A national survey published earlier this month reveals that 69% of Americans support "forcing" AI firms to transfer 50% of their stock to a public sovereign wealth fund. This widespread public sentiment emerges even as tech corporations continue to shed workers while ramping up capital expenditure for AI expansion. Benjamin Leff, chief executive officer of research firm Verasight, which carried out the June survey of 1,690 adults, noted that "AI Sovereign funds are seen as a tool to distribute the gains from the AI industry back to broader society."
This call for redistribution comes as tech layoffs in the U.S. continue to generate frustration and job security worries among workers. Goldman Sachs Senior Global Economist Joseph Briggs estimates that over 9% of the labor force, approximately 15 million workers, could lose their jobs during a 10-year AI transition period. Briggs, whose bank published this report last month, compared this to "automation and reallocation shock" seen in previous periods of significant technological change.
Capital's Relentless Expansion
Despite these projections of mass displacement, AI company executives report insatiable demand for their products and services. Pat Gelsinger, general partner at Playground Global and former Intel CEO, told CNBC on Wednesday that he considers AI demand "almost unlimited." He added that energy availability is "the only real limiter" to how much "economic value" can be extracted from increased intelligence across every imaginable industry.
Chip stocks have experienced a blistering rally over the past year, as investors pour capital into the semiconductor sector, recognizing its central role in the global AI infrastructure buildout. Lumentum CEO Michael Hurlston stated that his company's products are sold out for the next five years. Lumentum's stock has surged around 600% over the last 12 months, fueled by investors targeting key bottlenecks in AI data center construction.
Marc Boroditsky, chief revenue officer at Nebius, which builds data centers using Nvidia's GPUs, confirmed the extraordinary demand. He noted, "There's much more demand than we're able to fulfil, and that's been our experience for some time now." Andrew Feldman, CEO of Cerebras Systems, echoed this, stating that "the demand for compute far outstrips available capacity, and we're short on data centers."
Sungyun Park, CEO of Rebellions, a company backed by Samsung and SK Hynix targeting an IPO next year, also affirmed that "AI infrastructure momentum [is] still huge." These executives see no signs of overcapacity, even as enterprises begin to scrutinize the return on their AI investments more closely.
The State and Symbolic Reforms
In response to the growing chasm between corporate profits and worker precarity, Senator Bernie Sanders proposed the American AI Sovereign Wealth Fund Act this year. If enacted, this legislation would grant the public a 50% stake in the largest U.S. AI companies. Sanders declared last month that the bill would "guarantee that the economic benefits generated by AI are used to improve the lives of all of us — not simply to make the richest people in the world even richer."
Sanders' proposal, though framed as a mechanism to distribute gains, operates within the existing framework of capital ownership. He stated that "the future of AI and the fate of humanity must not be decided behind closed doors in Silicon Valley by billionaires seeking to maximize their power and profit." This acknowledges the concentrated power of capital but offers a reformist solution rather than a fundamental challenge to the system of private ownership.
Corporations, meanwhile, are shifting from "tokenmaxxing" — encouraging employees to use AI indiscriminately — to "valuemaxxing," focusing on AI applications that demonstrably create value to justify spending. Boroditsky explained that "the CFO bringing the hammer down and slowing spend should actually be looking for value." This rationalization of AI spending is not a slowdown in demand, but a refinement of how capital extracts surplus value.
Feldman predicted that future AI use will likely split by workload, with expensive frontier models reserved for "more advanced problems" and cheaper alternatives handling "easier tasks." This strategic deployment aims to optimize profit margins, ensuring that capital continues to dictate the terms of technological advancement and its application.