AI-assisted incident response is increasingly attractive because it can do exactly what teams want in the middle of the night: inspect alerts, compare telemetry, and propose or even apply a fix without waking an engineer for every noisy page. But a recent essay argues that the same capability may create a quieter long-term problem by leaving responders less practiced when a rare, messy outage finally slips past the automation.
The core concern is straightforward. If AI systems handle routine incidents well enough, humans get fewer chances to build intuition about how their own services fail. That matters because the hardest incidents are often the ones that do not look familiar. In those moments, engineers must still interpret signals, coordinate people, and reason about a system they may no longer know as intimately as they once did. The author calls that gap a form of “comprehension debt”: the more work software handles, the less the people around it may understand it.
The essay connects that risk to a classic automation problem identified by human-factors researcher Lisanne Bainbridge in 1983. Her argument, summarized in the piece, is that automation can reduce opportunities to practice ordinary work while still leaving people responsible for the unusual, high-stakes edge cases. In other words, machines can make the easy parts easier and the hard parts harder for the humans who remain.
The article also borrows from aviation, where automation is common but training is relentless. Pilots still rehearse rare failures in simulators, and they do so regularly, because the assumption is not that automation eliminates emergencies. It is that it changes the kind of competence operators need. The author uses that analogy to argue that software teams should think less about replacing judgment and more about preserving it.
That preservation can be active rather than passive. The essay describes incident simulations built with incident-management tools and LLM-powered stakeholders, where engineers rehearse outages in a controlled environment. Those exercises are meant to recreate pressure, ambiguity, and coordination demands without waiting for a real outage to provide the lesson. The point is not to watch an AI explain what happened after the fact; it is to give humans a chance to do the work themselves before the stakes are real.
The broader claim is not that AI incident tools are bad. The author says they are useful and often magical, especially when they fix routine problems and keep people asleep. The warning is that speed on the easy cases can mask a training deficit on the difficult ones. If average response times improve while deep troubleshooting skill decays, teams may discover too late that their systems have become understandable to machines but less legible to the humans responsible for them.
For engineering leaders, that suggests a simple operational shift. AI can handle more of the first pass, but teams still need regular hands-on incidents, simulated failures, and structured exercises that force people to work through ambiguity. Automation can be the assistant. It should not become the substitute for practice.
