diogo almeida helped build the methods behind chatgpt at openai. today he launched something that isn't a chat model at all. jev never generates a single word of text. feed it unstructured state plus a set of typed questions, and it returns a probability-scored answer to every question at once, in one parallel pass, not token by token the way every frontier chat model works. 70 to 500 milliseconds end to end. frontier conversational models on comparable tasks run 3 to 329 seconds. $0.042 per million input tokens, output entirely free, since there's no text being generated to bill for in the first place. his own framing: "think of jev as a frontier-intelligence function call, unstructured state in, typed probabilistic decisions out." trained on a method he calls rlcd, reinforcement learning for calibrated decisions, a genuine departure from the rlhf and rlvr most labs currently use. the honest limit worth naming directly. "hallucination-free" here means something narrower than the phrase suggests. bounded, typed outputs literally can't hallucinate text, since there's no text. but picking the wrong option within a valid schema is still entirely possible. this isn't a replacement for a chat model. it's the classification and routing layer that sits in front of one, cheap enough to run before and after every actual llm call. typesafe ai emerged from two years of stealth with $40 million in seed funding led by dcvc. almeida's actual question, the one that started this: why haven't superhuman chat models led to agi. his answer isn't a bigger chat model. it's a different category entirely, one optimized for the boring, high-volume decisions that sit underneath every real automation pipeline, and that decomposing those decisions might matter more for building genuinely useful ai systems than compressing everything into one more capable conversation. https://x.com/CompleteSkeptic/status/2099925682726002904/video/1