The better AI gets, the smaller its share of the economy might get – Alex Imas and Phil Trammell

Name: The better AI gets, the smaller its share of the economy might get – Alex Imas and Phil Trammell
Uploaded: 2026-06-04T16:37:38.000Z
Duration: 1 h 16 min 8 s
Channel: Dwarkesh Patel

Dwarkesh PatelJune 4, 20261h 16m

Topics46

Introduction and Core Questions0:00 Scarcity and the Relational Sector0:34 Labor Share and Predictive Challenges2:00 Historical Context: David Ricardo and Automation3:03 The Value of Economic Models Over Forecasts5:01 Labor Share and Capital Share Definitions6:01 Complements and Network-Adjusted Factor Shares8:02 Sectoral Implications and Satiation9:31 Task-Based Model and Relational Value10:06 Increasing Variety and Capital Demand11:32 Moore's Law and the Value of Compute14:01 Intrinsic Preferences for Human Connection16:33 Jane Street's Training Model18:33 The "Messy Middle" Scenario19:31 Political Economy and Transition Dynamics21:04 Conditions Required for the Messy Middle25:02 Redistribution Mechanisms: Complexity and Timeline26:04 Universal Basic Capital and Targeting Challenges27:32 Wealth Tax Political Sustainability28:03 Optimal Taxation Approaches29:03 Evidence on White-Collar Automation30:06 Coordination Effects and Layoff Narratives31:30 O-Ring Theory and Task Complementarity32:02 Jevons Paradox and Demand Elasticity33:34 Citrini Recession Scenario35:01 Gemini Omni and Multimodal Models38:31 O-Ring Automation and Reliability Requirements39:30 Gans and Goldfarb Automation Model40:30 Political and Licensing Frictions42:30 AI Preferences and Evolutionary Dynamics43:02 Wealth, Capital, and Savings Behavior46:32 Returns to Capital and General Equilibrium49:31 Interest Rates and Investment-Specific Technical Change52:50 Increasing Varieties and Capital Demand53:34 Wealth Accumulation Preferences and Dissipation55:04 Intrinsic vs. Instrumental Reasons for Accumulation56:34 Von Neumann Probes and Accounting Questions59:02 RL Credit Assignment and Cursor Composer 2.51:00:34 Policy Advice for Countries Outside the AI Supply Chain1:01:33 The Messy Middle and High Interest Rates1:03:00 Difficulties of Indexing the Economy1:04:33 Electricity vs. Social Media Analogy1:06:34 Open Models, Runaway Gains, and Recursive Improvement1:08:34 Retraining vs. Indexing Strategies1:09:35 Feasibility of Indexing and Privatization Trends1:11:30 Commoditization, Safety, and Political Economy1:13:00

In a Nutshell

As AI automates non-relational production, labor share could collapse toward zero if demand for new capital varieties outpaces saturation in human-intrinsic services like empathy and performance. Historical stability of the ~60% labor share may not persist once entire supply chains become fully automated, and redistribution via negative income taxes or broad capital indexing faces targeting, political, and timing challenges. Developing countries benefit more from early indexing into AI-driven assets than from domestic retraining, while gradual displacement creates a politically fragile "messy middle" even if aggregate wealth eventually expands.

AI-Generated Notes

These notes were generated by AI and may contain inaccuracies.

Alex Imas is Director of AGI Economics at Google DeepMind and Professor of Economics at the University of Chicago. Phil Trammell is Head of Economics at Epoch and research scholar at Stanford. The discussion focuses on what economics predicts about wages, labor share, taxation and redistribution of AGI-generated wealth, and what will remain scarce in a highly automated world.

Scarcity determines where value accrues. The relational sector includes services and goods where human involvement is intrinsically part of the value. Even if automation eliminates scarcity elsewhere, scarcity persists in activities requiring human participation. In a world where AI and robotics handle all physical production, humans would have no reason to participate in the machine economy. However, humans may still value human involvement in services like performances or cafe experiences. This creates a human economy where humans provide services to each other, though some wealth flows out to purchase automated goods from the machine economy.

The human economy is not a closed loop. Machines do not demand human services, so the human-only economy's share may shrink. Individual economic forecasts are unreliable. A blog post by Andrey Fradkin, Brian Jabarian, and Andrew Koh found substantial disagreement among economists' labor market predictions. Prediction markets may better aggregate forecasts and capture crowd wisdom. Economic forecasting has historically been poor.

This debate dates back 200 years to David Ricardo during the Industrial Revolution. Ricardo initially predicted automation would lower prices and benefit everyone, then reversed course, predicting mass unemployment and political unrest. Ricardo correctly identified that jobs existing in his time would be automated, but he missed the structural change effects. Automated tasks became cheap, freeing income for new spending on services. This illustrates the lump-of-labor fallacy—the incorrect assumption that automation reduces total work available.

Rather than making point predictions, models should examine boundary conditions. Models can explore scenarios where labor share reaches zero or remains constant. Current data limitations are severe: consumer demand elasticities are unknown, job creation and destruction tracking is inadequate, and the O*NET database is outdated and low quality. A "Manhattan Project for data" is needed. The useful approach is mapping potential scenarios by identifying which dimensions of scarcity generate each outcome.

Labor share is the portion of total goods and services paid as wages. Capital share includes rents on buildings, shareholder returns, and payments to owners of machines and land. For centuries, wages have accounted for approximately 60% of the economy, with 30-40% going to capital owners. A Kaldor fact is that labor share remained remarkably stable near 60% despite centuries of automation. Some debate whether recent declines are real or accounting artifacts. Atkinson has shown that holding accounting methods constant eliminates apparent labor share decline.

Sign in to read the full notes

Get access to AI-generated notes, topic timestamps, and more.

The better AI gets, the smaller its share of the economy might get – Alex Imas and Phil Trammell

Introduction and Core Questions00:00

Scarcity and the Relational Sector00:34

Labor Share and Predictive Challenges02:00

Sign in to read the full notes