Taxing AI to Help Workers Sounds Good, But Public Deserves More

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Rep. Greg Casar’s (D-Texas) proposed AI Tax and Work Protection Act has a solid premise: Firms capturing gains from AI-driven labor force reductions should carry some of its social costs. But the proposed tax on AI tokens illustrates why taxing artificial intelligence use is a poor proxy for taxing automation itself. Lawmakers should instead give the public an equity stake in the firms that capture its economic gains.<br>Casar’s proposal is an attempt to correct an imbalance by levying a tax on AI companies and using the proceeds to support workers. It’s a sound idea. To the extent AI allows firms to replace human labor while shifting some of the resulting costs — unemployment, lost tax revenue, and the like — onto workers and society writ large, those costs resemble an externality.<br>We’ve seen this type of tax before: Governments tax cigarettes in part for their negative health impacts and tax gasoline to finance the wear and tear on infrastructure vehicles cause. And we’ve spilled much ink debating carbon taxes as a method of internalizing carbon emissions in manufacturing.<br>The problem with Casar’s bill is in the particulars, as it doesn’t really tax worker displacement. It imposes a levy equal to the greater of two amounts: a percentage of the fair market value of tokens processed in covered transactions, or a percentage of the revenue and related-party value associated with those transactions.<br>The tax rate is keyed to the unemployment rate, which produces an appealing feedback loop: As AI contributes to greater unemployment, the tax rises and generates more money to put people back to work.<br>Unemployment may spike for various reasons, though. Absent some mechanism to account for that, AI companies may end up bearing the costs of labor market contractions they didn’t cause. The proposed bill includes a safety valve, allowing the Treasury Department to adjust the escalator where unemployment traces to war, pandemic, or an unrelated shock.<br>But that requires someone at the Treasury to determine — in real time and on a political calendar — how much of a given labor contraction is because of AI. That’s a causal allocation the tax code doesn’t otherwise have much occasion to make. More problematically, Casar’s proposal requires the Treasury to put a value on an extraordinarily unstable unit of measurement. A token is a technical unit, not a standardized commodity.<br>An equity-based approach ducks much of the mismatch inherent in trying to tax AI use as a proxy for AI-driven automation.<br>Photographer: Justin Sullivan/Getty Images

AI services increasingly encompass text, code, images, audio, and video. And tokens aren’t necessarily sold individually in an arm’s-length market that would supply the Treasury with a convenient unit price.<br>Rather than trying to determine the value (and implicitly, the social cost) of each instance of AI use, governments could capture a continuing share of the economic returns AI produces by taking an equity stake in the companies engaged in producing it. Professors Jeremy Bearer-Friend and Sarah Polcz proposed such an approach, and Sen. Bernie Sanders (I-Vt.) recently incorporated a more aggressive version into legislation aimed at creating a sovereign wealth fund.<br>The underlying architecture deserves serious consideration. Instead of trying to tax the meter, government can own a piece of the franchise.<br>The recent embrace of public ownership makes Bearer-Friend and Polcz’s approach less exotic than it may have sounded even five years ago. An equity-based approach ducks much of the mismatch inherent in trying to tax AI use as a proxy for AI-driven automation. Under Casar’s bill, a company selling access to a foundation model can generate covered transactions whether its customers use that model to displace workers, augment them, or do something with no meaningful effect on labor.<br>If we were to focus solely on internalizing the externalities of automation, we might consider taxing labor displacement itself. But that creates an even less enviable position for the Treasury, which would need to determine whether a given employee was terminated because of AI, conventional software, outsourcing, falling demand, or an ordinary business decision. And what...