I see the major issue with AI as one of "lowering the bar".
For example - I'm a mechanical engineer. I took a programming class way back in university, but I honestly couldn't tell you what language was used in the class. I've gotten up to a "could hack a script together in python if need be" level in the meantime, but it comes in fits and spurts, and I guarantee that anyone who looked at my code would recoil in horror.
But with chatGPT/copilot covering up my deficiencies, my feedback loop has been drastically shortened, to the point where I now reach for a python script where I'd typically start abusing Excel to get something done.
Once you start extending that to specific domains? That's when things start getting real interesting, real quick.
You confuse syntax with semantics. Being able to write produce good quality small snippets of python will not enable you to produce a successful piece of Software. It's just an entirely different problem. You have to unterstand the problem, the environment in which it exists to create a good solution. ChatGPT doesn't (as of now).
That's the thing though, it is successful. To my exact needs at the moment. It's not necessarily reliable, or adaptable, or useful to a layperson, but it works.
Getting from "can't create something" to "having something functional and valuable" is a huge gap to leap over, and as AI is able to make those gaps smaller and smaller, things are going to get interesting.
I had hoped to have ChatGPT do my work today, but even after a number of iterations it was having compiler errors and referring to APIs not in the versions it was having me install.
A bit different from stack overflow, but not 10x. It was flawless when I asked it for syntax, e.g. a map literal initializer in Go.
On the other hand, I asked it to write a design for the server, and it was quite good, writing more quantity with and more clarity than I had written during my campaign to get the server approved. It even suggested a tweak I had not thought of, although that tweak turned out to be wrong it was worth checking out.
So maybe heads down coding of complex stuff will be ok but architects, who have indeed provided an impressive body of training data, will be replaced. :)
For example - I'm a mechanical engineer. I took a programming class way back in university, but I honestly couldn't tell you what language was used in the class. I've gotten up to a "could hack a script together in python if need be" level in the meantime, but it comes in fits and spurts, and I guarantee that anyone who looked at my code would recoil in horror.
But with chatGPT/copilot covering up my deficiencies, my feedback loop has been drastically shortened, to the point where I now reach for a python script where I'd typically start abusing Excel to get something done.
Once you start extending that to specific domains? That's when things start getting real interesting, real quick.