Technologies for Ensuring the Security of the Artificial Intelligence Chip Supply Chain: A Working Paper

Key Chapter Title List

1. Executive Summary
2. Introduction
3. Hardware-Enabling Mechanisms
4. Challenges and Opportunities
5. Recommendations for Policymakers
6. Appendix: Hardware Security and Hardware-Enabling Mechanism Proposals
7. About the Authors
8. Introduction to the Technology and National Security Program
9. Acknowledgments

Document Introduction

Advanced artificial intelligence systems, built and deployed using specialized chips, demonstrate significant potential in driving economic growth and scientific progress. However, they have also sparked extensive discussions among U.S. policymakers regarding associated risks, leading to the implementation of comprehensive export control measures targeting AI chips and semiconductor manufacturing equipment to China.

Nevertheless, current export controls on AI chips suffer from numerous deficiencies. On one hand, controls lack precision; exporters and the U.S. Department of Commerce lack reliable means to track the end-users and purposes of chips after export, forcing them to adopt a one-size-fits-all approach that imposes a heavy burden on exporters and end-users. On the other hand, enforcement is difficult. Malicious actors can circumvent Entity List scrutiny by rapidly establishing shell companies, while investigations and blacklisting often take years. Furthermore, the rapid iteration of AI technology necessitates continuously expanding the scope of export controls, further exacerbating these issues.

Hardware-Enabling Mechanisms (HEMs), as security components embedded in data center AI hardware, offer a new path to address the aforementioned problems. Such mechanisms are already widely used in various fields including defense, consumer electronics, and commercial AI, such as the Secure Enclave in Apple iPhones and chip verification systems in Google data centers. Well-designed Hardware-Enabling Mechanisms can effectively detect and curb the smuggling of AI chips to China, enable more targeted export controls, reduce the risk of "de-Americanization" in the chip supply chain, and provide reliable governance and security solutions while protecting privacy.

The report provides a detailed analysis of the application scenarios and corresponding policy objectives of specific Hardware-Enabling Mechanisms such as location verification, computational cluster size limits, and offline authorization. It also points out the challenges and opportunities HEMs face in terms of design, security protection, and technical implementation. Based on this analysis, the report proposes three core recommendations to U.S. policymakers: accelerate HEM and hardware security R&D through direct funding and public-private partnerships; create industry R&D incentives through conditional export licenses; and further refine AI hardware security standards to harmonize industry security features. These recommendations aim to promote Hardware-Enabling Mechanisms as an effective governance tool for balancing national security, economic competitiveness, and privacy protection.