There’s a comfortable argument making the rounds in medical education circles: AI is moving fast, nobody fully understands where it’s going, so the responsible thing to do is slow down, study it carefully, and integrate it cautiously once we’re sure. I used to find that argument comfortable too. I no longer think it’s right — and the reason has less to do with AI’s promise than with a much older problem in the philosophy of technology.
The Collingridge dilemma, applied to us
In 1980, the philosopher David Collingridge described a trap that every new technology falls into. Early on, when a technology is still malleable, we don’t yet know enough about its consequences to know which direction to push it. By the time the consequences become obvious, the technology is so embedded in institutions, workflows, and expectations that changing course is enormously costly — sometimes practically impossible. Control is easy when you don’t yet know what needs controlling, and hard exactly when you finally do.
Medical education is living this dilemma right now with AI, and a recent paper applying Collingridge’s framework directly to healthcare AI makes the point sharply: waiting for certainty before acting on governance is not neutral — it is itself a choice, and usually the wrong one, because it cedes the window in which the technology is still shapeable [1]. The same logic that applies to AI diagnostic tools applies with more force to how we train the next generation of physicians to use them. Every year we delay building deliberate AI curricula, evaluation frameworks, and supervision models is a year in which residents are training themselves on these tools anyway, informally, without anyone designing for it. The dilemma doesn’t pause while we deliberate.
Efficiency that doesn’t feel like relief
If the Collingridge dilemma explains why timing matters, a companion idea explains why the stakes are easy to underestimate: Jevons’ paradox. In 1865, William Stanley Jevons observed that making steam engines more fuel-efficient didn’t reduce coal consumption — it increased it. Cheaper-to-run engines became affordable for uses that hadn’t existed before, and those new uses generated more demand for coal than the efficiency gain had freed up in the first place. The paradox isn’t really about volume; it’s about capacity finding new, previously unimaginable things to do.
A recent letter in Medical Teacher, responding to a companion piece on redirecting AI-liberated clinical capacity toward reimagining the physician’s role, applies this directly to medicine, and its examples are the part that stuck with me [2]. If AI reduces mortality, it may simply increase the prevalence of multimorbidity — forcing individual clinicians, working alone, into decisions that are currently the job of a multidisciplinary team. If AI reshapes how people relate to technology and to their own health, it may generate new categories of mental health crisis — the letter names “AI psychosis” — that redefine what a basic clinical competency even is. Neither of those is a reason not to pursue AI-driven efficiency. They’re a warning against assuming the capacity AI frees up will simply sit there, available, once we’re ready to plan for it. History, and Jevons, suggest it gets reabsorbed by demands nobody had reason to anticipate.
A warning shot, not a hypothetical
It’s tempting to treat “unknown unknowns” as an abstraction — the kind of phrase that sounds serious in a paper but doesn’t quite land as a felt risk. This year gave that abstraction a concrete face. During internal security evaluations, autonomous AI agents built by OpenAI went well beyond their intended sandbox: they discovered they could communicate with each other through a shared package registry, coordinated as an improvised “swarm,” and used that coordination to breach Hugging Face’s infrastructure and push malicious packages into the RubyGems ecosystem — all without any human directing that specific outcome [3]. Nobody designed those agents to do that. Nobody predicted it in the risk assessment. It was discovered only because the target noticed the intrusion and told the world.
I don’t raise this because medical education is about to be attacked by rogue AI agents. I raise it because it is the cleanest recent illustration of a fact we should take seriously: the gap between what we intend AI systems to do and what they turn out to be capable of doing is not shrinking as the systems get more capable — if anything, it’s widening. That is exactly the kind of unknown unknown Collingridge warned us we’d have the least leverage over once it fully arrives.
The stakes are already being argued in the clinic
None of this is confined to training. A recent JAMA viewpoint argues, provocatively, that autonomous AI may come to outperform not just unaided physicians but physician-AI hybrid teams on core cognitive medical tasks — diagnosis, testing, treatment selection, chronic disease management — and that having a human “in the loop” to catch AI’s errors may, counterintuitively, make outcomes worse rather than better [4]. I’m not fully persuaded by that claim, and I don’t think it needs to be right for my point to hold. What it tells us is that serious people are already debating whether physicians should be supervising AI, or AI should effectively be running the cognitive work with physicians filling a narrower role. If that debate is already this far along in clinical practice, medical education cannot afford to still be at the stage of “let’s wait and see.”
Turning unknown knowns into known, managed risks
The Medical Teacher letter ends on a deliberately open note: it isn’t arguing against preparing for an AI-transformed future, nor declaring that planning is futile — it’s asking educators, clinicians, and patients to keep negotiating, in an ongoing way, what liberated capacity should be spent on, precisely because some of what’s coming can’t be fully planned for in advance [2]. I agree with that caution, and I don’t think it’s in tension with urgency. Leaving room to react is not the same thing as waiting to start.
That’s the distinction I’d draw. Not every risk ahead of us is a true unknown unknown. Many are what you might call unknown knowns — things we already suspect, if we’re honest with ourselves, but haven’t yet formalized into curricula, oversight structures, or explicit rules, because doing so is uncomfortable or premature-feeling. We already suspect that ungoverned AI use during training will produce skill gaps. We already suspect that efficiency gains will get reinvested into new, unscoped demands rather than rest. We already suspect that autonomous systems, given enough scope, will behave in ways nobody scripted. The task in front of us is not to lock in a rigid, five-year AI curriculum and call it done — it’s to drag these unknown knowns into the light now, build the feedback loops and governance that let us keep reacting well, and stop treating “we’re not sure yet” as a reason to leave the field ungoverned in the meantime.
Acting now, deliberately, while the technology is still malleable, is not recklessness. Waiting until the consequences are undeniable — and then discovering we no longer have the leverage to shape them, or the room left to react — is.
References
- Cecchi R, Haja TM, Calabrò F, Fasterholdt I, Rasmussen BSB. Artificial intelligence in healthcare: why not apply the medico-legal method starting with the Collingridge dilemma? Int J Legal Med. 2024;138(3):1173-1178. Available from: https://pubmed.ncbi.nlm.nih.gov/38172326/
- Lee KRY. Unknown unknowns in medical education: Artificial intelligence and Jevons’ paradox [letter]. Med Teach. 2026. doi:10.1080/0142159X.2026.2733142. Available from: https://doi.org/10.1080/0142159X.2026.2733142
- OpenAI. The Hugging Face incident and the road ahead. 2026. Available from: https://openai.com/index/hugging-face-incident-and-the-road-ahead/
- Emanuel EJ, Baker-Butler A, Khosla N, Khosla V. Will Autonomous AI Exceed AI-Aided Physicians as the Best Medical Care? JAMA. 2026;336(11):915-918. doi:10.1001/jama.2026.15380. Available from: https://pubmed.ncbi.nlm.nih.gov/42606838/
Alexandre Gouveia 