# ChatGPT inventor's new AI model skips language entirely > Diogo Almeida left OpenAI to build Jev, a model that outputs probabilities instead of text, making AI cheaper, faster, and hallucination-free for software automation. - Title: ChatGPT inventor's new AI model skips language entirely - Summary: Diogo Almeida left OpenAI to build Jev, a model that outputs probabilities instead of text, making AI cheaper, faster, and hallucination-free for software… - Keywords: diogo almeida, typesafe ai, jev model, openai, large language models, reinforcement learning from human feedba, software automation, ai model architecture, synthetic data training, model routing, hallucination prevention, technology - Source: TechCrunch — https://techcrunch.com/2026/09/18/a-new-kind-of-ai-model-from-a-chatgpt-inventor-is-thrilling-developers - Author: Tim Fernholz - Published: 2026-09-18T18:49:30+00:00 - Read time: 2 min - Topics: diogo almeida, typesafe ai, jev model, openai, large language models, reinforcement learning from human feedba, software automation, ai model architecture, synthetic data training, model routing, hallucination prevention, technology ## ChatGPT inventor's disappointment Diogo Almeida helped build ChatGPT and RLHF, but felt the technology wasn't useful for automation because AI was optimized for human language, not computer tasks. > We have lightning in a bottle, and yet it is not useful. ## TypeSafe AI launches Jev Two years after leaving OpenAI, Almeida founded TypeSafe AI and released Jev, a transformer model that outputs probabilities instead of text, eliminating hallucinations entirely. ## No text output means no hallucinations Because Jev outputs predefined probabilities rather than generated text, it cannot hallucinate. Users define outputs in advance, making the model reliable for automation. ## Dramatically cheaper and faster Jev's output tokens are free and input tokens cost per billion instead of per million. Demand was so high the company briefly lost API availability. ## Vercel sees 5-18x speed gains Vercel replaced OpenAI's ChatGPT Luna 5.6 with Jev for safety classification and got results 5 to 18 times faster with greater accuracy. ## Confidence scores enable automation Jev returns real probability scores that developers can use to decide whether to act on results. Bryo AI found Jev 10-20x cheaper than Gemini for email classification. > it is the only one that hands back a real probability which makes it ideal for automating workflows!! ## Monitoring AI agents affordably Jev can monitor other AI agents to prevent jailbreaks and misbehavior. Using expensive LLMs for this task is costly, but Jev makes agent-monitoring economically sensible. ## Model routing becomes practical Jev can predict which workload requires which model in real-time. This routing task would be expensive with LLMs but is now feasible with Jev's low cost and speed. ## Named after an economic paradox Jev is named after William Stanley Jevons, whose paradox describes how falling costs lead to increased use. Almeida hopes cheap AI will be deployed everywhere. ## Trained on synthetic data exclusively Jev uses reinforcement learning from calibrated decisions on synthetic data. Almeida calls this bet better than RLHF and says half the company is a lab focused on this technique. > Half of [our company] is a lab that basically owns this entire subfield of statistically well-understood synthetic data ## Competitors will follow soon Jev currently stands alone, but observers expect competitors to emerge now that its utility is proven. LLM subsidies have discouraged this kind of innovation until now. > We should have seen this earlier in many ways, but presumably because the LLMs are so cheap and subsidized, you often don't have to be creative yet ## Key takeaway By abandoning natural language output in favor of calibrated probabilities, Jev achieves 5-18x faster performance and lower costs than LLMs for specific automation tasks.