I have had both frustration and joy working with AI. Just some anecdata:

I have found it is far better at understanding - and, with prodding, determining the root causes of bugs - big sprawling codebases than it is at writing anything in even simple code bases.

Recently I asked an AI to compare and contrast two implementations of the same API written in different languages to find differences, and it found some very subtle things and impressed me. It got a lot wrong, but that was because one of the implementations had lots of comments that it took at face value. I then wrote a rough spec of what the API should do and it compared the implementations to the API and found more problems. Was a learning experience for me writing specs too.

I repeated the exercise of comparing two implementations to track down a nasty one-line bug in a objc -> swift port. I wasn't familiar with the codebase, or even remember much about those languages, so it was a big boon and I didn't have to track down people who owned code until I was fairly sure that the bug had been found.

Also recently I asked an AI to compare two sets of parquet files and it did sensible things like downloading just bits of them and inspecting metadata and ended up recommending that I change some of the settings when authoring one of the sets of parquet files to dramatically improve compression. It needed esc and prodding at the halfway point but still it got there. Was great to watch.

And finally I've asked an AI a detailed question about database internals and vectorising predicates and it got talking about 'filter masks' and then, in the middle of the explanation, inserted an image to illustrate. Of 'filter masks' in the PPE sense. Hilariously wrong!