We have a bad universal classifier now (via Jev). 0->1, one might say.

A bad universal classifier does suggest a good one later. And that is exactly what I would call "theoretically doable"

That said, I don't think that Jev is a magic breakthrough or anything. I think it is just a particularly good narrative with an easy way to try it out.

Jev is interesting in that it's much cheaper and faster than a frontier LLM.

But I've seen nothing to indicate that the upper bound on classification tasks of a Jev-like model can exceed a frontier LLM with reasoning tokens. That seems nearly impossible even in principle (since Jev-style models are still based on LLM pretraining).

So while they're definitely on the Pareto frontier, which is valuable, they're at the "cheap" end of the spectrum more than the "good" end and I don't expect that to change.