Protecting Engineers' Skills in the AI Era

The author draws on their experience leading controls design for a planned U.S. nuclear plant with a full digital-control system. To keep operators proficient, the team intentionally left manual steps inside automated sequences, reasoning that an operator who only supervises automation gradually loses the mental model and will be unprepared when automation hands control back—which tends to happen on the worst day. The plant was never built, but the author says the design principle now applies to AI taking over the formative work that once built engineering expertise.

The piece cites two studies linking AI to a shrinking entry-level pipeline. A Harvard working paper covering 65 million workers across 280,000 U.S. firms found that junior employment fell about 9 percent within six quarters after firms adopted generative AI, relative to nonadopters, while senior employment continued to grow. A Stanford analysis of ADP payroll records similarly found that the youngest workers in the most AI-exposed occupations lost ground after late 2022, while experienced colleagues held theirs, and that the losses are concentrated where AI automates work rather than augments it.

Researchers at the New York Fed attribute much of the rise in young-graduate unemployment to remote work, arguing that firms hesitate to hire inexperienced people they cannot train and mentor at a distance. The author notes that both explanations—AI absorbing formative work or distance severing mentorship—point to the same broken mechanism: the apprenticeship channel through which expertise passes from senior to junior. The core dilemma, as they frame it, is that you cannot become a senior engineer without first doing junior work; expertise is earned through failed builds and hands-on practice.