Artificial Intelligence and Technological Unemployment

Ping Wang and Russell Wong

No 26-01, Working Paper from Federal Reserve Bank of Richmond

Abstract: How large are the effects of artificial intelligence (AI) on labor productivity and unemployment? We develop a labor-search model of technological unemployment where AI learns from workers, raises productivity, and displaces them if renegotiation fails. The model admits three steady states: no AI; some AI with limited capability, more job creation but higher unemployment; unbounded AI with endogenous growth and employment gains. Calibrated to U.S. data, the model implies a threefold productivity gain but a 23% employment loss, half within five years. Plausible parameters give rise to global and local indeterminacy with endogenous cycles in productivity and unemployment, underscoring the uncertainty of AI's impacts in line with a wide range of empirical findings. Equilibria are inefficient despite the Hosios condition; subsidizing jobs at risk of AI displacement is constrained optimal.

Keywords: generative artificial intelligence; technological unemployment; search and bargaining; en dogenous growth; constrained efficiency; indeterminacy (search for similar items in EconPapers)
JEL-codes: E20 J20 J64 L20 O30 O40 (search for similar items in EconPapers)
Date: 2026-02-23
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DOI: 10.21144/wp26-01

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