It's failing when there is no data in the training set, and there are no patterns to replicate in the existing code base.

I can give you many, many examples of where it failed for me:

1. Efficient implementation of Union-Find: complete garbage result 2. Spark pipelines: mostly garbage 3. Fuzzer for testing something: half success, non-replicateable ("creative") part was garbage. 4. Confidential Computing (niche): complete garbage if starting from scratch, good at extracting existing abstractions and replicating existing code.

Where it succeeds: 1. SQL queries 2. Following more precise descriptions of what to do 3. Replicating existing code patterns

The pattern is very clear. Novel things, things that require deeper domain knowledge, coming up with the to-be-replicated patterns themselves, problems with little data don't work. Everything else works.

I believe the reason why there is a big split in the reception is because senior engineers work on problems that don't have existing solutions - LLMs are terrible at those. What they are missing is that the software and the methodology must be modified in order to make the LLM work. There are methodical ways to do this, but this shift in the industry is still in baby shoes, and we don't yet have a shared understanding of what this methodology is.

Personally I have very strong opinions on how this should be done. But I'm urging everyone to start thinking about it, perhaps even going as far as quitting if this isn't something people can pursue at their current job. The carnage is coming:/