Now that we're hitting against the hardware supply limits of global economy, I expect more people to go back and revisit the things left along the way in the mad rush to "just throw more compute at it / make a bigger model" - and thus many more cases like Jev to show up in the next few years.
I've been playing with this for the last year or so. Started with a personal email classifier, also did benchmarks with some public datasets, then created a couple classifiers that could play Doom, and now I've been trying out some other experiments, like a request proxy/router to automatically choose a classifier and fallback to LLM to handle unseen requests
Jev did a great job at creating hype, but also at shaping the concept and space of "decision engine" or "decision model". People were already doing this with LLMs, which is very inefficient for most tasks like that, and the Jev guys figured there was a market there. It seems like they were right, and now there's a rush to flood the space, taking advantage of the hype window
In general, training a general purpose classifier is something lots of people have worked on for a long time. Large Transformer models themselves are typically "generalists" already, so structured generation and constrained decoding have given you the ability to use an LLM as a general classifier for years. It's an incredibly common pattern for working with LLM judges or any sort of branched decision making workflow.
A lot of people who are a bit less familiar with the field saw the hype around Jev and presumed that the reason it was so exciting was that it was a fundamentally new interface for working with an LLM. And that additional excitement drove even more attention to Jev. But fundamentally, TypeSafe's announcement was that they found a particular architecture/training paradigm that resulted in a model for this particular interface that had incredible accuracy, very low latency, and for which they could offer inference at a super low cost.
I've not kept up with the flood of Jev clones that have been released, but I think this is just typical for any new component in deep learning that gets popular. There are an absurd number of open source autoregressive LLMs and fine tunes you can use. The thing that makes one more popular than the other is typically the general performance of the individual model.
But training a model for this purpose, or emulating the procedures described in Jev's papers, isn't something that would be beyond the capabilities of any lab. It's not an entirely alien architecture or approach.
The bigger question for TypeSafe as a company would be if other teams are producing Jev-like models that win on performance or cost. Like I said, I haven't followed the reports super closely, so no idea if that's the case or not.
If CF's benchmark is representative and sufficient, Clef outperforms Jev!
Models by themselves don't guarantee market capture. Rather, its how they integrate. I think a lot of folks are burned by the closed nature of many models.
The hard part is the data and evaluation. Sure, it’s not that hard to build a fast model with good predictive power. But fast at doing what? You probably don’t care about classifying whether a hotdog is a sandwich (which is the Jev demo).
Latency won't be that good, but could still work similarly. Simply force the structured output of a LLM to the given schema.
Probably also easy to train because we can use stronget LLMs to generate input/output data, or even synthetic data is easy to generate.
It's not really a new technology, it's more like a new use-case.