Jev: a model that outputs only decisions and confidence, no text
Jev is positioned for the many LLM calls an agent makes each day just to get a yes/no: it takes a typed question and returns an answer with a confidence score, and a demo dashboard shows 6 agents running 17,233 decisions at a 3 ms median.
Jev AI is a $40M model built by an ex-OpenAI researcher that can't write a single word. It can't chat, can't code and can't read an image, and it launched just 2 weeks ago from Diogo Almeida, who worked on the RLHF research behind ChatGPT and then built the opposite. Your AI agent spends most of its day not working but deciding: is this urgent, which tool is next, which agent takes it, are we done, and every one of those is a full LLM call burning thousands of tokens of prose to get back 1 word: "yes." Jev skips the prose, so you send state and a typed question and get back an answer with a confidence score and nothing else, which means it can't hallucinate a format it was never allowed to write. The demo dashboard shows 6 agents running 17,233 decisions at 3 ms median and $0.00003 per call, while TypeSafe's own benchmark puts it at 193.6x faster and 444.6x cheaper, with input at $0.042 per 1M tokens and output free. Your LLM is the slow brain that thinks, and Jev is the reflex that routes it. But the real tell is the name, because Jev comes from William Stanley Jevons, who noticed in 1865 that better engines didn't cut England's coal use but made it burn more. Make a decision 444x cheaper and nobody makes fewer decisions, they build agents that make 444x more. Cheap reflexes don't shrink the swarm, they breed it.