For medical information, general AI frontier models (Google, OpenAI, Anthropic) outperformed specialized @EvidenceOpen and @UpToDate as assessed by 12 US clinicians, randomized and blinded to which model and extensive testing/benchmarks. This was not anticipated. @NatureMedicine nature.com/articles/s41591-0…

Today is the first time our Intelligence Frontier chart has moved backward.

The US government, citing national security authorities, has issued an export control directive to suspend all access to Fable 5 and Mythos 5 by any foreign national, whether inside or outside the United States, including foreign national Anthropic employees. The net effect of this order is that we must abruptly disable Fable 5 and Mythos 5 for all our customers to ensure compliance. Access to all other Claude models is not affected. We apologize for this disruption to our customers. We believe this is a misunderstanding and are working to restore access as soon as possible. Read our full statement: anthropic.com/news/fable-myt…

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The release of Anthropic's Mythos-class Claude Fable 5 is the latest signal that we are in a phase of exponential growth in AI capabilities, "takeoff mode". The biggest leaps are in engineering and scientific reasoning: frontier models now match or exceed expert-level performance on many technical tasks, and increasingly act as collaborators that plan, code, simulate, and design (as we show in our own research on self-improving agentic discovery systems). Understand these technologies deeply is critically, both the foundations and how they're applied to critical industrial problems at scale, and to use them to drive innovation and technology development. This July 27–30, I will teach Applied AI for Materials Discovery at @MITProfessional (live online so you can participate from anywhere). It's a hands-on deep dive into the shift from predictive ML to agentic, closed-loop AI-native discovery and innovation. Highlights: ▶ AI scientists & recursive self-improving swarm intelligence: massively parallel agents that read literature, formulate hypotheses, write and run code, and critique each other's work ▶ Generative AI for inverse design: diffusion and flow matching for proteins, alloys, metamaterials, and crystals ▶ Foundation models that "think" physics: graph transformers, neural interatomic potentials, neural operators and PINNs ▶ Bridging the reality gap across scales: connecting atomic-scale agents to physics simulators and product-scale (DFT, MD, FEA) for automated verification of AI-generated designs ▶ Building custom reasoning models: fine-tuning, RL; incorporating first-principles physical agency (e.g. MCP, tool use) ▶ Unlocking dormant knowledge: turning unstructured data (papers, patents, lab notebooks, legacy PDFs, etc.) into structured, actionable insight ▶ Interpretability, reliability, and enterprise deployment The course will provide you with ready-to-use agent templates, dozens of code notebooks, repos, and curated datasets you can deploy immediately in your organization. More details on the course and registration link, see reply.