I think the problem with this logic is it's based on the capabilities of LLMs today and really fails to address the prospect that they will continue to improve.

I used to be a PM and am technically literate enough but can only very minimally write code. I have been using LLMs to build (or try to, at least) internal tools for my business since GPT-4.

In the early days, I'd get a little ways, then the LLM would start breaking things, and I'd try but fail to get it to fix things. But over successive generations, I was increasingly able to get it unstuck by offering suggestions on where it may have gone wrong. With Opus 4.7, I don't even really have to do that - if something isn't working it's usually sufficient to just tell it what's broken. It can figure out how to fix it without my input. And of course fewer things are broken in the first place.

So I think I'm very well positioned to understand how these things are improving - better able to get the LLM to do what I want than the post OP quoted from /vibecoding (though I am 99% sure that post is actually AI slop), but less so than most of the people posting in this thread. As they've improved, whatever ability I have to guess at the causes of problems based on my experience having seen things go wrong with products I've PMed has become less necessary to getting the right outcome.

I expect that trend to continue - increasingly the LLM won't need the guidance of people with a great deal of technical expertise. I basically no longer have to attempt to diagnose problems in order to get them fixed, though with the caveat that I am building internal tools for which I am the only user, so certainly much simpler in scope than the stuff OP is talking about.

> Without guidance, LLMs tend to paint themselves into a corner, because they’re generating code to solve individual prompts, not thinking holistically about an application’s architecture.

The crux of what I'm trying to say here is that I absolutely believe that this line is 100% true today, but I would be deeply cautious about assuming that it will continue to be true given the improvements in LLMs over the past few years.