AI May, Paradoxically, Increase Demand for Higher Ed

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AI May, Paradoxically, Increase Demand for Higher Ed<br>There are scenarios where college demand increases in the face of AI

Jimmy Alfonso Licon<br>Apr 09, 2026

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About the Author

Jimmy Alfonso Licon is a philosophy professor at Arizona State University working on ignorance, ethics, cooperation and God. His forthcoming book, Better Not to Know: Why Knowing Less is Sometimes Best, is with Peter Lang Publishing. Before that, he taught at University of Maryland , Georgetown , and Towson University . He lives with his wife, a lawyer, at the foot of the Superstition Mountains. He also abides.

A decade ago, AI expert Geoffrey Hinton predicted that artificial intelligence would decimate radiology. Why employ expensive specialists when algorithms could read X-rays and CT scans faster and more accurately than humans? The logic appeared airtight at the time: AI substitutes for pattern recognition, radiology is pattern recognition, and so, therefore radiologists would go the way of elevator operators. And yet, a decade out, and the opposite has happened . Between 2012 and 2022, the number of radiologists in the United States increased by roughly 15 percent, and their median salaries rose faster than inflation. AI made radiologists more productive, shifted their work toward higher-stakes interpretation and consultation, and created demand for specialists who could integrate imaging AI into clinical workflows. The very technology predicted to eliminate the profession instead raised the premium on radiological expertise.<br>This outcome holds a lesson that extends far beyond medical imaging. Every few months, someone announces that AI will finish what the internet began and render higher education obsolete. Why pay tuition when a model can summarize Kant, draft business plans, debug code, and generate essays on demand? (Even though something similar could be said for the many, many free, high-quality college courses available online for the last decade or so). If AI substitutes for cognitive labor, and college primarily produces cognitive labor, then demand for college should fall. That inference is not crazy, but it should not be taken as inevitable either. The economic future of higher education turns on factors more complicated than substitution, and include complementarity, scarcity, signaling, and the structure of knowledge itself. And once those mechanisms are examined, it becomes plausible that AI will increase demand for certain forms of higher education rather than shrink it.<br>Frontier Knowledge Becomes Scarce When General Knowledge Becomes Cheap<br>Begin with a point about the nature of knowledge that Friedrich Hayek emphasized throughout his career. AI systems aggregate vast quantities of general information. They are extraordinary at synthesis across domains. But Hayek’s insight was that much of matters economically is local, tacit, and, contextual—something that is very hard to aggregate and automate, like an inside joke you ‘had to be there’ to appreciate. Markets coordinate this dispersed knowledge through prices precisely because no central authority can internalize it all.<br>Universities, at their best, are institutions that generate, curate, and transmit frontier knowledge and locally embedded expertise . Laboratories, research, and collaborative projects produce knowledge that does not yet exist in any training corpus. So, if and when AI dramatically lowers the cost of accessing what is already widely known, the effect is that the relative value of what is not widely known increases in relative terms.<br>This is a clear substitution effect, such that, if AI automates general knowledge, then returns shift toward what is difficult to encode and what requires situated judgment. And institutions that are organized around producing and validating such knowledge—research universities, advanced professional schools, specialized laboratories—may thus become more valuable. Consider how this worked in radiology. As diagnostic AI improved, the work of reading standard chest X-rays became partly automated. This allowed radiologists to focus on complex cases and communicating uncertain findings to clinicians under time pressure.<br>Powerful Tools Demand Sophisticated Users<br>A second mechanism involves credentialing. Public discourse often assumes AI (fully) substitutes for educated labor. However, the history of general purpose technologies at least suggests complementarity is just as, if not more, common. Electrification increased the marginal productivity of engineers and computers allowed finance professionals to focus on quantitative finance, risk modeling, and algorithmic trading. The more...