
Those of us of a certain age once kept a small directory in our heads, stocked with the phone numbers of our best friend, our grandparents, Domino’s, and the cute guy/gal we crank-called during slumber parties. And then, when our phones began remembering the numbers for us, that directory was cleared out so gradually that most of us can’t say when it happened or what moved into the vacated space in our brains.
That trade-off is relatively harmless; the contacts app on my iPhone rarely fails when it’s time for me to call the dogs’ groomer. But the same sort of switcheroo looks considerably different when the skill being traded away is the ability to fly an airplane, which is precisely what worried an American Airlines captain named Warren Vanderburgh nearly thirty years ago.
Following the Line
Vanderburgh, who spent 27 years in the Air Force before flying for American, was asked by the airline in the mid-1990s to figure out why so many accidents, incidents, and violations seemed to trace back to what came to be called “automation dependency.” In 1997 he stood in front of a class at the American Airlines Training Academy in Dallas and delivered a presentation he titled Children of the Magenta Line, borrowing the name from the magenta course line that a plane’s flight computer draws across the cockpit display. His concern was that pilots had grown so accustomed to following that line – managing the automation rather than flying the aircraft – that their own hand-flying skills were wearing away. And his team’s analysis attributed 68% of the problems they reviewed to automation mismanagement.
What makes the talk worth watching today (it lives on YouTube in all its late-nineties video glory) is that Vanderburgh was no Luddite. He laid out three levels of automation, from manually flying the plane, to the autopilot, to the full flight management computer, and argued that the professional skill lay in choosing the right level for the moment and being willing to step down a level when the situation called for it. The problem, as he saw it, was a culture that pushed pilots to always operate at the highest level of automation – a culture, he pointed out, that the industry had built for itself.
The Magenta Line Comes to the Office
The office version of the magenta line is the AI-generated first draft. It’s the meeting summary we skim in lieu of attending the meeting, and it’s the tidy synthesis of a forty-page report we never open.
And it arrives with a similar promise of reduced workload.
By their own account, employees have started to feel the drift Vanderburgh described. In a survey of 2,500 workers and IT leaders released this spring by GoTo and Workplace Intelligence, half of employees said they rely too much on AI, 39% said that reliance is eroding their skills and making them less intelligent, and 28% said they’ve begun trusting AI more than their own judgment. (That the research was commissioned by a company selling AI-enabled software gives the findings an almost confessional quality.) Six in ten also reported feeling pressured to use these tools in the name of productivity, which suggests that the culture pushing everyone toward the highest level of automation is not confined to cockpits.
Measured Decline
Self-reported worry is one thing and measured decline is another, and medicine has started producing the latter. In 2025, gastroenterologists Krzysztof Budzyń and Marcin Romańczyk of the Academy of Silesia in Poland and their colleagues published a study in The Lancet Gastroenterology & Hepatology examining what happened after four endoscopy centers introduced AI tools that flag polyps during colonoscopies. When those same doctors later performed colonoscopies without the AI, their adenoma detection rate fell from 28% to 22%. The researchers came across the finding by accident and were careful to frame it as a hypothesis about deskilling rather than a verdict, but a six-point drop among experienced physicians is not the sort of number we want to merely shrug off.
None of this would have surprised Lisanne Bainbridge, the cognitive psychologist whose 1983 paper Ironies of Automation remains one of the most cited works in human factors research. Her central irony is that automation absorbs the routine work and leaves humans responsible for the rare, difficult exceptions, which happen to be the moments when skills that have gone unused for months are least likely to show up.
The better a system performs day to day, in other words, the less prepared its human operators are for the day it doesn’t.
Who Learns to Hand-Fly?
For HR, the most uncomfortable part of the magenta line problem has less to do with the experienced workers sitting in our companies today than with the people coming up behind them.
Carl Hendrick, who spent eighteen years teaching in classrooms and now works on the science of how people learn, has been circling these ideas for a while – his March piece carries Bainbridge’s ironies straight into the modern knowledge-work office – and this summer he borrowed Vanderburgh’s phrase for a terrific essay in his Substack, The Learning Dispatch, about what chatbots are doing to students. In it, he draws a distinction that ought to make anyone responsible for workforce development a little uneasy. The pilots Vanderburgh worried about had learned to fly by hand long before the flight computers took over, so what they lost was ready access to knowledge they already possessed.
Novices who reach for the chatbot before building any foundation of their own, Hendrick argues, never acquire that knowledge at all. And nobody can drop down a level to fly a plane they were never taught to fly.
Skipping Ground School
Hendrick’s subject is the classroom, but the argument travels easily into the office. The Polish endoscopists, like the pilots, had years of unassisted procedures behind them before the AI arrived.
That 24-year-old claims analyst you’ve just hired, on the other hand, may never write the clunky first draft, reconcile the spreadsheet line by line, or struggle through the first set of investigation notes that once built judgment through sheer repetition.
We tend to think of AI deskilling as a loss – the gradual wearing away of something a person once had. But for a growing share of the workforce, the skills aren’t eroding at all … they can’t erode because they were never laid down in the first place. Medical educators have been wrestling with the same problem among residents and trainees, and the vocabulary has made its way into the New England Journal of Medicine: deskilling describes the loss of a skill someone already had, while “never-skilling” describes the failure to acquire it at all, because AI did the work during the period when the skill should have been forming (not to be confused with its unfortunate cousin, “mis-skilling,” in which the trainee learns the AI’s mistakes as if they were facts).
And our HR processes and systems aren’t built to notice these things either. Performance management rewards output, and the magenta line produces a great deal of output. Competency models assume that a skill, once demonstrated, stays put. And most L&D programs (and budgets) are planned around adding new capabilities rather than protecting existing ones from atrophy.
Manual Override
Aviation eventually responded by encouraging pilots to step down a level and hand-fly on purpose, and I think it’s fair to wonder what the workplace equivalent might look like. Perhaps a periodic exercise in drafting reports from scratch? Or a performance conversation that asks someone not only what they produced but whether they can explain how they produced it?
Pilots do have one advantage the rest of us lack though, because when an autopilot disconnects, an alarm sounds and the crew knows the plane is theirs to fly. When Claude or Grok or ChatGPT hands the work back to us – with a fabricated citation or a plausible but wrong summary – it doesn’t point out that it’s wrong. No alarms are buzzing on our desktops.
I’m not sure how many of us can recognize those moments when they come … let alone remember what to do with the controls.



