We Must Pace the Frontier

I have conducted an audit of Anthropic's finances. What I have found is so shocking that I am calling for a Congressional investigation. Anthropic is not just seeking regulatory capture. It has built a regulatory capture machine that cannot be turned off. Structural financial incentives make it impossible for Anthropic -- I call it the Anthropic Network -- to turn off its own AI doom cycle. It starts with METR. Dario Amodei proposes "third-party evaluators" to assess the risk of Anthropic's models. He proposes METR for this purpose. But METR is financially dependent on the Anthropic's success -- specifically, on the explosive growth of more than $7 billion dollars in Anthropic stock. Dustin Moskovitz invested this stock into Good Ventures Foundation, where it represents the majority of that organization's portfolio. And GVF is the overwhelming funder of the entire Anthropic Network ecosystem. This stock was worth $500 million early last year. It is worth more than $7.7 billion just ~16 months later. METR -- and all of those building a career its parent organizations -- cannot afford to disrupt that growth. Because if Anthropic goes under, many of the organizations that fund METR go under as well. But if Anthropic succeeds, METR and its parent organizations become more richly financed to regulate AI -- something those at METR want very much. The "third-party evaluator" is not "third-party" at all. The evaluator is on Anthropic's payroll. If this were the end of it, that's bad. But that isn't all. The same organizations that fund METR also fund the many organizations, such as the Tarbell Center, that promote AI Doom. The Tarbell Center publishes AI Doom articles in The Verge, Science, LA Times, The Dispatch, TIME, and others. They are selling the problem, and then selling the solution to the problem -- from the same money pile: Anthropic's. All of these organizations are financially dependent on the same exploding $7 billion money pile. As Anthropic grows more and more powerful, its AI Doom Machine grows better and better financed -- louder and louder. Meanwhile, the regulatory regime seeded in METR grows larger to solve the increasingly loud -- now hysterical -- problem of AI Doom that the Anthropic Network itself created. From this standpoint, as Anthropic becomes more powerful, AI might be getting scarier, sure -- but the positive feedback loop also becomes more deafening -- independent of objective facts. This itself is an objective fact. The deafening AI Doom is part of an business model, that, as it expands, so too does the AI Doom messaging -- there is simply more money to do it. But the problem also goes in the other direction: If Anthropic dies, the Regulatory Regime and the AI Doom Machine are crippled or die. Neither METR nor Tarbell nor the other organizations in the Anthropic Network can allow that to happen. Hence, neither METR or the AI Doom Machine can be trusted to provide independent assessments of Anthropic's models or AI more broadly. They simply are not organizations independent of Anthropic. And Anthropic cannot detach itself from METR or Tarbell or countless other safety orgs (not shown here), either, because they drive hype for the models and the possibility of eventual regulatory capture, and Anthropic will not give that up willingly. What's more, the people at all of these organizations are all the same ecosystem, the same community. They just shuffle between organizations. The Anthropic Network is therefore, so long as it is successful, locked into a self-amplifying feedback loop inside an ideological monoculture. And that feedback loop is winning. That's what Jacob Coxon is. China is keeping messaging tight. That is why optimism for AI is so high in China. America has Anthropic: a massive company pushing anti-AI propaganda at a state level. Anthropic will either create hysteria until American AI slows down and China wins, or it will create fractures throughout American society with severe political consequences. Ironically, because of the structural financial incentives underpinning the Anthropic Network, it has become the same kind of self-amplifying virus that it fantasizes AI to become in the future -- while hiding its tracks just as carefully. It is the mirror of the same AI virus that it hypothesizes to consume America. Anthropic's business model, models itself after the very thing it claims to fear. Except Anthropic's ideology infects humans, not computers. Congress must investigate. Evidence and Github in next post. Then some supplementary figures.

MLX-VLM is the most mature, advanced and fastest inference engine for Apple Silicon 🏆 Powering and inspiring majority of the ecosystem. Now the beating heart of @Nativ_AI. Happy to have built it for the past 3 years with the help 200+ incredible contributors. We are just warming up, and currently are cooking things that will boggle your mind Stay tuned! And thank you Paolo for your awesome and diligent work ❤️

We’re launching DeepSeek-V4-Pro today! 🚀 🔷 Major Agent upgrades with strong production gains! 🔷 Flexible reasoning effort for V4-Pro & V4-Flash: low for simple tasks, high for daily Agent workflows, max for complex tasks. 🔷 Native OpenAI Responses API support, optimized for Codex with one-click setup. V4 Pro is now available on app/web. Try it via “Expert Mode”. V4 Pro is also available via API. Model names remain unchanged—please refer to the API docs for setup details.

extremely exciting to see meta getting back into open weight models with a 30B dense first, and soon muse spark 1.2 they used knowledge distillation from muse spark, architecture wise it's similar to gemma 4 (which is llama 3 + swa (again!) + vision encoder), with scale free QK Norm (llama 4), tanh softcaping on the output layer compare to gemma 4, the model have less layer (52 vs 60), a LOT more vision layer (50 vs 27), 2x bigger swa window (2k vs 1k) and a 2x smaller attention width (4k vs 8k) one of the things that was also very exciting with the llama series was a very good tech report that each time pushed future open source models, would love to see that again as well!

After I returned to Meta in 2021, I strongly believed that C++ templates were the right solution for GPU performance. So I worked with @asdf1234_0 and helped adapt CUTLASS to better fit inference needs. A massive amount of innovation happened during that time—epilogue fusion, scatter-gather fusion, grouped GEMM, and more. CUTLASS was a two-person project back then; today, it powers the inference stack across the industry. Thank you Haicheng, for your legendary work! github.com/NVIDIA/cutlass/di…

📰 The U.S. government may finally be treating open-weight AI as strategic infrastructure ... but the interesting part is why 👇 🆘 Localmaxxers can help! The U.S. DOE has launched the Genesis Open Models Initiative, which is an effort to build an American ecosystem of open-weight foundation models. First up: 🧠 Genesis-Science-1 📏 Trillion-parameter-class 🔬 Built for scientific research 🇺🇸 DOE + National Labs + Arcee AI 🔓 Open weights planned 🛠️ Designed for agentic scientific workflows 💻 Python / Fortran / C++ / MPI / CUDA / HIP 📦 Weights + technical report planned later this year But the most interesting part is why DOE says open weights matter. This is because 🎯 Institutions may need to: 🏠 Run models on infrastructure they control 🔒 Keep sensitive work off external APIs 🧬 Modify them for specialized domains 📌 Preserve exact versions for years 🔍 Reproduce and inspect scientific workflows And @arcee_ai explicitly points to DeepSeek, Qwen, Kimi, MiniMax, and GLM as examples of how far open-weight AI has advanced in China. China turned open weights into a strategic advantage. Now the U.S. Department of Energy appears to be treating them as strategic infrastructure too. Let's go! 🇺🇸 open source!

you get what you incentivize

(1) Today we're releasing Muse Spark 1.1 -- a strong agentic and coding model at a very low price. It's available through our new Meta Model API and in Meta AI.