AI Efficiency Could Cost Us the Next Generation of Experts
IEEE Spectrum Richard Mitchell
Opinion — commentary, not a factual news event.
The article argues that generative AI and automation reduce the formative work junior engineers do, leading to expertise “atrophy” and worse performance when systems fail. It cites a Harvard working paper of about 65 million workers showing junior employment fell about 9% within six quarters after generative AI adoption. It proposes adding deliberate human “manual gates” into AI workflows—slower, chosen checkpoints like debugging with the AI assistant off—to preserve skills and ensure competent operators on the bad day.
Why it matters
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,
