After AGI, what’s still scarce? An economics-first look at inequality, taxes, and global gains
This preview is based on the episode notes, not the full audio. Dwarkesh talks with Alex Imas and Phil Trammell about the economics of AGI: capital’s share, redistribution, demand, inequality, and what countries outside the AI supply chain should do.
*This is a preview based only on the published show notes and timestamps, not a recap of the full conversation.* If you’re interested in AGI but want more than the usual technical or philosophical framing, this episode looks aimed at the economic questions that come after capability breakthroughs. The setup is straightforward but high stakes: if AGI generates enormous wealth, who captures it, how should it be taxed and redistributed, and can inequality be prevented from spiraling? From the notes, the conversation with Alex Imas and Phil Trammell appears to focus on places where economic reasoning may challenge common intuitions. The timestamped topics suggest a structured discussion of whether capital’s share rises, what a “Messy Middle” scenario might look like, and why a collapse in demand may be less likely than some people expect. The later sections seem especially useful for listeners thinking about political economy and global development. The notes point to questions about integrating human workers into a machine economy, what happens if some humans or AIs pursue wealth accumulation for its own sake, and what developing countries should do if they’re not central to the AI supply chain. This episode may be a good fit if you want a framework for thinking about AGI distributional effects rather than a debate about timelines or model architectures. Based on the notes alone, it promises a serious pass at the hardest downstream question: after AGI, what remains scarce—and who gets it?
About this episode
<p>Economics of AGI episode w <a target="_blank" href="https://www.aleximas.com/">Alex Imas</a> and <a target="_blank" href="https://philiptrammell.com/">Phil Trammell</a>.</p><p>There’s a bunch of important questions about how we deal with AI that only economics can answer.</p><p>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?</p><p>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.</p><p>It was very helpful to chat through these things with Alex and Phil.</p><p>Watch on <a target="_blank" href="https://youtu.be/Jj-kBHzUohs">YouTube</a>; read the <a target="_blank" href="https://www.dwarkesh.com/p/alex-imas-phil-trammell">transcript</a>.</p><p><strong>Sponsors</strong></p><p><a target="_blank" href="https://janestreet.com/dwarkesh">Jane Street</a> 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 <a target="_blank" href="https://janestreet.com/dwarkesh">janestreet.com/dwarkesh</a></p><p><a target="_blank" href="https://gemini.google">Google’s Gemini Omni</a> 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 <a target="_blank" href="https://gemini.google">gemini.google</a> or in Flow at <a target="_blank" href="https://flow.google">flow.google</a></p><p><a target="_blank" href="https://cursor.com/dwarkesh">Cursor</a> 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 <a target="_blank" href="https://cursor.com/dwarkesh">cursor.com/dwarkesh</a></p><p>Timestamps</p><p>(00:00:00) – Will capital share increase?</p><p>(00:19:36) – Messy Middle scenario</p><p>(00:25:57) – How to tax and redistribute AI wealth</p><p>(00:30:02) – Why demand collapse is unlikely</p><p>(00:39:26) – Human employees would be hard to integrate into the machine economy</p><p>(00:43:08) – What if some humans (or AIs) value wealth accumulation intrinsically?</p><p>(01:01:28) – What should developing countries do?</p> <br/><br/>Get full access to Dwarkesh Podcast at <a href="https://www.dwarkesh.com/subscribe?utm_medium=podcast&utm_campaign=CTA_4">www.dwarkesh.com/subscribe</a>