Not the Alligator Closest to the Boat

United Educators’ 2026 Top Risks Report made news this month because athletics rose to be a top campus concern, displacing Title IX. The other responses from 267 college and university leaders were familiar: admissions and retention, data security and cybersecurity, compliance (non–Title IX), operational pressures, facilities and deferred maintenance, funding and financial stability, student mental health, recruitment and hiring, and public safety.

Every item on the list is a challenge that most institutions already know how to name, staff and plan for, even athletics. But none are the alligator closest to the boat.

AI is the threat leaders should be worried about because it imperils the essential purpose of the university faster than demographics, plant maintenance or changes in incremental funding. No other item on the list attacks and splinters the core transaction: the awarding of a degree based on content delivery, feedback cycles and assessment. AI threatens to make the business model and the value proposition fail together.

Once upon a time, course content was delivered by an instructor. Students would demonstrate learning and the instructor would offer feedback and ultimately give a final assessment: a grade. This was the central contract. But increasingly, students are receiving content from AI and AI is generating demonstrations of learning. The instructor is still required to give feedback and a grade, but if the instructor has lost control of content delivery, how can a grade by that instructor be considered valid, even without a broken feedback loop?

The AI reality is that every student has access to the equivalent of 500 or more college professors in their phone or laptop ready to answer questions any time, day or night, for free, or $20-plus per month, depending on the pro plan. What AI professors don’t offer is an evaluation system and degree-granting authority. But as content delivery becomes cheap, fast and widely available outside the institution, the value of university-branded courses drops and the institution’s largest cost category—instruction—is put under a microscope. What value are faculty delivering?

Enter legitimacy. Legitimacy, not prestige, is the scarce asset worth paying for. Prestige depends on how an institution is ranked above other institutions. Legitimacy depends on whether employers believe the diploma means what it says. A prestigious university can lose legitimacy if employers stop trusting its transcripts. A nonprestigious university can build legitimacy if it can demonstrate that its graduates actually know what the degree says they know. AI attacks legitimacy directly because it makes performance easy to counterfeit.

The speed of this attack is part of what makes AI the closest alligator. AI can change expectations about academic quality in a single admissions cycle. A competitor can repackage AI-supported instruction quickly. A small number of students can broadcast their misuse of AI and overwhelm existing integrity systems. Employers can rapidly revise their trust in a credential once they suspect transcripts no longer represent student capability. The downside arrives faster than slow governance processes can respond. The risk is existential rather than marginal.

I presume higher ed leaders don’t see the AI alligator because the usual risk categories assume the academic machine stays intact. Faculty can point to the large bites that have already been taken out of it, and it still survives. With the prevalence of online programs at scale, nobody really knows who is doing the content delivery, assessment and validation anyway. It might as well be AI.

What AI is threatening is organizational structure. Institutions will face a choice between two paths, and the choice will be determined by how they treat faculty.

If an institution’s structure is optimized for student services while treating faculty as a managed cost, the AI alligator will spur leadership to replace the cost. Some will say fire the professors and put 500 AI professors in every dorm room. Ask AI to deliver lectures, grade papers, respond to routine questions, provide feedback on drafts. Faculty labor will be cut wherever possible in the name of efficiency. Instructional quality declines. This is already starting to happen.

But cutting faculty does not solve the certification problem. No degree, no university. Faculty alone enable the university to legitimize assessment and grant a degree. Certification requires expert judgment. Advisers can support persistence; residential life can support social development; career services can translate skills to employers. None of those functions can certify mastery in a credible way when AI allows the whole academic performance to be counterfeited.

In short, if an institution responds to AI by cutting faculty lines and keeping large enrollment throughput, it saves cash and spends credibility. Employers respond by discounting the credential, which pushes institutions to add surveillance and compliance layers, which adds cost and friction, which further degrades trust, which further degrades outcomes. The spiral is operational and financial. It all ends with a flimsier credential that signals less.

Alternatively, universities could restructure themselves around faculty excellence and faculty leverage. The return to blue books is a recognition that AI can draft, revise and polish all written take-home work. Grades and transcripts have no meaning without verification. Every verification format requires faculty attention and class formats built around small groups. Oral exams, studio critiques, clinical rotations and lab work don’t scale the way lectures do. They’re expensive. But if students are already choosing to have content delivered to them by AI, the faculty role becomes synthesis, discussion, critique and engagement. Education becomes personalized. It may be that advisers, counselors and the “belonging” infrastructure matter less when faculty matter more.

Put another way, as AI drives the marginal cost of content delivery toward zero, the cost of delivering legitimacy rises. That means using AI to make faculty time go further in the places where faculty judgment is irreplaceable. It also means building infrastructure that makes faculty more effective: AI tools configured to institutional standards, shared banks of oral exam questions and rubrics, secure testing environments, and support staff who help faculty redesign courses around verification.

The alligator closest to the boat is AI-forced restructuring. Universities used to be purveyors of courses bundled into degrees. There was an assumption that the degree alone had value. Now, universities need to step up and double down on offering verified capability developed under expert supervision, in addition to the social and professional networks that make the capability legible in the world. AI can help faculty deliver content at scale, and employers can trust that your graduate actually knows what the transcript claims.

The United Educators report asks what keeps campus leaders up at night. The answer they give is the answer they know how to give: the risks that fit existing categories, existing budgets, existing governance structures. AI does not fit. It is not a line item. It is a transformation of what the university sells and how it sells it. That is why it is the alligator closest to the boat—and why it does not appear on the list. I for one welcome the AI alligator, because mentorship paired with expert judgment is the only defensible value proposition left.