I'm sort of shocked at how little of my argument seemed to land with you in any way. I'm wondering how many cycles of software hype have you been through? Were you here for the PC revolution, the .com era, smartphone mass adoption?

There's a lot of what-ifs, and worst case scenarios in your reply that I simply don't find likely. I am not drinking the koolaid from the AI maximalists or the doomers. I could be wrong of course, no one can predict the future, but to me the very real, novel and broad utility of LLMs that we are just learning to harness combined with the investment outlook are leading to a mania that has people overestimating where things will land when the dust settles. If I'm wrong then I guess I'll join the disenfranchised masses picking up pitchforks, but I'm not going to waste time worrying about that until I see more evidence that it's actually going that badly.

So far what I see is that software engineers are the ones getting the most actual utility of AI tooling. The reason is that it still requires a precision of thought and specificity to get anything sustainable out AI coding tools. Note this doesn't mean that engineers can design better apps than proper designers, rather my point is that designers and other disciplines still can not go much further than prototypes, they still need engineers to write the prompts, test the output, maintain the system, and debug things when they go wrong. I have worked long enough with large cross-functional teams to know that the vast majority of folks in non-engineering functions simply can not get enough specificity and clarity in their requests to allow an LLM to turn it into a working system that will work over time. The will hit a wall very quickly where new features add bugs faster than they improve things, and the whole thing collapses under its own weight like a mansion of popsicle sticks. And by the way, I don't consider AI-assisted coding to require less qualification than regular coding. Sure you don't need to know as much syntax or algorithms, but you absolutely need to know data modeling, performance, reliability, debugging, consistency, and migration knowledge in order to use AI to contribute to any software that powers a real business, and yeah you might need to develop your product and business sensibilities, but to me that's what been happening throughout the history of computing. Wiring up ENIAC, certainly required qualifications that were not needed for assembly programing, which in turn required certain things that C programmers did not need and so forth, but harnessing the increasing compute power and complexity required new qualifications. I don't think AI will ultimately be that different, it will change the way we work, it doesn't replace what senior engineers do.