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

2026-07-10 · Source: Dwarkesh Podcast · Field: Finance & Economics — Economic Analysis & Policy, Capital Markets & Investment Management · Depth: Advanced, extended

Summary

Alex Imas (Google DeepMind) and Phil Trammell (EFAC/Stanford) discuss the economic implications of advanced AI and automation. They explore what remains scarce post-AGI, emphasizing the "relational sector" where human involvement adds intrinsic value, such as ballerinas or doctors providing empathy. The discussion highlights the historical difficulty of economic forecasting, noting David Ricardo's mispredictions during the Industrial Revolution regarding labor displacement. They examine the surprising historical stability of labor share, typically over 60%, and the potential for a "messy middle" scenario where automation displaces jobs without sufficient wealth creation. Strategies for taxation and redistribution, including negative income tax and universal basic capital, are considered. Currently, there is little evidence of widespread white-collar job automation or unemployment due to AI, with demand elasticity and O-ring theory providing explanations. The experts also touch on the Citrini recession scenario, AI preferences, and global implications for developing countries, stressing the importance of indexing AGI gains.

Key takeaway

For Policy Makers assessing AGI's economic impact, understand that historical labor share stability and demand elasticity are crucial, but AGI's wealth distribution effects are uncertain. You should prioritize developing robust data collection on consumer preferences and job task automation. Implement flexible redistribution mechanisms, such as a negative income tax, while exploring strategies for broad-based capital ownership to ensure equitable access to AGI-driven wealth, mitigating "messy middle" risks and fostering inclusive prosperity.

Key insights

Human-intrinsic value and demand elasticity will critically shape economic scarcity and labor share in an AGI-automated future.

Principles

• Economic forecasting is notoriously difficult.
• Labor share has historically remained stable despite automation.
• Demand elasticity dictates automation's economic impact.

Method

Economists should use prediction markets for aggregate forecasts and build models from specific AGI scenarios to identify critical data needs, especially for consumer demand elasticities.

In practice

• Index AGI gains for broad wealth distribution.
• Collect data on consumer demand elasticities.
• Account for regulatory layers in job automation.

Topics

• AGI Economics
• Labor Share
• Economic Forecasting
• Wealth Redistribution
• Automation Impact
• Demand Elasticity
• Relational Sector

Best for: AI Scientist, Research Scientist, Policy Maker

Related on AIssential

• Universal Basic Income Was Always Controversial - AI Just Changed The Entire Argument — AI's transformative impact on labor markets necessitates a re-evaluation of Universal Basic Income's role.
• AI's First Vaccine Hits Humans — Advanced AI's economic impact hinges on demand elasticity, the "relational sector," and capital's evolving returns, challenging historical labor share stability.
• Study: Firms often use automation to control certain workers’ wages — Firms often automate to reduce high wages, increasing inequality while muting productivity gains.
• Debatable: AI’s impact on the economy — AI's economic impact is debated, with evidence suggesting both job creation and significant displacement.
• Question recently I was thinking- about economic outcomes of AI — AI-driven job displacement raises critical questions about consumer demand and economic sustainability.
• Post-Labor Economics in 60 minutes — Post-labor economics describes an economic future where automation eliminates human labor as a binding constraint on output.

See Counsel's argued verdicts on the open AI decisions leaders are weighing →

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Dwarkesh Podcast.