There’s a question I ask engineers when they hand me an AI generated PR: “walk me through what this does.”
If they can, the PR is usually fine. If they can’t, the PR is almost always broken. Not visibly broken. Not the kind of broken that fails a test. The kind of broken that ships, sits in production for six months, and then takes down a service at 2am because nobody knew the cache invalidation only ran on the leader.
AI doesn’t fix that. AI makes it worse.
AI is a multiplier. It multiplies whatever you bring to it. If you understand the problem, AI gets you to a working solution faster than any tool we’ve ever had. If you don’t, it gets you to something that looks like a working solution faster than any tool we’ve ever had. Those are very different things, and the difference doesn’t show up until later.
The engineers I see thriving with AI right now read every line. They push back when the model takes a shortcut they don’t like. They ask “why” three times. They notice when the generated code uses a pattern they’ve never seen, and they stop to learn it before they accept it. AI doubles their throughput because they were already good. It also doubles their learning rate, because every prompt is a chance to see how an experienced engineer (which is what the model is impersonating) would have approached the same problem.
The engineers I see drowning treat the model like an oracle. They accept the diff. They run the tests. The tests pass, so they ship. They have no mental model of what they just merged. Six months in, they’ve shipped a hundred PRs and learned nothing. The codebase has grown faster than their understanding of it, and the gap is widening.
Here’s the test I’d give anyone using AI heavily. Pick a PR from last week. Open it without the model. Explain every block out loud. Why this data structure? Why this error path? What happens if this call times out? What does this loop do on the empty case? If you can’t answer those questions about your own merged code, AI isn’t helping you. It’s helping the model, and you’re the typist.
This isn’t just an engineering problem. Engineering managers and product managers fall into the same trap, one rung up. If you used AI to write a JIRA ticket and you can’t explain it five minutes later, your problem is different than you think. The ticket isn’t the artifact. The thinking behind the ticket is. If the model did the thinking and you hit save, you’ve handed your team a task that nobody on your side actually owns. The first time an engineer pushes back with a real question, the whole thing falls apart, and the engineer figures out fast that there’s no one home behind the requirements.
Use AI. Lean into it. Just don’t outsource the part of the job that was ever actually yours.