If AI Removes Junior Jobs, Who Becomes the Seniors?
What does the world look like when young people can't train up?
The sharpest number we have about AI and work is hiding in payroll files.
Stanford economists, reading the records of ADP, which pays one in six American workers, found that employment for workers aged 22 to 25 in the occupations most exposed to AI is down 16% relative to everyone else.
For older workers in the same jobs: nothing.
And there is no wave of firings underneath the number. The mechanism they point to is quieter: firms are simply not replacing the juniors who leave.
Read as a jobs story: that is sad but survivable; entry-level work has been automated before.
Read as a capability story: it is something else. Because the junior years were doing two jobs at once: the cheap labour everyone saw, and the slow manufacture of the one asset a firm cannot buy in - someone who has made mistakes and learned.
You remove that junior rung of the career ladder today and nothing fails because seniors are still on the payroll. But those failures are coming, and they’re scheduled for the year they retire.
Using AI Reduces Skills in Doctors
For the cleanest evidence of what AI assistance does to the skill underneath it, look at medicine. Here’s a prime example:
19 doctors in four Polish clinics, each with at least 2000 colonoscopies behind them, started working with an AI that watches the procedure screen and flags suspect tissue in real time.
Detection improves while it watches; that is why clinics buy it.
The unsettling number came from the procedures the AI sat out. Before the tool arrived, working unaided, these doctors found pre-cancerous growths in 28.4% of exams.
After three months of routine AI use, across 648 unaided procedures, the rate was 22.4%.
A fifth of the unaided skill, gone, in a quarter of a year.
The study, published in The Lancet’s gastroenterology journal in August 2025, is small and careful and the first of its kind, which is why its least excitable sentences matter most.
Marcin Romańczyk of the Academy of Silesia, one of the doctors who ran it, called it the first study to suggest that routine AI use can erode a clinician’s ability at a task that matters to patients.
The UCL specialist who reviewed it independently went further: the first real-world evidence of the skill itself wearing away.
The machine hadn’t taken anyone’s job. All it it had done is taken some of the looking and assessing off the doctors minds. All 19 doctors were still there, still credentialed, still scoping. What had thinned was the thing underneath, the trained eye that did the finding before there was a prompt to confirm.
The White Collar Example
If that sounds like a story about endoscopes, Microsoft and Carnegie Mellon ran the white-collar version. They surveyed 319 knowledge workers across 936 real tasks done with AI and found the obvious thing - people lean on the tool - and a quieter thing underneath it.
The more a worker trusted the AI, the less critical thinking they reported doing.
And the thinking that survived had moved to a different task: away from producing the answer, toward verifying the machine’s answer, weaving it in, deciding whether to ship it.
The finding of the study was something predictable: workers stop scrutinising the output precisely where they lack the skill to inspect it.
So for those of us that us AI daily in our jobs, the work itself is becoming checking the AI’s work and output. The checking is a skill. And the skill has exactly one factory.
The Skills Factory
That skill is manufactured, slowly and expensively, by doing the work the AI now does. The junior analyst who builds the model by hand for three years - getting the assumptions wrong, being corrected, watching a senior tear the thing apart on a Friday afternoon - is how a firm produces the person who can later glance at a machine’s output and feel, before they can prove, that a number is off.
The years spent learning the tools and frameworks was never a cost centre that AI has at last liberated us from. The apprenticeship is the verifier factory, it’s what creates people with deep domain knowledge and the ability to judge what ‘good’ looks like.
Unfortunately, nobody seems to be thinking about this second order effect. The junior’s work is, by definition, the most routine work in the building, the most automatable, so it goes first.
This would matter less if checking were a small movement, but it’s not. It is becoming the strategy.
MIT Sloan Management Review put out a video this week on what it calls AI atrophy, the erosion of critical thinking in heavy AI users, and how CIOs should fight it. Every cure on the list is a verification job:
Treat the model’s output as a hypothesis to stress-test,
Build independent verification checkpoints,
Do your own analysis first, then consult the tool.
All those points are very sensible advice! But all of it assumes the one input the same playbook is destroying: people who can actually do the checking.
As we build more and more AI products and processes, we HAVE TO rely on verifiers with domain knowledge. However in practice, the very same AI tools and processes are creating an environment where we stop producing them.
The Future
Five years from now there will be a room - a credit committee, a clinical review, a Friday post-mortem - where the machine’s answer looks clean and is wrong, and what decides everything will be an ordinary question: who in this room can tell?
Whoever raises a hand will have learned the skill the slow way, on exactly the kind of work firms are deleting the requirement for right now. So count your bench while it is still a bench: who here can tell you the machine is wrong - and where, exactly, is the next one of them supposed to have learned it?
