All of this at the end reduces to a simple fact at the end of the discussion.
You need some of way of precisely telling AI what to do. As it turns out there is only that much you can do with text. Come to think of it, you can write a whole book about a scenery, and yet 100 people will imagine it quite differently. And still that actual photograph would be totally different compared to the imagination of all those 100 people.
As it turns out if you wish to describe something accurately enough, you have to write mathematical statements, in other words statements that reduce to true/false answers. We could skip to the end of the discussion here, and say you are better of either writing code directly or test cases.
This is just people revisiting logic programming all over again.
> You need some of way of precisely telling AI what to do.
I think this is the detail you are not getting quite right. The truth of the matter is that you don't need precision to get acceptable results, at least in 100% of the cases. As everything in software engineering, there is indeed "good enough".
Also worth noting, LLMs allow anyone to improve upon "good enough".
> As it turns out if you wish to describe something accurately enough, you have to write mathematical statements, in other words statements that reduce to true/false answers.
Not really. Nothing prevents you to refer to high-level sets of requirements. For example, if you tell a LLM "enforce Google's style guide", you don't have to concern yourself with how many spaces are in a tab. LLMs have been migrating towards instruction files and prompt files for a while, too.
Yes, you are right. But in the sense that a human decides if AI generated code is right.
But if you want a near 100% automation, you need precise way to specify what you want, else there is no reliable way interpreting what you mean. And by that definition lots of regression/breakage has to be endured everytime a release is made.