Not exactly. It takes time for those words to become mainstream for a generation. While you'd have to manually add those words in dictionaries, LLMs can learn these words on the fly, based on frequency of usage.
At this point we're already using different definitions of grammar and vocabulary - are they discrete (as in a rule system, vide Harper) or continuous (as in a probability, vide LLMs). LLMs, like humans, can learn them on the fly, and, like humans, they'll have problems and disagreements judging whether something should be highlighted as an error or not.
Or, in other words: if you "just" want a utility that can learn speech on the fly, you don't need a rigid grammar checker, just a good enough approximator. If you want to check if a document contains errors, you need to define what an error is, and then if you want to define it in a strict manner, at that point you need a rule engine of some sort instead of something probabilistic.
I’m glad we have people at HN who could have eliminated decades of effort by tens of thousands of people, had they only been consulted first on the problem.
Also, it takes at most few developers to write those rules into a grammar checking system, compared to millions and more that need to learn a given piece of "evolved" language as it becomes impossible to avoid learning it. It's not only fast enough to do this manually, it also takes much less work-intensive and more scalable.