I'm generally skeptical of Simon's specific line of argument here, but I'm inclined to agree with the point about communication skill.
In particular, the idea of saying something like "use red/green TDD" is an expression of communication skill (and also, of course, awareness of software methodology jargon).
Ehhh, I don't know. "Communication" is for sapients. I'd call that "knowing the right keywords".
And if the hype is right, why would you need to know any of them? I've seen people unironically suggest telling the LLM to "write good code", which seems even easier.
I sympathize with your view on a philosophical level, but the consequence is really a meaningless semantic argument. The point is that prompting the AI with words that you'd actually use when asking a human to perform the task, generally works better than trying to "guess the password" that will magically get optimum performance out of the AI.
Telling an intern to care about code quality might actually cause an intern who hasn't been caring about code quality to care a little bit more. But it isn't going to help the intern understand the intended purpose of the software.
I'm not making a semantic argument, I'm making a practical one.
> prompting the AI with words that you'd actually use when asking a human to perform the task, generally works better
Ok, but why would you assume that would remain true? There's no reason it should.
As AI starts training on code made by AI, you're going to get feedback loops as more and more of the training data is going to be structured alike and the older handwritten code starts going stale.
If you're not writing the code and you don't care about the structure, why would you ever need to learn any of the jargon? You'd just copy and paste prompts out of Github until it works or just say "hey Alexa, make me an app like this other app".
In particular, the idea of saying something like "use red/green TDD" is an expression of communication skill (and also, of course, awareness of software methodology jargon).