The Yale Budget Lab, led by Martha Gimbel, argues that existing tax code should be reformed before creating new taxes on artificial intelligence. The lab demonstrates that labor income and capital income are taxed differently, a gap that becomes critical as AI naturally generates more wealth through capital than through wages. Under a rapid AI adoption scenario, US federal tax revenues could rise by up to $216 billion by 2030, but this amount could be roughly twice as large if gains were distributed more evenly between labor and capital. The report identifies two main flaws: unrealized capital gains are not taxed until they are realized, and certain retirement savings vehicles receive preferential tax treatment that further reduces the effective rate on capital income.
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