OpenAI released a 13-page policy paper arguing that governments must redesign economic rules as AI systems approach superintelligence, with the company tying its proposals to labor-market disruption and shifts in who captures productivity gains. The report’s “people-first” blueprint included ideas such as shifting tax reliance from labor toward capital and exploring levies related to automated labor. The proposals echo similar elite-industry tax ideas circulating in Silicon Valley. Vinod Khosla’s earlier proposal—eliminating federal income taxes for most Americans earning under $100,000 while reshaping tax treatment of capital gains—overlaps with the paper’s core theme: existing tax and safety-net structures are “dangerously unprepared” for the scale of change described. The paper landed amid public skepticism about conflicts of interest, including commentary from AI policy experts that OpenAI has a stake in how regulation develops. OpenAI framed the document as a starting point for democratic discussion rather than a final recommendation set. For higher education stakeholders, the policy debate matters because university governance, labor planning, and workforce training strategies are increasingly shaped by federal and state AI regulation—especially when proposals link tax and work-hour redesign to AI deployment.