FWIW, I think inaccuracy - what people call hallucination - will probably be limiting in some applications, like public facing broad based chat applications with stringent requirements on factuality.

There are lots of other applications - in particular I'm thinking about turning unstructured data into structured data for aggregation and analysis, where you can tolerate some level of error and LLMs can work quite well to automate it.

Beyond that, obviously copilot or assistant type stuff is already something people use. It doesn't have to be flawless for that, the main use case for chatGPT is not making high stakes decisions blindly based on the answers it gives out. It makes mistakes and does dumb stuff sometimes but it's also helpful, the flaws aren't a dealbreaker even if people would like them to be.

At the same time, I don't expect it to get much better, it's going to be about getting useful work out of what we have.