TypeSafe launched Jev because sequential LLMs are “totally useless for computers”

When TypeSafe emerged last week after two years in stealth, backed by $40 million in seed funding led by DCVC, to launch its first model, Jev, it claimed something that counters just about everything the industry has built since ChatGPT: The model doesn’t write. It decides.

The first of what the organization calls a new class of System One models, Jev is a text-only model that machines can use natively to make decisions inside software applications. Developers can send Jev structured questions and get typed decisions with calibrated probabilities, meaning software can account for uncertainty.

TypeSafe has built a new architecture for Jev, a new sampler (an algorithm that selects tokens from a model’s predicted probability distribution to control randomness, creativity, and consistency), and a new training algorithm known as Reinforcement Learning for Calibrated Decisions (RLCD).

Sequential LLMs are totally useless for computers

Co-founder and CEO of TypeSafe, Diogo Almeida, is ex-OpenAI, where, according to TypeSafe, he co-invented RLHF and InstructGPT, the methods behind ChatGPT and GPT-4.

Almeida posted on X on September 15 to state, “The improvements are clear if you see them [LLMs and Jev] side by side. Ask a System One model a ton of structured questions just like you would an LLM. Get the answers back near instantly. Meanwhile, LLMs take hundreds of times longer to respond. Look at how the LLM generates sequentially, which is great for a natural conversation, but totally useless for computers.”

After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI?

I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev

• 20-200x faster

• 40-400x… pic.twitter.com/JSybNG2BKJ

— Diogo Almeida (@CompleteSkeptic) September 15, 2026

“Look at how LLMs generate sequentially, which is great for a natural conversation, but totally useless for computers.”

Almeida said the inspiration for Jev came from asking himself: why haven’t superhuman chat models led to artificial general intelligence yet? He said that Reinforcement Learning from Human Feedback (RLHF) chat has led to LLMs that are “optimized for human preferences” and include issues such as mode dropping, overconfidence, and an overall lack of reliability.

TypeSafe: System One models can’t hallucinate

Almeida’s launch post lists headline stats for Jev as 20-200x faster, 40-400x cheaper (with output tokens free), and frontier composable intelligence, optimized for decisions. Almeida further claimed that TypeSafe System One models output decisions with probabilities and confidence instead of words, and they “can’t hallucinate” because they’re “a lot more like code”, i.e., reliable, fast, self-consistent, and type-safe.

Frontend cloud company Vercel has noted that, within 24 hours of launching on AI Gateway, “Jev from TypeSafe AI reached more than twice as many paid teams as any previous model launch, making it the fastest-adopted model in gateway history. Jev passed every other comparison model in its first twelve hours and continued to widen its lead for the rest of the day. By hour 24, nearly 13% of paid teams were using it. That’s 2x the GPT-5.6 family and more than 6x Fable 5.1’s share.”

Developers can set thresholds for when Jev acts autonomously vs. when it needs human oversight and review. They can then combine those decisions in code to build larger workflows, with control over how the intelligence is used. Software engineers can use Jev to select an agent’s next tool or subagent; they can also use it to confirm the veracity of a model’s output and set guardrails.

In a blog post titled “A deep dive into Jev, TypeSafe’s System One model,” independent software developer Flavio Copes noted that Jev is “not a chatbot” like ChatGPT, and it is not a coding model. It does not write replies, explanations, or code.

Jev is a smart if statement

“The simplest way to describe it: Jev is a smart if statement,” wrote Copes. “The important difference is where the AI sits. With ChatGPT or a coding agent, the AI is the main interface or worker. Jev is a small component inside a regular application. You add it where code needs one judgment, while the rest of the product stays ordinary code.”

“You add it where code needs one judgment, while the rest of the product stays ordinary code.”

Copes reiterates TypeSafe’s stated performance levels: most calls to Jev complete in about 100 milliseconds, input tokens cost $0.042 per million, and (as already noted) output tokens are free.

You send it some data and a list of typed questions, and it sends back one answer per question: a yes/no probability, one option picked from a list you defined, or a position on a scale you defined. Every answer comes with probabilities.

One developer gave Jev the “one thing it can’t handle”

To put Jev to the test, AI engineer Bartosz Mikulski tells The New Stack that because Jev is advertised as a text-only model, he gave it the one thing it can’t handle: pictures.

“I turned 400 hand-drawn sketches into Scalable Vector Graphics (SVG) coordinates and asked what they were,” Mikulski says. “It got about 35% right, where just ‘guessing’ typically returns 10%, but it answered ‘airplane’ for more than half of the drawings, so that number is part real ability, and part a heavy bias toward one label.”

To be fair, Mikulski notes that TypeSafe says in its own documentation that Jev reads text only and handles words better than numbers.

“I fed it numbers that encode pictures, which is close to the least fair test anyone could design, and it still beat chance by a wide margin. I mean that as a compliment, not as a benchmark. It tells you nothing about how Jev does on the text classification it’s actually sold for,” Mikulski clarifies.

“I fed it numbers that encode pictures, which is close to the least fair test anyone could design, and it still beat chance by a wide margin. I mean that as a compliment, not as a benchmark.”

Why is TypeSafe Jev called Jev?

Jev is named after the 19th-century economist William Stanley Jevons and his Jevons Paradox: the economic principle that as technology increases the efficiency with which a resource is used, that resource’s total consumption actually rises rather than falls.

When steam engines became more efficient, we used more coal, not less; when LED lighting dropped lighting costs, we used more lighting; when data compression algorithms lowered the bandwidth needed to stream video, global web data traffic increased… and so on.

Almeida concluded his X video post with a nod to developer productivity and said that, “As we say at TypeSafe, we’re building prod, not God.” The company’s comedy disclaimer is shown below.

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