The way I see it, generally every new data point (on which the production model inference gets run once) becomes part of the data set which then gets used in training every next model, processing the same data point many more times in training, thus training unavoidably taking more effort than inference.

Perhaps I'm a bit biased towards all kinds of self-supervised or human-in-the-loop or semi-supervised models, but the notion of discarding large amounts of good domain-specific data that get processed only for inference and not used for training afterward feels a bit foreign to me, because you usually can extract an advantage from it. But perhaps that's the difference between data-starved domains and overwhelming-data domains?