The rapid advancement of artificial intelligence is expected to generate trillions of dollars in new economic value. This potential wealth has sparked a significant public and academic debate regarding how Americans can benefit from this growth, especially since current market gains have not been equally distributed.
Policy Proposals for Equitable AI Distribution
While U.S. Senator Bernie Sanders recently proposed that the general public should own half of artificial intelligence, experts note that such a policy is unlikely to be adopted soon. Nevertheless, this proposal highlights a broader conversation among policymakers, economists, and technology researchers: how can the American populace capitalize on AI-driven economic expansion?
A variety of ideas have emerged, ranging from implementing new corporate taxation structures to paying individuals directly for the data they contribute to training AI systems, and even establishing novel labor unions designed to hold the industry accountable.
Shifting Public Sentiment and Community Concerns
Public sentiment regarding AI development is showing marked signs of change. Data suggests growing public skepticism about large-scale technological infrastructure projects. An Emerson College poll released recently indicated that only 27% of Americans support building data centers in or near their communities, compared to 63% who are opposed.
This shift contrasts sharply with a similar survey conducted in December 2025, which found that while 33% supported such developments, opposition stood at 42%. Many citizens feel they stand to lose more than they gain from the industry. For example, Will Hollingsworth, a resident of Northeast Ohio, criticized a proposed 257-acre data center campus in Portage County, stating: “We’re being asked to sacrifice the lifeblood of our city so that a trillion-dollar company can save a fraction of a cent on its margins.”
Models for Shared Ownership and Compensation
Responding to concerns about wealth concentration favoring corporations, multiple experts have put forth mechanisms aimed at creating fairer outcomes. One such model is “data dignity,” advocated by computer scientist Jaron Lanier. This concept proposes that individuals should receive compensation for the information and contributions they provide to create AI systems.
“Whether [his proposal] would be a good idea depends on the nature of the government that would be responsible for routing benefits to people,” Lanier said, suggesting that if the government itself were to become merely “just another AI company,” he preferred a more “distributed economic model.”
Paying Individuals for Data Contributions
A challenge in developing compensation systems is determining how to accurately value individual data points. Raul Castro Fernandez, an assistant professor of computer science at the University of Chicago, argues that tracking and compensating contributors is feasible. He stated: “The strongest version of profit sharing is not a tax but a compensation system tied to the human contributions that make AI systems valuable in the first place.”
Fernandez suggests that instead of calculating the exact value of every individual data point, a collective-management structure—similar to music royalties—could be implemented. Under this model, AI companies would contribute a share of their profits into a central pool. Payments would then be distributed across creators, publishers, or other intermediaries based on audited measurements of data contribution.
However, researchers Nicholas Vincent and Brent Hecht cautioned against this approach in a 2023 study, arguing that assigning monetary value to personal data can be extremely subjective and potentially confusing. They concluded: “If a technology is reliant on the collective contributions of millions or billions of people, we already know each individual value will be very small, so why bother spending time and energy performing [potentially costly] data value estimation?”
Creating New Labor Associations
Matt Prewitt, president of the RadicalxChange Foundation, proposed creating a new class of legal rights—a 21st-century version of labor unions. These associations would grant people powers to influence how AI operates, ensuring that these rights cannot be waived on an individual level but must be exercised collectively.
Economist Glen Weyl notes that efforts focused solely on public or private ownership models are insufficient. According to RadicalxChange staff, attempts to either “divide and fractionalize ownership” or “consolidate ownership” merely offer temporary fixes (“only band-aids”).
Alternative Economic Adjustments
Some policy experts suggest that established mechanisms can create a more equitable AI economy without relying on untested ideas. Dean Baker, an economist and co-founder of the Center for Economic and Policy Research, points to strengthening corporate taxes, antitrust enforcement, and labor protections.
Baker proposed implementing a workable corporate income tax at a higher rate for all companies. He suggested that the payment mechanism could involve requiring corporations to turn over non-voting shares equal to the targeted tax percentage (for example, 25% of shares for a 25% tax rate).
Furthermore, Baker argued that strict antitrust enforcement provides a viable path toward fairer profit distribution. He suggested that if cheap foreign goods displaced blue-collar workers in the past, similarly restrictive policies should not prevent large technology companies from accumulating excessive wealth.
Revisiting Work Structures
Finally, an alternative to discussions of universal basic income is shortening the standard work week. Baker noted that while the 40-hour work week has remained unchanged for ninety years, many other nations have successfully reduced working hours. He concluded that if AI generates significant productivity boosts, society should consider lowering the required hours to 32 or even less, and potentially doubling the overtime premium from 50% to 100%.
