The most expensive mistake in AI-assisted QA isn't a bad test. It's not knowing why you wrote it.
Every week I watch engineers open ChatGPT, paste a user story, and type "write test cases for this." Ten seconds later they have 40 lines of confident-looking test code. Nobody asked what actually needed to be tested. Nobody asked what could actually break. The AI didn't fail — the question did. A generated test that verifies the wrong thing is worse than no test at all, because it gives you a green checkmark and a false sense of safety.
This isn't an anti-AI article. I use AI to write tests every single day. But after 8+ years designing automation frameworks and watching teams adopt AI tooling, I've learned that the skill that matters now isn't "can you prompt an LLM" — it's "can you tell, before you prompt it, whether this is even a job for AI." That's the decision most junior engineers skip. Let's fix that.
The Core Question Nobody Asks
Not "can AI write this test?" — it almost always can.
