Jeremy Bearer-Friend and Sarah Polcz discuss their proposal to require leading AI firms to pay taxes in equity, reshaping how the gains from AI are distributed
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Artificial intelligence is rapidly reshaping the economy, but two Stanford Law alumni argue that existing tax frameworks are failing to capture—or fairly distribute—the value it generates. Jeremy Bearer-Friend, JD '14, a professor at George Washington University Law School, and Sarah Polcz, JSM '12, JSD '20, a professor at UC Davis School of Law, join co-host Professor Richard Thompson Ford to discuss a proposal that would require leading AI companies to pay a portion of their taxes in equity rather than cash, with those shares placed into a public trust, and their work with U.S. Senate members to make this happen.
The conversation explores a central question: If AI was built on vast amounts of human-generated text, images, and creative work, who is entitled to share in the wealth it produces? Bearer-Friend and Polcz connect their proposal to broader concerns about wealth concentration and whether the gains from AI will flow to a narrow class of tech executives and investors—or to the public at large.
The episode also examines how an equity-based tax could work in practice, including questions of governance, political insulation, and the mechanics of a sovereign wealth fund, and what it would mean to give the public a direct stake in the companies shaping the future of artificial intelligence.
Links:
Connect:
(00:00:00) A Tax Paid in Stock
(00:02:14) IP, Inequality, and the AI Boom
(00:05:56) Why the Public Deserves an Equity Stake
(00:11:58) How the Tax Would Actually Work—Stock, Rates, and Governance
(00:16:59) Sanders, Trump, and a Race to Co-opt the Idea
(00:21:06) Objections, Safeguards, and the Road Ahead
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