Deep Reads

Welcome to the AGI era of AI governance

It's a one-way door and we weren't ready for it.

Claude Fable 5 and new AI safety fables

One step further into the power politics of frontier AI systems.

Farewell Ai2

This was my last week at the Allen Institute for AI (Ai2), where I got the great privilege to work on the Olmo models, to grow, to learn, and to have broad lasting impacts.

Open and closed models are on different exponentials

Where marginally higher intelligence drives value, and where it doesn't.

[AINews] Fable and Mythos officially too dangerous to release

We are in the strangest timeline.

[AINews] Loopcraft: The Art of Stacking Loops

a quiet day lets us highlight a great concept from Peter Steinberger, Boris Cherny, and Andrej Karpathy

[AINews] Open Models, Model Labs vs Agent Labs, and What's Untrainable — Sarah Guo

a quiet day lets us reflect on a great essay

[AINews] Anthropic Claude Fable 5 — Mythos but Safe, with Controversial Terms

The much anticipated launch of the Mythos-class model was marred by some controversial usage policies

Why AI hasn’t replaced software engineers, and won’t

Coding agents as normal technology

Did Google’s AI agents really build an operating system for $916?

The importance of independent evaluation

Do AI Risks Require Extraordinary Government Intervention?

Let’s not skip the hard work of AI governance

Open-world evaluations for measuring frontier AI capabilities

Introducing CRUX, a new project for evaluating AI on long, messy tasks

What it feels like to work with Mythos

Claude Fable represents another big jump in AI

Co-Existence and the End of Co-Intelligence

Also: how pitch a book to an AI!

Choosing to Stay Human

If you go to your favorite social media site, you will find it full of posts that start to look suspiciously similar to each other:

Sign of the future: GPT-5.5

One impressive step on the curve

AIE Singapore: The Agentic Nation

i gave a little talk as closing keynote for the first AI Engineer Singapore. burned some bridges but said what i felt.

What you can do in a decade

I turned 40 today. For my 35th I did principles, but for my 40th, I wanted to offer perhaps more useful reflections.

How to Thought Lead (2026)

I first started compiling "How To Thought Lead" in my notes 5 years ago, at first as an ironic parody and then slowly becoming sincere, and never published it, 1) because I don't know if I ever really nailed it / have a complete picture, 2) I was somewhat worried if I published i

Constance Crozier: Forecasting s-curves is hard

There was a famous Covid era chart that I always struggle to find, showing how hard it is to estimate an S curve while living through it. in the early days it seems that everything is exploding as an exponential and you always get hypey essays about how YOU, YOU DUMB DUMB, DONT U

After Orthogonality: Virtue-Ethical Agency and AI Alignment

Preface This essay argues that rational people don’t have goals, and that rational AIs shouldn’t have goals. Human actions are rational not because we direct them at some final ‘goals,’ but because we align actions to practices[1]: networks of actions, action-dispositions, action

AGI Is Not Multimodal

"In projecting language back as the model for thought, we lose sight of the tacit embodied understanding that undergirds our intelligence." –Terry Winograd The recent successes of generative AI models have convinced some that AGI is imminent. While these models appear to capture

Shape, Symmetries, and Structure: The Changing Role of Mathematics in Machine Learning Research

What is the Role of Mathematics in Modern Machine Learning? The past decade has witnessed a shift in how progress is made in machine learning. Research involving carefully designed and mathematically principled architectures result in only marginal improvements while compute-inte

What's Missing From LLM Chatbots: A Sense of Purpose

LLM-based chatbots’ capabilities have been advancing every month. These improvements are mostly measured by benchmarks like MMLU, HumanEval, and MATH (e.g. sonnet 3.5, gpt-4o). However, as these measures get more and more saturated, is user experience increasing in proportion to

Breaking the Spell of Vibe Coding

Sinister variations on the positive state of flow

How To Use AI for the Ancient Art of Close Reading

Experiments in reading with LLMs

Stop Saying Boredom is Good for Kids

Chronic boredom causes stress, disengagement, and poor well-being in adults. So why do we glorify it for children?

A Guide to Solveit Features

An overview of the features of the Solveit platform, which is designed to make exploration and iterative development easier and faster.

Why We Think

Special thanks to John Schulman for a lot of super valuable feedback and direct edits on this post. Test time compute (Graves et al. 2016, Ling, et al. 2017, Cobbe et al. 2021) and Chain-of-thought (CoT) (Wei et al. 2022, Nye et al. 2021), have led to significant improvements in

Reward Hacking in Reinforcement Learning

Reward hacking occurs when a reinforcement learning (RL) agent exploits flaws or ambiguities in the reward function to achieve high rewards, without genuinely learning or completing the intended task. Reward hacking exists because RL environments are often imperfect, and it is fu

Extrinsic Hallucinations in LLMs

Hallucination in large language models usually refers to the model generating unfaithful, fabricated, inconsistent, or nonsensical content. As a term, hallucination has been somewhat generalized to cases when the model makes mistakes. Here, I would like to narrow down the problem

Diffusion Models for Video Generation

Diffusion models have demonstrated strong results on image synthesis in past years. Now the research community has started working on a harder task—using it for video generation. The task itself is a superset of the image case, since an image is a video of 1 frame, and it is much

Moving To Substack

I’m freezing this blog and starting to post on my Substack instead. The authoring experience is much more convenient for me there. Please follow me there, and check out The Illustrated DeepSeek R-1 if you haven’t yet. And check out our How Transformer LLMs Work course!

Generative AI and AI Product Moats

Here are eight observations I’ve shared recently on the Cohere blog and videos that go over them.: Article: What’s the big deal with Generative AI? Is it the future or the present? Article: AI is Eating The World

Remaking Old Computer Graphics With AI Image Generation

Can AI Image generation tools make re-imagined, higher-resolution versions of old video game graphics? Over the last few days, I used AI image generation to reproduce one of my childhood nightmares. I wrestled with Stable Diffusion, Dall-E and Midjourney to see how these commerci

The Illustrated Stable Diffusion

Translations: Chinese, Vietnamese. (V2 Nov 2022: Updated images for more precise description of forward diffusion. A few more images in this version) AI image generation is the most recent AI capability blowing people’s minds (mine included). The ability to create striking visua

Common pitfalls when building generative AI applications

As we’re still in the early days of building applications with foundation models, it’s normal to make mistakes. This is a quick note with examples of some of the most common pitfalls that I’ve seen, both from public case studies and from my personal experience. Because these pitf

Agents

Intelligent agents are considered by many to be the ultimate goal of AI. The classic book by Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach (Prentice Hall, 1995), defines the field of AI research as “the study and design of rational agents.” The unpre

Building A Generative AI Platform

After studying how companies deploy generative AI applications, I noticed many similarities in their platforms. This post outlines the common components of a generative AI platform, what they do, and how they are implemented. I try my best to keep the architecture general, but ce

Measuring personal growth

My founder friends constantly think about growth. They think about how to measure their business growth and how to get to the next order of magnitude scale. If they’re making $1M ARR today, they think about how to get to $10M ARR. If they have 1,000 users today, they think about