It would explain a lot of misunderstanding between "programmers" though.
But then you go onto say this:
> But that is not programming then? Doing voice recognition in the 90s, missile guidance systems, you name it, those are hard things, but it's not the "programming" that's hard. It's the figuring out how to do it. The algorithms, the strategy, etc.
That implies LLMs can't help with design of something that needs voice recognition and missile guidance systems. If that's your claim, it's wrong. In fact they are far better at being sounding boards and search engines than they are at coding. They make the same large number of mistakes in design tasks as they do in software coding, but in design tasks the human is constantly in the loop, examining all of the LLM's output so the errors don't matter so much. In vibe coding the human removes themselves from the loop to a large extent. The errors become shipped bugs.
Whether you have to solve a problem that is hard today because there aren't many available resources, or something well discussed that you can research with Google or an LLM, I don't think it changes anything about their argument that once you know what to do, actually turning it into working code is comparatively mundane and unchallenging, and always has been to some degree.
No.
I suspect the underlying meme behind this is "LLMs can't think, they are just stochastic parrots". If you define an LLM to be the thing the model builders train by ingesting and compressing information on the internet, in a way that yields a very convenient if somewhat unreliable search engine, then that's true. But if you define them that way, the modern models like Opus, Gemini, GLM and so on aren't pure LLMs, and haven't been for a while. They still have an LLM inside them, but have other things bolted on like attention heads, context windows, and Chain of Thought. These things outperform humans at some tasks that require "thinking".
Glasswing is an example. Before Glasswing human "researchers" could find a solution to a particular problem, the "lets find an exploit in this software" problem, as a particular rate. Now with Glasswing, it's orders of magnitude faster than that rate. The same thing has happened in other areas, like protein folding and weather prediction. These models aren't reproducing something they've seen before. They are producing things we consider new - like creating an exploit for a vulnerability they discovered.
With all the evidence around if you are still hanging onto the idea these models can't help with many tasks by doing what we call "thinking", you are in denial.