new rival to the costly architecture of the frontier labs may be emerging.

Last week, Typesafe AI, a company founded by Diogo Almeida, former OpenAI researcher and co-inventor of the reinforcement learning tactic foundational to ChatGPT, introduced Jev, a model that interacts and delivers outputs to other software, rather than delivering chat responses back to humans.

The company emerged from stealth on Tuesday with $40 million in funding, and its model is available in early access for select developers.

The most notable part of Typesafe's launch is the efficiency gains it claims. The company said that its models are hundreds of times cheaper and faster than the leading models from frontier AI companies like OpenAI and Anthropic, while achieving similar levels of intelligence.

• For example, Jev costs just over 4 cents per million input tokens, roughly 238 times cheaper than GPT-6 Astra and Claude Fable 5.1 at $10 per million input tokens. For outputs, Typesafe says Jev is free because it is "too cheap to meter," compared to $50 per million from the same competitors.
• On speed, Typesafe claims Jev is two orders of magnitude faster than existing models, with an end-to-end response time between 70 and 500 milliseconds, compared to 3 to 329 seconds for existing LLMs.

Jev achieves these gains by not relying on the traditional systems that modern LLMs are built upon. In fact, Typesafe says Jev is neither small nor an LLM, instead replacing "sequential generation with parallel computation," The company said that its model is optimized with a tactic called "Reinforcement Learning for Calibrated Decisions," which answers queries with "epistemically honest probabilities," rather than "programmatically verified" outputs that are written to human preferences.

Additionally, Jev can produce hundreds of outputs in parallel from a single prompt and provide confidence scores for each, giving developers control over when tasks should and shouldn't be autonomous. Typesafe says Jev is best suited for tasks such as AI-powered workflows and real-time applications than for human-in-the-loop or chatbot tasks.

"I spent years working on models designed to make AI better at interacting with people," Almeida said in a statement. "But if AI is going to fundamentally change how work gets done, people can't be the only consumers of intelligence."

Typesafe's debut adds to a growing number of neolabs looking beyond traditional LLMs for efficiency breakthroughs. Another example is Pathway, a company betting on post-transformer architecture to deliver comparable performance at a fraction of the cost and resources of the frontier labs.

Our Deeper View

Typesafe and Pathway both challenge the norms that AI is costly to build and costly to use, and that, because of the value it brings, it is worth the elevated token price tag. These innovations also come at a time when the cost of AI has come sharply into focus for enterprise, with many clamping down on the tokenmaxxing attitude that drove the industry six months ago. Taken together, these shifts are flashing warning signs for frontier labs, who have staked their missions on pure scaling: more money is more compute is more intelligence is more value. With record-breaking trillion-dollar IPOs from Anthropic and OpenAI on the horizon, the question remains whether the industry will seize new innovations or be swept up in the gravity and influence frontier labs have generated.