![\"Global](\"https:\/\/www.europesays.com\/ai\/wp-content\/uploads\/2026\/06\/1780832655_45_0x0.jpg\")
<\/p>\n<\/p>\n
CHICAGO, IL – AUGUST 24: A trader monitors offers in the Standard & Poor’s 500 stock index options pit at the Chicago Board Options Exchange (CBOE) on August 24, 2015 in Chicago, Illinois. Uncertainty among traders after big losses in the Asian markets caused a sharp drop in the S&P at the open. (Photo by Scott Olson\/Getty Images)<\/p>\n
Getty Images<\/p>\n
The people most likely to be devastated by AI are not factory workers or call center agents. They are the Manhattan law partners, the tenured academic class, and every professional who spent decades mastering the art of following the rules. That is the central point in a keynote delivered by economist Tyler Cowen<\/a> at the Sana AI Summit<\/a> in New York, May this year – a talk that has since spread rapidly across X and through Sand Hill Road group chats, where its rebuke of conventional elite wisdom proved either galvanizing or uncomfortable depending on the viewer.<\/p>\n The talk\u2019s viral reach reflects a genuine fault line in how VC investors are placing bets. If Cowen is right, the most defensible venture categories over the next decade are not productivity software for knowledge workers but infrastructure for a world remade by initiative, physicality, and the experimental testing of AI-generated ideas.<\/p>\n Cowen, the Holbert L. Harris Professor of Economics at George Mason University<\/a> and chair of the Mercatus Center<\/a>, argued that AI is about to execute what he called a “status remix” across society. The mechanism is simple and brutal: smart, credentialed professionals who played by institutional rules are the ones whose skills AI replicates most easily. “There was an older world we all grew up in, where if you were very smart, went to a good school, worked hard and followed the rules,” he told the audience. “Those are actually the people who might lose the most.”<\/p>\n The biggest losers in his framing are the highest earners in rule-governed professions. A partner at a white-shoe firm earning $2 million a year in Manhattan does not become unemployed; instead, they get commoditized and relocated. “They will be sent to Houston. At 37 they’ll be an energy company executive, earning $350k a year.” The academic class, in Cowen’s telling, faces a similar status deflation.<\/p>\n The categories receiving the most capital in 2025 and 2026 have disproportionately targeted enterprise knowledge work: legal tech, contract analysis, financial research automation. Anthropic’s $50 billion <\/a>Series H announced in May 2026 compressed national VC statistics so severely that New York’s $2 billion in monthly deal flow represented only 3% of US activity that month, against an underlying share closer to 12% when Anthropic is stripped out. The concentration of capital into frontier model infrastructure is rational if AI commoditizes the high-skill professional class. But Cowen’s framework suggests the next layer of value creation sits elsewhere.<\/p>\n Two job categories, in his view, are badly underinvested. The first is experimental testing of AI-generated ideas, particularly in biomedicine. “AI will have many, many new ideas as it does already,” he said. “We will need to test those ideas. In the world today, only PhDs run experiments. That’s on the way out.” The implication for investors is a large market for platforms that democratize experimental infrastructure, from lab automation to clinical trial coordination for non-specialist operators.<\/p>\n The second category is data collection from the physical world. Most physical reality remains undigitized. Cowen framed this as a constraint on AI capability rather than a solved problem: “Most of the world is not currently expressed in the form of data. Insofar as we turn the world into data, AI can do much more for us.” Sensor networks, field data services, and physical-digital translation tools are the unglamorous plays this thesis generates.<\/p>\n On macroeconomics, Cowen positioned himself explicitly against Silicon Valley consensus. His projection for AI\u2019s contribution to GDP growth is half a percentage point, from roughly 2% to 2.5%, a forecast that provokes derision from founders and investors who believe transformative technology should produce transformative growth. His counter-argument is: “The smarter the AI is, the harder it is for you to work with it, the harder it is to get organizations to adopt it, it is a Human Bottleneck problem.” He estimates that government, higher education, healthcare, and the nonprofit sector constitute at least 40% of US GDP, and that these sectors will be slow to restructure. The FDA’s average drug approval<\/a> timeline of a decade is his anchor example.<\/p>\n