Can you precisely define what you mean by "generating" and "pretends", in such a way that this neural network does both these things, but a conventional modern audio codec doesn't?

"Pretends" is a problematic choice of words, because it anthropomorphizes the algorithm. It would be more accurate and less misleading to replace "pretends to be" with "approximates". But then it wouldn't serve your goal of (seeming to) establish a categorical difference between this approach and "regular algorithms", because that's what a regular algorithm does too.

I apologize, because the above might sound rude. It's not intended to be.

I was avoiding the word "approximate", because that implies a connection to the original raw data.

A generative model guesses what data should be filled in, based on what is present in its own model. This process is totally ignorant of the original (missing) data.

To contrast, a lossy codec works directly with the original data. It chooses what to throw out based on what the algorithm itself can best reproduce during playback. This is why you should never transcode from one lossy codec to another: the holes will no longer line up with the algorithm's hole-filling expectations.

Not really. Any lossy codec is generating data that pretends to be close to the raw data.

Yes, but the holes were intentionally constructed such that the end result is predictable.

There is a difference between pretending to be the original raw data, and pretending to be whatever data will most likely fit.

And that's why Packet Loss Concealment is only used to fill in the occasional lost packet. That way, the occasional vowel or the end of a syllable could be bridged over. Other improvements exist to prevent packet loss in the first place that are much less in make-samples-up territory.

Yes, I agree that this use case is entirely reasonable. I also agree that it's reasonable to be concerned in the first place.