That reliance on someone with specialized knowledge takes on particular urgency when the guidance becomes a warning. The people we turn to for an explanation of how something works may also be positioned to recognize where it could go wrong. Some of the people who helped build AI systems are now warning the public about what those systems may become. For higher education leaders already incorporating AI into institutional life, their warnings raise an immediate question: what should institutions do with knowledge that warrants attention but cannot yet offer certainty? Waiting for proof may mean waiting until foresight has become hindsight.
When builders become witnesses
These insiders occupy a distinctive position as participant-witnesses. They have helped create the technologies whose implications they now ask society to examine. Their warnings carry the knowledge acquired through that participation, along with the interests and limitations that accompany it. Institutional leaders must weigh that testimony and decide what it requires them to investigate. Geoffrey Hinton’s 2024 Nobel banquet speech made this tension visible. Being honored for foundational contributions to machine learning gave him an occasion to warn about the possibility of losing control of systems more intelligent than humans. The achievement being celebrated was also the source of his concern. His warning asked the audience to consider responsibilities that extended beyond the scientific accomplishment itself.
Dario Amodei’s argument in “We Must Pace the Frontier” brings that responsibility into the organizations developing AI. He describes a change in his assessment of the pace of development and proposes ongoing access for external evaluators who could examine systems from within. His acknowledgment that companies still choose what to include and omit in their disclosures identifies a problem for anyone relying on those disclosures: access to the technology does not necessarily provide access to the evidence needed to judge it.
A witness’s warning should initiate scrutiny; it should not become its substitute. For higher education, that means translating concern about AI’s development into examination of specific institutional uses. Leaders do not have to resolve every prediction about AI’s future to investigate the changes already occurring on their campuses. The warning creates a reason to examine those changes while choices remain open.
When adoption precedes deliberation
AI can enter institutional life through individual initiative before a formal adoption decision has been made. A staff member seeking help with documentation or a faculty member managing competing demands may begin using a product without waiting for institutional approval. What appears to be a personal productivity choice can change the conditions under which colleagues participate in shared work.
Consider an employee who brings an AI assistant into a meeting. The tool produces a transcript, summarizes the discussion, and emails both to attendees. It has addressed a real workload problem, but it has also created and circulated a detailed record before the group has discussed whether that record should exist. Comments previously captured selectively in meeting notes are now available for forwarding, potentially separated from the context in which they were spoken.
An institution might subsequently approve a product and designate who may operate it. That decision provides a starting point for examining what participants should know, who can access the record, how long it is retained, and how errors can be corrected. The convenience that prompted the tool’s entry deserves consideration alongside the consequences of making it part of institutional practice.
Colleges and universities cannot rely solely on vendors to explain what their systems do, nor can IT authorization alone settle whether a use is appropriate. People with sufficient access must be able to examine the system, its permissions, its records, its failures, and the institutional conditions under which it operates.
Giving discernment an institutional home
That examination draws on knowledge distributed across an institution. A provost can consider how an AI application changes academic judgment. A vice president of student affairs can examine whether its use affects students’ willingness to speak candidly or seek help. Admissions leaders can investigate how recommendations are produced and challenged. IT contributes essential knowledge about system behavior and access, while faculty, staff, and students can identify consequences that a technical review alone may miss.
Bringing these perspectives together gives warnings somewhere to go. An adviser who notices unsuitable course recommendations or a staff member who questions an unexpected transcript should have a way to bring that observation into institutional review. Their proximity to the work may make a problem visible before it appears in a vendor report or reaches senior leadership.
Leaders could begin with a few questions:
- What evidence supports this use, and what remains uncertain?
- Who has sufficient access and expertise to examine its effects independently?
- How can people affected by the system report a problem and obtain a response?
- What findings would prompt the institution to narrow, revise, or suspend the use?
These questions become useful when someone has responsibility for pursuing the answers. Review takes staff time and access to records. It also requires a route from findings to decisions. An institution that invites concerns but cannot act on them has created a place to receive warnings without developing the capacity to respond. That gap may allow a problem already recognized by some to take hold and spread until its consequences are borne by others.
Learning before the reckoning
AI researcher Stuart Russell gives the concern a memorable form in what he calls the “gorilla problem.” Gorillas’ futures depend heavily on human decisions; Russell asks whether humans could find themselves similarly dependent on machines with substantially greater intelligence. His warning returns us to the participant-witness: someone whose work within AI informs his concern about humanity’s ability to shape its own future. For higher education, that concern sharpens the question of whether institutions are developing the capacity to understand and influence the systems they increasingly rely upon.
Institutions can build that capacity together. A networked improvement community offers a way to organize shared inquiry around a specific problem, with a common aim and systematic testing across settings. For AI meeting assistants, participating institutions could compare transcript errors, correction procedures, participant experiences, and the circumstances in which recording should be excluded. Carnegie’s improvement approach provides a basis for learning from such differences rather than assuming that one institution’s policy will work everywhere.
Even a small group could make its experience useful to others by documenting what it tried, what it learned, and why it changed course. That work would give leaders evidence grounded in institutional practice as they interpret warnings from people developing the technology. It would also make revision an expected part of responsible adoption.
The search for guidance under uncertainty has a long history. Ifá offers one culturally grounded way to enter that history; the Greek oracle offers another familiar reference. The enduring question concerns what people do with the interpretations they receive. For higher education, discernment becomes meaningful when it informs decisions while those decisions can still be changed. The eventual reckoning will concern what institutions did with the warnings available to them. Leaders have an opportunity now to make those warnings the beginning of sustained inquiry, with people able to examine emerging problems and findings capable of changing practice. Certainty may arrive later. The conditions for responsible judgment can be built now.
In my companion column, “A Kill Switch Is Not Governance,” published Tuesday, September 15, 2026, here in The EDU Ledger, I continue the governance discussion, examining how lawmakers are responding to these warnings and what their proposed interventions mean for higher education’s decisions about delegating authority to AI.
Sophia Rahming, Ph.D., is an educational futurist, speaker, and consultant who helps colleges and universities design, scale, and evaluate STEM education initiatives while reimagining teaching, learning, and work in the age of artificial intelligence. She is the editor of Black Sisterhoods: Paradigms and Praxis and the author of The Girl Who Loved Math: The Story of Euphemia Lofton Haynes and The Unfinished Days: A 90-Day Practice for Burnout, Healing, and Beginning Again Rooted in Wabi-Sabi. She writes as a private citizen, and the views expressed are her own.
