AI Code Ages Faster Than You Think, and the Bill Comes Due at Refactor Time

Fast, working code from an AI assistant is not the same as code that fits your system. Each generation solves the task in front of it, blind to the sibling module three folders over that already does most of the job. Nothing looks wrong at merge time. The cost shows up weeks later, when a twenty minute change takes an afternoon because nobody ever made the new piece agree with the old ones.

The Details:

• The model's context window is bounded, so it optimizes for the local ask, not the shape of the whole codebase. Without someone actively holding that bigger picture and forcing the new code to match it, you get functional code that quietly duplicates logic, naming, and helper functions elsewhere.

• Orienting the AI before it writes anything changes the output dramatically. Asking it to read existing files and name the conventions first, or pointing it at a similar endpoint and telling it to flag any deviation, produces code that barely needs reshaping compared to an open ended request.

• Individual habits do not scale across a team, so the fix has to live in shared, machine readable form: a living conventions file at the repo root that gets a new line every time the AI drifts the same way twice, plus lint rules and directory structures that make the correct pattern the easiest one to reach for.

• Consolidation has to be a scheduled task, not a hope. Naming an explicit ticket like 'find the three places we do this and propose one' works because the AI can do that analysis well; it just will not volunteer it while busy building the next feature.

Bottom Line: Cheap first drafts do not eliminate the cost of coherence. They just defer it to the day someone has to refactor, and that day is more expensive than it needed to be.