<strong>58% of surveyed U.S. workers now support creation of a government-administered AI wealth fund, a 19-point swing from eighteen months prior, as technology sector layoffs exceeded 90,000 positions through March 2025. The survey, conducted across 2,400 employed adults in January, marks the first time majority sentiment favors direct sovereign capture of automation-driven productivity gains. The proposed fund structure would tax AI-generated corporate profits at a separate rate, channeling proceeds into a distributed national account—functionally a technology dividend paid to citizens regardless of employment status.

The timing reflects visible decoupling between equity performance and headcount. The Nasdaq-100 delivered 31% total return over the trailing twelve months while constituent technology firms reduced North American payrolls by 7.2% in aggregate. Alphabet, Meta, Amazon, and Microsoft—collectively representing $8.1 trillion in market capitalization—announced sequential workforce reductions totaling 34,000 roles since October, citing efficiency gains from large language model deployment in customer service, content moderation, and code generation. Median affected worker tenure was 4.1 years, indicating cuts reached beyond recent hires into established engineering and operations cohorts.

The sovereignty question matters because it reframes technology policy from regulatory constraint to fiscal participation. Traditional labor protections—retraining subsidies, extended unemployment insurance, wage insurance—treat automation as temporary displacement requiring a bridge back to employment. A wealth fund structure assumes permanence: that AI-driven productivity creates lasting, non-recoverable job losses in specific sectors and that the state should claim a portion of resulting corporate value creation before it reaches shareholders. Alaska's Permanent Fund offers the only U.S. precedent, distributing oil revenue since 1982; annual payouts averaged $1,600 per resident over the past decade. An AI equivalent scaled to federal tax receipts could generate $40 billion to $120 billion annually by 2028, depending on rate structure and scope, per Congressional Budget Office modeling published in September.

Allocators should track three legislative pathways emerging in parallel. Senator Warner's draft bill, circulated to 14 co-sponsors in February, proposes a 12% surcharge on profits attributable to AI-generated revenue, defined as income from products or services where machine learning systems contribute over 25% of functional output. House counterpart legislation, expected by June, leans toward a narrower scope—only generative AI products sold directly to consumers—but at a 16% rate. The third path runs through state capitals: California, New York, and Illinois are each drafting AI profit-sharing mechanisms that would apply to in-state operations, creating potential compliance fragmentation for multinationals. Corporate tax teams at affected firms are already modeling $2 billion to $6 billion in incremental annual liability under federal scenarios.

The telling detail is not the survey result but the constituency. Support reached 67% among workers aged 25-34, the cohort most likely to hold equity compensation and most fluent in AI tooling. They are voting for redistribution of gains they partially own, which suggests the perceived unfairness exceeds personal financial interest. That calculation changes quickly if tech multiples compress or if fund payouts undershoot expectations, but the direction is now established.

First markup of Warner's bill is scheduled for the Senate Finance Committee in late May, with floor consideration possible before August recess.