[chii]

> 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

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.

[bauerm97]

I'm sure in the early days of compilers (I wasn't around back then, so I'm just assuming) they could also be fairly unreliable. Maybe they translated something in a completely idiotic way, lots of bugs, etc... But over time they improved and improved to the point that 99% of programmers never worry about anything except high level abstractions. This could be signalling the beginning of another such paradigm shift to a higher level abstraction

[sguthery]

Would that that were the case (compilers getting better). See http://embed.cs.utah.edu/csmith/ C compilers for the PDP-11 were pretty good if only because they were both simple, single threaded, and C was essentially a verbose version of the PDP-11 instruction set. Languages got more abstract and instruction sets (including their models of execution) got more complex. Optimizing the compilation of an abstraction with a faulty understanding of either the abstraction or the instruction set (or both) begets bugs you can't see as reported in the reference above. OB: I personally would like to see these folks point their AI code machine at netlib including the Collected Algorithms of the ACM. Generating numerical methods code is, in my experience, not the same as generating much of what is found on Github.