The Half-Life of Expertise

In radioactive decay there is one number that does the work, the half-life. After one half-life, half the original atoms remain. After two, a quarter. After ten, roughly one-thousandth. The mathematics is simple. The implication is severe. A substance with a half-life of five minutes and a substance with a half-life of five thousand years follow the same equation, but one is a laboratory curiosity and the other is geological. The difference is the rate constant.

Professional expertise has a half-life. We are about to find out the rate constant, in a few different professions, more or less at the same time. There is no published number for any of them.

The honest version of the argument runs like this.

We have within-cohort signals. Last summer The Lancet Gastroenterology and Hepatology ran a study from a Polish multicenter trial called ACCEPT. Four endoscopy centers had introduced an AI polyp-detection tool at the end of 2021. Three months later, the same endoscopists, doing colonoscopy without the AI, were detecting at least one adenoma in six fewer patients per hundred than they had three months earlier. The proportion fell from 28.4 percent to 22.4 percent. The doctors had not retired. They had not gotten worse at colonoscopy in any general sense. Their visual attention had reorganized around a tool that, for these procedures, was no longer present. The decay happened inside one cohort, in three months, against a baseline they themselves had set.

In June 2009 the captain of Air France 447 had flown 346 hours in the previous six months. Of those, by William Langewiesche's reporting in Vanity Fair, about four hours were on the controls. The other 342 were spent monitoring an autopilot that had not failed. When the autopilot failed, the captain did not recognize the airplane was in a stall. Same career, same person. Decay measured against his earlier self.

Both of those are within-career stories. The slow version is what happens when one cohort trains under another whose own expertise was developed in an AI-mediated environment. That is the version we have not measured because we cannot measure it yet. The first cohort that trained primarily in AI-supported workflows is, in most professional fields, three or four years into their careers. The cohort behind them is finishing residency or grad school. Whatever the generational rate constant is, it has not had time to compound.

What we do know is the mechanism, and the mechanism is exponential.

Each generation of professional develops its expertise in the training environment available to it. The radiologist who has read fifty thousand chest X-rays builds a perceptual library that no textbook conveys. The radiologist who has read fifty thousand AI-pre-screened chest X-rays builds a different library, one organized around the AI's flags rather than the unfiltered image. Both libraries are real. They are not the same library. When the AI-pre-screened radiologist trains the next cohort, she teaches what she knows. She cannot teach what she does not know. The next cohort develops in an environment whose pedagogy has been further mediated, and trains the cohort after them, and so on.

The exponent does not require any individual to make a wrong decision. It is what happens when each generation faithfully transmits the form of expertise it has, and the form changes in a single direction, and the change is invisible from inside the cohort that produced it.

I have been asked, by every general counsel and chief medical officer I have spoken to in the last year, what the rate constant is. I do not know. Nobody knows yet. The within-cohort findings are suggestive of the mechanism. They do not give us the curve. The curve will become measurable in roughly a decade, when the cohort that trained primarily under AI mediation supervises the one behind them. By the time it is measurable in published data, several rate-constant cycles will have elapsed, and the decision to do something about it will be made by people whose own calibration was set inside the decay.

The asymmetry between decay and recovery is the thing that turns this into a problem rather than a transition. Decay is passive. It happens because the training environment is what it is. Recovery is active. It requires a deliberate decision to slow training, reintroduce unmediated practice, accept the cost in efficiency. The decision to recover has to be made by people who, increasingly, do not know what was lost.

Polanyi's version of this argument runs through tacit knowledge, the kind of knowing that lives in practice rather than rules. The thing radiologists develop after thousands of unprocessed images. The thing pilots develop after hundreds of hours hand-flying. The thing junior associates develop after the fifteenth contract drafted from scratch. Tacit knowledge cannot be transmitted by description. It can only be transmitted by apprenticeship, and apprenticeship requires that the master have the knowledge to transmit. Two generations of mediated training and the master does not.

I do not have a recommendation that addresses this at scale. The strategy advice I give clients is the conservative version. Identify the few processes where the slow, unmediated, expensive version of training is worth protecting. Subsidize it inside the firm. Treat the people produced by that training as the option you are paying to keep open, not as a cost line you are looking to optimize. Be willing to lose, on quarterly metrics, to a competitor who does not preserve the option, on the bet that you will outlast them in the decade when their pilots can no longer fly without the autopilot.

That is a long bet. It is the only honest one I can identify.