That idea is not going anywhere fast - Sanders ally Jaron Lanier, who serves as Microsoft Research's Chief Technical Officer and Prime Unifying Scientist, 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." But the proposal has forced a broader question into the open: Who gets the money when AI changes the economy?
Currently, the stock market has seen rapid accumulation of AI-generated wealth, but many Americans have only limited access to those gains. Public sentiment has turned markedly negative within the past year.
In April, Will Hollingsworth, a resident of Northeast Ohio, spoke at a public comment session concerning a proposed data center complex occupying 257 acres in Portage County. "When I see the data center proposal, I don't see progress," he said. "I see a gamble where the big tech companies get the gold while Portage County foots the bill." He added, "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. 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."
Economists and researchers are sketching out concrete ways to spread the wealth.
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One approach is paying people for their data. Jaron Lanier has proposed a model known as "data dignity," under which individuals get paid for the data and contributions used to build AI systems. He said, "Good data and supervision 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."
University of Chicago computer science assistant professor Raul Castro Fernandez contends that AI firms already gauge the value of data. He suggests a system like 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."
Not everyone is convinced. In a 2023 study, researchers Nicholas Vincent (Simon Fraser University) and Brent Hecht (Northwestern University) cautioned that assigning a value to each individual's data is highly subjective and could backfire. They state, "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."
Dean Baker, an economist with the Washington D.C.-based Center for Economic and Policy Research, offers another approach. He suggests using existing tools: higher corporate taxes, stronger antitrust enforcement, and labor protections. His specific proposal would require companies to hand over non-voting shares equal to 25% of their profits as a tax.
He also points to the work week. The U.S. has stuck with 40 hours for roughly 90 years, while other countries have moved to shorter weeks. Baker argues that if AI really boosts productivity, the standard work week should drop to 32 hours, and overtime pay should double from 50% to 100% premium.
Matt Prewitt, president of the RadicalxChange Foundation, advocates for something like 21st-century unions. He wants new legal rights that let people form associations to negotiate with AI companies - gaining shares, governance, and real power. His group warns against simply splitting up ownership, calling that a temporary fix that keeps the same old incentives.
The bottom line: There is no shortage of proposals, but no consensus yet on what will actually work.
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