When you say "trained classifiers", you are referring to models which are trained (or fine tuned) to work on one specific problem, right? That is the opposite of "general purpose".

Would Jev be more accurate in a specific task if it had been developed only for that task, as opposed to general purpose? Of course it would. So, sure, Jev is trading accuracy for generality. According to you "nobody thought it's a good tradeoff", which again is false, there was huge demand for a cheap and accurate general purpose classifier.

> work on one specific problem, right? That is the opposite of "general purpose".

A business doesn't need Jev for the sake of Jev. Most business are solving specific problems.

And fine-tuning got a lot cheaper these days - I've seen claims here on HN that ~500 examples is enough to beat Jev.

> "nobody thought it's a good tradeoff", which again is false, there was huge demand for a cheap and accurate general purpose classifier

There wasn't. The hope is that there was a latent demand, but we've yet to see if it's truly latent or just manufactured.

Noone is saying "hell yeah, finally we got a general purpose classifier, my business needed it so much". The typical message is "this seems cool, let me see where I can apply it".

The fact that name itself is a play on Jevons Paradox illustrates that there was no demand until Jev was released.