This largely fits with a pattern I've been seeing with LLM coding. The models are often helpful, sometimes extremely so, when it comes to creating prototypes or other small greenfield projects. They can also be great at producing a snippet of code in an unfamiliar framework or language. But when it comes to modifying a large, messy, complicated code base they are much less helpful. Some people find them a useful as a beefed up autocomplete, while others don't see enough gains to offset the time/attention to use them.

I think a lot of arguments about LLM coding ability stem from people using them for the former or the latter and having very different experiences.