Diogo Almeida：Jev模型与RLCD训练范式深度访谈

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We have an unusual relationship with today’s guest: for years since coauthoring the InstructGPT paper, Diogo Almeida had been saying that API-available frontier models have been going down the wrong path, everything from the alignment to refusals to reliability perspectives, that we have dropped every mode other than autoregressive chat-tuned LLMs because of the overwhelming success of ChatGPT.

In a launch video now viewed ~40M times (by comparison, GPT4o was 22M, Fable 5 was 15M, Navier Stokes was 74M, and 6 Astra was 137M), Diogo introduced Jev and it immediately took over the AI timeline — we’ll skip full Jev explainers because our favorite AI influencer/educator has probably already done one. We also collected:

• the official patterns and cookbooks you should see first, from Allie
• Jev usecases
• speed based - games and computer use
• the voice + computer use example we discuss at 1h34 mins
• voice + browser control
• The must not miss Doom demo
• Driving cars in games
• Excalidraw
• virtual try-ons
• “Smart Games”/smart NPCs
• guided responses in text messages
• Jev for coding agents has an official guide
• jev for linting
• compacting tool calls
• reasonable pushback from Theo - Diogo has published a note on the Tyranny of the KV Cache that you should read as a followup after the pod for Jev + coding agents, because of his belief that Cache Rules Everything
• Programming Languages built atop Jev (Diogo’s fave)
• Jev for analytics replay and user journey review
• “dark data”
• entity resolution
• natural language search
• “smart software”
• a core goal of Jev is to “disappear into the background” - eg as unremarkable as regex
• Jev as a judge
• Jev memes
• Jev vs LLM capabiltiies
• blending transformers and classifiers
• about the confidence api
• Jev vs GLiNER (note difference/pushback, agreed, agreed, agreed)
• Jev on trolley problem
• Jev Bush

Instead we’ll focus on what we can uniquely offer — a broader philosophical and mission-based understanding of how and why Jev was created, and what you should expect next in terms of future models from TypeSafe (ReasoningJev?) and what usecases and ideas you should work on vs the 55th low effort clone of Jev’s API or doing a generic JevBench benchmark - something Diogo has rejected publicly.

Why RLCD: Three kinds of RLHF, and why they are ALL the wrong north star

Diogo knows a good deal about RLHF, given that he was on the team that pioneered post-training at OpenAI — and traces the three branches to Christiano et al 2017 (the robot backflip demo), Stiennon et al 2020 (learning to summarize) and his baby, Ouyang et al 2022 (InstructGPT). From there on, every innovation from Function Calling to Structured Outputs to Reasoning felt like a hack on top of the string based, sequence to sequence prediction paradigm. As he mentions on the pod, from 2023-2024 he struggled unsuccessfully, due to both personal and organization underestimation, to train a model that

Jev’s core innovation is "Reinforcement Learning for Calibrated Decisions”, a novel, unpublished technique that optimizes for “answers with epistemically honest probabilities on System One tasks” rather than human rated feedback (RLHF) — which causes hallucinations, sycophancy, and permanent reliance on humans — or programmatically verifiable outputs with rubrics (RLVR) — which solves Navier Stokes but exacerbates jagged intelligence and doesn’t integrate well with other software.

We’ve talked about the calibration problem before on the pod, but probably the single best place to understand why RLCD became necessary is Diogo’s AIE talk, which discusses why a generation of training helpful AI assistants for humans has impaired them for training models for composable, programmable AI for automation.

At the end he also teases his contrarian opinion on scaling laws - which teases how to build a modern neolab without the billions of dollars the major labs have…

The Bitterest Lesson: Tasks and Data beats Compute

We spend a good amount of time discussing Diogo’s essay on the Bitterest Lesson:

His point is that “You get what you optimize for and the bitterest lesson in ML is that the most important part of it isn’t ML at all.” - and picking the right north star, eg upvoting for user preference vs being integrated into tool calls - makes everything else fall in line.

We’re excited to catch up with a freshly dyed Diogo to discuss:

• Why AI can solve extraordinarily hard problems but still fail to automate basic work
• What System One Models are and why Jev is built for software rather than chat
• RLHF, mode collapse, calibration, and the hidden costs of optimizing for human preferences
• Why refusals become a problem when AI is buried inside software dependencies
• Why TypeSafe rejects public benchmarks and optimizes for intelligence per dollar
• The “bitterest lesson”: why the right task and the right data can matter more than compute
• Why TypeSafe thinks of itself as a data lab rather than a model lab
• RLCD vs. RLHF and RLVR as fundamentally different North Stars for AI
• Why reliability and robustness matter more than simple determinism
• Jev’s programming primitives and how intelligence maps into software control flow
• Why developers should decompose AI workflows into small, measurable decisions
• How structured state replaces giant prompts and system messages
• Why Diogo thinks AI should eventually disappear into the background of software
• The “inverse SaaS-pocalypse” and how AI could supercharge existing software
• System One vs. System Two intelligence and the limits of reasoning models
• Dark data, computer use, real-time intelligence, and Jev’s biggest early use cases
• Why Jev could reshape coding agents built around a single-model architecture
• Why Diogo says he wouldn’t pre-train with $1 billion
• The OpenAI journey that led to TypeSafe and why he thinks many neo-labs are approaching AI incorrectly
• Coding agents beyond the KV cache, shared state, sub-agents, and the multi-agent future

Timestamps

00:00:00 Jev Launch Week and the AI Economic Revolution

00:02:50 What Is Jev? System One Models and Programmable AI

00:05:54 RLHF, Mode Collapse, Calibration, and Yann LeCun

00:10:29 Programmatic AI, Refusals, and Safety Alignment

00:17:21 Why TypeSafe Rejects Public Benchmarks

00:20:43 The Bitterest Lesson: Data, Compute, and the Right Task

00:24:59 RLCD vs. RLHF and RLVR

00:28:42 Why Powerful AI Still Hasn’t Automated the Economy

00:39:55 Reliability, Robustness, and Determinism

00:48:11 Model Versioning, LTS, Speed, and Intelligence per Dollar

00:54:04 Inside Jev’s API and Programming Primitives

00:58:28 How to Build with Jev: Structure, Decomposition, and Small Decisions

01:18:28 The Inverse SaaS-pocalypse and AI Disappearing into Software

01:33:21 Computer Use, Dark Data, and Jev’s Biggest Use Cases

01:38:48 How Jev Could Reshape Coding Agents

01:41:00 AI Safety, Frontier Pacing, and the Limits of RLVR

01:48:03 Why Diogo Wouldn’t Pre-Train with $1 Billion

01:55:19 The OpenAI Story Behind TypeSafe

02:01:41 Why Diogo Thinks Most Neo-Labs Are Getting AI Wrong

02:08:00 Coding Agents Beyond the KV Cache and the Multi-Agent Future

Transcript

Introduction: Jev Launch Week and Developer Momentum

Swyx [00:00:00]: Okay, we’re in the studio. A special occasion because this week, Diogo, my good buddy, launched Jev, and it’s been taking over the complete timeline. How do you feel? What’s it like to be you right now?

Diogo Almeida [00:00:16]: Emotionally?

Swyx [00:00:17]: Yeah.