Every industry conversation about AI right now seems to circle the same warning: it will do most of the routine work, services revenue will compress, and headcount-based business models must change to stay competitive. That warning isn’t wrong. It’s just half the story. It only describes what AI may take away from the market as businesses transform. Almost nobody is talking about what AI is creating at the same time: a large, durable, currently unclaimed demand for the specific skill of checking whether AI did the job correctly.
There’s a 40-year-old piece of research that predicts exactly what’s happening now, from a field that has nothing to do with software. In 1983, cognitive psychologist Lisanne Bainbridge published a short paper called “Ironies of Automation,” based on years of studying industrial process control rooms. Her finding was this: the more comprehensively you automate a system, the more demanding, not less, the remaining human role becomes. Why? Because humans are left holding exactly the tasks nobody could figure out how to automate, plus a brand-new job nobody trained them for: supervising a system whose failure modes they no longer see often enough to recognize. Skills that go unpracticed deteriorate. An experienced operator who spends their days watching automation work, instead of doing the work themselves, quietly becomes an inexperienced operator without ever noticing the transition. The automation is usually right, until the day it isn’t.
Aviation gave this idea real stakes. In 1987, a Northwest Airlines flight crashed on takeoff from Detroit, killing 154 of the 155 people on board. The crew had grown accustomed to an automated system that checked whether the flaps and slats were correctly configured for takeoff. That day, the automated check had been silenced by a tripped circuit breaker. The crew, used to the machine catching that error, didn’t manually verify it themselves. The plane took off unconfigured and didn’t make it. Nothing exotic went wrong that day: just a very ordinary, very human failure to keep practicing a check that automation had quietly made feel unnecessary.
That’s the pattern. And it’s showing up again, right now, in every field that has adopted AI assistants at scale, and people are already living through it. Junior lawyers are offloading legal research and first drafts to AI, the exact repetitions that used to build legal judgment, and firms are openly worried their new hires aren’t developing the ability to evaluate AI output at all. Several 2026 industry surveys on software engineering point to a similar problem from a different angle: junior developers who lack grounding in architecture and security can’t reliably judge whether AI-written code is good, and default to trusting the AI over their own instincts, precisely because they never built the instincts to trust instead. It’s been put more bluntly at industry security events: junior engineers raised on AI-assisted coding increasingly lack basic grounding in networking and protocols, to the point where teams struggle to even explain a security risk internally, let alone catch one.
So, the question people are asking but very few are providing an answer: yes, the mundane, repetitive tasks are going to get automated, that part of the story is true and it’s not really in dispute. But who is going to have the skill to validate what AI produced? Who’s going to be able to look at an autonomous system’s output and know, from real hands-on grounding, whether it’s right? And when something goes wrong, when the AI needs to be stopped, corrected, or restarted mid-task, who still has the muscle memory to do that? Everyone is racing to build automation. Who’s building the capacity to check it?
