The faster an AI assistant lets you revise code, the faster the reasoning behind that code disappears. Speed was never the problem. It became dangerous only once it stopped giving anyone a reason to pause.
The Details:
Manual refactoring used to enforce its own brake. Because changes were slow and costly, someone would recall why an approach failed weeks earlier and stop the team from repeating it. AI assistants remove that friction entirely, so five versions of a function can pass in minutes with nobody, human or model, remembering why version two was scrapped.
Each AI session works from whatever is currently loaded: open files, recent prompts, maybe a test run. It has no access to the conversation three revisions back where someone flagged a race condition or a transaction boundary. So it re-suggests the discarded fix, tests pass, and the rejected constraint vanishes without anyone noticing.
Three signals expose the decay before it compounds. A bug that returns in a different shape means a protective constraint got erased, not just a code symptom. A confident explanation nobody can verify against original notes means the reasoning was never written down. And revisions feeling easier week over week, when systems should be getting harder to change as they mature, usually means the team is quietly dropping constraints it forgot it had.
The fix is treating the assistant as a capable collaborator with total amnesia. Repeat constraints even if stated an hour ago. Record the rejected approach, not just the accepted diff, since a two-line note explaining why an option breaks a batch job carries more value than the cleanest merge. Save the prompt as an artifact alongside the code, and have one person read every AI-assisted batch cold before committing, asking whether it would still make sense in six months.
Bottom Line: AI will keep your code running long after its design stops making sense; only deliberate documentation of rejected choices keeps that design legible to the humans who still have to change it.
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