Jev, an AI for making quick decisions, has been a viral hit in Silicon Valley. But OpenAI is hot on its heels

Jev, an AI model built for quick decisions, went viral among AI developers and Silicon Valley cognoscenti when the startup TypeSafe AI released it in September.

These early adopters were quick to experiment with the AI model, which could perform all kinds of classification decisions—like sorting online customers into buckets based on certain data points or categorizing documents—at a fraction of the cost of many leading AI models. Programmers showed it sorting email, spotting writing mistakes and even playing games.

Within a day of arriving on Vercel’s AI Gateway, a service for accessing different models, nearly 13% of its paid teams had tried Jev. Vercel said Jev reached more than twice as many paid teams in its first 24 hours as any previous model launch.

Perhaps the clearest sign that TypeSafe was on to something: on Oct. 6, just three weeks after Jev’s launch, OpenAI rolled out a competing product, called Decisions API .

These services are competing to make AI practical for the thousands of routine judgments businesses might want to automate each day. At that volume, small differences in cost, speed and error rates can change whether automating a task is worthwhile. TypeSafe trained Jev specifically for this work. OpenAI’s Decisions API uses GPT-6 Luna, an existing model in its lineup.

An AI model that skips the prose

TypeSafe CEO Diogo Almeida told Fortune he began thinking about Jev after helping develop the technology behind ChatGPT at OpenAI. Almeida saw a gap between models’ ability to answer people’s questions and their usefulness in automating routine work. Almeida said he wanted to address AI’s “massive over-promise under-deliver issue” and avert an “AI winter.”

“Building a company just happened to be the most effective way to do that,” he said.

Before joining OpenAI in 2020, Almeida took a year and a half off to travel, meditate and play games. He says he reached “top-2 in Hearthstone,” the digital collectible card game, an accomplishment he joked might have been harder than building Jev.

Almeida has said he had tried to work backward from a future in which AI had transformed the economy. In that future, would most requests to models come from people, or from code running automatically? He concluded that the vast majority would come from code.

A customer-service program, for example, could ask a model whether an incoming message concerns a billing issue or a technical problem, then use the answer to route it to the appropriate team.

Developers could set a threshold for the model’s confidence about its routing decision. Above that threshold, the message would be routed automatically to billing or technical support. Below it, and it would be flagged for a human to check..

This sort of classification task is something that rudimentary AI, or machine learning systems, have handled for decades. Computer science students are often taught to build these classifiers in machine learning courses. Email services have long used these simple models to filter spam.

A model can estimate how likely a message is to be spam, and the software can move it to the junk folder when that estimate crosses a threshold set by its developers.

The problem with these simple models is that they tend to be relatively inflexible, without much understanding of a document’s text, and they require some technical know-how to create.

In some ways, large language models (LLMs), which are what today’s best known AI systems are based on, represented an improvement on these simple classification systems. LLMs can perform classification tasks too, sorting customer-service messages based on written descriptions of categories such as billing questions or technical problems. They have a much deeper understanding of the text they are processing, so can make finer-tuned decisions.

Developers can also change the LLM’s instructions to use the same model for other jobs. So that makes these systems more flexible than the old classification models.

But LLMs take more computing power to process these decisions, which makes them both more expensive and slower than the old, simple machine learning classifiers. When the LLM has to handle thousands of these requests throughout the day, the cost and delay of each call add up.

TypeSafe says Jev is designed to handle these judgments faster and more cheaply than general-purpose language models, but with the same ability to set up the classifier through easy, natural language instructions that LLMs offer. Developers describe what they want Jev to evaluate, and Jev returns choices, scores or probabilities their software can use. The company says it produces those outputs together, avoiding the time spent generating an answer piece by piece.

Developers still have to establish whether its judgments are accurate enough for their particular task.

Almeida initially thought making the models behind Jev reliable would take him a week. Instead, TypeSafe spent two years developing Jev. “Making the