A growing debate among economists, technology researchers, and policymakers centers on a straightforward question with complex implications: How can ordinary Americans benefit if artificial intelligence generates trillions of dollars in new economic value?
While AI-driven wealth has accumulated rapidly in the stock market, many Americans remain largely excluded from those gains. The disconnect has fueled various proposals, from Senator Bernie Sanders’ recent suggestion that the public should own half of artificial intelligence to more targeted approaches involving data compensation or expanded tax policies.
Recent survey work indicates that a majority of U.S. workers now favor holding corporations more accountable through an AI sovereign wealth fund. Meanwhile, unconfirmed reports suggest that OpenAI, ahead of its highly anticipated IPO, has discussed offering the government a 5% equity stake. Amazon founder Jeff Bezos told CNBC that eliminating federal income taxes for the bottom half of U.S. earners represents the best policy to level the economic playing field.
Public Sentiment Shifts Against AI Infrastructure
The question of AI wealth distribution is embedded in a rapid shift in public opinion toward the technology itself. An Emerson College poll released this week found that only 27% of Americans support data centers being built in or near their communities, with 63% opposed. That represents a significant deterioration from a similar poll conducted in December 2025, when 33% voiced support and 42% opposed such developments.
The sentiment was captured in viral comments from Will Hollingsworth, a Northeast Ohio resident, at an April public comment session regarding a proposed 257-acre data center campus in Portage County. “When I see the data center proposal, I don’t see progress,” Hollingsworth said. “I see a gamble where the big tech companies get the gold while Portage County foots the bill.”
“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,” he added, according to CNBC.
Competing Visions for Data Compensation
Among the proposed solutions is a concept called “data dignity,” advocated by computer scientist Jaron Lanier, who holds the Office of the Chief Technical Officer Prime Unifying Scientist at Microsoft Research. The model would compensate people for the information and contributions that help create AI systems.
Lanier said that whether Sanders’ proposal would work depends on the nature of the government responsible for routing benefits to people. He prefers what he calls a more “distributed economic model” unless the alternative is a future where people’s contributions are anonymized and dismissed in favor of pretending AI did all the work.
Raul Castro Fernandez, an assistant professor of computer science at the University of Chicago, argues that accurately tracking and compensating data contributors is feasible. “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 said.
He suggested a mechanism analogous to music royalties: AI companies would pay a share of model profits into a pool, with payments distributed across creators, publishers, and platforms according to audited measures of data contribution. “They already estimate how much data matters through scaling laws,” Fernandez noted.
Skepticism About Individual Compensation
However, researchers Nicholas Vincent from Simon Fraser University and Brent Hecht from Northwestern University caution against individual data valuation approaches. In a 2023 study, they argued that attaching valuations to each person’s data can be extremely subjective and potentially counterintuitive.
“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?” they concluded.
Matt Prewitt, president of RadicalxChange Foundation, advocates for creating new legal rights that give people collective power to shape how AI worksβa 21st century version of unions where people must join associations to exercise these rights rather than signing them away individually.
Traditional Policy Remedies
Some economists argue that existing mechanisms could create a more equitable AI economy without untested approaches. Dean Baker, an economist and co-founder of the Center for Economic and Policy Research, points to stronger corporate taxes, antitrust enforcement, and labor protections.
Baker suggested requiring companies to turn over non-voting shares equal to a targeted tax rate as one approach. “The best way to do this is require companies to turn over non-voting shares equal to the targeted tax rate, for example 25% of shares for a 25% tax rate,” he said.
As AI wealth continues to concentrate, the pressure to develop workable solutions appears likely to intensify alongside growing public skepticism about the technology’s local impacts.
Source: www.cnbc.com β https://www.cnbc.com/2026/07/26/how-can-ai-wealth-be-shared-with-all-americans.html
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