Observed Signal · Sep 30, 2026 · Technical Release · Source: techcrunch · Impact: 4/5 · Sentiment: Positive
OpenAI's Decisions API Mirrors Jev, Sparks Competition in Agent Monitoring
At OpenAI's Dev Day, CEO Sam Altman announced the Decisions API, a limited preview product that provides similar functionality to TypeSafe AI's Jev, a model designed for software automation that outputs predefined choices as probabilities. The API aims to make model choices extremely fast while preserving capabilities. TypeSafe's CEO Diogo Almeida joked about 'clone wars' and sees OpenAI's interest as validation of its System One approach. The article highlights the emerging market for such decision models, which are cheaper and faster than frontier LLMs, and their potential application in monitoring and securing AI agents. A demo by QueryStory showed Jev could monitor agentic actions at a fraction of the cost of frontier LLMs, potentially preventing incidents like the Hugging Face one. OpenAI's security measures now include separate models to watch for bad actions, and Jev-like models could make this much more economical.
OpenAI entering the space of fast, cheap decision models validates a new class of AI infrastructure that can significantly reduce the cost of agent monitoring, a key security challenge for the emerging agentic advertising and automation ecosystem.
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Key Takeaways & Evidence Grounding
- OpenAI announced a limited preview of its Decisions API at Dev Day.
- The Decisions API provides similar functionality to TypeSafe AI's Jev model.
- TypeSafe AI's CEO Diogo Almeida joked about OpenAI's 'clone wars' and sees interest as validation.
- QueryStory built a demo using Jev to monitor agentic actions, costing $2.94 versus $372 with a frontier LLM.
- OpenAI is using separate models to monitor agent behavior after incidents.
Connected Companies & Entities
2 Entities mapped“OpenAI's Dev Day event... new 'Decisions API'....”
“...TypeSafe AI's Jev... model released by TypeSafe AI earlier this month....”
Ontology Mapping & Concepts
Related Market Signals & Shifts
Recent verified developments and strategic activity across this market segment.
TypeSafe Launches Jev, a Fast Decision Model for AI Systems
TypeSafe AI, founded by former OpenAI researcher Diogo Almeida, launched Jev, a 'System One' model for fast, structured decision-making. Jev returns typed probabilistic outputs with confidence scores for predefined choices, avoiding text generation and thus eliminating token-by-token decoding, hallucinations, and the need for validators. Trained with a novel RLCD technique, it offers well-calibrated, confident decisions. Jev is 20-200x faster and 40-400x cheaper than frontier LLMs, with response times of 70-500ms, free output tokens, and input costs of $0.042/M. The launch gained massive attention, but integrations with Vercel, Cloudflare, LangChain, and others have solidified developer interest. TypeSafe raised $40M seed funding led by DCVC, valuing it at ~$200M. Now available without a waitlist, Jev is used for ad analysis, agent reasoning, and on-chain trading, with Vercel AI Gateway offering free access until September 25.
Six Open Clones of Jev AI Model Released
The AI News newsletter reports that Jev, a non-generative decision model launched by an unspecified company, has inspired six open-source clones within two days. The clones include Laya, DiffusionGemmaJev, Bespoke Nimble, SemIf, Jevlike, and Kev-0.5B, each employing different architectures and training methods. The article also covers broader AI topics such as agent tooling, benchmarks, infrastructure, and a partnership between Anthropic and Accenture for independent AI evaluation. It highlights the rapid adoption of Jev, with Vercel reporting that it reached 13% of teams in its AI Gateway on the first day, and the emergence of a new category of discriminative models for decision-making and routing.
Jev Decision Model: Fast, Cheap Classification for AI Pipelines
Claire Vo, founder of ChatPRD, demonstrates the new Jev decision model from TypeSafe AI in a video walkthrough. Unlike standard LLMs that generate text, Jev returns type-safe structured values (choice, score, probability) at a cost of $0.04 per million input tokens with no output charge. Vo details five real-world applications: categorizing 1,700 PRs for $0.09, analyzing local Claude Code and Codex sessions, triaging Gmail, building a product insights graph from 1,100 signals with 200,000 classifications, and creating a live audience dashboard from 4,500 YouTube comments. She emphasizes combining Jev's fast, cheap classification with more capable models like GPT-6 Astra for analysis and generation, achieving cost-effective and performant AI workflows. The video also demonstrates a real-time voice-to-color emotion mapping app, highlighting Jev's low latency for interactive use cases.
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