founded by former OpenAI researcher Diogo Almeida (@CompleteSkeptic) who helped invent Reinforcement Learning from Human Feedback (RLHF), just released a model that is ~200x faster and 450x cheaper than leading LLMs, while matching near-frontier performance on decision-making (System 1) tasks. It hits that speed and cost profile by processing data in parallel rather than generating token by token which is how most current models like Claude or ChatGPT function. Most importantly it outputs confidence scores when it’s uncertain meaning the model tells the user how confident it feels in the decisions it is making. This will become increasingly more important as AI regulations pick up over concerns about safety. Why this matters: most knowledge work is about decision making not text generation. Software that people use has always just been static workflows. Every time you click a button, you've made a decision. When you click “Send Email” the software just executes the fixed path of actions you committed to that some developer has programmed in so you can send an email halfway across the world. When people talk about AI replacing the “human in the loop” what they actually need is a system that can evaluate choices and quantify risk in real time: Should we flag this transaction? Should the car turn right? Does this patient require surgery? Fast, dirt cheap decisions paired with calibrated confidence scores are the missing link for real-time agentic workflows - especially right now, when ROI is the single biggest question hanging over the AI buildout. We have seen open source models pick up a lot of steam as companies have tried to lower costs and delegate simple work to open source and high level work to the frontier. I think Typesafe AI’s model integrates nicely into this future providing cheap and fast decision making as well as providing a way to monitor the confidence of the decisions being made for when we still need that human touch. In order to achieve AGI, the goal is to first create a computer that thinks like a human. The human brain runs on about 20W of power and it has gotten us from sticks and stones until now. AI uses ~1000W. That leaves a lot of room to be made on improving AI models. The human brain provides decisions but also a confidence in those decisions. You may believe the Eagles are going to beat the Chiefs in the Super Bowl, but the level of confidence you have in that answer matters. Depending on your confidence in the Eagles ability to win, you may feel more or less nervous betting those $5 with your friends. Furthermore, no human thinks one word at a time. We think in concepts and then figure out how to express ourselves. We also have saved behaviors (call it muscle memory or reflexes) that trigger when someone throws us a ball, tells us to turn right etc. rather than involving a multi-step reasoning process every time. I think JEV ( model) and the work on looped transformers represent a big step in the right direction towards AGI.