US AI Export Controls Escalate: Global AI Supply Chain Splits into Dual Tracks

U.S. AI Model Export Controls Escalate: From Chip Blockades to “Fencing” Models—Global AI Industrial Chain Accelerates Toward Dual-Track Fragmentation

Recently, the United States’ regulatory logic for core AI technologies has undergone a pivotal shift—its oversight focus is systematically expanding from the hardware layer (GPU chips) to the software layer (large language models themselves). Anthropic, acting on explicit U.S. government instruction, has fully suspended foreign users’ access to Fable 5 and Mythos 5 (Source 12); OpenAI faces a coordinated antitrust and data misuse investigation launched by attorneys general across multiple U.S. states (Source 13); and AWS has urgently delisted numerous closed-source models targeted at international markets (Source 10). These three incidents are not isolated or coincidental—they constitute synchronized actions under a unified policy paradigm. This marks the arrival of a new phase in U.S. AI technology governance: the era of “fencing.” Rather than restricting only compute supply, this approach directly governs model access rights, training-data flows, and inference-service boundaries. Its implications extend far beyond technical compliance—reshaping global AI R&D paradigms and industrial division of labor.

The Essence of “Fencing”: From Physical Blockade to Cognitive Sovereignty Control

Looking back over the past two years, U.S. AI controls targeting China have pivoted on the CHIPS Act and revised Bureau of Industry and Security (BIS) export rules, focusing on physical cutoffs of high-end GPUs such as the A100 and H100. While this strategy effectively slowed the expansion of computing capacity among leading Chinese enterprises, it failed to halt model iteration: through domestic chip adaptation, pooled compute scheduling, and algorithmic compression, China’s large models continue advancing at an average pace of two to three generations per year. U.S. policymakers clearly recognize that constraining the “muscles” (compute) alone cannot restrain the “brain” (model capability). Anthropic’s proactive shutdown represents the first time “Model-as-a-Service” (MaaS) has been formally incorporated into the national security review framework. Its legal basis rests on an expanded interpretation of the International Traffic in Arms Regulations (ITAR), particularly its definition of dual-use technologies “capable of military application.” In other words, Fable 5 and Mythos 5 have been designated as “cognitive infrastructure” with potential strategic risk—their external service provision now constitutes a new frontier of sovereign extension. Treating models themselves as strategic assets subject to direct control elevates AI governance from industrial-product regulation to the ideological and epistemic sovereignty level.

Accelerating Global AI R&D “Dual-Track System”: Barriers Spur Autonomous Ecosystem Restructuring

These tightened controls impose dual pressures on Chinese tech firms: First, API access and fine-tuning permissions for cutting-edge closed-source models have been substantively revoked, causing capability gaps in vertical domains—such as financial risk control and biomedical research—that rely heavily on high-precision inference. Second, even open-source communities are affected: Hugging Face has implemented geographic fencing (geofencing) on certain models containing sensitive training data, further exacerbating inequities in technical information access. Yet history shows external pressure often catalyzes indigenous innovation. Today, leading domestic vendors are building alternative pathways across three dimensions:

1. Accelerating iteration of domestically developed foundational models: Baichuan Intelligence’s Baichuan3 and Zhipu AI’s GLM-4 have approached the performance of Llama-3-70B on rigorous benchmarks—including mathematical reasoning and code generation.
2. Building integrated “training-inference” autonomous ecosystems: Huawei’s Ascend chips paired with MindSpore, and Cambricon’s MLU chips coupled with its Cambricon SDK, enable hardware-software co-optimization—reducing training time for billion-parameter models by 40%.
3. Advancing the “Model-as-a-Factory” paradigm shift: Tongyi Lab (Alibaba) has open-sourced the full weights and training scripts for its Qwen2 series, empowering enterprises to perform domain-specific fine-tuning using proprietary data—thus reducing reliance on general-purpose foundation models. Notably, this substitution is not mere replication but a holistic stack reconstruction grounded in security and controllability. For instance, the Institute of Automation, Chinese Academy of Sciences’ multimodal large model “Zi Dong Tai Chu” adopts a heterogeneous architecture built on Phytium CPUs and Kunlunxin GPUs; its training data is drawn exclusively from publicly available Chinese-language corpora—eliminating compliance risks at the source.

Surge in Compliance-Driven Alternatives: Cross-Border Compute Leasing, Model Distillation, and Edge Inference Emerge as New Blue Oceans

As centralized cloud services face constraints, distributed, lightweight, and localized AI deployment models are seizing strategic opportunity. Markets are responding structurally: Orders on cross-border compute leasing platforms—such as Cloud-Edge Nexus, registered in Singapore and the UAE—have surged 210% month-on-month. Their core value lies in delivering hybrid architectures (“offshore training + onshore inference”) compliant with both GDPR and local data laws. Demand for model distillation technology is surging: MiniMax’s TinyLLaMA series (1.3B parameters) achieves 92% of Qwen2-7B’s accuracy on medical Q&A tasks while shrinking model size by 98%. Edge-AI chip shipments are skyrocketing: Horizon Robotics’ Journey 5 SoC, equipped with a dedicated Transformer acceleration engine, enables real-time execution of 10-billion-parameter models on automotive terminals—with power consumption below 15W. Collectively, these technical pathways signal a broader trend: AI capability is migrating from “cloud-based leviathans” toward “edge-based swarms.” Underpinning this shift is the imperative to avoid political risks inherent in centralized model services—and to meet nations’ non-negotiable requirements for data sovereignty and localized deployment.

Capital Market Reconfiguration Signals: The Logic of Hard-Tech Self-Reliance Makes a Strong Comeback

Markets have reacted with remarkable sensitivity to this policy pivot. The U.S. AI Software Index has declined for nine consecutive trading days (Source 16), reflecting investors’ deep concerns about the long-term profitability of SaaS companies dependent on overseas model APIs. Meanwhile, U.S.-based leveraged ETFs focused on Chinese AI hardware stocks have filed密集 applications (Source 9), with key holdings including InnoLight (optical transceivers), Cambricon (AI chips), and Sugon (liquid-cooled servers). This capital flow reveals a fundamental shift in investor consensus: amid the irreversible trend of “model fencing,” AI value-chain profits are migrating upstream—from applications toward hard-tech components. Notably, several ETF prospectuses explicitly cite “domestic substitution progress” and “IT innovation ecosystem compatibility rates” as critical evaluation metrics—signaling a decisive pivot in investment logic: from growth narratives to survival-capability assessments.

The escalation of U.S. AI model export controls is no short-term tactical adjustment—it represents a systemic upgrade in its strategy to preserve technological hegemony. As “fences” expand from chips to models, global AI development has inevitably entered a new normal of dual-track parallelism: one track comprises the “Compliance Alliance,” anchored in Western technical standards and ecosystems; the other forms the “Resilience Network,” rooted in self-reliance and controllability. For China, the challenges are formidable—but historical experience demonstrates that genuine technological breakthroughs often emerge precisely from the most constrained environments. As external channels narrow, endogenous innovation momentum intensifies. This globally fragmented AI industrial chain—sparked by regulatory intervention—will ultimately redefine what true technological leadership means.