I think is more about how people are using LLMs.

If you are using it to write code, you really care about correctness and can see when it is wrong. It is easy to see the limitations because they are obvious when they are hit.

If you are using an LLM for conversation, you aren’t going to be able to tell as easily when it is wrong. You will care more about it making you feel good, because that is your purpose in using it.

> If you are using it to write code, you really care about correctness and can see when it is wrong.

I heavily doubt that. A lot of people only care if it works. Just push out features and finish tickets as fast as possible. The LLM generates a lot of code so it must be correct, right? In the meantime only the happy path is verified, but all the ways things can go wrong are ignored or muffled away in lots of complexity that just makes the code look impressive but doesn’t really add anything in terms of structure, architecture or understanding of the domain problem. Tests are generated but often mock the important parts the do need the testing. Typing issues are just casted away without thinking about why there might be a type error. It’s all short term gain but long term pain.