However, I think very little programming labor is employed in the construction of new algorithms or even most business logic, even a casual stroll through github reveals a staggering amount of reimplementation.
I think the promise here is the ability to code in a more conceptual way with less fiddling with the finicky details.
This is basically how product managers code. Or former engineers turned engineering managers. Or even team leads. Hell, maybe like an architect?
You come up with a rough sketch, design the system, think through a couple edge cases, tell the computer what you need, and the computer figures out the details for you. Similar to being a high level engineer that designs/defines/codes the broad strokes of something and then lets the lower level minions handle details.
We made a similar leap when compilers were invented.
and then the impl. turns out to have a bunch of details wrong that you didn't catch initially. And you wonder why there's so many bugs in software these days!
I think the AI model is helpful, but the specification being ambiguous or under-specified is the problem, and the effort to sort that out is hard. I'm not sure an AI can help in that aspect, and that's where most of the value of programming comes from.
In the same way, that's why I think it would be a useful tool: it promises to automate away the kind of coding that most programmers can do with eyes closed and that's the most boring and repetitive part of the job.
Like, without trying to demean it, it sounds like a great boilerplate generator.