Classification models lend themselves to sparsity and explainability. The good ones are very simple and economic to run on a laptop. If someone told you before 2022 that a json classifier was a product you would have laughed it off.

To add to this the more difficult problems in classification done on scale have always been about collecting "good" -ve examples, enough data to calibrate on every confidence interval and debugging outliers. And those are solved on a case by case basis by the company pursuing its own peculiar version of the problem.

Am I the only one who thinks this is just all hype?

I think it’s about sample efficiency. You could finetune your own jev using Lora with very little data