How Might Fiscal Policy Respond to the Rise of Artificial Intelligence?
Karen Dynan, Douglas Elmendorf, Louise Sheiner · National Bureau of Economic Research · 1 Jul 2026
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Where this sits in the debate
- Will AI actually boost productivity and growth? · its finding: it depends (why studies differ)
- Will AI destroy more jobs than it creates?
- Will AI widen inequality? · its finding: it depends (why studies differ)
- Should governments act on AI now? · its finding supports the claim (why studies differ)
Direction of the finding as read from the abstract by an AI model (AI-written).
Key takeaway AI-written
This NBER paper lays out long-term US scenarios combining faster productivity growth, greater income inequality, job displacement and a higher capital share of income, and works through what each would mean for federal debt. It then weighs policy responses on growth, income distribution, support for laid-off workers and the taxation and ownership of capital, favouring policies that hold up across scenarios given how uncertain AI's effects are.
Abstract
Artificial intelligence will probably generate major changes in the US economy, although the nature, timing, and magnitude of those changes are highly uncertain.We analyze a set of long-term scenarios involving different combinations of faster productivity growth, greater income inequality, job displacement, and a higher capital share of income.For each scenario, we assess the implications for federal debt and potential policy responses related to faster economic growth, the distribution of income, support for workers who are laid off, and taxation and ownership of capital.Given the uncertainty surrounding AI's economic effects, policies that are robust to different scenarios would be especially valuable.
About this publication
Academic journal. Published in an academic journal — typically peer-reviewed before publication, though indexing alone doesn't guarantee review depth.
- Published
- 2026 (4 months ago)
- Citations
- 0 (OpenAlex)
0 citations recorded by OpenAlex — too few 2026 papers in this collection for a fair comparison.
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