How AI Wealth Could Be Shared: Data Payments to Shorter Workweeks

The distribution debate intensifies

While Senator Bernie Sanders' proposal for 50% public ownership of artificial intelligence remains unlikely to become policy, it has catalyzed serious debate among economists and technology researchers about how Americans can share in AI's anticipated economic windfall. The question has grown urgent as public opposition to AI development rises sharply.

Recent polling reveals a dramatic shift in sentiment. An Emerson College survey found only 27% of Americans now support data centers in their communities, with 63% opposed—a substantial decline from December 2025, when opposition stood at 42%. The shift reflects a widespread perception that AI benefits accrue to corporations while communities bear the costs.

Compensating data contributors

One approach centers on paying individuals for data used to train AI systems. Raul Castro Fernandez, a University of Chicago computer science professor, argues this is technically feasible. "AI companies already estimate how much data matters through scaling laws," he said, proposing a collective-management system similar to music royalties where companies pay into a pool distributed based on audited data contributions.

Microsoft researcher Jaron Lanier advocates for this "data dignity" model, though he acknowledges its effectiveness depends on government structure. "Good data and supervision can result in enough real money having a significant impact on people's lives," Lanier said.

However, researchers Nicholas Vincent and Brent Hecht caution that valuing individual contributions may be impractical. Their 2023 study found that "seemingly minor design choices can seriously change the distribution of data values," questioning whether the effort is worthwhile when individual values will inevitably be small.

New collective bargaining models

Matt Prewitt of RadicalxChange Foundation proposes creating new legal rights that require collective action, functioning as 21st-century unions. These regulated associations would have "a very serious seat at the table with AI companies" and power to negotiate shares, remuneration, and governance.

Economist Glen Weyl argues against simply fractionalizing or consolidating ownership, viewing these as "band-aids" that either spread extractive incentives or concentrate risk.

Traditional policy tools

Dean Baker, co-founder of the Center for Economic and Policy Research, advocates for proven mechanisms: stronger corporate taxes, antitrust enforcement, and labor protections. He suggests companies could turn over non-voting shares equal to the tax rate rather than cash payments.

Baker also champions a straightforward solution with global precedent: shortening the workweek. "We set the 40-hour work week 90 years ago and it has not changed since," he said, proposing a reduction to 32 hours or lower if AI delivers promised productivity gains.

Why it matters

The debate extends beyond theoretical economics. Public opposition to AI infrastructure is mounting precisely because communities see costs without benefits. Unconfirmed reports suggest OpenAI has discussed offering the government a 5% equity stake ahead of its IPO, indicating the industry recognizes the legitimacy of these concerns. How this wealth gets distributed—or doesn't—will shape both AI development trajectories and public acceptance of the technology.

These details were first reported by CNBC.