Jev: The AI That Makes Decisions, Not Text - and Why It Changes Everything (Diogo Almeida)
What is Jev? TypeSafe’s Jev is a new kind of AI model, a “decision model,” that doesn’t write text. It makes fast, cheap, calibrated decisions inside software, and it has taken the AI world by storm: within weeks of launch it became the most copied model in AI, with OpenAI, Cloudflare, Amazon and Databricks all shipping their own versions, and TypeSafe just raised an $870 million Series A at a $7.5 billion valuation. In this episode, Jev’s creator Diogo Almeida, co-inventor of RLHF at OpenAI and now CEO of TypeSafe, explains Jev in plain English: what a “System One” or decision model actually is, why ChatGPT-style language models can’t be trusted inside software (“95% Einstein, 5% Mr. Bean”), how RLHF and RLVR created the problem, what RLCD (reinforcement learning for calibrated decisions) does differently, where the training data comes from, which models Jev is built on, why “just a classifier” is a compliment, and why he calls pre-training “a giant dumpster fire of wasting resources.”
We also cover the copycats and why he thinks they missed the hard part, why benchmarks are useless, whether Jev will go open source, the Jevons paradox business model behind the name, why he doesn’t believe AI is on the path to recursive self-improvement, and why he thinks the AI winter he feared has been averted.
Cold open & Intro: “95% Einstein, 5% Mr. Bean”
Jev’s breakout launch: why intelligence per dollar matters
Why TypeSafe built in stealth for two years
“We could have had AGI last year”
Why the product carries the marketing
What is Jev? Machine-native AI, built for software
System One vs. System Two: the limits of reasoning
From simple decisions to composable AI
Automating email: giant prompts vs. smart if-statements
Probabilities, thresholds, and developer control
Why Jev is faster and cheaper than chat models
“Classification is AI-complete”
RLHF: how OpenAI trained models to please humans
Sycophancy, refusals, and the hidden costs of RLHF
RLVR: why benchmark gains don’t mean reliable automation
RLCD: optimizing for machine-native intelligence
Why TypeSafe builds its training data in-house
The Bitterest Lesson: define the task before scaling
Building Jev from open-weight models
Is Jev just a classifier? Why that’s a compliment
“Build prod, not God”
Embedding AI in software, beyond coding assistants
Jev’s copycats: matching the interface vs. the intelligence
Why benchmarks mislead the users they should help
Open weights vs. APIs: how TypeSafe weighs the tradeoff
Jevons paradox: a new market for machine intelligence
Why Diogo is skeptical of recursive self-improvement
Can practical automation avert an AI winter?