This new AI model isn't an LLM. It's 20x cheaper and plays Doom in real time (Jev explained)
TypeSafe just launched Jev, and it is not an LLM. It is a "System 1" model: it does not generate text, it answers in about 100 milliseconds, and it costs $42 per BILLION input tokens with free output. Allie Laabs from TypeSafe explains how it works, then we stress test it live. In this video: what a System 1 model is, the Choice / Score / Noul primitives, why it is 238x cheaper on input than GPT-6 Astra by TypeSafe's own price chart, the Doom demo running in real time, and what we built with it in 48 hours, including a tweet classifier doing 12 tweets a second. 00:00 "All of my life's work for under $40" 00:16 Allie Laabs joins: the Jev launch 01:25 What is a System 1 model? 02:48 Live demo: classifying 12 tweets a second 04:18 20x cheaper than the fastest LLM I can find 05:43 How it works: not streaming tokens 11:52 Why Jev will never generate text 13:24 Coining "System 1 models" 14:58 Jev for real-time computer use (Cua) 16:21 The primitives: Choice, Score, Noul 21:49 Noul and determinism 26:40 Jev plays Doom in 100 ms 30:19 Co-hosts react 34:18 Hallucinations and limits 36:45 $42 per billion tokens, output free Guest: Allie Laabs, DevRel at TypeSafe AI Jev: https://typesafe.ai This is a segment from ThursdAI, the weekly live AI news show, September 17, 2026. 🔗 Subscribe to our show on Spotify: thursdai.news/spotify 🔗 Apple: thursdai.news/apple 🔗 Youtube: thursdai.news/yt And for the full show notes and links visit 👉 sub.thursdai.news 👈
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Chronologie de l'épisode
TypeSafe Labs launches JEV, a new class of System 1 model, after two years in stealth.
- TypeSafe Labs, co-founded by Diogo Almeida (a co-creator of RLHF and ChatGPT), emerged from stealth after two years to launch JEV, a new class of model they call a System 1 model.
- The name System 1 comes from Daniel Kahneman's Thinking Fast and Slow, referring to fast intuition rather than the slow deliberative reasoning of LLMs.
- JEV is priced at roughly $42 per billion input tokens, with output tokens considered 'too cheap to meter,' a pricing model that is orders of magnitude cheaper than typical LLM pricing.
Alex demos a real-time JEV-powered tweet classifier running on his X timeline.
- Alex built a browser extension that scores every tweet on his timeline with JEV in real time, processing about 12 tweets per second at essentially zero cost.
- JEV is 4-5 times faster than a fast model like Qwen running on Cerebras LPU hardware and about 20x cheaper, enabling a 'real-time cognitive firewall' for social feeds.
- Because JEV returns probabilities rather than generated text, categories appear before an LLM could even begin responding, enabling use cases like semantic ad blocking.
Ali explains the core innovation: machine-to-machine AI without a human interface translation layer.
- Ali argues that using LLMs to call tools is wasteful because it translates machine code into human interface language and back again, like using a bipedal robot instead of a dishwasher.
- JEV exposes latent AI intelligence as machine-readable, type-safe probabilities and typed objects, enabling machine-to-machine use without ever returning to user space.
- The training approach is called reinforcement learning for calibrated decisions (RLCD), doing parallel processing of all questions at once rather than generating text character by character.
Concepts clés
- System 1 model— TypeSafe's new class of model inspired by Kahneman's fast-thinking concept, designed for real-time decision making rather than text generation.
- JEV— The first public System 1 model from TypeSafe Labs that outputs calibrated probabilities instead of generating text.
- TypeSafe Labs— The stealth startup behind JEV, co-founded by Diogo Almeida, a co-creator of RLHF and ChatGPT.
Citations remarquables
I think that Jev can categorize all of my life's work in terms of the text that I outputted as a human with less than 40 dollars.
🤯— A lifetime of human writing can be processed by JEV for under $40, revealing an almost absurd gap between human effort and machine cost.
It's so weird to me that we are using these, like, big old language models to create the text or the instructions to interact with an interface that was already on the other side of the interface is machine code.
💡— Overturns the assumption that LLMs are the natural way to automate tasks, exposing the wasteful translation layer between human language and machine code.
Actions à entreprendre
🧠AI Architecture
System 1 models like JEV represent a new class of AI that outputs calibrated probabilities instead of generating text, enabling machine-to-machine communication without a human-language translation layer.
This week, identify one task in your workflow where you currently use an LLM to generate text instructions for a machine, and sketch how a probability-based approach could replace it.
JEV's three primitives — choice, score, and null — let developers define questions in natural language and get structured, comparable, nearly deterministic outputs.
Sign up for TypeSafe's API and build a small prototype using the choice primitive to classify a set of text samples you already have.
💰Cost Optimization
JEV is priced at $42 per billion input tokens with output tokens free, making it roughly 20x cheaper than comparable models and enabling use cases that were previously cost-prohibitive.
Calculate the monthly cost of your current LLM-based classification or moderation tasks and compare it to JEV's pricing to see if you can eliminate that line item.
Real-time inference under 100 milliseconds opens up applications like live content filtering, game AI, and interactive tools that were impossible with slower LLMs.
Prototype a real-time classifier for a live data stream you work with (e.g., chat, social media, logs) using JEV and measure the latency.
La transcription et les insights sont générés par IA et peuvent contenir des erreurs. La précision dépend de la qualité audio et de la clarté des intervenants — en cas de doute, l’audio original fait toujours foi.
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