Welcome! I write about rationality, coordination, and AI. I’m particularly interested in the coordination challenges associated with AI safety.
This is what takeoff feels like. Anthropic and OpenAI have been explicit about their intention to create an intelligence explosion, and employees at both companies have recently confirmed that their models are significantly accelerating their own development.
This week we’ll talk about what that means, considering the trajectory of future progress, our increasing inability to measure the capabilities and risks of the frontier models, and some ideas for how humanity can successfully navigate what is coming.
A Closer Look at the “Societies of Thought” Paper
Today I’m going to take a deep dive into an intriguing paper that just came out: Reasoning Models Generate Societies of Thought by Junsol Kim, Shiyang Lai, Nino Scherrer, Blaise Agüera y Arcas and James Evans. Here’s how co-author James Evans explains the core finding:
“These models don't simply compute longer. They spontaneously generate internal debates among simulated agents with distinct personalities and expertise—what we call "societies of thought." Perspectives clash, questions get posed and answered, conflicts emerge and resolve, and self-references shift to the collective "we"—at rates hundreds to thousands of percent higher than chain-of-thought reasoning. There's high variance in Big 5 personality traits like neuroticism and openness, plus specialized expertise spanning physics to creative writing.”
First, an administrative note: I’m starting to write longer pieces on specific topics. I’ll link to them in each week’s newsletter, but you can subscribe to them directly if you like.
We have so much to talk about this week. The internet is taking a break from losing its mind over agents to instead lose its mind over Moltbook (social media for robots, but also much more and much less than that). Dario Amodei has an important new piece about the dangers of AI, and not everyone is happy about it. Lots of people have interesting thoughts about Claude’s Constitution. And lots more—so much more.
Wearable AI Pins: I’m Skeptical
AI-focused personal devices are back in the news: OpenAI has announced that they’re working on some new AI-focused devices with Jony Ive and rumor has it that Apple is working on something similar.
I love gadgets and I love AI, and I’m very open to the idea that an AI-first device would look very different from anything that currently exists. But I’m deeply skeptical about the pin form factor.
The big news this week is that Anthropic has published Claude’s Constitution (previously known as the soul document). It’s very very good and I expect there will be a lot of commentary about it once folks have had a chance to read and digest it.
We also have some very interesting new interpretability work to unpack, a couple of interesting pieces about the politics of AI, a nice summary of the arguments in If Anyone Builds It, Everyone Dies (and the main counterarguments), and much more. And of course lots of news about agents, which people are still losing their minds over.
This week’s newsletter goes deep on two specific topics. We start with AI and employment: will AI be like past technological revolutions that changed our jobs but didn’t eliminate them, or are we headed for permanent mass layoffs? Next, we’ll do our best to keep up with the breakneck progress of Claude Code and other coding agents.
The AI news doesn’t slow down just because we have a new special interest, so we’ll also check in on how AI forecasters performed last year, assess the environmental impact of AI, review how to pick the best model for the job, and much more. Oh, and we’ll talk about how to understand and manage burnout. That seems pretty relevant right now.
People continue to lose their minds about Claude Code. We’ll begin this week’s newsletter with a look at what people are using it for and where they think it’s headed. Here’s my short take: Claude Code’s present usefulness is 30% overhyped. A lot of the amazing things people are reporting are genuinely amazing, but they’re quick working prototypes of fairly simple tools. But…
Sometime in the past couple of months, AI crossed a really important capability threshold. By the end of 2025, it was clear to any programmer who was paying attention that our profession has completely changed. By the end of 2026, I think that same thing will be true for many professions. Most people won’t realize it right away, and it may (or may not) take a few years for the changes to really take hold, but the writing is now very clearly on the wall.
Happy New Year! It would be silly for me to wish you an uneventful year, but I hope most of your surprises are good ones.
We begin this week’s update with our final roundup of year-end retrospectives. After that we’ll get to a new (and somewhat lengthened) timeline from the AI-2027 team, gaze in wonder at the state of the art in image generation, hear a beautiful but heartbreaking story about AI-related job loss, and contemplate the possibility of a war over Taiwan.
On paper, this was a quiet week: there were no major releases, and no big headlines. Online, though, there’s been a big shift in the vibe since the release of Opus 4.5 a month ago. It’s now undeniable that AI is transforming programming, and it feels increasingly likely that the same will happen to all other knowledge work before too long. We’ll check in with some industry leaders to see how it feels in the trenches.
But that’s not all—we review the latest evidence of accelerating progress, gaze upon the wreckage of once-proud benchmarks, and try to figure out what to do about AI-related job loss. And shoes! If you’ve been wanting more fashion reporting in these pages, today is your lucky day.
As 2025 draws to a close, we look back on one of humanity’s last “normal” years with Dean Ball, Andrej Karpathy, and prinz. We have lots of AI-assisted science news including a big new benchmark, a look at AI in the wet lab, and a new startup working on emulating fruit fly brains.
Lest we get too carried away with holiday cheer, UK AISI reports on rapid growth in dangerous capabilities, Windfall Trust notes early signs of labor market impacts, and Harvey Lederman meditates on automation, meaning, and loss. Plus lots of political news, a few new models, and much more.
It’s the time of year when people start publishing retrospectives—we have a great review of Chinese AI in 2025, an in-depth review of technical developments, and a report on the state of enterprise AI deployment. Stand by for more of these over the next few weeks.
If you’re looking for data, we have overviews of on when prediction markets think AGI might arrive (hint: soon) and safety practices at the big labs (hint: not great). Plus AI crushes another major math contest, some guidance on integrating AI into education, and lots more. But let’s ease into it with a fun conversation about model psychology.
First, some housekeeping: I’ve started Monday Brief, which is a shorter and less technical version of Monday Radar. You can get the email newsletter here if you’re interested.
There was only one big new release last week, but there’s still lots to catch up on. We’ll look at a couple of new metrics from CAIS and Epoch as well as progress reports on AI-powered science, coding productivity, and autonomous cars. Plus some great pieces on cyberwarfare, the vibecession, alignment, and AI companions.
This week’s most interesting news is Claude’s “soul document”, which Anthropic used to train Claude on ethical behavior. There are so many facets to this story including how the document was discovered, what this tells us about Claude’s ability to introspect, and the complexities of codifying ethical behavior in the real world.
We also have a deeper look at Opus 4.5, plenty of political developments, some fascinating but troubling papers on safety and alignment, and a guide to giving money to support AI safety.
Welcome to the first issue of Monday Radar. It’s been a busy week, with significant releases from all three of the big labs. We also have deep dives on the bleeding edge of AI productivity, AI scientists, challenges with controlling even well-aligned AI, and much more.