Before Washington starts drafting new taxes on AI-generated wealth, it should probably fix the tax system it already has. That, in short, is the message from Martha Gimbel, executive director of the Yale Budget Lab, who made the case in a Bloomberg interview on August 12, 2026.
The argument is less about AI and more about a structural problem hiding in plain sight: labor income and capital income are not taxed the same way, and that gap is about to matter a lot more as AI shifts economic output toward the latter.
The $216 billion question
The Yale Budget Lab published a report on July 20, 2026 modeling several scenarios of AI-driven economic growth. Under a rapid AI adoption scenario, the lab projects federal tax revenues could rise by up to $216 billion by 2030.
If the gains from AI productivity were distributed more evenly between labor income and capital income, rather than flowing disproportionately toward capital, the projected revenue increase could be roughly twice as large. The reason is straightforward: wages get taxed at higher rates than investment returns, and AI, by its nature, tends to generate wealth through capital rather than paychecks.
