As Washington and Silicon Valley race to harness artificial intelligence, a growing chorus of economists and technologists is debating how to spread AI’s expected trillions in value beyond shareholders. Proposals range from “data dignity” royalties that compensate people for the information used to train models, to new collective rights and data unions that could secure remuneration and governance power. Others press for traditional levers—higher corporate taxes paid via nonvoting shares, tougher antitrust enforcement, and stronger labor standards—while a shorter, 32-hour workweek emerges as a straightforward way to share productivity gains. The push comes amid souring public sentiment: most Americans now oppose local data centers, and support is rising for mechanisms such as an AI sovereign wealth fund. Skeptics warn that valuing individual data contributions is impractical and yields trivial payouts, but advocates argue pooled royalty systems—akin to music licensing—can align incentives. The broader political backdrop includes ideas from eliminating income taxes for lower earners to renewed scrutiny of tech power, signaling that AI’s social contract is becoming a front-line economic issue.




























