Budget Lab Models AI Effects on $6,595 Billion 2030 Revenue Base
Updated
Updated · The Budget Lab at Yale · Jul 20
Budget Lab Models AI Effects on $6,595 Billion 2030 Revenue Base
2 articles · Updated · The Budget Lab at Yale · Jul 20
Summary
The Budget Lab released a model estimating how AI could change the tax system and the federal revenue-to-GDP ratio, using CBO’s $6,595 billion FY2030 revenue baseline.
The analysis finds the near-term net effect hinges on adoption speed: AI could make IRS audits cheaper and more effective, but taxpayers could also use AI to uncover new avoidance strategies.
Its framework applies Karger et al. 2026 AI-driven GDP growth scenarios through the Budget Lab’s small macro model, then layers the implied revenue-to-GDP change onto the baseline; the setup has little to no effect on 2030 GDP growth.
The model simplifies several channels—including labor-income dispersion, capital income, retirement distributions and corporate tax assumptions—and the authors say a more comprehensive version is planned.
GitHub code and a full methodology were published with the study, which says longer-run simulations would likely show larger effects on inequality than the current 2030-focused exercise.