INTRODUCTION

The United States has spent three years building an elaborate system of export controls designed to slow China's AI development or as they say “to prevent national security risks associated with AI”. On January 13, 2025, the Biden administration published its most comprehensive framework yet, a 40-page rule establishing ECCN 4E091 to control the export of advanced computing chips and certain closed AI model weights to cultivate a secure and trusted technology ecosystem for the responsible use and diffusion of AI. However, the rule carves out a broad exclusion for AI models whose weights are publicly released. As a result, the new controls apply only to a narrow class, leaving most open-source AI development outside the export control regime. (Bureau of Industry and Security)

This is the open-weight paradox. The U.S. government cannot effectively restrict the global spread of advanced AI capabilities once model weights are publicly released. Some of the most powerful models are already freely available to anyone with an internet connection. Meta's Llama 3.1 405B, which is trained on approximately 3.8×10²⁵ FLOPs or computational operations, can be downloaded from Hugging Face around the world. DeepSeek-V3, a Chinese model built in part on techniques influenced by the U.S. open-weight releases, approaches GPT-4-class performance on several benchmarks. DeepSeek-V3 was trained for just $5.576 million in reported marginal compute costs,(arXiv,DeepLearning.AI) which is roughly 1/18th of GPT-4’s estimated training expense. (arXiv,BentoML) This excludes prior R&D and ablation experiments, with total infrastructure investment estimated at roughly $1.3 billion by SemiAnalysis. The current export control regime does not restrict these models because, once weights are published, meaningful enforcement becomes impractical. (Bureau of Industry and Security)

The math makes the problem worse. Training a frontier AI model costs tens of millions of dollars and requires thousands of high-end GPUs running for months. By contrast, fine-tuning that same model for specialized applications, including military ones, can cost as little as $200 using techniques like QLoRA. The computational asymmetry makes export controls focus on model creation rather than downstream adaptation use, even though most capability diffusion occurs...

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