OpenAI's chief global affairs officer, Chris Lehane, believes the 2028 presidential election may prove pivotal for AI. He is urging politicians to develop substantive policies addressing Americans' anxieties about the technology, rather than offering empty rhetoric in response to anti-AI sentiment and growing backlash against data centers.

In a newly published essay, Lehane draws a parallel between AI's potential to transform the economy and President Franklin D. Roosevelt's New Deal—the sweeping set of federal programs and reforms enacted during the 1930s after industrialization and the Great Depression upended the existing economic order. He notes that Americans today share a similar unease about losing control of their economic futures, and he calls on political leaders to chart a "compelling third path" that lets people share in AI's benefits. His essay outlines five policy priorities covering worker protections, education, AI safety, and data center development.

The essay arrives shortly after a surprising Democratic victory in the midterm elections, where Democrats seized control of Congress amid broad voter frustration over the cost of living. Not surprisingly, many of Lehane's proposals would promote broader AI adoption and support the infrastructure expansion that companies like OpenAI require to grow.

The original New Deal established worker protections and placed limits on corporate power. It remains unclear how Lehane's vision would hold AI companies accountable for the economic disruption their technology could cause. "Invoking a New Deal metaphor is to invoke state capacity to actually benefit the public," said Brennan. "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."

Although AI was not a dominant issue in the midterms, opposition to data centers became a significant bipartisan concern in several races. Lehane may be correct that AI will feature more prominently in political conversation over the next two years. Affordability is already emerging as a central issue for the 2028 election as Americans contend with inflation, tariffs, and the rising cost of living. AI could become an increasingly significant part of that discussion as voters worry about data centers driving up electricity bills, AI displacing workers, and the technology's economic gains flowing only to a handful of wealthy corporations.

Lehane acknowledges many of these concerns, but he provides few concrete proposals to prevent AI's benefits from becoming concentrated. Instead, he largely presents AI as a route to greater prosperity for Americans willing to embrace it. Consider his tax code proposals: he suggests incentives for workers to learn AI skills, easier capital access for entrepreneurs, and new approaches to employee ownership and profit-sharing. Those fall short of binding requirements that would guarantee workers share in AI-generated wealth or that companies bear the costs of displacing workers. "I was struck by how much of this policy is for a future…that is already predicated on mass AI adoption and AI benefitting society, which we have yet to see," Brennan said. "All of it is predicated on benefitting from AI's upside."

Brennan suggested it would make more sense to consider a revised corporate tax rate, tax incentives for hiring human beings rather than automating jobs, and measures to prevent companies from accumulating excessive market power.

Lehane's focus on expanding AI's economic opportunities rather than limiting potential harms carries over to data centers. He acknowledges concerns about data centers while emphasizing the construction jobs and local tax revenue they could generate. He then cautions that politicians supporting a data center moratorium could jeopardize America's advantage over China, even comparing that position to the 2002 vote authorizing the Iraq war.

Lehane also places child safety and education at the center of his proposed agenda, emphasizing the importance of age-appropriate protections and safeguards around AI companions for minors. He warns against excluding AI from classrooms, contending that doing so could create inequality between wealthy students and "children in rural communities." But this argument assumes that AI improves learning—a claim that remains unproven and one that critics say warrants more scrutiny than additional funding or individualized attention from human teachers.

Lehane further calls for enforceable AI safety standards, independent evaluations, public oversight, and the ability to restrict AI systems or use a kill switch to shut them down—though recent studies indicate that OpenAI and other AI labs have not published comprehensive plans for what they would do if a model escaped human control. Brennan described these positions as "disingenuous," citing OpenAI's history of opposing legislation designed to impose such measures. In 2024, for instance, OpenAI opposed California's AI safety bill, SB 1047, which would have required catastrophic risk evaluations and shutdown capabilities. Last year, OpenAI also sought to limit state regulation, arguing that states could undermine American innovation. The company has more recently adjusted some of those positions, backing several state safety laws and expressing openness to independent evaluators embedded within the company.

Lehane also contends that people should retain control over consequential decisions made using AI, rather than delegating judgment entirely to machines. But this raises the question of what human control truly looks like in a society where humans are increasingly required to use AI in education, work, health care, and government.

Throughout his essay, Lehane frames AI as a technology that could restore Americans' sense of agency and economic opportunity. Yet he links average Americans' "middle class" success to participation in an economy increasingly shaped by companies like OpenAI. Whether his policies would meaningfully redistribute power or subtly entrench it remains unclear.