
AI-generated code doesn't come with a QA team attached.
I've been in conversations with engineering leaders who are shipping faster than ever thanks to AI coding assistants. The code volume is up. The test coverage is not. That gap is where production incidents live.
Here's what I see consistently: teams assume that because AI wrote clean-looking code, it's been validated. It hasn't. AI-generated code can compile perfectly and still contain flawed business logic, missed edge cases, and integration assumptions that fall apart in real environments. The output looks confident. That's not the same as correct.
My team's job doesn't change when the code was written by a model instead of a developer. We still define what "working" means, build the test strategy, and own the quality signal before anything ships. If anything, the volume of AI-generated code makes systematic QA more critical, not less, because the surface area grows faster than any single team can review manually.
The question I'd ask any engineering leader right now: who on your team is accountable for quality when no human wrote the function you're about to deploy?
#SoftwareTesting #QualityAssurance #AIEngineering #TestAutomation

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