The United States has been approaching chip exports to China all wrong. Rather than obsessing over keeping advanced semiconductors a generation behind, policymakers should focus on maintaining America's overall computing advantage, according to analysis of current export control frameworks. This shift in thinking could reshape how the US competes in the AI race while avoiding unintended consequences that actually help China catch up .

What's Wrong With the Current Chip Export Strategy?

Today's export controls operate on a simple premise: prevent China from accessing the latest generation of AI chips. The Commerce Department's Bureau of Industry and Security (BIS) enforces rules designed to keep advanced processors like Nvidia's H200 and Blackwell generation GPUs out of Chinese hands. But this approach has a fundamental flaw. By focusing narrowly on blocking specific chip models, the US may actually be accelerating China's ability to develop competitive alternatives .

The current framework treats chip performance thresholds as the primary security concern. However, this ignores a more important metric: total compute advantage. When the US restricts access to the newest chips, it inadvertently pushes Chinese companies like Huawei to innovate faster and develop their own solutions, such as the Ascend 910C processor. Meanwhile, companies like Biren Technology and other Chinese AI startups are optimizing for different use cases than their American counterparts, focusing on industrial deployment and inference efficiency rather than raw training power .

How Should Export Controls Actually Work?

• Relative Advantage Framework: Shift focus from keeping chips one generation behind to maintaining America's total computing power lead across all categories, including data-center compute and AI infrastructure spending.
• Licensing Flexibility: Allow strategic chip exports through Commerce Department licensing when doing so preserves US competitive advantage, rather than imposing blanket restrictions that drive innovation elsewhere.
• Supply Chain Monitoring: Track not just individual chip sales but China's total compute capacity, including domestically manufactured alternatives and inference efficiency improvements that reduce reliance on cutting-edge hardware.

The stakes are high. China's AI startups are already seeing faster industrial adoption than their US counterparts, shaped by a different economic environment and customer base. Companies optimizing for manufacturing AI adoption and enterprise solutions are deploying Mixture-of-Experts models and other inference-efficient architectures that don't require the absolute latest chips .

Why High-Bandwidth Memory Matters More Than You Think?

While policymakers focus on GPU restrictions, a critical gap has emerged in export controls around high-bandwidth memory (HBM), a specialized type of ultra-fast memory essential for advanced AI systems. Modern AI chips require HBM to function effectively, yet the US export control framework has significant gaps in this area. The US leads in both HBM production and innovation, but these regulatory gaps are helping China catch up faster than necessary .

HBM is packaged directly onto GPU chips and is vital for AI data-center performance. Companies like SK Hynix, Samsung, and Micron produce HBM, but export restrictions on semiconductor manufacturing equipment, including ASML immersion DUV lithography tools, create uneven enforcement. When one component of the supply chain is restricted while another remains open, China can work around the bottleneck .

"Current export rules focus on keeping chips a generation behind. They should focus on keeping America's total compute ahead," the analysis stated.

AI Frontiers Policy Analysis

The problem extends beyond individual components. The Bureau of Industry and Security (BIS) enforces rules through the Foreign Direct Product Rule (FDPR), which attempts to control not just US-made goods but also foreign products containing US technology. However, inconsistent application across memory, processors, and manufacturing equipment creates loopholes that sophisticated actors can exploit .

What Does This Mean for the US-China AI Competition?

The geopolitical implications are significant. If the US continues restricting specific chip generations while leaving other pathways open, China will simply optimize around those restrictions. Chinese AI startups are already demonstrating this adaptability, building systems designed for inference efficiency and industrial deployment rather than competing directly on training performance .

A relative advantage framework would require continuous assessment of China's total computing capacity, not just monitoring of individual chip sales. This includes tracking domestically manufactured alternatives, licensing agreements with international suppliers, and the efficiency gains from architectural innovations. The goal would be to maintain a meaningful US lead in aggregate compute power, rather than playing whack-a-mole with specific products .

The current approach also creates perverse incentives for US companies. Nvidia and other semiconductor manufacturers face pressure to sell chips globally to maintain revenue, but export restrictions limit their addressable market. A more strategic licensing approach could allow controlled sales that preserve US advantage while generating revenue and maintaining relationships with international partners .

Ultimately, the question facing policymakers is whether export controls should focus on blocking specific technologies or maintaining strategic advantage. The evidence suggests the latter approach would be more effective, more enforceable, and less likely to accelerate Chinese innovation in unintended directions.