If you truly believed in a 10% chance of human extinction in 3 years, you would not be spending your time selling and integrating it into a B2B CRM SaaS platform Watch what they do, not what they say

We built high-throughput materials labs in Menlo Park to create a loop between experiments and models. The labs generate fresh data, the models learn from it, and then help us decide what to try next. Using only 1,300 H200s, plus months of our experimental data, we mid-trained and RL’d an open-source model to surpass GPT-6 Astra on our analysis benchmark. We call it Neon. This is real footage from our lab. We’re focusing first on hard problems in materials science, including superconductors, magnets, and semiconductor materials. Read our blog posts below.

fwiw I think the actual reason leaders of AI frontier labs suddenly seem to agree to pace AI development is (a) safety measures are currently so crap that they're likely to end up in court if not jail, and they all agree that no one wants that (b) they see no major new model advancement coming up soon anyway and need an excuse (c) shift in public opinion the talk of existential threat is mostly there to keep you distracted and the stock market happy.

The world deserves confidence that American companies developing increasingly capable AI will act responsibly, especially as the trajectory of progress has steepened. Every frontier lab must deliver on this, and there is no reason any of us should come to work if we cannot. We welcome a federal framework that sets consistent safety requirements for frontier AI. But we do not believe we need to wait for an anti-trust exemption or legislation to begin the work of providing this confidence. Consistent rules to manage frontier risk so that we can maximize the benefits are a good idea (and we are excited by ideas like independent auditors). Years ago, companies like ours developed things like Responsible Scaling Policies and Preparedness Frameworks. Those were good for that moment, and focused primarily on the deployment of completed models, not what happens during their development process. Today's shift to focusing on safe development and evaluation will need new tools. For example, at OpenAI we now formulate explicit safety cases in advance of frontier reinforcement learning runs we expect to significantly increase capability, in addition to the safety work we have long done in advance of model releases. We hope that other companies will learn from our approaches and propose their own; we think shared standards for misalignment, monitoring, and safety will lead to better outcomes. We look forward to collaborating with our colleagues across the industry to formulate the best version of these. When we talk about “pacing”, we do not mean “stopping”. Progress has been rapid and will continue to be. But it should be slower than it otherwise could be; interventions like safety cases and monitoring have significant costs. Pacing will be well worth this cost; no amount of American competitive pressure should justify recklessness, or let capabilities get ahead of alignment and monitoring. Where we will need the help of our government is for international coordination. But first we should do what we can ourselves.

We’re launching AlphaGenome Atlas: an AI-powered searchable database mapping the predicted impact of all 9 billion possible single-letter DNA changes. Here’s how it could help researchers better understand our biology 🧵

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.

Three 🥑 in a row from Muse Spark 1.1 to 1.3 within two months. Huge momentum of the team pushing on the model capability especially on coding, agent, instruction following, and long context capability. Try it out at developer.meta.com/ai/models… and let us know what you think.

What happened when GPT 5.6 Terra and Luna were heavily discounted on OpenRouter? Token usage exploded by 13.8x Jevons Paradox = as technology makes the use of a resource more efficient, total consumption of that resource increases rather than decreases 🧵

given the $$$B investments the industry over all is using gpus rather stupidly.

The 1.5T that could! Bigger and even better models on the horizon.

Announcing Discovery Loop! I am very excited to announce that, along with my longtime friends and collaborators @Sanjay_Ghemawat, @OriolVinyalsML and @quocleix, we are founding Discovery Loop (@DiscoLoopAI), a Public Benefit Corporation whose mission is to automate machine learning, science, and engineering to accelerate discoveries and progress. The four of us have worked together for 14 to 30 years, and have helped build some of the world’s most used products, infrastructure and AI models, and we’re excited to turn our attention to this ambitious endeavor. ♾ Learn more at: discoveryloop.com

💻 Meet Qwen-CUA — our native computer-use agent for (almost) everything. Code, APIs, and computer use are three of the most important interfaces for agents. Today’s models are already highly capable with the first two. Qwen-CUA is built to unlock the third: graphical interfaces designed for people. 👀 Native Perception — screenshots only. No DOM, accessibility tree, or other hidden machine-readable state. 🖱️ Native Interaction — keyboard and mouse events across browsers, desktop apps, and professional software. No task-specific APIs. 🧠 Native Intelligence — maintains long-horizon visual context, verifies progress, and learns from large-scale interactive experience with verifiable outcomes. To make native computer use trainable at scale, we built approximately 40K verifiable tasks and rollout infrastructure with nearly 100K vCPUs, supporting tens of thousands of concurrent environments. Across eight computer-use benchmarks spanning everyday desktop use, long-horizon workflows, personalized computing, scientific research, web interaction, macOS, and adversarial robustness, Qwen-CUA demonstrates strong and broadly competitive capabilities. Scaling the same recipe to Qwen-CUA-Max pushes this frontier further. Not just “clicking the screen” — native computer use unlocks software and workflows that previously required a human at the keyboard. Together with code and APIs, it completes the interface stack for more general agents. Joint work by Qwen Team × XLang Lab. 📖 Technical Report: github.com/xlang-ai/Qwen-CUA… 💻 Code: github.com/xlang-ai/Qwen-CUA