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New commentaries expose a distinction in the proposal: collecting money from AI-linked businesses is not the same as taxing the activity that displaces workers.
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The Make AI Work for Americans Act, introduced September 24, 2026, would create a federal trust fund for workers and families affected by AI, financed through taxes on data processing, covered digital advertising and certain excess profits. Economist Heather Boushey argues major AI beneficiaries should help fund adjustment even while employment effects remain uncertain; Drexel law professor Andrew Leahey counters that the proposed tax bases may collect revenue without tracking AI’s contribution or actual worker displacement. The disagreement puts the bill’s central design question—how to fund support while connecting liability to harm—at issue.
The bill defines a computational-processing unit as 10 kilobytes of binary data; Leahey argues that measures information volume, not the computing work performed.
A proposed 5% tax on covered ads shown to US users would not distinguish revenue generated with AI from other advertising revenue.
The excess-profits provision would tax income above 40% of gross receipts at 50%; coverage also depends on AI activity, revenue thresholds and substantial electricity use.
Sen. Mark Kelly’s proposed AI taxes would finance support for workers facing disruption. Fresh commentary exposes a divide over how to collect that money. On October 7, economist Heather Boushey endorsed asking large AI companies to contribute. A day earlier, tax scholar Andrew Leahey argued that the bill’s measures poorly connect technology activity with labor-market harm.
Kelly introduced the Make AI Work for Americans Act on September 24, 2026. It would establish a federal trust fund to help workers and families navigate AI-related disruption. Its proposed funding combines taxes on computational processing, covered digital advertising revenue and certain excess profits.
Writing for the University of Pennsylvania’s Kleinman Center, Boushey calls the legislation an important step toward supporting workers and communities. Her case emphasizes who should contribute: the companies positioned to profit most from AI. She also acknowledges that there is no consensus on precisely how the technology will affect workers.
Boushey cites estimates that more than 6 million workers, primarily in clerical and administrative roles, may have greater difficulty adapting. She also points to Bureau of Labor Statistics categories identifying over 200 occupations with very high AI exposure. Those categories track how closely AI capabilities match job tasks, rather than establishing that those jobs will disappear.
The proposal uses three different tax bases—the quantities against which a tax is calculated. Leahey’s October 6 Bloomberg Tax column describes them as follows:
Lawmakers must distinguish between raising money from the AI economy and measuring the activity understood to produce harm.
Andrew Leahey, assistant professor of law at Drexel, writing in Bloomberg Tax
Leahey’s objection goes beyond the processing definition. Even a better measure of computing work would need a defensible connection to labor disruption, he argues. Under the profits provision, a highly profitable covered company could owe tax without having displaced workers. A company that did reduce employment could fall outside the coverage thresholds.
He does not insist that every tax perfectly match its policy goal. Instead, he says a weaker connection requires a stronger justification for charging that taxpayer. His proposed interim alternative is to capture some AI upside through a public claim on company profits or an equity interest, while evidence linking specific AI uses to economic costs improves.
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