AI Summary
5 min read“We have been famously terrible at forecasting.” That line from Alex Imas, director of AGI economics at Google DeepMind, lands early in this conversation with Phil Trammell of Epoch, and it sets the episode’s central discipline. Rather than predict whether artificial general intelligence will create utopia or catastrophe, the two economists work backward from possible endpoints—zero labor share, full employment, or something in between—to ask what must be true about scarcity, demand, and human preferences for each scenario to hold. The result is a careful, data-hungry framework for thinking about what remains scarce after AGI, and why the answer determines who gets the wealth.
The ballerina problem and the relational sector
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What you'll learn
- 1 Timestamped Outline
- 2 (00:15) **Introducing the Guests and Core Question** - Alex Imas (DeepMind/Chicago) and Phil Trammell (Epoch/Stanford) join to discuss what economics predicts about a world with advanced AI
- 3 (00:44) **The Relational Sector Hypothesis** - Humans may remain scarce in services where human involvement is part of the value (ballerinas, baristas, therapists)
- 4 (02:28) **Why Individual Forecasts Are Unreliable** - Alex argues economists have been famously bad at predicting automation's effects, citing David Ricardo's 1820 error
- 5 (06:30) **Defining Labor Share and Its Remarkable Stability** - Labor share has stayed ~60% for centuries despite massive automation
- 6 (09:48) **The Relational Sector Refined: Tasks, Not Jobs** - Alex reframes the relational sector as about tasks within jobs, not entire occupations
- 7 (12:27) **The Mongolian Economist Thought Experiment** - Phil warns against holding variety fixed when forecasting what will be scarce
+ Full timestamped outline available in the app
Show Notes
Economics of AGI episode w Alex Imas and Phil Trammell.
There’s a bunch of important questions about how we deal with AI that only economics can answer.
What is the optimal way to tax and redistribute the wealth that will be generated? How should countries not in the AI supply chain index into the gains? Is there any world where inequality doesn’t explode?
It might seem like these questions have obvious answers, but the first thing economics teaches you is that your intuitions can often be entirely wrong.
It was very helpful to chat through these things with Alex and Phil.
Watch on YouTube; read the transcript.
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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?
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