This is useful for classification problems; any time you need to write software that looks at some fuzzy data and needs to make a probabilistic decision. It's far more cost-efficient and performant to use this type of model instead of an LLM.

Before now you had to train a model on your specific classification problem, now these new models don't require any specific training at all to do pretty well on novel problems.

"You could do this before" "No, you couldn't, this gives you something new" "Yeah but I don't want it"

It is an absolute requirement if you want to build something that (1) uses a classifier as part of a larger system, (2) can be let off the leash with no human supervision and (3) is not an AI slop demo that will be [dead] and [flagged] in seconds after being posted to HN.

For instance if you have a predictive model for market prices that is not calibrated that's... nice. If you have a calibrated model you can add a Kelly better and you have a trading strategy that makes money. Similarly if you are classifying articles or images or other contents to make a feed you might believe that 70% or 95% or some other level of precision is "good enough" and you can set the knob and turn on the cruise control.