A new model shipped on 15 September that never writes a sentence. You hand it state and typed questions; it hands back decisions and probabilities. It costs $0.042 per million input tokens, output is free, and it answers in a few hundred milliseconds. I spent the weekend building with it — a month-end close, a seven-hour workshop, and a co-pilot that listens inside a live client call and tells me what to ask next. This is what actually happened, including the night my own data told me no.
Jev, by TypeSafe AI. Not a chatbot, not a smaller LLM, and not a replacement for one. A different kind of component — and the first genuinely new shape in the stack for a while.
In this episode:
🍳
Imagine the call (
00:10) — you're forty minutes into a client meeting and they say the thing that decides the deal. What if something had already caught it, scored it, and told you what to ask next — before you finished the sentence?
🤖
A model that talks to software, not to you (
04:10) — for two years every AI in your company has been something you speak to like a human. Jev inverts that. State in, typed judgment out. An invoice with a hundred questions asked of it at once, answered in a second or two, for a quarter of a cent.
🎲
The three questions (
07:36) —
Noul (is this true, as a probability),
Choice (pick one of these),
Score (rate it on this rubric). That's the whole vocabulary. Everything else is your code. Which is the point people miss: Jev judges, your code decides.
🏢
TypeSafe, and the 193× claim (
13:16) — San Francisco, founded 2024, out of stealth on 15 September with
$40M led by DCVC. Founder Diogo Almeida worked on RLHF and InstructGPT at OpenAI. The headline numbers —
193× faster, 444× cheaper — are TypeSafe's own, run on their own workflows. I say so on air, and you should too.
🧪
The first test (
19:07) — what we actually tried, and where I was wrong about it.
💸
2.6 cents (
22:09) — the number that reframes everything. That same job on a large language model isn't 2.6 cents. It's two or three dollars.
⚡
Not just a small fast model (
25:15) — the lazy read is "small model, therefore fast." That isn't the story, and this is where it gets interesting.
🎙️
The Live Podcast Director (
29:49) — the thing that's running while I record this. Listen → Judge → Decide → Guide → Prove. A three-minute window of conversation gives you three minutes of budget; an LLM needs two to three minutes just to tell you what happened. Jev does it in about
1.3 seconds. That ratio is why a live co-pilot is possible at all.
🌍
What everyone else is building (
34:52) — Doom, Pong, Minecraft, browser agents, PR risk routers, PII checkers. Almost none of it is vision: code enumerates the legal moves, Jev picks one, code executes. Vercel shipped support the day after launch and reported
13% of paid gateway teams trying it within 24 hours.
⚠️
Should you trust it? (
39:28) — the honest part. "Cannot hallucinate" means it cannot break your schema. It can still be wrong. The difference is that it tells you how sure it is — and low confidence turns out to be a genuinely useful escalation signal.
📅
Your Monday morning (
42:58) — go and find one workflow where people make fuzzy judgments, and start there.
Stay happy. Stay chaotic. And go and try Jev.