Forecasting the Economic Effects of AI

Jason Abaluck, Kevin Bryan, Rebecca Ceppas de Castro, Basil Halperin, Todd Jones, Ezra Karger, Otto Kuusela, Dan Mayland, Ananaya Mittal, Connacher Murphy, Matt Reynolds, Josh Rosenberg, Philip Tetlock, Phil Trammell and Ria Viswanathan
Additional contact information
Kevin Bryan: https://discover.research.utoronto.ca/15527-kevin-bryan
Rebecca Ceppas de Castro: https://forecastingresearch.org/team/rebecca-ceppas-de-castro
Ezra Karger: https://www.chicagofed.org/people/k/karger-ezra
Otto Kuusela: https://forecastingresearch.org/team/otto-kuusela
Matt Reynolds: https://forecastingresearch.org/team/matt-reynolds
Josh Rosenberg: https://forecastingresearch.org/team/josh-rosenberg
Philip Tetlock: https://forecastingresearch.org/team/philip-tetlock

No WP 2026-07, Working Paper Series from Federal Reserve Bank of Chicago

Abstract: We elicit forecasts of how AI will affect the U.S. economy, comparing the beliefs of five groups: academic economists, employees at AI companies, policy researchers focused on AI, highly accurate forecasters, and the general public. The median respondent in each group expects substantial advances in AI capabilities by 2030, small declines in labor force participation consistent with demographic shifts, and an annual GDP growth rate of 2.5%, which exceeds both the typical medium-run (2.0%) and long-run (1.7%) baseline forecasts from government agencies and private-sector forecasters. Conditional on a “rapid” AI progress scenario, in which AI systems surpass human performance on many cognitive and physical tasks, experts forecast substantial, though not historically unprecedented, economic shifts: annualized GDP growth rising to around 4% and the labor force participation rate falling from its current level of 62% to 55% by 2050, with roughly half of that decline—equivalent to around 10 million lost jobs—attributable to AI. A variance decomposition suggests that expert disagreement about these effects is driven primarily by different beliefs about the economic effects of highly capable AI systems rather than by disagreement about the pace of AI progress. These forecasts map onto notably different policy preferences across groups: experts strongly favor targeted measures such as worker retraining, whereas the general public supports both targeted programs and broader interventions, including a job guarantee and universal basic income.

Keywords: Artificial intelligence; Economic forecasting; macroeconomic impacts (search for similar items in EconPapers)
JEL-codes: E27 J21 O33 O38 O40 (search for similar items in EconPapers)
Pages: 225
Date: 2026-03
New Economics Papers: this item is included in nep-ain
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DOI: 10.21033/wp-2026-07

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