AI has no magic powers to figure out what the “API/etc contracts” are in a nasty old codebase. This problem is fundamentally hard and not always well defined.

AI is also (at least for now) not consistently good at writing tests that test the behavior that matters. Recent models seem to be getting slightly less bad, but again, this is a hard problem.

I’ve personally used AI to rewrite gnarly code where the goal is to have exactly identical results to the old code. GPT-5.6 Sol could do this correctly with some extra help guiding it to rest the right things, and I haven’t tried this particular task on a newer model.

But ancient code like in the article is probably an enormous buggy mess full of accidental behaviors and implementing protocols in ways that do not fully match the spec. Some, but not all, of the mismatches will be cases where the spec is wrong. Sure, an AI can probably generate a new implementation with a similar set of bugs, but is this what is actually wanted? Also, depending on the gnarly set of bugs, a clean implementation may be unable to reproduce the bugs, leading to a messy implementation instead.

But there is pretty big risk of false positives and negatives; some "bugs" are design decisions that were not documented well/at all, and inevitably some new ones will appear when it reasoned something wrong.

An LLM doesn't have enough context to know what you so and don't want. If you give it call logs for every function that was called and how, maybe, but a seasoned engineer will have more context around what the company needs.