OpenAI published a policy document on 6 April titled 'Industrial Policy for the Intelligence Age: Ideas to keep people first', laying out its most detailed vision yet for how governments should respond to the economic disruption caused by AI. The 13-page paper calls on the US government to modernise the tax system by shifting the tax base from labour income and payroll taxes to corporate income, capital gains, and taxes on automated labour — effectively a robot tax, an idea first floated by Bill Gates in 2017.
The centrepiece proposal is a Public Wealth Fund that would give every American citizen a direct stake in AI-driven economic growth. The fund would be nationally managed, seeded in part by AI companies, and invested in diversified long-term assets across both AI firms and businesses deploying the technology. OpenAI frames this as a mechanism to ensure that the productivity gains from AI do not concentrate exclusively among shareholders and early adopters.
The document also proposes subsidising a transition to a four-day work week — 32 hours at full pay — arguing that rising AI-driven productivity makes this feasible without reducing output. This aligns with a broader argument that AI should improve quality of life, not simply increase corporate margins. The proposals sit within a wider policy landscape where governments worldwide are grappling with how to regulate AI's economic impact, from the EU's AI Act to the UK's more innovation-friendly approach.
For context engineers, the implications are worth tracking regardless of whether these specific policies gain traction. If automated labour is taxed, the economics of AI agent deployment change — every agentic workflow that replaces human labour becomes a tax event, not just an efficiency gain. And if public wealth funds create broad-based ownership of AI infrastructure, the incentive structures around open-source versus proprietary models shift in unpredictable ways. Whether you agree with OpenAI's proposals or not, the fact that the company building GPT is now actively shaping the policy conversation about AI's economic consequences is itself a significant development.
