alt Hacker News>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.
I don’t understand how you’ve reasoned your way here.
How could Jev have possibly built something out of reach of a frontier lab providing the same or 5x as much resourcing to one of their teams to achieve? Which they can do because Jev has only received $40M of funding recently, so a round that is approximately what OpenAI is spending per math problem they try cracking.
In addition to that, these frontier labs have got extremely good at generating synthetic data and running generalised training pipelines. I can only imagine how easy it would be for them to build this internally vs Jev building it from scratch.
And then the final thing: one of the best places you might apply Jev is within a harness, behind layers that customers increasingly have abstracted from them. Frontier labs have huge incentives to do this as it could make their offering much better and cheaper. And whoever gets this first wins another big attraction for users.
My take on this is Jev is either acquired almost immediately for the benefit of the next 1-3 months head start for whichever lab acquires them or we get a similar model offered from all labs in 3-6 months or sooner.