# AI Review Loops Don't Always Stabilise > Source: > Published: 2026-08-25 17:42:15+00:00 # AI Review Loops Don’t Always Stabilise It’s tempting to think that having AI review code and then implement fixes in a loop will create a flawless diamond at the end of the process. Some are accidentally doing the slow version of this loop where dev A creates an AI-generated PR, dev B lazily AI-reviews it, then dev A AI-fixes it, *ad infinitum*. With careful guardrails this can work, but doing it naively will often create a mess for a few reasons: - AI does not have a consistent set of opinions. What it considers good code can change from run to run. In the worst case, it can flip-flop from review to review, causing a never-ending loop. - Reviews can often encourage scope creep, from ‘Hey, you don’t have a test for this!’, to ‘Hey, you don’t have a CI/CD pipeline!’, to ‘Hey, you don’t have an Android app for this!’. - Any false positives or hallucinations in reviews can introduce new defects. As a quick test I asked Opus 5 to generate a small amount of ‘perfect, non-trivial’ code and passed it through three review-fix loops. Here is an [AI-generated writeup](https://gist.github.com/KMahoney/3098f0f12638d0a83a5ef3b91bef601d). Note the defect count *increases* with each review!