Savage, but maybe fair. If one of Chomsky's underlying claims is indeed that language requires innate hard parsing rules and can't just be derived from probabilistic sampling of a bunch of data - that seems completely dead in the water.
It is entirely likely that the way we operate is probability-first, only deriving rules loosely after taking in lots of experiential data to speed up and simplify that initial fake-it-til-you-make-it understanding. The fact LLMs can get the quality we see using just this approach is a strong indicator that this method of understanding may be a fundamental approach of biological systems too.
(and if you're arguing this is unfair because humans created the language that's being used for probabilistic training - well, look at image models trained on photographs instead and tell me those aren't an example of extreme quality derived purely from mass-inferenced data. Rules-based architectures don't necessarily need apply.)
But honestly, this seems like a silly claim to begin with if it really was claimed. We have formal language theory complexity classes of probabilistic algorithms for a reason - they work! It shouldn't be surprising that the model can stretch down to the fundamentals too. Far fewer programmers (and linguists) were raised to think with these models than deterministic rules-based ones, but the field has been progressing alongside for decades, and now they get to play with powerful LLMs that take probabilistic inferencing to the extreme and will likely prove it works (very elegantly) for everything. This shouldn't be surprising in retrospect.
Chomsky may very well be right that there always exists some fundamental elegant formula underlying any phenomenon (or at least any language). But it's undeniable at this point that simplistic statistical approaches can be applied at scale to those phenomenon and derive highly-useful general models, which also will very likely converge upon the elegant formulas he envisioned. The two are intrinsically linked, neither inseparable.