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Alex Imas and Phil Trammell – What remains scarce after AGI?
Brief
On policy, they weighed quick‑acting instruments (negative income tax, short‑run transfers) against longer‑run claims (universal basic capital), noting severe indexing/targeting challenges if AGI rents concentrate in a few private firms. They discussed the tradeoff between commodifying frontier models (wider access, easier indexing, diffusion of returns) and safety/regulatory dynamics where fewer, larger labs could be easier to govern. Other topics included developing countries’ options (indexing vs. retraining), political economy of redistribution during a gradual vs. rapid takeoff, and speculative selection dynamics if long‑lived capital‑optimizing agents dominate future preferences. Both agreed more data, clearer accounting (network‑adjusted shares), and grounded scenario building are essential — many substantive questions remain open, especially about demand elasticities, indexability of AI returns, and how political institutions will respond during transitions.
Why it matters
Alex Imas (Google DeepMind; Prof. of Economics, UChicago) emphasized that labor share has historically hovered around ~60% of GDP and called for a “Manhattan project for data” — better consumer demand elasticities and task-level data — plus prediction markets to aggregate forecasts rather than relying on individual experts.
Key details
- Phil Trammell (EFAC, Stanford) pointed out that network‑adjusted capital shares are far from 100% today (example: U.S. computer & electronic products show ~50% network‑adjusted capital share), and argued we haven’t yet seen fully automated supply chains — though a qualitative shift is possible if entire supply chains can be automated.
- Alex described an incentive‑compatible conjoint experiment on art prints: a single human‑made print commanded a large premium over an AI print, but when supply rose to 500 copies the human premium fell sharply — evidence that relational/human intrinsic value can be supply‑sensitive and needs systematic measurement.
- Both speakers argued the “messy middle” (piecemeal automation that causes mass layoffs without enough wealth creation to compensate displaced workers) is a narrow / unlikely window: if AI can automate many white‑collar tasks it will likely also expand the technological frontier and generate substantial capital returns, though short‑run political frictions could still produce transitional harms.
- Technical and production notes discussed: H100 rental prices have recently risen despite hardware gains (reflecting rising opportunity cost of compute as model capabilities grow); Google’s Gemini Omni was cited as an example of multimodal models improving world models and video editing; Cursor/Composer 2.5 (Sasha Rush) uses targeted ‘hint’ tokens to rerank trajectories for credit assignment during training.
- Policy options debated: negative income tax/UBI provide immediate floors but carry political risks; universal basic capital faces hard indexing/targeting problems (which firms/assets to include); consumption or broad taxes could fund government purchases of diversified AI exposures (indexing), but concentration of private returns and political feasibility remain open concerns.