When I hear this I do wonder to myself how they're using AI.
For me, as long as I'm properly RPI looping it and not blindly pressing "yes" then it will nearly always reach the solution, usually a fair bit quicker, because it effectively becomes an ideation machine that can keep more thoughts and knowledge in it than my brain can.
However, I'm using it through giving it the context, it has access to the repos, access to the sytems, I'm telling it where the logs are, I have docs to show it what each part of the system is for.
Along with that, it does depend on my own instincts/knowledge for me to read its response and for me to say "no, you've over-thought/engineered this and this is actually the better solution", but its very are nowadays for me that it can't find the 1 liner, as long as I've fed in the right context, mostly pre-done because I've spent a bit of time building out the context tree for the repos/systems in a way that allows it to know what it needs to know.
I'm not doubting that people spin their wheels and couldn't find the one liner, but if its as simple as you say, that does seem like people who aren't great at LLMs along with a lack of instinct/experience.
I guess in essence, I think you can use LLMs in an old school troubleshooting way, and I find it still speeds that up the majority of the time. Its basically how I use it most of the time. And like old-school troubleshooting, if you build out the LLMs context over time, it also grows in capability, as long as its being used as a tool and not blindly trusted.
Should I not assume that most senior developers who are serious about LLMs do this?