I can't speak to finance. In programming, it can be useful but it takes some time and effort to find where it works well.

I have had no success in using it to create production code. It's just not good enough. It tends to pattern-match the problem in somewhat broad strokes and produce something that looks good but collapses if you dig into it. It might work great for CRUD apps but my work is a lot more fiddly than that.

I've had good success in using it to create one-off helper scripts to analyze data or test things. For code that doesn't have to be good and doesn't have to stand the test of time, it can do alright.

I've had great success in having it do relatively simple analysis on large amounts of code. I see a bug that involves X, and I know that it's happening in Y. There's no immediately obvious connection between X and Y. I can dig into the codebase and trace the connection. Or I can ask the machine to do it. The latter is a hundred times faster.

The key is finding things where it can produce useful results and you can verify them quickly. If it says X and Y are connected by such-and-such path and here's how that triggers the bug, I can go look at the stuff and see if that's actually true. If it is, I've saved a lot of time. If it isn't, no big loss. If I ask it to make some one-off data analysis script, I can evaluate the script and spot-check the results and have some confidence. If I ask it to modify some complicated multithreaded code, it's not likely to get it right, and the effort it takes to evaluate its output is way too much for it to be worthwhile.

I'd agree. Programming is a solid use case for AI. Programming is a part of my job, and hobby too, and that's the main place where I've seen some value with it. It still is not living up to the hype but for simple things, like building a website or helping me generate the proper SQL to get what I want - it helps and can be faster than writing by hand. It's pretty much replaced StackOverflow for helping me debug things or look up how to do something that I know is already solved somewhere and I don't want to reinvent. But, I've also seen it make a complete mess of my codebase anytime I try to build something larger. It might technically give me a working widget after some vibe coding, but I'm probably going to have to clean the whole thing up manually and refactor some of it. I'm not certain that it's more efficient than just doing it myself from the start.

Every other facet of the world that AI is trying to 'take over', is not programming. Programming is writing text, what AI is good at. It's using references to other code, which AI has been specifically trained on. Etc. It makes sense that that use case is coming along well. Everything else, not even close IMO. Unless it's similar. It's probably great at helping people draft emails and finish their homework. I don't have those pain points.