You are like, science denial-ism level of ignorance when it comes to this conversation, or perhaps, it might be that you don't actually understand the underlying philosophy of scientific inquiry to understand why LLMs basically were able to break the back of both UH and innate acquisition.

You seem like the kind of person who cheer-leads "in the spirit of science" but doesn't actually engage in it as a philosophical enterprise. You didn't seem to notice the key point that @Not_mikey@slrpnk.net made, which is that unlike UG, LLM's are actually testable. That's the whole thing right there, and if you don't get the difference, that's fine, but it speaks to your level of understanding to how one actually goes about conducting scientific inquiry.

And if you want to talk about incurious:

You might as well say that the longer you stare at a printed painting, the harder it is to deny that printers make art. LLM’s do not “understand” their outputs or their inputs. If we feed them nonsense, they output nonsense. There’s no underlying semantics whatsoever. LLM’s are a mathematical model.

Specifically "There’s no underlying semantics whatsoever" is the key lynch pin that UG demands that LLM's demonstrate are not strictly necessary. Its exactly why Chomskys house of cards crumbles with the the counter-factual to UG/ Innate acquisition that LLM's offer. I had a chance to ask him this question directly about 6 months prior to that op-ed being published. And he gave a response that's about as incurious about why LLM, and basically, big complex networks in general, are able to learn as you've offered here. His was response was basically the same regurgitation on UG and innate acquisition that he offers in the op-ed. And the key point is that yes, LLM's are just a big bucket of linear algebra; but they represent an actually testable instrument for learning how a language might be learned. This is the most striking part of Chomskys response and I found it particularly galling.

And it is interesting that yes, if you feed ~~LLM's~~ transformers (I'm going to start using the right term here: transformers) unstructured garbage, you get unstructured garbage out. However, if there is something there to learn, they seem to be at least some what effective at finding it. But that occurs in non-language based systems as well, including image transformers, transformers being used to predict series data like temperature or stock prices, even even DNA and RNA sequences. We're probably going to be having transformers capable of translating animal vocalizations like whale and dolphin songs. If you have structured series data, it seems like transformers are effective at learning patterns and generating coherent responses.

Here's the thing. Chomsky UG represented a monolith in the world of language, language acquisition and learning, and frankly, was an actual barrier to progress in the entire domain, because we now have a counter factual where learning occurs and neither UG or innate acquisition are necessary or at all relevant. Its a complete collapse of the ideas, but its about as close as we'll get because at least in one case of language acquisition, they are completely irrelevant.

And honestly, if you can't handle criticism of ideas in the sciences, you don't belong in the domain. Breaking other peoples ideas is fundamental the process, and its problematic when people assume you need some alternative in place to break someone elses work.