Diogo Almeida, a former OpenAI researcher pivotal in developing ChatGPT, faced disillusionment despite the chatbot’s impressive capabilities. Almeida, who played a key role in creating reinforcement learning from human feedback (RLHF), found that while the technology demonstrated remarkable language proficiency, it fell short in practical applications. "We’ve harnessed incredible potential, yet it remains unhelpful," he remarked to TechCrunch. "Our optimization focuses on human language, but for automation, computers speak a different language."
Two years after departing OpenAI, Almeida founded a startup called TypeSafe AI to address these challenges. Recently, the company unveiled Jev, a novel transformer-based model that diverges from traditional large language models (LLMs). Instead of generating text, Jev produces probabilities, which the team refers to as “calibrated decisions.”
By avoiding text altogether, Jev offers significant advantages: it is fast, cost-effective, and eliminates the problem of hallucinations since users specify outputs in advance. This model’s input tokens are measured in billions instead of millions, making it a highly efficient option.
Developer interest has surged following Jev’s release, with demand temporarily overwhelming the API capabilities. The model stands out in software automation, providing developers a cost-efficient and reliable means to integrate intelligence into their applications. For instance, Pranit Sharma, a software engineer at Vercel, shared that after replacing OpenAI’s ChatGPT Luna 5.6 with Jev, the company achieved results five to eighteen times faster with enhanced accuracy.
Another developer, Nikhil Mudholkar, who serves as CTO at Bryo AI, compared Jev with Gemini for classifying business emails. Although Gemini was marginally more accurate, its cost was ten to twenty times higher. Mudholkar was particularly impressed with Jev’s confidence scores, noting, "It's the only one providing a real probability, making it excellent for workflow automation."
Beyond potentially replacing LLMs in specific applications, Jev can also augment existing systems by serving as a safeguard against inaccuracies. Almeida advocates for its use to monitor LLM agents, suggesting that employing Jev for this purpose is a sensible approach. “This method shifts the hallucination issue partly onto the user’s shoulders,” explained Armin Ronacher, CTO of Earendil, which develops the open-source model harness Pi. He remarked that users can assess the reliability of outputs based on probability scores.
Jev also presents opportunities in model routing, enabling predictions about workload requirements without the high costs associated with LLMs. Almeida's vision for Jev is clear: by lowering the cost of intelligence, it should foster widespread adoption, akin to the early days of the internet rather than the current trend of developing mega apps.
The model takes its name from William Stanley Jevons, a 19th-century economist known for his paradox, which outlines how decreasing costs can lead to increased usage. Almeida contends that this principle applies to intelligent software as well. "We envision smart software becoming ubiquitous, manifesting in a distributed manner, similar to the early internet," he expressed.
While he remains reticent about Jev’s underlying architecture, industry insiders suggest it might be built on a foundation of open-weight LLM technology. The company characterizes Jev as a "System One model," emphasizing intuition over reasoning and fine-tuned for specific tasks. Almeida revealed that Jev is trained exclusively on synthetic data through a method he terms “reinforcement learning from calibrated decisions.”
"Our commitment to generating all of our data has proven to be one of the best decisions I've ever made—better than our launch or even RLHF," he said. "Half of our organization is dedicated to mastering this field of statistically well-understood synthetic data, which has become my passion."
At present, Jev occupies a unique niche, although Ronacher anticipates that other players will emerge now that its practical benefits are evident. "Perhaps we should have recognized this sooner, but the affordability of LLMs has, until now, stifled the need for innovation," he noted.
TypeSafe is committed to developing additional versions of Jev across new modalities. When asked if TypeSafe positions itself as a cutting-edge lab, Almeida responded, "Frontier labs often produce fear or hype. I prefer our focus to be on creating genuine intelligence, steering clear of aspirations to create a 'god' in a data center or chasing infinite wealth."
