Yale Budget Lab urges tax-code reform before new AI taxes

Policymakers should address existing gaps in the US tax code before designing new taxes aimed specifically at artificial intelligence, according to Yale Budget Lab executive director Martha Gimbel. The concern is that AI-driven growth may flow disproportionately to capital income, which is generally taxed differently from wages. That could reduce the amount of federal revenue generated by productivity gains compared with a scenario in which more of the gains reach workers as labor income. A Yale Budget Lab analysis estimated that rapid AI adoption could increase federal tax revenue by as much as $216 billion by 2030. The potential increase could be larger if the economic gains were distributed more evenly between labor and capital. Gimbel pointed to the treatment of unrealized capital gains and tax-advantaged savings as existing features that can limit revenue from capital-heavy growth. Her argument is that repairing those structural gaps would prepare the tax system for AI without immediately creating a separate AI-specific levy. Disclosure: This article was edited by Editorial Team. For more information on how we create and review content, see our Editorial Policy.