This often doesn't scale well. And coming up with all the necessary context in writing is a bit harder than when programming.

Well, it's where we are now with AI technology. Perhaps a superior future AI will need less of it. For now I give it all that I think it won't reliably figure out on its own.

It's actually where we have always been with software development, which is why so many software professionals think the media narrative is so stupid.

Oh, you want to fire your engineers? Easy, just perfectly specify exactly what you want and how it should work! Oh, that's what the engineers are for? Huh!

Yup, but the management, essentially the ones who control the hiring and firing, are more clued into the media narrative, with a bias toward dismissing what engineers think.

> AI needs appropriate and sufficient guidance to be able to write code that does the job.

Note that the example (shitty Microsoft) implementation was not able to properly run tests during its work, not even tests it had written itself.

If you have an existing codebase that already has a plenty tests and you ask AI to refactor something whilst giving it the access it needs to run tests, it can already sometimes do a great job all by itself.

Good specification and documentation also do a lot, of course, but the iterative approach with feedback if things are actually working as intended is a game changer. Not unsurprisingly also a lot closer to how humans do things.

The iterative approach has one problem -- it is onerous to repeat the lengthy iterative process with a different model, as it will lead to an entirely different conversation. In contrast, when the spec is well-written up-front, it is trivial to switch models to see how the other model implements it differently.