AI Efficiency Could Cost Us the Next Generation of Experts

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 non-adopters, 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 through failed builds, dead-end debugging sessions, and the “why on earth did that work” moments that a capable AI will now happily spare the newcomer.

Spare them enough of those, and you produce a cohort that can supervise a model on paper but never developed the gut sense to know when the model is confidently, catastrophically wrong. Most of the commentary stops at the diagnosis, or reaches for policy solutions that treat the loss of junior jobs as an economic problem.

Yet it’s also an engineering problem, and safety-critical fields have already spent decades learning how to solve it. Aviation’s Lessons About the Automation Paradox My own career started at the sharp end of automation. My first job out of school was verifying and validating the software in the digital jet-engine controller that decides, faster than any pilot could, how a fighter plane’s engine responds.

Even then, in the late 1980s, the central tension was visible: The machine outperforms the human in routine cases, but the human is all that stands between the aircraft and disaster in the cases the machine didn’t anticipate. This tension is known as the automation paradox, in which increasingly capable automation gives human operators less practice, while leaving them only the most difficult situations.

Aviation learned, repeatedly and expensively, what happens when human skills atrophy inside that gap. The canonical example is Air France flight 447, which fell into the Atlantic in 2009. The proximate cause was mundane. Iced-over airspeed sensors fed the autopilot bad data, and it did what it is designed to do: it disconnected and handed control of the airplane back to the crew.

What followed was not a hardware failure. It was a competence failure. A recoverable situation became an unrecoverable one because the pilots, conditioned by thousands of hours of watching the automation fly, could not read a high-altitude aerodynamic stall and hand-fly their way out of it. The airplane was working. The training the automation had quietly eroded was not.

The industry’s response is instructive, and it’s the same move we made in that nuclear control room. It did not rip out the autopilot. It built deliberate manual practice back in. In 2017 the FAA issued Safety Alert for Operators 17007, “Manual Flight Operations Proficiency,” declaring that “manual flight is the foundation upon which other technical flying skills are built.”

The alert formally recognized skill decay as a hazard in its own right. Some airlines amended their procedures to encourage hand-flying both the initial climb and initial descent in benign conditions, knowingly trading a sliver of fuel efficiency to keep the crew’s raw flying skills alive. That trade is the whole point. A perfectly optimized system that produces incompetent operators is not optimized at all.

It has simply moved its failure mode somewhere the spreadsheet can’t see it. Manual Gates Could Preserve Engineering Skills Put the aviation lesson and the nuclear instinct side by side and they point to one design pattern we now need in AI-augmented work: the deliberate “manual gate.”

A manual gate is a point in a workflow where a human takes the controls, not because it is the fastest way to get the task done, and not only as a safety interlock, but specifically to exercise and preserve a skill that would otherwise decay. The distinguishing feature is that it is chosen.

You decide, as a matter of design, which competencies your organization must keep alive in human beings because those are the ones you will need on the bad day. Then you engineer the friction required to keep them warm. Picture how this might work on a software team that leans on AI for most of its code.

The team places a manual gate around the skill it can least afford to lose: debugging. When a defect surfaces in a critical module, the assigned engineer—deliberately, often a junior one—must first reproduce the failure, trace it to root cause, and write an automated test that captures the bug, all with the AI assistant switched off.

Only after the engineer commits to a diagnosis does the model come back on, to propose the fix, generate alternatives, and sweep the codebase for similar bugs. The engineer then compares their diagnosis against the model’s. When the two disagree, that’s the design working, surfacing the disagreement before the bad day instead of during it.

This approach reframes the junior engineer entirely. The instinct today is to let AI do the entry-level work because it is faster and cheaper. But some of that work is not overhead to be eliminated.

It is the training apparatus of your future senior staff, and you should protect it the way you’d protect any other piece of critical infrastructure. It may not be efficient this quarter, but dismantling it quietly mortgages your capability a decade out. Why Companies Must Keep Training Junior Engineers None of this is free, and pretending otherwise would insult the people who have to sign the budgets.

A deliberate manual gate is, by construction, less efficient in the near term than full automation. Keeping juniors doing formative work and running the manual sequences costs something now to protect something later. That’s a hard sell in a market that judges most leaders on quarterly results.

A hired executive who carries “unnecessary” humans that AI could replace will hear about it from the board long before the payoff arrives. The math only works for someone insulated from that pressure: a founder with control, a private company, an institution with a genuinely long horizon, or a regulator willing to require workers to demonstrate their skills regularly, as pilots must.

Which means the organizations most likely to preserve their own expertise are the ones structurally able to spend short-term margin on long-term capability; everyone else will need that outside push. So here is the argument, in one line: Deliberate inefficiency is not waste. In safety-critical engineering we have always known it as insurance, and we buy it on purpose.

As AI takes over the work where expertise is forged, the smart move is not to resist the automation. It is to keep our hands on the controls by design—so that when the automation fails, as it always eventually does, there is still someone in the chair who knows how to fly.