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

艾力克斯·伊马斯与菲尔·特拉梅尔——在通用人工智能之后,什么仍然稀缺?

Dwarkesh Podcast

2026-06-05

1 小时 16 分钟
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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. Sponsors Jane Street 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 Google’s Gemini Omni 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 or in Flow at flow.google Cursor 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 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? Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe
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  • 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 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?

  • 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 and 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 never 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