Anyone who has worked in ML for 10+ years would already know that the usage of LLMs for everything is lazy, wasteful and a high degree of marketing on it.

There have been other "universal"/general classifiers like GLiNER, GLiFormer, etc based on BERTs (Laya itself is based on ModernBERT!), but I do think there's something underrated about slapping classification on a "big" model like I've seen post-Jev announcement, lots of Qwen stuff, but the most interesting to me so far is razorback16/openjev using DiffusionGemma. There's a level of generalization that lots and lots of parameters get you that you can't really get out of small models.

I've come to the same conclusions as you.

> I see it as a wake up call for the tech community to go back to basics for most tasks instead of relying solely on generic LLMs.

I always say the cheapest LLM request is no request at all.

The data labeling objection baffles me. Even if you don’t need labels for training, how do you know your model is working if you’re not evaluating it?

My company specializes in statistical long document text classification, but nowadays we mainly work with audit trail requirements because we got tired of hearing complaints about our 5 example learning curve. Seems like the industry standard is telling an llm to label and telling an llm to eval, and crossing your fingers that it’s correct.

> it’s just BERT with more data

Let's take that as a given. Is BERT with more data not useful?

> I can see why people would want ready made one shot classifier, and I can see the value of sending multiple classifier in one call, but I wouldn’t call it breakthrough

Are those things that people want less useful because of what someone else calls it?

> I see it as a wake up call for the tech community to go back to basics for most tasks instead of relying solely on generic LLMs.

Maybe, or maybe to use Jev, which is useful?

Whether something is overmarketed or undermarketed, novel or derivative, it does not change its function.

> a bit cheaper

Gemini 2.5 Flash Lite is $500/Gt, Jev is $42/Gt. AKA an order of magnitude cheaper.

> BERT with more data

It is specifically not just that, in the same way that models which have been chat/task-optimized via RLHF (which made these models much more useful for a huge variety of tasks) are not just "the base transformer model with more data".

the huge benefit in real systems for Jev like solutions i see is the cybersecurity / prompt injection mitigation. since the output will always be well structured, there is no way prompt injection might make the system do something crazy.

probably a prompt injection can still affect the output though, in unforeseeable ways.

>I believe many labs will replicate it in no time

I really doubt this actually. To me, Jev is a great example ofcounter positioning. When you consider just how hyper optimized the labs are around auto regressive LLMs, and just how much money they have already invested and are pre committed to investing in an entire stack for auto regressive transformers... then responding to Jev becomes nearly impossible actually. They would just be giving up too much.

Just think, everything from their current sources of revenue, the sales use cases they tout, the marketing on the websites, the messaging to customers, then technically to the APIs, their internal batching and scheduling algos, their GPU configs, the chips themselves. ALL OF IT is designed with generative text models in mind. Jev breaks all of it.

I think basically no chance of a response any time soon.

This, intuitively, feels like a "lower level, basal, reflex" layer for the LLM's intellection.-

It would be amazing to have big BERTha with per-token pricing on GCP or AWS. There are many times I am reaching for a cheap classifier with the general behavior of an LLM.