Research

A comprehensive survey of the emerging field of Post-Labor Economics, which begins with the premise that human labor will largely disappear rather than merely shift between sectors. Reviews economic structures and possibilities in a future where AI substantially reduces or eliminates the need for human labor.

A collection of 21 essays from leading economists and technologists on AI's economic transformation. Contributors include David Autor, Joseph Stiglitz, Yoshua Bengio, Eric Schmidt, and Betsey Stevenson. Covers labor displacement scenarios, universal basic capital, fiscal policy for a post-labor tax base, and safety nets for permanently displaced workers.

From the most-cited labor economist of his generation: outlines scenarios ranging from gradual augmentation to full labor displacement. Examines how transformative AI could fundamentally alter the relationship between human expertise, productivity, and compensation.

If paying people for work no longer distributes prosperity, give everyone an ownership stake in AI-driven capital. Makes the case for "universal basic capital" — not a handout, but a share in the productive assets of the automated economy. The policy expression of "own the capital, not the labor."

Develops a "threat model" centered on mass unemployment from AI-driven labor automation. Analyzes both "capability-modifying interventions" that shape AI development and "adaptation interventions" that help society adjust, including taxation of automation, education reform, and social substitutes for work.

Uses machine learning to identify workers displaced from routine-task occupations and tracks their outcomes. Finds significant polarization in welfare from automation, with displaced workers increasingly dropping out of the labor force or moving to low-wage work. Evaluates redistributive policy responses.

Argues that unemployment is driven by inequality of wealth and political power, not technology per se. After WWII, 42% of UK jobs were redundant but full employment was maintained through social policy. Makes the case that economic democracy, not just income transfers, is needed to manage the automation transition.

Models the economic feasibility of a transition through unconditional basic income in a post-labor world. Distinguishes between capital that complements labor and capital that fully substitutes for it — a distinction central to understanding which workers benefit and which are displaced.

A task-based model analyzing automation's distributional and political economy implications. Finds that workers prefer higher capital taxes in the long run but lower taxes during the transition, and that capital owners may actually benefit from UBI — suggesting alignment between the capital class and redistribution.

Models how output and wages behave under different scenarios culminating in Artificial General Intelligence. Finds that if automation outpaces capital accumulation, wages decline even before full automation is reached. If full automation arrives, wages collapse entirely — making capital ownership the sole source of income.

Analyzes whether technological progress can be steered to complement labor rather than displace it. Finds that steering becomes more desirable when safety nets are weak — but beyond a critical threshold where technology devalues labor, steering becomes ineffective and policy must shift toward redistribution.

Finds that approximately 80% of the U.S. workforce could have at least 10% of their tasks affected by LLMs, with around 19% of workers seeing at least 50% of their tasks impacted.

Examines why AI hasn't yet shown up in aggregate productivity statistics despite rapid advances, and argues the lag is consistent with historical patterns of general-purpose technology adoption.

A framework for thinking about how AI will affect output, wages, and inequality. Argues that the economic effects of AI will be more modest than many predict unless the technology moves beyond task automation.

Develops a framework showing that automation displaces workers from existing tasks but can be counterbalanced by the creation of new tasks. The balance between these forces determines labor's share of income.

The landmark study estimating that 47% of U.S. jobs are at high risk of automation. While the timeline has been debated, the framework for assessing job vulnerability remains influential.

Documents the significant decline in labor's share of national income since 2000, exploring globalization, technological change, and institutional factors as potential explanations.

Argues that the decline in labor's share is driven by industry concentration, as "superstar firms" with high productivity and low labor shares capture increasing market share.

Published in Econometrica, isolates a new mechanism: automation increases inequality by raising returns to wealth, not just through wage effects. The benefits of new technologies accrue to capital owners in the form of higher capital incomes, while wages at the bottom stagnate — the academic foundation for "own the capital, not the labor."

Examines what governments should tax when AGI erodes the traditional tax bases of labor income and consumption. The premise — that labor income could cease to be a viable tax base — is itself a landmark acknowledgment of the post-labor scenario from within mainstream economics.

Former chief economist of the U.S. Department of Labor argues that a successful transition requires solving three problems: how ordinary people seek a better life if work is devalued, how resources will be distributed, and where people will find meaning and purpose. The right policy response can address all three.

Proposes a two-tiered response to AI labor displacement: "adjustment insurance" for transitory job losses, and a "digital dividend" for those permanently without work. The digital dividend can be scaled up as displacement accelerates — a practical bridge to a post-labor economy.