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

Policymakers should address existing gaps in the US tax code before creating new taxes for artificial intelligence, Yale Budget Lab executive director Martha Gimbel said in an August 12 Bloomberg interview.

Gimbel said AI-driven growth may shift economic output toward capital income, which is taxed differently from wages, limiting the federal revenue generated by productivity gains.

A Yale Budget Lab report published July 20 estimated that rapid AI adoption could increase federal tax revenue by as much as $216 billion by 2030. The report found that the increase could be roughly twice as large if the gains from AI productivity were distributed more evenly between labor and capital income.

The report identified the tax treatment of unrealized capital gains and tax-advantaged retirement savings as factors that reduce the effective taxation of capital income. Unrealized gains are generally not taxed until an asset is sold.

In a May 6 analysis, the Budget Lab said AI-driven productivity could expand the tax base and slow growth in US debt. It said the outcome would depend on how the gains are distributed and whether the tax system captures them.

The analysis also said labor-market disruption could reduce payroll-tax receipts and increase spending on safety-net programs, offsetting part of the fiscal benefit from higher productivity.