This is an interesting question. As an NLP researcher who is in contact with companies that demand NLP applications, I think we are not there yet. For example,

This is an interesting question. As an NLP researcher who is in contact with companies that demand NLP applications, I think we are not there yet. For example, a company that wants to extract information from medical records cannot use solutions like GPT-4 or Claude that involve sending protected data to third parties in foreign jurisdictions. Modest local models (like 7B models) don't work so well for things like named entity extraction yet. And furthermore, companies typically want some explanation and accountability for the results (especially in sensitive domains...) and sure, LLMs can explain, but you have no guarantee that the explanation isn't hallucinated. When I mention the possibility of hallucinations, companies typically balk and say that they prefer the classic way. My (potentially biased) opinion is that classic NLP still has life left in it. For how long, I don't know. If small, locally-runnable LLMs get much better and more reliable, addressing the hallucination problems, what you mention might become largely true for most engineering applications. Also note that beyond engineering, things like syntactic parsing are also useful for scientific pursuits too, and at the moment they seem to be out of reach of LLMs.