The way I have experienced this, starting from circa 2018, it was a bit more incremental. First, LSTMs and then transformers lead to new heights on the old tasks, such as syntactic parsing and semantic role labelling, which was sad for the previous generation, but at least we were playing the same game. But then not only the old tools of NLP, but the research questions themselves became irrelevant because we could just ask a model nicely and get good results on very practical downstream tasks that didn't even exist a short while ago. NLP suddenly turned into general document/information processing field, with a side hustle in conversational assistants. Already GPT2 essentially mastered the grammar of English, and what difficulties remain are super-linguistic and have more to do with general reasoning. I would say that it's not that people are bitter that other people make progress, it's more that there is not much progress to be had in the old haunts at all.