RESOLVING THE LEDGER
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Bernie Sanders' recent proposal that the public should own half of artificial intelligence is unlikely to become policy anytime soon, but it reflects a broader debate that is gaining momentum among economists, technology researchers, and policymakers: How can Americans benefit if AI creates trillions of dollars in new economic value? There is no shortage of ideas, most untested. But the risks, and rising public opposition to AI, make the question an important one.
AI wealth has accumulated quickly in the stock market, but many Americans are still limited in how much they benefit from that growth. Recent survey work indicates that a majority of U.S. workers now want to hold corporations more accountable via an AI sovereign wealth fund . There have been unconfirmed reports that ahead of a highly anticipated IPO, OpenAI has discussed offering the government a 5% equity stake . Meanwhile, Jeff Bezos recently told CNBC that the best policy idea to level the economic playing field is simply eliminating federal income taxes for the bottom-half of earners in the U.S.
Recent survey data indicates that this question is embedded in a rapid change in public sentiment towards AI. An Emerson College poll released this week found that only 27% of Americans support data centers being built in or near their community, with 63% opposed. Public sentiment has soured substantially in less than a year. A similar poll conducted in December 2025 found that while 33% said that they would support such developments, only 42% voiced their opposition. Many Americans feel as though they have nothing to gain and everything to lose from AI.
"When I see the data center proposal, I don't see progress," said Will Hollingsworth, a Northeast Ohio resident, speaking at an April public comment session regarding a proposed 257-acre data center campus in Portage County. "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," Hollingsworth said in comments that went viral. "We're being asked to drain our reservoirs so [that] a chatbot can write a poem or so [that] our sheriff can generate a picture of himself standing next to Bigfoot."
Among economists, tech industry researchers, and public policy experts, there are multiple proposals that address Hollingsworth's sentiment that the potential outcomes from AI development are dramatically skewed in favor of corporations. These include partial public ownership models in AI and other shared equity mechanisms.
Computer scientist Jaron Lanier, who currently holds the Office of the Chief Technical Officer Prime Unifying Scientist at Microsoft Research, has argued for a model sometimes referred to as "data dignity," where people receive compensation for the information and contributions that help create AI systems.
"I spent some time with Sen. Sanders when he visited the AI community at Stanford," Lanier said. "Whether [his proposal] would be a good idea depends on the nature of the government that would be responsible for routing benefits to people," he said. If the government were to simply become "just another AI company," he prefers what he calls a more "distributed economic model."
"Good data and supervision," Lanier says, can result in enough real money having a significant impact on people's lives.
"But if the future is to be the normative Silicon Valley one, where people will be fictionally treated as becoming useless because their contributions have been anonymized and dismissed in favor of pretending AI did all the work, then it is better for some kind of support to come through a government structure with a participatory/democratic element," Lanier said.
The challenge is figuring out how such a system would work. AI models are trained on enormous amounts of information from millions or billions of sources. Determining which individual contributions created value, and how much they should be paid, might run into the same criticisms that dogged efforts to compensate people for search histories — while the sums are massive in the aggregate, the economic value of any single individual's data is low.
Raul Castro Fernandez, an assistant professor of computer science at the University of Chicago who has recently written about how to fairly compensation the public for AI, refutes the argument that it's infeasible to accurately track (and compensate) the enormous amounts of data points collected by AI models from human contributors. "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.
"They [the AI companies] already estimate how much data matters through scaling laws," Fernandez said. "A plausible mechanism would look less like calculating the exact value of every individual 'token' and more like a collective-management system, analogous in spirit to music royalties: AI companies would pay a share of model profits into a pool, the aggregate share would be anchored by evidence about how much model performance depends on data, and payments would be distributed across creators, publishers, platforms, or other intermediaries according to audited measures of data contribution," he said.
But Nicholas Vincent and Brent Hecht, researchers from Simon Fraser University and Northwestern University, respectively, caution against this approach. In a 2023 study examining whether it's possible to adequately compensate individuals for their contributions to an AI system, Vincent and Hecht argue that attaching valuations to each person's data can be extremely subjective and potentially counterintuitive.
"Seemingly minor design choices can seriously change the distribution of data values, a serious concern for any hu...
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