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
