It sounds like you read some of Emily Bender, et al's works and then simply regurgitated them. Unfortunately, everyone in this camp simply has large walls of text for their arguments, and basically no data, proofs, or any evidence whatsoever. The 'evidence' around "system X doesn't understand, like I do" amount to 'I don't like this' and 'trust me bro'.
In formal lingiistics, Chomsky has a point against ML for LMs, which is based around the lack of interpretability. His works did push our understanding forward - because they included formal mathematics in proving his statements. However, they tended to be more specific about what limits exist around expressivity, rather than your issues laid out here.
There are strong arguments to say that we do learn and represent language in a similar way to these NNs, at least at some level. 1) the distributional hypothesis and 'words defined by company they keep' is intuitive for how we learn language, and has provided enormous advances in NLP starting with w2v. 2) Dozens of academic papers from around the globe in different labs all reproducing the result in various ways that the distributional hypothesis word vectors (either contextual as in Bert/Gpt, or not as in gensim) map to brain activations by fMRI. This result also tracks in more resolute experiments in visual modalities for more invasive experiments in non-human primates.
I could go on providing data and experiments, but it would be nice if the opposing side to this would provide any at all that were convincing.