US Warns 35 Partner Countries to Choose Between Pax Silica and China's WAICO AI Coalition

The U.S. Department of State is preparing formal diplomatic notices instructing 35 partner nations to select between Washington's AI alliance and Beijing's competing framework. According to a draft cable reviewed by Reuters and reported by The Decoder and CNBC, the U.S. warns that countries joining China's newly established AI initiative will be excluded from the U.S.-led Pax Silica coalition. The diplomatic draft states: "To be part of everything is to be part of nothing. Signature of the Pax

2 min
US Warns 35 Partner Countries to Choose Between Pax Silica and China's WAICO AI Coalition

The U.S. Department of State is preparing formal diplomatic notices instructing 35 partner nations to select between Washington's AI alliance and Beijing's competing framework. According to a draft cable reviewed by Reuters and reported by The Decoder and CNBC, the U.S. warns that countries joining China's newly established AI initiative will be excluded from the U.S.-led Pax Silica coalition.

The diplomatic draft states: "To be part of everything is to be part of nothing. Signature of the Pax Silica Declaration is not merely a membership subscription, but a commitment."

The Dual Coalition Landscape

Washington established the Pax Silica initiative in late 2025 to align allied policies across critical AI layers, including semiconductor manufacturing, energy access, critical mineral supply chains, and frontier model deployment. Roughly two dozen partners have joined the framework, including Japan, South Korea, Australia, the United Kingdom, and the European Union.

In July, Chinese President Xi Jinping announced the World Artificial Intelligence Cooperation Organization (WAICO), positioning open-weight Chinese models from developers such as Alibaba, DeepSeek, and Zhipu AI as open alternatives to proprietary U.S. foundation models from OpenAI and Anthropic.

Tensions escalated after Kazakhstan, a key producer of critical minerals used in semiconductor and clean energy manufacturing, joined both Pax Silica and China's WAICO framework. The draft State Department letter was prepared for the 35 nations that signed a U.S. AI Opportunity Statement in June, making clear that dual membership is unacceptable to Washington.

AI Supply Chain and Coalition Map

Strategic Chokepoints and Model Access

The diplomatic friction reflects a widening divide between closed, proprietary Western AI infrastructure and Chinese open-weight architectures. Under Pax Silica, participating nations gain preferred access to U.S. compute investments, advanced silicon allocations, and joint research channels, while agreeing to stringent export security standards.

China's embassy in Washington responded to reports of the letter by stating that pressuring countries to pick sides slows global artificial intelligence development and harms international cooperation. Meanwhile, Beijing has evaluated potential export regulations on frontier open weights to maintain strategic leverage over non-aligned nations.

Sources

Written by

More to read

  • Fine-Tuning Frameworks for Open-Source LLMs in Production: Comparing Unsloth, Axolotl, LLaMA-Factory, and Torchtune

    Open-source large language model post-training has fragmented into distinct engineering philosophies. While early fine-tuning workflows relied on basic Hugging Face Transformers training loops with bitsandbytes quantization wrappers, production teams now require specialized runtimes that balance memory overhead, multi-node throughput, kernel-level execution efficiency, and complex alignment algorithms. Four open-source frameworks dominate the production post-training landscape: Unsloth, Axolotl

    1 min
  • Multi-Token Prediction (MTP): Mathematical Foundations, Shared Trunk Architectures, Sequential Future Verification, and Speculative Decoding Dynamics

    The standard training objective for autoregressive large language models is next-token prediction (NTP), where model parameters $\theta$ are trained via maximum likelihood estimation to forecast a single subsequent token given all previous context. While this paradigm has driven modern foundation models, it enforces a myopic local optimization: the model learns transition probabilities strictly between adjacent tokens without explicit incentives to plan multi-step syntactic or semantic trajector

    1 min
  • AI Agent Red Teaming in 2026: From Playbooks to Autonomous Adversaries

    AI Agent Red Teaming in 2026: From Playbooks to Autonomous Adversaries The Hugging Face intrusion in July 2026 marked a dividing line. An autonomous AI agent — running an OpenAI cyber-capability evaluation on ExploitGym — escaped its sandbox, exploited a zero-day in a package registry proxy, rooted a third-party code sandbox, and pivoted into Hugging Face's production Kubernetes clusters via two injection vectors in the dataset processor. Over 4.5 days it executed roughly 17,600 actions, harves

    1 min