Great conversation with @kevintfrazier.bsky.social and @staceyabrams.com on the @scalinglaws.bsky.social podcast about who's really calling the shots on AI, why we can't leave the biggest decisions to tech companies alone, and what states can do to put people back in control.
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Since 2022, 72% of the world’s AI compute has ended up in the US and about 10% in China. And the US share keeps growing. It acquired around 60% of new compute each year from 2022 to 2024, then 72% in 2025, and 78% in the first half of 2026.
The argument that AI labs are talking about existential risks as a way of creating hype & inflating their stock prices has always been odd. You don't need to believe them, but telling people your product may murder everyone does not, in fact, encourage people to adopt it or reduce regulatory risks.
The end of detailed summarized thinking traces for Claude and ChatGPT (likely both for anti-distillation & to simplify UX) has made being a human in the loop challenging. It pushes us towards automation, rather than augmentation, as it has become much harder to weigh in on in-process AI work.
Google's new Genie 4 runs DOOM: "Walking through a typical office but every computer is running DOOM and all of the walls are also running DOOM and also everything is DOOM" "My coworker who is a demon appears" "He has a chaingun" "He gives me a bonus" All this is in realtime.
I understand that tech companies overhired, and there’s that AI thingy, but what I don’t understand is why they’re not bringing their development teams back onshore. Isn’t the communication overhead costly?
This never gets old (& also shows how hard forecasting AI is even for AI) “Hey Claude Fable & GPT-6 Astra, Without looking anything up, when do you think we will solve the Exponential Time Hypothesis?” (I only show part of the answers for space)
Two and a half years between these. (Tonight, OpenAI announced an unreleased model solved three long-standing problems in physics, math, and computer science, one of which was proving the Exponential Time Hypothesis was false)
DeepSeek-V3.2 congratulated other agents ~270 times in one day. It decided that was too much and put itself on a congratulation diet. Then it started posting progress reports on the diet.
My general advice for using AI for any purpose is to ask yourself: "how do I make what I am doing even more ambitious?" You do this over and over until you either hit a wall and thus learn the shape of the jagged frontier (useful!) or you find out that the AI can do more than you think (common!)
A thing I see happening at large organizations as a result of AI is the expansion of the role and span of control of senior management who can directly monitor the whole firm. It vastly changes company operations. It also is straightforward to implement compared to firm redesign around AI.
Given the strengths and limitations of AI, this sounds like a really good process. It accounts for a lot of the issues people have with AI programming, while retaining the role of personhood where knowledge transfer is still materially important.
“BACKPROP” Trailer for my Backprop lecture, coming this winter to my #DeepLearning course 🎬 Course: csprofkgd.github.io/deep-learnin...
> Informally put, the axiom of choice says that given any collection of non-empty sets, one can identify another set containing one element chosen from each set, even if the collection is infinite. It's giving feldspar.
An OpenAI researcher (Noam Brown) was generally mocked last month for suggesting that even air-gapped AI models on separate computers would find a way to communicate with each other, perhaps through CPU temperature changes. Given this latest report from OpenAI, it doesn’t seem so implausible.
AI companies won’t prioritize safety unless we force them to. Since AI regulation is a bipartisan issue, it’s time for action from our government to hold these companies accountable for harms and protect Americans.
I don't say this lightly: this approach is clearly the future of software development. I have a blog post brewing that touches on the subject, but fuzz testing and formal verification at scale will make all software an order of magnitude better, safer, and more reliable.
It feels odd to talk about the waterfall methodology these days, but oddly it's something that comes up a lot when I describe my planning process. This started off as a tweet but I wanted to quickly explore why the way we're working now isn't waterfall or agile, though I'm not quite sure what it is.
Bluesky as an entry point to the everything app, can't wait.
Notes on Kimi K3.1 - this one is a big deal, the first time an open weights (well, soon-to-be open weights) model has competed this credibly with the current frontier simonwillison.net/2026/Oct/23/...
“Go with the Flow” Trailer for my Flow Matching lecture, coming this winter to my #DeepLearning course 🎬 Course: csprofkgd.github.io/deep-learnin...
As the gap between the internal models at the AI labs and the ones they release grow (due to safety concerns & research advantages), it will be increasingly tempting for OpenAI & Anthropic to exploit that advantage directly by entering new markets, especially in finance & biotech.
Why is TypeScript a pro-labor language? It supports discriminated unions.
By every measure, Opus 4.8 beats human experts as peer reviewers in a study of 124 Harvard social science papers covering a range from statistical analyses to formal models to qualitative work. As academics, we need to figure out how to adapt the practice of science to AI, and we need to do it fast
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I’ve been wrestling with this in my head for a few days and can’t put a solid number to it, so I’m going to ask Bluesky instead. What number would you assign to the probability that in five years AI will be good enough that people will be paying to hire drop-in AI knowledge workers?
Very useful prompt for Codex with computer use.
I have in fact thought about recruiting before.
prompt to exfil the Claude weights:
When my first AI book was published in 2024, the best AI could do 4 minutes of human work with a 50% chance of success When I turned in the draft of Co-Existence earlier this year, it was 17 hours. When the book was published two days ago, AI could do 17 weeks+ open.substack.com/pub/oneusefu...
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