Fast AI-generated code does not eliminate thinking. It postpones it, and postponed thinking charges interest.
A prompt-review-ship cycle can take twenty minutes and feel like a clean win. Two weeks later the same code breaks, and the person debugging it is often the same person who approved it, now reading it like a stranger wrote it. That gap between approving and understanding is where the real cost hides, and it never shows up on a velocity chart because it lands under a different ticket in a different sprint.
The Details:
AI output mimics your own style closely enough that your brain files it as understood rather than skimmed. With a colleague's pull request you know you are reading unfamiliar work and stay alert. With AI output that matches your conventions, the guard drops, and locally correct, fluent-looking code is far harder to catch a flaw in than code that looks obviously wrong.
The fix is treating every AI-generated block like a stranger's pull request the moment it lands, not after production fails. That means narrating out loud what each part does and why, and naming the assumptions it makes about inputs, state, and callers. Most two-in-the-morning failures live at those boundary assumptions, not inside the logic itself.
Writing a three-line contract before opening the chat window turns vague vigilance into a specific check. State the expected input shape, the callable states, and the return format first, then compare the generated code against that contract rather than against a general impression. It takes under a minute and catches drift immediately.
Naming three tests before accepting code, the happy path, a bad input, and a failed dependency, is a fast proxy for real comprehension. If none come to mind, that is the signal you are skimming, and it is far cheaper to notice at acceptance time than during an outage.
Bottom Line: The thinking has to happen somewhere; the only choice is whether it happens now, while the context is fresh, or later, while the system is down.
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