OpenAI could eat Jev's lunch by folding general classification into its models
OpenAI is about to eat Jev's lunch – Arcturus Labs
TypeSafe's Jev, a general-purpose classifier built on a large language model, has been adopted faster than any model in AI Gateway history. But OpenAI has used LLMs as implicit classifiers for years—tool calling, token-level decisions—and could replicate Jev's trick, then embed it inside its own models for smarter, faster, cheaper agents. The real moat may be TypeSafe's training data and calibration process, not the architecture.
OpenAI has for years used their LLMs as implicit classifiers; they just haven't trained them for general classification tasks and they haven't packaged up general classification as a stand-alone product.
- orbital-decay
Every major AI shop has a ton of in-house classifiers already, big, small, generalist, specialized. Some are used in inference pipelines (e.g. safeguards), some are used in data preparation, training, analysis and investigation, research, various one-off and intermediate tasks etc. Offering them on a public API doesn't always make business sense. I don't see much substance to this buzz, looks like people that are new to all this are discovering that classifiers exist, they are more efficient at classification, and many tasks commonly done with generative models are classification in disguise. Which is not bad at all, a fresh look at their use is great to have.
- prodigycorp
This article is extraordinarily hard to read. It’s tummelvisioned on OpenAI and things like tool calling which are only relevant to the extent that llms have been tuned to make relative choices, but this applies to all LLMs. Also, some really outdated references. LLM written, perhaps?
Also, moat discussion is the lowest form of discussion. I don’t care if jev has a moat. Did it get the interface right? What other past ideas have we overlooked that if given some love, could kick the door down like jev did?
Really silly stuff.. people wanting to talk about moats when there’s no castle. Moat talk merely projects the illusion of being engaged but, much more often than not, it’s hollow engagement.
- andy12_
I find it unlikely. OpenAI is all in training models with reasoning with RL, and Jev-like models are the total opposite. They are made to not reason at all to be fast. If you want to add reasoning on top, you might as well use a conventional LLM because you lose the price and speed benefits when you output auto-regressive tokens. I don't think OpenAI will even bother with this.
> My main assumption is that Jev is using something quite close to a conventional large language model. As evidence of this, Latent Space reports that many of the early clones are indeed LLM-based.
Not proof that this is the case with Jev though. It might use non causal text encoder for the state, which could make sense given that it's very good for its price.
- rdevsrex
There is one benefit that Jev has, that it is not OpenAI and thus it's probably less likely to steal your own work.
- prometheus1992
Why would OAI need to follow Jev? I really think this is paid by Jev. Jev itself won't have lunch money in a shortwhile because there are literally 10s of free alternatives available which can be run locally on basic consumer hardware. Terrible utility aside, there is no sensible business proposition in Jev.
- dmix
For context on what "Jev" is: https://news.ycombinator.com/item?id=49717558
- abroszka33
If OpenAI releases something similar to what Jev does, then that would be like admitting defeat. Their whole spin is AGI and world ending danger. Why would somebody with an AGI at home make something like Jev which is intended to be a part of some SW the AGI is going to replace anyway.
- 60secs
I'd be surprised if they weren't aqui-hired by one of the big labs as a unicorn.
The ability to use classifiers under the hood for the larger models has the potential to dramatically improve cost and throughput, allowing them to increase margin on a very similar service.