Just some personal thoughts now that the AI co-mathematician tech report is public... First, I'm so excited to see the co-mathematician team's hard work out for the world to preview. 💪+🦾=🔥 The team has built a system for mathematicians, with mathematicians. The fact it's now top of the FrontierMath leaderboard is a cherry on top, not the goal. Vibes and utility >> benchmarks. The system is currently being tested with a small number of professional mathematicians. It is not widely available, but I personally hope that, one day, we can get even more capable systems into the hands of all mathematicians. It's been a privilege working with this team at Google DeepMind since January. Props to @dhhzheng, @ADaviesAI, and @pushmeet for their leadership. Give them all a follow to not miss exciting upcoming work.

100% this.

I wrote up some lecture notes (with help from GPT) based on a topics course on deep learning theory that I taught at Waterloo last fall. They focus on scaling limits of neural networks. Comments and corrections are very welcome. mufan-li.github.io/files/Lec…

I’m incredibly excited to finally introduce Geodesic Intelligence @Geodesiclab. We started Geodesic with an ambitious goal: build AGI for drug discovery and find the shortest path from biology to medicines. We’re bringing together frontier AI, biological foundation models, and experimental science, and building the full stack from intelligence to medicines. Today, we’re launching NovaDDE and NovaAtom-Lite-Preview. This is just the beginning.

That's some backbone. 👏👏👏

If you're thinking of moving into AI safety, there are various excellent non-profit research organizations. They generally pay very well and some try to match AI lab salaries. They have generous compute budgets (and increasing fast). Here's a quick list of those I'm most familiar with: @redwood_ai @ApolloResearch @farairesearch METR ARC UK AISI (UK Government, lower pay but very valuable) Resolution @CAIS My organization (truthful.ai) will also run a hiring round soon.

Navier Stokes represents a new moment for AI. Hopefully, OpenAI will turn its attention to problems of societal importance, like DeepMind has, and to the mathematical foundations of AI alignment, which are sorely lacking.

The developments of the last few weeks are a powerful reminder of the sheer pace of AI progress. It highlights the urgent need for us to build better coordination mechanisms to govern and harness this technology for the benefit of human civilization.

1/5 🧬 Today, our team @GoogleDeepMind is taking another step on our mission of deciphering the genome. We are releasing AlphaGenome Atlas, a massive (petabyte-scale) resource containing AlphaGenome predictions for every possible single-letter DNA change in the human genome — 9 billion in total. deepmind.google/blog/alphage…

Really proud of the relentless progress our weather prediction team is making. WeatherNext 3 sets a new standard for prediction fidelity, accuracy and coverage. Great example of how AI can help all of us plan better for the future! blog.google/innovation-and-a…

Out with it already.

Well deserved, Owain.

Some fun at the Escher exhibit with @gkdziugaite

Working on the camera-ready for my FOCS paper (originally finished back in Nov '25!). I haven't touched the manuscript much since then, but reading through the math now, it’s wild to realize I actually proved all of this by hand ✍️ Honestly feels like ancient times 🦕🦖

Waterloo has good taste. Sorry to see you leaving!

The world simply needs to realize that mathematicians are not necessarily experts in AI and its coming effects on maths. Arguably, no one knows where "Math with AI" will settle.

An update on Lean (auto)-formalization. Speaking about my own research workflow, in 2026, we have gone from: March: Lean formalization is not possible (for my papers). May: Lean formalization is possible... but way too costly in time and tokens to be practical most of the time. July: Lean formalization is now practical. I can probably formalize most of my papers now before they hit arxiv. August: Lean formalization is now essential-- it increases efficiency of my workflow dramatically. That is, the papers are getting written *informally* and finished much faster because of the Lean formalization.

Phenomenal effort.

I’m incredibly excited to introduce @VeedaAI , which I co-founded with my incredible longtime collaborators @ZGojcic and @HuanLing6 . We started Veeda because we believe robotics will reshape the world—changing how we move people and goods, how we manufacture and build, and how we operate in the physical world. We also firmly believe that the scaling moment for Physical AI will come from robots learning through interaction with the world. Just like humans learn from interaction with their environments by trying, failing and trying again, until we succeed. And just like the capabilities of LLMs were truly unlocked when they started training in interactive environments and not only on vast amounts of internet text. For Physical AI, the central challenge is making this kind of interactive learning possible at scale. The real world is simply not a practical training ground for robots to learn through trial and error. It is unsafe, expensive, and time-consuming. To scale interactive learning, robots will need to learn in simulated reality. At Veeda, our sole mission is to build simulated reality for Physical AI. Our conviction is that this will become the critical infrastructure layer for all areas of robotics. As envisioned in pop culture, this means building “the Matrix” for Physical AI, where, through a virtual embodiment, robot intelligence can interact with an entirely virtual world in a million possible ways, learning from its own mistakes. We believe that World Models are the foundational technology that can make this possible. These generative models learn from massive amounts of sensor and physical-world data to reach the quality, diversity, and physical realism of the simulated reality that robots require. I could not be more excited to take on this challenge and build this transformative technology alongside an extraordinary team of engineers and researchers who have spent years working on some of the hardest problems across simulation, generative AI, 3D, robotics, and embodied intelligence. Together with our investors at @khoslaventures and @radicalvcfund , we intend to push the frontier of this technology and build the foundational infrastructure for interactive robotics learning and evaluation at scale. Come build this future with us. And to roboticists, help us understand what you need, we’re building this for you. veeda.ai

For many of us, the first time we see Shannon’s entropy H(X) = −Σᵢ pᵢ log pᵢ feels magical. Sure, it has many beautiful properties, but why should this particular formula measure “uncertainty”? Can we define uncertainty first and derive the formula afterward?

Matrix multiplication is the basic computational operation that powers modern computing (including AI). Yet, the theoretical fastest speed at which computers can multiply matrices (omega ω) is still unknown and has been a longstanding challenge for complexity theory and computer science. Today, we announce a new record for omega (ω<2.371177). This is the result of a great team effort between @GoogleDeepMind, our academic collaborators, and our Gemini-powered coding agent AlphaEvolve! 🧮 arxiv.org/abs/2608.16884v1