The account nobody checks
Imagine a bank that publishes a glowing dividend report every quarter. Payouts up. Returns up. Customers delighted. Then imagine that the bank has stopped publishing its balance sheet, and no regulator, board member or journalist has thought to ask why. The dividends are real money, deposited in real accounts. What nobody can tell you is what they are being paid out of.
That is where most organisations stand with AI today. Every adoption dashboard in every leadership deck shows the same pleasing shape: usage climbing, throughput climbing, time-to-draft collapsing. All of it is true. And all of it measures the yield. Almost none of it measures the principal: the accumulated human capability, built over years of difficult, slightly painful practice, that the yield is quietly being drawn against.
This edition is about that second number. Call the first one the Atrophy Dividend: the short-term performance gain AI pays out today, funded by a long-term skill withdrawal that no instrument in your organisation is built to detect. The unnerving part is not that the withdrawal happens. The unnerving part is that, by design, the people making it feel richer while they do.
A fifth of an edge, gone
In August 2025, The Lancet Gastroenterology & Hepatology published a study that deserves to be taught in every business school, although it is about colonoscopies. Researchers observed 19 experienced endoscopists, each with more than 2,000 procedures behind them, working across four centres in Poland. Before AI-assisted detection was introduced, these doctors found adenomas, the precancerous growths they are paid to find, in 28.4% of the colonoscopies they performed unaided. After a period of routine AI use, the researchers looked at the same doctors performing procedures without the AI. Their detection rate had fallen to 22.4%. That is a six-point absolute drop and roughly a 20% relative decline in a life-and-death skill, in people who were already experts.
Nobody did anything wrong. The AI worked: with the system switched on, detection came in at 25.3%. These were not careless clinicians. They were excellent clinicians who had, without deciding to, started leaning on a very good crutch, and a body of expertise that had taken years to build began to dim in a matter of months. The study’s lead clinician, Dr Marcin Romańczyk, described it as the first to suggest a negative impact of regular AI use on health professionals’ ability to complete a patient-relevant task. An accompanying commentary warned of “the quiet erosion of fundamental skills.”
“You are gaining something in the short term that might result in losses in the long term.”
Mohammad Hossein Jarrahi, University of North Carolina at Chapel Hill, on the cognitive debt of delegating decisions to AI (as reported by HCA Magazine)
Jarrahi’s phrase, cognitive debt, is the right one, with one amendment. A debt implies a statement: a number you can look at, a repayment schedule, a creditor who will eventually call. Skill atrophy has none of these. It is a debt with no ledger, which is exactly why it compounds.
We have seen this film before
None of this is new. It is simply arriving in a new industry. In 1983, the psychologist Lisanne Bainbridge published a short paper in the journal Automatica titled “Ironies of Automation.” Her argument was devastating in its economy. Automate the routine parts of a task, she wrote, and you leave the human with the exceptions: the rare, difficult, unrehearsed cases the machine cannot handle. But the human can no longer practise on the routine cases, because the machine has absorbed them. So the person you most need at the worst moment is also the person who has had the least chance to prepare for it. The better the automation, the crueller the irony.
Aviation learned this by crashing. After a series of accidents in which crews struggled to fly manually when the automation failed or confused them, the US Federal Aviation Administration issued a safety alert in January 2013 encouraging airlines to promote manual flight operations and to give pilots more hands-on time. Pilots now log deliberate manual flying not because autopilots are bad, but because autopilots are so good that human proficiency decays without being protected on purpose.
Notice what aviation did that most workplaces have not. It admitted the decay was structural. It did not blame individual pilots for getting rusty, and it did not ask them to “be more careful.” It built practice into the system. The industry treated skill the way a bank treats capital: as something that must be actively held in reserve, not assumed to be there.
Knowledge work has nothing like it. We handed our drafting, analysing, summarising and coding to a tool with extraordinary range, and we did it faster than any profession has ever adopted anything. We have no equivalent of manual flight hours. We barely have the vocabulary.
The mechanism: supervising is not practising
Why does the decay happen so reliably? Because expertise is not a possession. It is not a file you saved once and can open again. It is a pattern of connections that is maintained by use, and specifically by retrieval and production under difficulty: the moment you stare at the blank page, the awkward dataset, the bug that won’t yield, and have to generate something from what you know.
AI removes precisely that moment. And what it leaves behind looks like work, feels like work, and is billed as work, but is a different cognitive activity. The AI produces; the human reviews. Review is recognition, and recognition is the easy half of expertise. You can approve a thousand well-formed analyses and become steadily worse at writing one, because nothing in the approving requires you to reach.
Three pieces of evidence show how this plays out.
Evidence one: the learning penalty. In January 2026, Judy Hanwen Shen and Alex Tamkin at Anthropic published a randomised trial with 52 mostly junior software engineers, each with at least a year of weekly Python experience, asked to learn an unfamiliar library. Half used an AI assistant; half did not. On a comprehension quiz afterwards, the AI group averaged 50%; the hand-coding group averaged 67%, a gap the authors described as nearly two letter grades (Cohen’s d of 0.738). The speed gain that was supposed to justify the trade was about two minutes and did not reach statistical significance. In other words: a large, measurable loss of understanding, purchased for almost no measurable time.
Evidence two: the scrutiny penalty. A team at Microsoft Research and Carnegie Mellon, led by Hao-Ping Lee, surveyed 319 knowledge workers about 936 real examples of using generative AI at work, and published the results at CHI 2025. Their central finding was a confidence paradox: the more confident someone was in the AI, the less critical thinking they reported applying to its output. Confidence in themselves pointed the other way. Trust in the machine and scrutiny of the machine moved in opposite directions, and scrutiny is where a surprising amount of professional skill actually lives.
Evidence three: the real-world penalty. The Lancet study, above, shows what these laboratory effects look like once they escape the lab: not a quiz score, but a missed cancer.
Three different populations. Three different measures. One direction.
What gets lost: the invisible reserve
If you take these findings seriously, a new category of organisational asset comes into view. Call it the reserve: the portion of your workforce’s capability that is not being used on any given day, but that you are relying on to exist when something goes wrong.
The reserve is the senior analyst who can sniff out that a model’s output is subtly off because she once built forecasts by hand. It is the engineer who knows what the stack trace means because he has read ten thousand of them without help. It is the editor who can tell the draft is hollow because she has written a thousand hollow drafts herself and learned what they feel like from the inside. None of these abilities shows up in a day-to-day output metric, because on a normal day the AI is right and the reserve sits idle. Its entire value is realised in the exception.
And that is the trap. Reserves look like waste to any optimisation process. A finance team looking at a human who “isn’t needed” for routine cases will see overcapacity. A well-meaning manager will see an opportunity to redeploy. The reserve is liquidated, slowly, by a thousand sensible decisions, and the first sign that it ever existed is the day it is needed and found missing.
There is a second, subtler loss: the pipeline. Today’s reserve was built by yesterday’s routine work. The junior who used to write the first draft, run the first reconciliation, debug the first ticket was not merely producing output. She was becoming a senior. Remove the routine cases and you remove the staircase by which novices climb. We explored this in an earlier edition under the name of the apprenticeship problem; the atrophy dividend is its adult cousin. One stops the reserve from forming. The other drains the reserve that already exists.
And there is a third: the calibration. If you no longer do a thing yourself, you lose your sense of how long it should take, what good looks like and where the traps are. You become a poor judge of the very output you are signing off. This is why the Microsoft/CMU finding matters so much. Skill is not only the ability to produce. It is the ability to know when something is wrong. Lose the first and the second goes with it.
A taxonomy of AI users: four ways to hold the tool
Not everyone who uses AI is on the same trajectory. The Anthropic trial is particularly valuable here, because it looked not just at whether people used the assistant but at how. Three interaction patterns preserved learning: generating code and then asking follow-up questions to understand it, asking for explanations alongside code, and asking only conceptual questions while debugging independently. What separates these from the low-scoring patterns is that the human stays cognitively in the loop. Drawing on that, and on the broader literature, it is useful to think of four archetypes.
The Passenger. The Passenger prompts, receives, pastes. The output arrives complete, and the Passenger’s role is to forward it. Passengers are the fastest people in the building and, over any horizon longer than a quarter, the most fragile. They accumulate output but not understanding, and their confidence in the tool, per the Microsoft/CMU data, is highest exactly where their scrutiny is lowest. A Passenger is one outage, one hallucination or one unusual client request away from a very visible problem.
The Proofreader. The Proofreader reads everything the AI produces and corrects the visible flaws. This feels responsible, and it is better than being a Passenger. But proofreading is recognition, not production, and the Proofreader’s underlying skill slowly erodes while their sense of diligence stays intact. Of the four archetypes this is the most dangerous, because it is the most self-deceiving. The Proofreader is working hard and getting weaker.
The Sparring Partner. The Sparring Partner attempts the problem first, or at least frames it, then uses the AI to challenge, extend and explain. They generate and then interrogate. They ask why, not just what. This maps closely onto the interaction patterns that preserved learning in the Anthropic trial. They are slightly slower on any single task and meaningfully faster at improving over time.
The Fire Marshal. The Fire Marshal is a role as much as a type: the person, usually a leader, who treats capability as infrastructure. They ask which skills the team could not perform without the tool, they schedule unassisted work on purpose, and they measure what the team can do with the system switched off. The Fire Marshal is the rarest archetype and the one organisations need most, because individuals cannot solve a structural problem with personal discipline alone.
It is worth being honest about the incentives. Almost everything in a typical workplace rewards the Passenger: faster output, quicker turnaround, visible adoption. Almost nothing rewards the Sparring Partner, whose benefits are deferred and invisible. If you leave this to individual virtue, the Passengers will win the performance review and the Sparring Partners will quietly outlast them. That is a bet most firms have not knowingly made.
The uncomfortable truths
The first uncomfortable truth: the loss feels like a gain. This is what sets skill atrophy apart from most operational risks. With the tool on, you perform better, and so you feel sharper. The decline only becomes visible when the tool is off, wrong or out of its depth, which is precisely the situation you were hoping never to face. Every individual signal says you are improving. The signal that says otherwise only arrives at the worst possible moment.
The second: your metrics are structurally blind to it. Output per head, turnaround time, tickets closed, drafts shipped: every standard measure improves while the reserve declines. You are optimising a number that is positively correlated with the problem. A dashboard that celebrates AI adoption without measuring unassisted capability is not neutral. It actively rewards the drawdown.
The third: it is irreversible faster than it is visible. Skills that took years to build can erode in months. Jarrahi is blunt about the end state: in many cases, he says, the skill is gone and he doubts it can be redeveloped. Whether or not that is always true, the asymmetry is real. Building expertise is slow. Losing it is fast. And the loss tends to be noticed after the window to prevent it has closed.
The fourth: this is not an argument against AI. The Lancet study found that the AI genuinely helped. Anthropic’s trial found that some ways of using it protected learning. Nothing here says to stop. It says to stop treating the yield as the whole story. A person who takes a dividend while checking the principal is an investor. A person who takes a dividend and never looks is, eventually, an heir to nothing.
Practical implications: running the reserve like capital
The aviation analogy is useful because it points to a solution that is neither Luddite nor naive. The answer is not to take away the autopilot. It is to protect the manual hours deliberately. Here is what that looks like at three levels.
If you are early in your career. The struggle you are tempted to skip is the apprenticeship. The practical rule is simple: attempt before you ask. Write the bad first draft, sketch the approach, form a hypothesis, then bring in the AI to challenge it. When you do ask for code or text, ask it to explain its choices, and do not close the window until you could reproduce the logic. This is the Sparring Partner pattern, and the evidence suggests it is not just virtuous but effective. Treat unassisted practice the way an athlete treats training: something you do precisely because the match does not need it today. You are building the reserve that will make you valuable when the easy cases are all automated.
If you hire and manage. Stop using speed and visible AI usage as proxies for competence. Add an unassisted component to hiring, promotion and review, sometimes, on the hard cases: a live problem-solving session, an annotated walkthrough of how someone reached a conclusion, a task where the tool is deliberately unavailable. Ask people to explain the AI’s output, not just ship it. Notice who can say where the output is likely to be wrong. That person has a functioning reserve. The one who cannot is, however fast, a Passenger. And be careful with workload: the single biggest driver of Passenger behaviour is overload, not laziness. People who are drowning do not practise.
If you lead. Treat capability as a balance-sheet item. Identify the five or six skills your organisation could not survive losing, then ask honestly how many people could perform them today without the tool. Schedule unassisted work: manual-flight hours, in aviation’s terms. That might be a monthly “tools-off” review of a critical process, rotation of juniors through unassisted work so the pipeline keeps forming, or tabletop exercises in which the AI is declared unavailable and the team must recover. Build a continuity plan that humans can actually execute, written by people who have done the work and reviewed by people who could do it again. Then measure it. Add a capability metric next to the productivity one, and watch the pair rather than either alone. If you only chart the dividend, you will keep paying it until the principal runs out.
One more practical point, for all three levels: pay attention to which tasks you give away, not just how many. Some tasks are pure friction and should be automated with a clear conscience. Others are the exercise that keeps the muscle alive. The skill is in telling them apart, and that judgement is itself a thing you only retain by continuing to practise.
The statement that never arrives
The cruellest feature of the atrophy dividend is its timing. Gains arrive immediately, loudly and with a dashboard. Losses arrive late, quietly and without a name. A financial institution that behaved like this would be shut down. A workforce that behaves like this is called “AI-forward.”
The endoscopists in Poland were not careless. They were excellent, and they were given an excellent tool, and in a matter of months a fifth of their unaided edge was gone. Nobody told them. No alarm sounded. The only reason we know is that someone thought to run the unassisted numbers again.
That is the whole assignment for the coming year. Not to resist the tool, and not to celebrate it uncritically, but to find out what you still own when it is switched off. Check the principal. Schedule the manual hours. Reward the people who can still do the thing, not only the people who can get it done.
Because every shortcut is a withdrawal from a skill account you cannot see, and the statement does not come in the post. The overdraft just shows up one day, in a room where the tool does not work and everyone is looking at you.