TypeSafe AI, a startup bestowed with $40 million in funding, on Tuesday declared itself a maker of frontier models with the release of Jev, a new type of AI model intended for interaction with machines rather than people.

Instead of returning natural language in response to a text-based prompt, TypeSafe AI designed Jev to return typed probabilistic decisions for use by other software or AI models.

Type safety in programming is a way to catch errors that arise when software processes an unexpected type of data, as might happen if an operation tried to divide an integer by a text string. By providing typed, structured values, Jev can avoid the parsing and validating that must be done to process text responses from LLMs.

This approach can be useful for scenarios where interactions with an AI system need to be constrained to a limited set of answers. It can, for example, play Doom, when fed structured data describing the player's game state. But it's more likely to be used for sorting customer service problems and other business workflows.

With Jev, the developer starts with a state value, which can be a JSON object or simply a string like "My card was charged twice." The model gets presented with state information through a set of question primitives (Choice, Score, Noul), each of which returns a different type of structured response with probabilities.

So a question asking the model to choose which of three departments should handle a particular customer service query might come back: {"billing": 0.08, "technical": 0.85, "sales": 0.07}, with a confidence score of 0.82.

That's not very useful for a person seeking the answer to a question but for a software developer, that might be just the thing to make a particular customer service function run more reliably.

Jev is a System One model, which relies on a different architecture called Reinforcement Learning for Calibrated Decisions (RLCD). Diogo Almeida, co-founder and CEO of TypeSafe AI, is a former OpenAI researcher and one of the co-inventors of reinforcement learning for human feedback (RLHF) and ChatGPT.

"TypeSafe was founded to pursue an alternative path for AI research, focused on machine-native AI," said Almeida in a statement. "I spent years working on models designed to make AI better at interacting with people. But if AI is going to fundamentally change how work gets done, people can't be the only consumers of intelligence."

While natural language responses from the likes of ChatGPT or Claude can be wrapped in other code to produce a constrained set of responses, Jev's appeal follows from its efficiency.

TypeSafe claims that LLMs generally have variable response times that can stretch into minutes. Jev's response time supposedly ranges from 70ms-500ms, 40x-200x faster than traditional LLMs.

The model achieves this speed by using parallel processing. LLMs like OpenAI's GPT-5.6 Terra predict the next text token in a sequence and thus operate sequentially. The System One architecture returns all outputs to a query at once. 

The demo posted on the TypeSafe website shows Jev returning a response in 0.114s, compared to 8.566 seconds for OpenAI's GPT-5.6 Terra.

Then there's the issue of cost. Jev charges $0.042 / MTok for input and $0 for output. OpenAI GPT-5.6 Terra costs $2.00 / MTok and $12 / MTok for output. And compared to a top tier model like Fable 5.1, Jev costs 238x less.

The name "Jev" is a reference to 19th century English economist William Stanley Jevons, known for Jevons paradox, who argued that technology efficiency related to coal usage increased coal consumption rather than reducing it. The name also reflects the AI industry's bet that greater token efficiency will increase token consumption even as token prices decline. 

That wager assumes the market for tokens will be as broad as the market for energy – an assumption that isn't yet settled given that many people who have access to AI tools just don't have a use for them or avoid AI tools due to moral objections.

TypeSafe claims that Jev is hallucination-free, which really isn't a fair comparison as its output is not natural language. Jev instead returns structured responses with probabilities, and that does not preclude the possibility of being incorrect. TypeSafe's model won't be the source of misinformation like fabricated legal citations but it also won't be used in the same way since its responses are intended to be consumed by software.

Where this becomes relevant is when handling AI agent tool calls and automation.

"Having a hallucinated tool call is inconvenient in an agent, but is an absolute deal-breaker if it’s part of a system with latency guarantees or it’s buried several layers deep in a dependency chain," the company claims in its blog post. "Existing models, no matter how smart, still hallucinate and have type errors."

Jev, TypeSafe claims, is well-suited for AI automation software, real-time applications that need quick decisions, AI map reduce jobs for classifying large corpora of data, verification of AI inputs, and AI model harnesses.

And if it can play Doom as well and as efficiently as advertised, it may not be long before users ask Jev to make drone targeting decisions. ®