I totally agree with this, both the part about having to be clever with the model implementation and how fast and usable it is. For my own research, it ended up that it was a type of set cover problem. To me it felt large, ended up with almost a million non-zero values to optimize (and had to do 10k of them), but the state of the art is so fast. CPLEX optimizes them in like 3 seconds and can tell me that it's provably optimal. Even open source solvers (HiGHS) solves in like 10 seconds

It is true that when I phrased the problem wrong (with multiplication not addition), it was taking forever, and I had to mess around a long time to figure it out. ChatGPT actually sorta helped a little

But it does feel like magic and not used enough. I think Julia JuMP goes a long way towards just expressing the problem easily. It might take more work/tricks to get it to work (big-M tricks are crazy to me) but at least as a first pass if it just works is worth a try, I think

And to be fair, I really wanted it to be provably optimal for the fun of it. But when I set even a 10 minute timelimit, it got a really good solution across the board much faster than I could have found with manual programming