Jev as a decision layer: LLMs generate, Jev decides
TypeSafe AI's Jev is a separate decision layer that returns typed answers with probabilities via Choice, Score and Noul, with cost examples such as classifying 1,018 papers for $0.08.
JEV engineering is not what you think it is. Most people hear "200x faster, $400 x cheaper" and think: cheaper routing. That's the wrong frame. What TypeSafe AI actually built is a split in how AI systems work. Not a cheaper alternative to the LLM. A separate layer that does the one thing LLMs are terrible at: deciding. Your LLM is a generative engine. It thinks, reasons, creates. But inside every agent loop, it's also doing something else, choosing which tool to run, rating whether a result is good enough, deciding whether to go again. Binary questions. No generation needed. And you're paying frontier model prices for every single one. Jev reads the system state and returns a typed answer with probability: Choice - which agent should act next? Score - is this result good enough to keep? Noul - is the goal met? No reasoning. No text. Just the answer. That's a completely different layer of the stack. The architecture it creates: LLM creates work. Jev decides what happens next. Agents execute. When you build it that way, the economics change completely. 1,018 papers classified for $0.08. 500 emails triaged for 3.5 cents. 300-agent swarms that run on autopilot without a human watching the loop. This is Jevons Paradox in AI infrastructure. Make decisions cheap enough and you stop rationing them. Systems that were financially impossible become routine. One model to think. One to decide. The diagram below maps how the split actually works in a running system.
This post quotes another post: x.com/mahaximus_/status/2085024744387092973