Bernie Sanders’ recent proposal that the public own half of artificial intelligence is unlikely to become policy anytime soon, but it reflects a broader debate gaining momentum among economists, technology researchers and policymakers: How can Americans benefit as AI creates trillions of dollars of new economic value? There is no shortage of ideas, most of which have not yet been tested. But the risks and increasing public resistance to AI make the question an important one.
AI wealth has been accumulating quickly in the stock market, but many Americans still have limited understanding of how much they can benefit from this growth. Recent polls suggest that a majority of U.S. workers now want to hold companies more accountable through an AI sovereign wealth fund. Unconfirmed reports suggest that OpenAI has discussed offering a 5% equity stake to the government ahead of a highly anticipated IPO. Meanwhile, Jeff Bezos recently told CNBC that the best policy idea to level the economic playing field is simply to eliminate federal income taxes on the bottom half of the U.S. workforce
Recent survey data suggests that this question is associated with a rapid shift in public opinion toward AI. An Emerson College poll released this week found that only 27% of Americans support building data centers in or near their community, with 63% opposed. Public sentiment has deteriorated significantly in less than a year. A similar poll in December 2025 found that while 33% said they would support such developments, only 42% opposed it. Many Americans feel they have nothing to gain and everything to lose from AI.
“When I see the data center proposal, I don’t see any progress,” Northeast Ohio resident Will Hollingsworth said at an April public comment session on 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 are being asked to sacrifice the lifeblood of our city so that a trillion-dollar company can shave a fraction of a cent off its margins,” Hollingsworth said in comments that went viral. “We are being asked to drain our water reservoirs [that] A chatbot can write a poem or something [that] Our sheriff can create a picture of himself standing next to Bigfoot.
There are several suggestions among economists, technology industry researchers and public policy experts that reflect Hollingsworth’s view that the potential outcomes of AI development are dramatically skewed in favor of corporations. Some of these include public ownership models in the area of AI and other shared equity mechanisms.
Computer scientist Jaron Lanier, who currently holds the position of Chief Technical Officer Prime Unifying Scientist at Microsoft Research, argues for a model sometimes called “data dignity,” in which people are compensated for the information and contributions that contribute to the development of AI systems.
“I spent some time with Senator Sanders when he visited the AI community at Stanford,” Lanier said. “Whether [his proposal] “What would be a good idea depends on the type of government that would be responsible for passing on the benefits to the people,” he said. If the government were to simply become “just another AI company,” he would prefer what he calls a more “distributed economic model.”
“Good data and oversight,” Lanier says, can mean enough real money has a significant impact on people’s lives.
“But if the future is to be normative Silicon Valley, where people are notionally treated as useless because their contributions have been anonymized and dismissed to make it seem like AI has done all the work, then it’s better that some kind of support comes from a government structure with a participatory/democratic element,” Lanier said.
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Pay people directly for AI training data
The challenge is figuring out how such a system would work. AI models are trained on massive amounts of information from millions or billions of sources. Determining which individual contributions added value and how much they should be paid could face the same criticisms that have dogged efforts to compensate people for search histories – while the total sums are huge, the economic value of any one person’s data is small.
Raul Castro Fernandez, an assistant professor of computer science at the University of Chicago who recently wrote about how to adequately compensate the public for AI, rejects the argument that it is impossible to accurately track (and compensate for) the enormous amounts of data points that AI models collect from human contributors. “The strongest form 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.
“She [the AI companies] “Already estimate how important data is using scaling laws,” Fernandez said. “A plausible mechanism would look less like calculating the exact value of each individual ‘token’ and more like a collective governance system similar in spirit to music royalties: AI companies would contribute a portion of model profits into a pool, the overall share would be anchored by evidence about how heavily model performance relies on data, and payments would be distributed among creators, publishers, platforms, or other intermediaries according to verified data metrics,” he said.
But Nicholas Vincent and Brent Hecht, researchers at Simon Fraser University and Northwestern University, respectively, warn against this approach. In a 2023 study examining whether it is possible to adequately compensate individuals for their contributions to an AI system, Vincent and Hecht argue that assigning ratings to each person’s data can be extremely subjective and potentially counterintuitive.
“Seemingly minor design decisions can seriously alter the distribution of data values, posing a serious problem for any human-AI system that wants to integrate such values for payments or other purposes,” it said. “If a technology relies on the collective contributions of millions or billions of people, we already know that any individual value will be very small, so why should we bother investing time and energy into execution?” [potentially costly] Data appreciation?” they concluded.
Creating new strong unions for the 21st century
However, direct payouts may not be the only way to achieve a fairer data collection process. Matt Prewitt, president of the RadicalxChange Foundation and one of the two authors of this policy paper, argues for the creation of a new class of legal rights that give people the power to shape how AI works, a 21st century version of unions in which “people cannot give up these rights at the individual level. Instead, people must band together in associations to exercise these rights.”
This would create a new class of regulated associations that “take a very serious seat at the table with AI companies and have the power to gain shares, compensation, governance and power,” Prewitt said.
Economist and technologist Glen Weyl, senior researcher at Microsoft and founder of RadicalxChange, argues that the goal should not necessarily be for companies to be state or publicly owned. According to RadicalxChange, efforts to “either divide and fractionalize ownership (i.e. giving more or different people a share in conventional property) or consolidate ownership (i.e. putting it in the hands of a representative of the public such as the state)” can do some good, but are best viewed as “just band-aids.”
“The fragmentation of ownership only ‘spreads’ the same old extractive incentives of conventional ownership, while the consolidation of ownership ‘puts all the eggs in one basket,’ increasing the risks of institutional capture and illegitimate representation,” RadicalxChange staff wrote in a policy article arguing for new models focused on shared ownership.
New corporate taxes, fewer working hours
Others say the mechanisms are already in place for policymakers to create a fairer AI economy without resorting to untested ideas. These include tougher corporate taxes, antitrust enforcement and labor protections, according to Dean Baker, an economist and co-founder of the Center for Economic and Policy Research.
While Baker said he remains unconvinced that there will be a mass displacement of human labor by AI, he added that he will still resort to “old remedies.”
Those remedies could include “a viable higher corporate tax rate for all companies,” Baker said. However, he added that the form of payment could be new. “The best way to achieve this is to require companies to give up non-voting shares at the target tax rate (e.g. 25% of shares for a 25% tax rate),” he said.
Furthermore, Baker says, if applied consistently, antitrust enforcement offers a plausible path to a more equitable economic distribution of AI profits. Baker made the analogy that cheap Chinese products are displacing workers. “We let Chinese-made goods in to screw over large swaths of the workforce. We shouldn’t engage in protectionism to keep Elon Musk and Mark Zuckerberg ridiculously rich,” he said.
Not repeating the policy mistakes of the past, including lax attitudes toward the rise of social media and the previous global outsourcing era, is high on the radar of some of the most senior policymakers in the U.S., who are betting that AI, and especially the employment aspect, will become more prominent as a campaign and social issue in the coming years.
While the idea of a universal basic income — or a “universal high income program,” as Elon Musk calls it — to combat mass unemployment has been around for years, there is a simpler labor market mechanism for distributing future economic efficiencies through AI that already has a global precedent.
The answer is not no work, but less work.
“We introduced the 40-hour work week 90 years ago and nothing has changed since then,” Baker said. “Other countries have shortened the work week and work year. If AI is to give us the promised productivity boom, let’s lower the threshold to 32 hours or possibly even lower. We can also double the overtime bonus to 100% instead of 50%,” Baker said.
https://www.cnbc.com/2026/07/26/how-can-ai-wealth-be-shared-with-all-americans.html
