Ro Khanna on AI and the Rise of a New Epstein Class

On April 17, 2026, Congressman Ro Khanna warned that unchecked AI development risks creating a novel ‘Epstein class’ of untouchable elites, echoing concerns about systemic impunity for the wealthy and powerful. Speaking at the National Press Club, he linked the lack of accountability in the Jeffrey Epstein scandal to the urgent need for AI regulation, wealth taxation, and antitrust action to prevent technology from exacerbating inequality and eroding democratic safeguards. His remarks follow growing evidence that AI-driven job displacement and data harvesting are concentrating power in ways that outpace legal and ethical frameworks, particularly in tech hubs like Silicon Valley and data center corridors in Northern Virginia and Arizona.

The Epstein scandal revealed how concentrated wealth and influence can distort justice systems, with key figures like Les Wexner and Leon Black avoiding criminal scrutiny despite credible allegations. As of 2026, over 3 million pages of Epstein-related documents remain heavily redacted, fueling public distrust. Khanna argued that the DOJ’s refusal to interview survivors or pursue investigations into named enablers proves a two-tier justice system exists—one where the wealthy operate with functional immunity. This dynamic, he warned, is being replicated in the AI boom, where tech leaders openly discuss displacing millions of workers while resisting calls for worker transition funds or data privacy safeguards.

AI’s Concentrated Power Mirrors Past Failures of Accountability

The analogy to an ‘Epstein class’ is not hyperbolic. AI systems trained on vast datasets harvested without consent are enabling unprecedented surveillance and behavioral prediction capabilities. In 2025, the AI Now Institute reported that just five firms control over 60% of global AI training data infrastructure, raising alarms about monopolistic control over knowledge and labor markets. Unlike the industrial barons of the first Gilded Age, who funded public libraries and universities, today’s AI magnates rarely reinvest in civic infrastructure. As Khanna noted, there is no ‘Sam Altman library’ equivalent to Carnegie’s legacy—a symbolic absence reflecting a broader retreat from social responsibility.

This imbalance has tangible local consequences. In Santa Clara County, California, where NVIDIA and other AI chipmakers are headquartered, municipal budgets face strain from rising housing costs driven by tech wealth, while public schools report declining per-pupil spending adjusted for inflation. Meanwhile, in Loudoun County, Virginia—home to the world’s largest concentration of data centers—residents have protested rising electricity rates and groundwater depletion linked to AI infrastructure. The county’s service authority reported in early 2026 that data center cooling consumes over 100 million gallons of water daily, prompting calls for stricter environmental review under the Chesapeake Bay Preservation Act.

“We’re not just talking about job loss—we’re talking about the erosion of local tax bases when AI displaces workers without reinvestment in community resilience. Cities need tools to negotiate benefit agreements with tech firms, just as they do with stadium developers or logistics hubs.”

Legal scholars are also sounding alarms. In a March 2026 paper, the Stanford Law School Center for Internet and Society argued that current antitrust laws are ill-equipped to address AI-driven market concentration because they focus on consumer pricing rather than control over labor, data, and democratic discourse. The report urged Congress to update the Clayton Act to include ‘non-price harms’ like algorithmic wage suppression and algorithmic management, which disproportionately affect gig and service workers in urban centers like Los Angeles and Miami.

“Antitrust enforcement must evolve. When an AI system sets wages for thousands of warehouse workers across state lines, that’s not just a labor issue—it’s a monopoly problem. We need regulators who understand code as well as commerce.”

Directory Bridge: Who Solves This?

The risks posed by unchecked AI concentration demand coordinated responses from professionals who understand both technology and public interest. Municipalities grappling with data center impacts need environmental impact consultants to assess water and energy use, while workers displaced by automation require career transition specialists to navigate retraining programs funded by emerging state AI impact funds. Most critically, communities seeking to challenge algorithmic discrimination or data misuse must consult civil rights attorneys versed in emerging AI accountability frameworks, including those building cases under biometric privacy laws in Illinois and Washington State.

These professionals are not just service providers—they are frontline defenders of democratic accountability in an age where power is increasingly invisible, embedded in algorithms rather than boardrooms. Their work ensures that the lessons of the Epstein scandal are not repeated: that no class, however technologically advanced, is beyond the reach of law, transparency, or civic oversight.

The true danger of AI is not that it will turn into sentient, but that it will amplify existing human impulses toward impunity and extraction—unless we build countervailing forces just as speedy. As Khanna suggested, regulating AI is no more radical than regulating electricity or aviation. It is the minimum condition for a society where innovation serves the many, not just the few who own the servers.