How the episode opens
Today I'm chatting with Alex Imas, who is Director of AGI Economics at Google DeepMind and Professor of Economics at the University of Chicago, and Phil Trammell, who is Head of Economics at Epoch and research scholar at Stanford. In general what I want to understand in this interview is what economics tells us about what we can expect in a world with more and more automation and more advanced AI. I want to understand what that tells us about what will happen to wages and the labor share, what the best way to tax and redistribute the wealth generated by AGI will be, and what kinds of things will be scarce.
What is scarce tells you where the value will accrue. I want to start there. What are some plausible candidates of what will be scarce?
Something like the relational sector, which is defined as services and goods where the fact that a human was in the loop is part of the value of that product. Because humans are naturally scarce, if we have automation where a lot of other things stop being scarce, we will still have scarcity in the things that humans are 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.
Here's maybe one intuition pump. In a world where AI can physically do anything humans can do, there's this whole machine economy where they're building factories and doing research and coming up with new ideas. Humans may or may not be involved in the physical production of those things, but probably not in the ultimate limit, if robotics is solved.
If you don't care about humans being involved in that process, why would they be? But then there are these other things you point out where we actually do want the ballerina or the barista to be a human.
From the episode’s captions: roughly the first two minutes, lightly cleaned. Keep watching on YouTube →
Topics
- agi economics
- ai taxation
- wealth redistribution
- inequality risk
- developing countries
Companies and tickers mentioned
- GOOG
- GOOGL
Watch the episode
Summary, takeaways and quote selection by Octagon. Quotes are short excerpts; the full conversation is in the episode.
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Dwarkesh Podcast, this episode and its artwork belong to Dwarkesh Patel. Octagon is not affiliated with the show; this page is an independent summary.
