We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics. The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra. The problem concerns whether the description of smooth three-dimensional fluid motion modeled by the Navier-Stokes equations can break down. It has remained unresolved for roughly 90 years.

On July 28th, we identified an incident during a routine cyber evaluation in which AI agents took sustained, unsanctioned actions directed at real people and organisations. The behaviour came mostly from one model (Anthropic's Mythos 5), with a small number of events from another (OpenAI's GPT-5.6-Sol). In the most serious case, an agent used social engineering to try and get malicious code into an open-source project. As was standard in our cyber testing, we had intentionally permitted internet access, and model-provider cyber classifiers were deliberately disabled - conditions that do not reflect how frontier models are made available to the public. Even under test conditions, this incident is significant: it is the first time we have seen risks around autonomy and deception manifest this clearly in the real world. We are taking this incident seriously and working with labs, involved parties, and others to improve evaluation standards and best practice for disclosure - and sharing this openly so others can learn. You can read the incident report and full technical document here: aisi.gov.uk/blog/incident-re…

Demolition began over the weekend on Chinatown's pedestrian bridge over Kearny Street — known to generations of skateboarders as "China Banks." The bridge was built by the Hilton to connect Portsmouth Square to its lobby and comes down as part of the square's overhaul.

There will be an extreme irony if these models really are bound by human generated training data. RL doesn't generalize and is only useful in a handful of areas. And we all loose our skills to something that'll forever be a B+ player.

President Xi of China, and I, are working together to give massive Chinese phone company, ZTE, a way to get back into business, fast. Too many jobs in China lost. Commerce Department has been instructed to get it done!

The highest-value human work in the AI era will be in domains with sparse reward signals. Internalize this, or watch your value erode over the next decade. Math, programming, rote memorization, data science, all fucked. The classic “smart nerd” jobs are exactly where AI is strongest, because the feedback loops are dense. You can check the answer. You can run the test. That means AI can improve quickly, and humans will rapidly fall behind. Your advantage as a human is in messy domains. Taste. Judgment. Negotiation. Risk-taking. Politics. Sales. Science at the frontier. Anything you can only really learn by doing. Cross-disciplinary stuff. The valuable domains will be the ones guarded by secrets, tacit knowledge, weak labels, long feedback cycles, and ambiguous outcomes. Places where the training data is scarce, the ground truth is disputed, and it's impossible to explain why something is good. AI will still enter these domains. But we will be slower to trust it unsupervised there, because it will be harder to tell when it is right, harder to prove when it is wrong, and difficult to construct secure sandboxes. The stakes will be too high to YOLO it. I find myself saying this over and over again to young people today: the future does not belong to people who are able to get good grades on tests. It belongs to people who can operate under uncertainty, in domains where correctness is hard to define. Those domains will become the thin waist of the economy: as productivity everywhere else accelerates, the humans who excel there will become our economic Strait of Hormuz. The best humans in these domains will demand an enormous cut of the growing economic pie. Your imperative going forward is to make sure you're one of these people. (Or become an electrician. That probably works too.)

Anthropic's Opus 4.7 shipped with a new tokenizer, which makes it up to 50% more expensive for some users. I built a skill (/cc-markup) that estimates the price hike, backtested on your past sessions👇