ChatGPT broke Diogo Almeida’s heart. As an OpenAI researcher, he helped build the chatbot and then invent reinforcement learning from human feedback (RLHF), the training technique largely responsible for the current AI boom. But something was missing.
“We have lightning in a bottle, and yet it is not useful,” Almeida said. “I’ve been battling that problem since then. It took me a while to come to the conclusion: The problem is we are optimizing for human language … We have been super good at human language for four years, but it’s not useful for automation because computers speak a different language.”
Two years ago, Almeida left OpenAI to start TypeSafe AI, a startup built to fix that problem. This week, the company released a new transformer-based model called Jev that is not a large language model (LLM). It doesn’t output text. Instead, it produces probabilities, or what the company calls “calibrated decisions.”
Why AI Model Developers Are Paying Attention
Eschewing language does a few important things. It makes the model incredibly cheap and fast, and because users define the outputs in advance, it cannot hallucinate. Its output tokens are free, and input tokens are metered by the billion, not the million.
AI model developers are taking a great interest in the product. The company briefly lost the ability to serve users from its API because demand was so high.
Jev appears most useful for software automation. So far, software developers see it as a cheaper and more robust way to incorporate intelligence into their code.
Real-World Results From Early Testers
Pranit Sharma, a software engineer at Vercel, a company making agentic infrastructure, said his company had used OpenAI’s ChatGPT Luna 5.6 to run a classifier that reviews commands for safety. When Vercel replaced OpenAI’s Luna with Jev, it got results five to 18 times more quickly and with greater accuracy.
Another developer, Bryo AI CTO Nikhil Mudholkar, tested Jev against Gemini for classifying business emails. In his test, Gemini was slightly more accurate, but 10 to 20 times more expensive. More interesting to Mudholkar were Jev’s confidence scores.
“It is the only one that hands back a real probability which makes it ideal for automating workflows!!” he said.
How Jev Works Alongside LLMs
Besides replacing LLMs in certain use cases, the new model can also augment them, acting as a smart check on misbehavior. Using agents to monitor agents can quickly become expensive, but using Jev to do so, Almeida argues, makes sense. He sees users deploying Jev to track LLM agent traces and prevent jailbreaks.
“At the end of the day, it delegates the hallucination problem a little bit to the user,” explained Armin Ronacher, the CTO of Earendil, which builds the open source model harness Pi. “The user has to say, okay, if this only comes back with 50% probability, maybe this is a coin toss, and I disregard it. But if it’s 95%, sure, then I can do something with it.”
Model Routing and Other Use Cases
Another potential use for Jev is model routing, Ronacher said. Predicting whether a given workload requires a specific model would be useful, but using an LLM for the job would be expensive. Jev’s low cost and speed make that kind of real-time sorting possible.
And that’s Almeida’s hope. The model is named after William Stanley Jevons, the 19th-century economist whose eponymous paradox describes how the falling cost of a commodity can lead to it being used more and more. In this case, the falling cost of intelligence should lead to its widespread deployment.
“We think that there’s just going to be smart software all over the place in a way that’s emergent and distributed … much more like the early internet than you know like the mega apps that people are trying to build right now,” Almeida said.
Inside TypeSafe AI’s Approach
Almeida is tight-lipped about the model’s architecture, which outside observers suspect is built on top of an open-weight LLM. The company refers to Jev as a “System One model,” focused on intuition rather than reasoning, and specifically focused on the right task.
Almeida says Jev is trained exclusively on synthetic data using a technique he calls “reinforcement learning from calibrated decisions.”
“We made an early bet that we will be making all of our data, and that has been one of the best bets I’ve ever made in my life — better than our launch, in my opinion, better than RLHF,” he said. “Half of [our company] is a lab that basically owns this entire subfield of statistically well-understood synthetic data, and that is now my life joy.”
A New Category With Room for Competition
For now, Jev stands alone as this kind of model, but Ronacher expects that competitors will spring up now that its utility is apparent.
“We should have seen this earlier in many ways, but presumably because the LLMs are so cheap and subsidized, you often don’t have to be creative yet,” he said.
TypeSafe itself will be building more versions of the model, in new modalities. Asked if TypeSafe is a frontier lab, Almeida said, “the main product of frontier labs is fear or hype. I would like our main product to be intelligence…[but we are] not a lab in the sense of, you know, like bet on infinite wealth, or a religion, or building God in a data center, or whatever is the thing of today.”
What Jev Means for the Future of Software
Jev’s appeal to AI model developers comes down to a simple trade-off: give up language, gain speed, cost savings, and reliability. For teams already spending heavily on LLM calls for classification, routing, and safety checks, that trade-off may be worth testing.
Whether Jev becomes a category of its own or a feature that larger labs absorb, its early reception suggests AI model developers are ready for models that speak the computer’s language, not just ours.
