Leading economist Tyler Cowen has issued a stark warning that colleges are failing to prepare students for an AI-transformed labor market, with consequences that extend beyond economics into psychological wellbeing. The George Mason University professor stated that universities are “producing a generation of students who will go out on the labor market and be quite unprepared for what they’re expected to do” during a conversation with podcaster Azeem Azhar that aired Tuesday.
Cowen emphasized that college is “the time to do it” when it comes to learning to work with AI, yet current teaching methods are “counterproductive, even.” His comments come as generative AI tools become embedded across white-collar industries, making proficiency with these systems a baseline expectation for new graduates. While Cowen doesn’t believe AI will destroy jobs outright, he predicts it will fundamentally reshape hiring practices, career trajectories, and productivity standards.
The economist warned that new graduates may struggle to get hired, but the deeper concern involves psychological damage. “The output will be lower, but I think many of the highest costs will be psychological — people feeling they do not fit into this world,” Cowen said. “And they’ll be somewhat correct.”
OpenAI’s VP of Education, Leah Belsky, echoed these concerns on the company’s podcast last week, stating that college graduates need to know “how to use AI in their daily life” and should be taught to use it “in ways that will expand critical thinking and expand creativity.” Google DeepMind research scientist Stefania Druga told Business Insider in May that if AI can complete a student’s assignment, teachers need to change it. “If an AI can solve a test, it’s the wrong test,” she said.
Druga, who designs AI education platforms for children, observed that young people are using AI to shortcut learning entirely rather than being taught to use it as a tool for co-creation. Educators are adopting diverging strategies in response: some are doubling down on analog tools like handwritten essays and oral exams to preserve academic integrity, while others are embracing AI to build more personalized, harder-to-game assessments. This split approach highlights the uncertainty and urgency surrounding how educational institutions should adapt to the AI revolution.
Key Quotes
Universities are producing a generation of students who will go out on the labor market and be quite unprepared for what they’re expected to do.
Tyler Cowen, professor at George Mason University, made this statement during a conversation with podcaster Azeem Azhar, highlighting the fundamental disconnect between current college curricula and the AI-transformed job market awaiting graduates.
The output will be lower, but I think many of the highest costs will be psychological — people feeling they do not fit into this world. And they’ll be somewhat correct.
Cowen emphasized that the consequences of inadequate AI preparation extend beyond economic impacts, warning that graduates may experience deep psychological distress from feeling disconnected from modern workplace expectations.
If an AI can solve a test, it’s the wrong test.
Google DeepMind research scientist Stefania Druga articulated a fundamental principle for AI-era education, suggesting that assessments must evolve to measure skills that complement rather than compete with AI capabilities.
College graduates need to know how to use AI in their daily life and should be taught to use it in ways that will expand critical thinking and expand creativity.
OpenAI’s VP of Education Leah Belsky outlined the educational approach needed, emphasizing that AI literacy should enhance rather than replace fundamental cognitive skills.
Our Take
Cowen’s warning represents a pivotal moment in the conversation about AI and education. The psychological dimension he emphasizes is often overlooked in discussions focused purely on job displacement or economic impacts. A generation entering the workforce feeling fundamentally unprepared could create long-term societal challenges that extend far beyond individual career struggles. The divergence in educational strategies—some embracing AI, others resisting it—suggests institutions are paralyzed by uncertainty rather than leading with vision. The most concerning aspect is the timeline mismatch: AI capabilities are advancing exponentially while educational institutions operate on semester-long or year-long cycles. By the time curricula are updated, the AI landscape may have shifted dramatically. The involvement of AI companies in shaping educational discourse raises important questions about who should define AI literacy and whether corporate interests align with broader educational and societal goals.
Why This Matters
This warning from a prominent economist signals a critical gap between higher education and workforce readiness in the AI era. As generative AI tools like ChatGPT, Claude, and others become standard in professional environments, students graduating without AI literacy face significant disadvantages in job markets across industries. The psychological dimension Cowen highlights is particularly concerning—a generation feeling disconnected from the modern workplace could lead to broader societal challenges including mental health issues, economic inequality, and social fragmentation.
The divergence in educational strategies reveals institutional uncertainty about how to respond to AI’s rapid advancement. While some educators resist AI integration to maintain traditional academic standards, others recognize that preparing students for an AI-augmented workplace is essential. This debate reflects broader tensions about AI’s role in society and whether institutions can adapt quickly enough to technological change. The involvement of AI industry leaders like OpenAI and Google DeepMind in this conversation suggests the tech sector recognizes its responsibility in shaping educational approaches, though questions remain about whether their interests align with broader educational goals.
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