As a counterexample of one, I would consider myself pretty much an "expert" in some sense at programming, and I've internalized the jargon of software engineeri

As a counterexample of one, I would consider myself pretty much an "expert" in some sense at programming, and I've internalized the jargon of software engineering and computer science to the degree that my conversation is sometimes nigh incomprehensible to even other people conversational with programming, even when I'm intentionally trying not to use jargon. I'm also typically someone who writes in an extremely careful and information-dense wa designed to specify what I mean as precisely as possible and hedge out any possible misinterpretations, even to the point of writing entirely too much. And yet, even when I combine all those factors with having a fairly simple task using common technologies in mind, I typically can't get large language models to produce anything I would consider good that I couldn't have written myself just as quickly as it took me to input the prompt and get the result. I also find translating programming ideas into words instead of code an impedance barrier when code is what I have in my head. Moreover, obviously it never does things how I want them done, which is very frustrating because every choice I make about how I write my code, down to the smallest detail, is 100% intentional and conscious, and so it just feels frustrating and disappointing to use code that a large language model has generated for me, because it's not what I wanted written in the first place. And if I had given enough specification about how to go about things to the model for it to produce exactly what I wanted, I might as well have written it myself, because the translation of my thoughts and intentions into code is largely effortless as it is. The only thing that typically slows me down noticeably is referencing APIs and extremely obscure bugs typically caused by a gotcha in whatever system I'm working on — but it would take just as much time to prompt, wait for, and then read the output of a model as it would to just tab over to the docs I already have open, click on a link for a function that seems likely, and read the description of the function or method, and as for obscure bugs, I doubt a large language model could help me debug them in the first place when I can't find anything on stack overflow or anywhere else on the internet about them. I very rarely have the sort of problems or produce the sort of bugs that would be easily helped by a large language model.