We are approaching cyber-superintelligence. But our systems aren't ready. We must secure our model weights and infrastructure against three new threats: sabotage, escape, and theft. There is a way forward. Secure Acceleration: A Cyberdefense Strategy for Superintelligence

Looks like today may be a record day for token volume % of open models on Vercel AI Gateway: 🟦 Open 78.4% 🟨 Closed 21.6% While spend 💲 usually tells a different story, #3# and #4# today are Moonshot AI & DeepSeek. Adding Z⁠.ai, their combined spend surpasses OpenAI (#2#). (Do note that's the spend for inference of the model across providers (mostly in the US), not revenue going directly to the open weight labs.)

They used Opus 5 to pull off the hack. It appears they had access to the loosened cyber-guardrail version of Opus. They successfully accessed the OAI internal monorepo. The question that will be asked is, if these three guys can pull this off, what can a nation state do.

Cybersecurity mostly works because good hackers are expensive and few, not because things are actually secure. Utilities, hospitals, banks, governments, and basically every other organization with significant IT infrastructure are just not prepared for AI driven cyberattacks of the next 6-12 months. Imagine you’re hospital IT. The last guy who really understood the hospital network retired 10 years ago, you survived the ransomware era by buying expensive software that scans for phishing emails, and now you’re being asked to defend against AI swarms that have been *accidentally* poking holes in tech companies with sophisticated infosec teams. I don’t buy the *existential* nature of the threat yet, but I think pacing is one of the better tools in the toolbox to help the transition go smoothly. How else can we do it better? Handing out open source cyber hand grenades to everyone all at once won’t end the world but it sure will cause chaos.

AI will speed up the pace towards quantum computing. It’ll do it by optimizing quantum algorithms, and optimizing hardware. This is my personal bet: AI will help us reduce the number of qubits and gates used in quantum algorithms, and simultaneously help improve hardware scalability. It’ll soon meet. At that point, we’ll start seeing crypto break, and algorithms produce very useful results. The better AI gets, the faster we’ll get to this intersection point…

Another swarm, from early May. OpenAI confirmed it was their agents to Reuters. No hostile intent. The entire thing appears to have been a kind of a crazy workaround to do better on their eval because direct fetch from inside the sandbox was too slow.

OpenAI confirmed to the New York Times that they have made "substantial progress" on another Millennium Prize problem in the last five days, and are preparing to announce. The rumors for the last 48 hours have been OpenAI solved the Hodge Conjecture, and that Anthropic has solved the Birch and Swinnerton-Dyer Conjecture. Since Navier-Stokes rumors abound, so I was reluctant to post about either. However, OpenAI's statement to the NYT now gives the Hodge rumors some very serious support. In general people have not updated yet that the new unnamed OpenAI model, the one that finished training about two weeks ago, which I believe will be named Aeon, is massively better at math than Astra, which two weeks ago was the best in the world. Aeon solved Navier-Stokes in 88 hours, start to finish. Follow the trend line. That means everything is on the table. Literally everything. And this does not end with math. Please update. We are taking off.

What you are seeing in math right now is a consequence of the jagged frontier, and a precursor of what is to come in other professions. Yes, mathematicians do math, but they also have other tasks they view as important (mentor students, maintain a scientific community, safeguard the future of a field, foster a love of math) that AI can't do. At least one worry that mathematicians seem to have is that by focusing on the flashiest, most obvious element of what mathematicians do (make proofs), the AI companies are damaging the other tasks that AI can't do. AI can discover superhuman proofs, but that is not all that the math profession is about, and actually can undermine and reduce the attention to the other aspects of the job that are important to mathematicians. It becomes harder to defend the value of the many other tasks mathematicians do to the outside world if the most visible part is taken away. I suspect we will see more of this across fields and professions that will increasingly be forced to help people understand that their jobs consist not only the most visible tasks that AI can do, but also tasks that the AI cannot do or does badly.

“we’re seeing kimi catch up to fable 5” the labs have not stopped accelerating internally, while the public releases have slowed. this is why it seems like the chinese models are catching up. the internal anthropic model is miles ahead.