Forecasting the Economic Effects of AI / Ezra Karger, Otto Kuusela, Jason Abaluck, Kevin A. Bryan, Basil Halperin, Todd R. Jones, Connacher Murphy, Philip Trammell, Matt Reynolds, Dan Mayland, Ria Viswanathan, Ananaya Mittal, Rebecca Ceppas de Castro, Josh Rosenberg, Philip Tetlock.

Format:

Book

Author/Creator:

Karger, Ezra.

Contributor:

Kuusela, Otto.

Abaluck, Jason.

Bryan, Kevin A.

Halperin, Basil.

Jones, Todd R.

Murphy, Connacher.

Trammell, Philip.

Reynolds, Matt.

Mayland, Dan.

Viswanathan, Ria.

Mittal, Ananaya.

de Castro, Rebecca Ceppas.

Rosenberg, Josh.

Tetlock, Philip.

National Bureau of Economic Research.

Series:

Working Paper Series (National Bureau of Economic Research) no. w35046.

NBER working paper series no. w35046

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2026.

Summary:

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.

Notes:

April 2026.

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