It does, and possibly this launch is a little window into the future!
Install scripts are a simple example that current generation LLMs are more than capable of executing correctly with a reasonably descriptive prompt.
More generally, though, there's something fascinating about the idea that the way you describe a program can _be_ the program that tbh I haven't fully wrapped my head around, but it's not crazy to think that in time more and more software will be exchanged by passing prompts around rather than compiled code.
What Naur meant by "theory" was the mental model of the original programmers who understood why they wrote it that way. He argued the real program was is theory, not the code. The translation of the theory into code is lossy: you can't reconstruct the former from the latter. Naur said that this explains why software teams don't do as well when they lose access to the original programmers, because they were the only ones with the theory.
If we take "a great description" to mean a writeup of the thinking behind the program, i.e. the theory, then your comment is in keeping with Naur: you can go one way (theory to code) but not the other (code to theory).
The big question is whether/how LLMs might change this equation.
Even bringing down the "theory" to paper in prosa will be lossy.
And natural languages are open to interpretation and a lot of context will remain unmentioned. While programming languages, together with their tested environment, contain the whole context.
Instrumenting LLMs will also mean, doing a lot of prompt engineering, which on one hand might make the instructions clearer (for the human reader as well), but on the other will likely not transfer as much theory behind why each decision was made. Instead, it will likely focus on copy&pasta guides, that don't require much understanding on why something is done.
That theory, or mental model, is a lot like a program, but of a higher kind. A mental model answers the question: what if I do this or that? It can answer this question with a different level of detail, unlike the program that must be executed completely. The language of a mental model is also different: it talks in terms of constraints and invariants, while the program is a step-by-step guide.
But if the person writing the prompt is expressing their mental model at a higher level, and the code can be generated from that, the resulting artifact is, by Naur's theory, a more accurate representation of the actual program. That would be a big deal.
It is much easier to use LLMs to generate code, validate that code as a developer, fix it, if necessary, and check it into the repo, then if every user has to send prompts to LLMs in order to get the code they can actually execute.
While hoping it doesn't break their system and does what they wanted from it.
Also... that just doesn't scale. How much power would we need, if everyday computing starts with a BIOS sending prompts to LLMs in order to generate a operating system it can use.
Even if it is just about installing stuff... We have CI runners, that constantly install software often on every build. How would they scale if they need LLMs to generate install instructions every time?
That's basically what I was thinking too: installation is a constrained domain with tons of previous examples to train on, so current agents should be pretty good at it.
(I have my own answer to this but I'd like to hear yours first!)