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TypeSafe AI offers System One Models for machine-native AI, focusing on calibrated decisions and cost-efficiency over traditional LLMs.
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TypeSafe AI is pioneering a new generation of AI models, termed "System One Models," designed for machine-native applications rather than human interaction. This approach diverges from traditional LLMs optimized for chat through Reinforcement Learning from Human Feedback (RLHF), which TypeSafe AI argues leads to issues like mode dropping, overconfidence, and unreliability. Instead, TypeSafe AI focuses on Reinforcement Learning for Calibrated Decisions (RLCD) to create AI that is reliable, fast, and type-safe, producing typed outputs that software can act upon.
TypeSafe AI highlights extreme cost-efficiency, with Jev priced at $0.000081 per Cost, $42 per Billion input tokens, and offering a 238x lower input price than models like Claude Fable 5.1. These prices are presented as significantly lower than traditional LLMs.
System One Models are a new class of AI models developed by TypeSafe AI, specifically designed for making decisions within software systems, prioritizing reliability and machine readability over human-like conversation.
Jev is TypeSafe AI’s first public System One Model. It is optimized for automation and provides typed decisions with associated confidence probabilities, enabling software to act autonomously or escalate when necessary.
Unlike LLMs that might be prompted to output JSON, System One Models are natively designed to produce typed, structured decisions with calibrated confidence, aiming for greater reliability and direct software actionability.
TypeSafe AI attributes the speed and cost-efficiency to a new architecture, sampler, and training algorithm (RLCD), focusing on decision-making rather than generating human-like text.
Yes, while designed for reliability, Jev provides confidence estimates for each decision. This allows software to manage uncertainty, escalating to human review when confidence is low, thus mitigating the impact of potential errors.
Users can interact with Jev by sending structured questions and receiving typed decisions. The website provides links to documentation and a console for interaction.
