Why it matters: AI has made writing code nearly free, but confirming that code actually works still takes as long as it always did, so teams that budget time as if generation is the hard part are quietly stacking up risk no dashboard will show them.
The Details:
Generation and verification used to move together. When a person writes a function, understanding builds line by line, so the code and the reasoning behind it live in the same head. AI code arrives without that reasoning attached, so every review starts from zero instead of from familiarity built during the writing.
Polished output hides unverified logic. AI-generated code looks reviewed already: clean names, tidy docstrings, sensible structure. That surface invites skimming instead of scrutiny, and a wrong assumption can sit quietly in four lines of an otherwise elegant file.
Letting the same model write its own tests doubles the blind spot rather than closing it. A test built by the system that built the code checks that code against the model's understanding of the problem, not against reality. The passing suite confirms internal consistency, not correctness, and it feels like proof precisely because it looks like one.
Verification cost does not scale with line count the way people assume. A five-hundred-line change is not ten times harder to audit than a fifty-line one; it behaves closer to a hundred times harder, because human attention breaks down well before the diff does. Keeping changes small is a concession to that limit, not a stylistic preference.
Bottom Line: The developers who thrive won't be the fastest generators. They'll be the ones who've made doubting their own output as automatic as producing it.
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