I work in tech and the biggest use case for AI for me is searching for information about the codebase. Essentially a fuzzy search and finding related information (ex: how does X service do Y operation).

I find if I use it for coding tasks it frequently does a worse job and ultimately slower than me doing it myself. But I also have over a decade of experience just in the language I'm using. Sometimes I use it to write a shell script of repetitive work, or writing some unit tests, but rarely larger more complex things. I really try to avoid using it for things that I can do myself in vim or whatnot because of the environmental impact that a LLM has.

Most stuff in life is already solved

– Diogenes, probably

I think it's because LLMs in single shot scenarios are always liars, fabulists, or both.

The only reason (and only way) it works with coding is because you can hook it up to tools to run static analysis on its bullshit and it can guess enough derivatives in a loop to eventually build something that kinda sorta works.

This doesn't apply for many fields outside of writing software. There just aren't many fields where it applies to have a machine produce permutations and then automatically run validation on the result.

Imagine if you put it in charge of manufacturing or something, you'd have so much wasted raw material before you got to an acceptable result that you'd go out of business.