Protecting Engineers’ Skills in the AI Era

A little over a decade ago, I led the controls design for a first-of-its-kind full digital-control system for a U.S. nuclear plant. It was, on paper, a beautiful machine—engineered to run itself the way a modern airliner does, with operators watching over a system that rarely needed them. And we made a decision that, to an efficiency-minded observer, looked backward: We deliberately left manual steps inside sequences the system could execute on its own.

We were solving a specific problem. An operator who only ever supervises automation slowly stops being an operator. The hands go cold. The mental model of what the plant is actually doing gets fuzzy. Then comes the day the automation hands control back. It’s always the worst day, because automation only quits when it’s confused or in trouble. But by then, you have a person in the chair who hasn’t truly operated the thing in years. The manual steps were there to keep the human current. It was inefficient by design, on purpose.

That plant, as it happened, was never built. It was shelved amid the politics and economics that surround nuclear power in this country, for reasons that had nothing to do with the engineering. But the design instinct outlived the project, and I’ve come to believe it’s the most useful idea I can offer to the argument now consuming every boardroom: What happens to human expertise when AI does the work that used to build it?

AI Is Disrupting the Engineering Career Ladder

The data has gotten hard to wave away. A Harvard University working paper covering some 65 million workers at more than 280,000 U.S. firms found that after companies adopted generative AI, junior employment fell roughly 9 percent within six quarters relative to nonadopters, while senior employment kept right on growing. A Stanford analysis of ADP payroll records points the same way: The youngest workers in the most AI-exposed occupations lost ground after late 2022 while their more-experienced colleagues held theirs. The Stanford researchers found that the losses concentrate where AI automates the work; where it merely augments, junior employment holds steady or rises.

The causal story is still contested, and honesty requires saying so. Researchers at the New York Fed attribute much of the rise in young-graduate unemployment not to AI but to remote work, arguing that firms are reluctant to hire inexperienced people whom they cannot train and mentor at a distance. But notice what the explanations share. Whether a model is absorbing the formative work or distance is severing the mentorship around it, both describe the same broken mechanism: the apprenticeship channel through which expertise passes from senior to junior. Either way, “entry-level” has quietly come to mean “three years of experience required.”

Strip away the noise and you’re left with one deceptively simple problem: You cannot become a senior engineer without first being a junior one. Expertise is not downloaded. It is earned…