OpenAI chief global affairs officer Chris Lehane is already trying to shape the 2028 presidential election, arguing in a new essay that the moment calls for policies addressing Americans’ anxieties about AI rather than lip service to anti-AI sentiment. He compares AI’s potential to reshape the economy with Franklin D. Roosevelt’s New Deal, saying Americans today feel similarly anxious about losing control over their economic futures, and urges politicians to find a “compelling third path” that lets people share in AI’s benefits.

Lehane’s essay lays out five policy priorities spanning worker protections, education, AI safety, and data center development. His tax proposals include incentives for workers to learn AI skills, easier capital access for entrepreneurs, and new approaches to employee ownership and profit-sharing. Kate Brennan, who leads AI and corporate power policy at the AI Now Institute, told TechCrunch that the rhetoric marks a shift from OpenAI‘s policy agenda last year, which focused on accelerating AI development, reducing regulatory barriers, and expanding infrastructure. She also noted that many proposals would encourage wider AI adoption and support infrastructure expansion that companies like OpenAI need, while offering few concrete measures to prevent AI’s gains from becoming concentrated. “Invoking a New Deal metaphor is to invoke state capacity to actually benefit the public,” Brennan said. “This is written by a private company that is trying to advocate for policies for its own benefit. It is ahistorical at best and harmful at worst.”

On data centers, Lehane acknowledges concerns but emphasizes construction jobs and local tax revenue, and warns that politicians supporting a data center moratorium could jeopardize America’s lead over China, comparing that stance to the 2002 vote authorizing the Iraq war. On education, he stresses age-appropriate protections and safeguards around AI companions for minors, but warns against keeping AI out of classrooms, arguing that could create inequality between wealthy students and children in rural communities. Critics note the argument takes for granted that AI improves learning, a claim that remains unproven.

Lehane also calls for enforceable AI safety standards, independent evaluations, public oversight, and the ability to restrict AI systems or use a kill switch. Brennan called these stances “disingenuous,” pointing to OpenAI‘s history of opposing legislation designed to impose such measures, including California’s SB 1047 in 2024. The company has since shifted some positions, backing several state safety laws and saying it would be open to independent evaluators embedded in the company.

Lehane argues people should maintain control over consequential decisions made using AI, but the essay leaves unclear what that human control looks like in a society where AI use is increasingly required in education, work, health care, and government. Throughout, he frames AI as a technology that could restore Americans’ sense of agency and economic opportunity, while tying middle-class success to participation in an economy shaped by companies like OpenAI. Whether those policies will meaningfully redistribute power or subtly entrench it is left unresolved.