If you're working on something where the cost of bugs is high and they're tricky to detect, LLM generated code may not be a winning strategy if you're already a skilled programmer. However, LLMs are great for code review in these circumstances - there is a class of bugs that are hard to spot if you're the author.

As a simple example, accidentally inverting feature flag logic will not cause tests to fail if the new behavior you're guarding does not actually break existing tests. I and very senior developers I know have occasionally made this mistake and the "thinking" models are very good at catching issues like this, especially when prompted with a list of error categories to look for. Writing an LLM prompt for an issue class is much easier than a compiler plugin or static analysis pass, and in many cases works better because it can infer intent from comments and symbol names. False positives on issues can be annoying but aren't risky, and also can be a useful signal that the code is not written in a clear way.