That's how I've been using and treating it, though I'm not primarily a developer. I work in ops, and LLMs write all sorts of disposable code for me. Primarily one-off scripts or little personal utilities. These don't get shared with anyone else, or put on github, etc. but have been incredibly helpful. SQL queries, some python to clean up or dig through some data sets, log files, etc. to spit out a quick result when something more robust or permanent isn't needed.
Plus, so far, LLMs seem better at writing code to do a thing over directly doing the thing, where it's more likely to hallucinate, especially when it comes to working with large CSV or Json files. "Re-order this CSV file to be in Alphabetical order by the Name field" will make up fake data, but "Write a python script to order the Name filed in this CSV to be alphabetical" will succeed.
That's exactly my experience as well. AI will read only the first 100 lines of a file, decide that's good enough, and spit out a garbage result. But ask it to write a bash one-liner and it will work perfectly.
I've had large successes in using it to draft electrical drawings faster (more a symptom of the tools I have now being mediocre)