I'd wager that the vast majority of the ML research community, especially anyone interested in "AGI", is familiar with Hofstader's work. And I don't think anyone working on contemporary language models would argue that they are somehow an assumption-less "pure" model--the particular inductive bias of the Transformer has been studied by a huge number of researchers and continues to be, and the same is true for things like training data bias.

I think the Hofstader's view of modern LLMs is actually a deeply human and touching one. Looking at his work over the years, his curiosity has always veered towards human thought. He could have written GEB with a focus on completely different examples of self-reference, but he chose three striking humans from history. When he's describing modern systems as "empty intelligence", I think there's a little bit of heartbreak in his perspective, because he sees them as fundamentally different from humans in a way that leaves the part he loves--the "I" in the loop--out of the equation. He gave an interview a few years ago where he explains his feeling as being "diminished" not in a "What will I do if I'm not the best at math?" kind of way, but more specifically as he puts it, that humans are "imperfect, flawed structures".