First, it's important to understand that Chomsky is a linguist (a syntactician) and that the goals of linguistics are very different from artificial intelligence. In particular, the two domains differ greatly as to what constitutes a satisfactory theory of human language. For the modern study of syntax, it is to have a rigorously correct formalization of all of the grammatical sentences in a natural language; it should also make correct linguistic predictions. If there is an exception to this, the theory is considered wrong. This is very different from artificial intelligence, which has much less rigorous goals as to what constitutes success.
Chomsky is pointing out in the video that it’s also important in linguistics that the theory make actual claims about human language. So if the theory is just as good at non-human languages (like programming languages or even
antibody therapies), it can’t make any interesting claims or predictions at all about human languages.
This of course ignores the fact that, notwithstanding the semantic hallucinations, LLMs have achieved strikingly accurate syntactic competence. So, even though the computer program to run the LLM makes no linguistic claims, the learning implicit in the neural network still has an implicit linguistic theory to manifest that competence.
But this in turn fails, because LLMs are just black boxes and do not in any way constitute satisfactory scientific explanation. No one has any idea how to translate billions of weights from an artificial neural network into a linguistic theory.