CNAS's "Secure, Governable Chips" report

Broadly capable AI systems, built and deployed using specialized chips, are becoming an engine of economic growth and scientific progress. At the same time, these systems also could be used by irresponsible actors to enable mass surveillance, conduct cyberattacks, and design novel biological weapons. This makes securing and governing the supply chain for AI chips important for mitigating risks to U.S. national security. But today’s semiconductor export controls are lackluster as a stand-alone solution. To be effective, they need to be far-reaching, which harms the competitiveness of U.S. firms, risks the “de-Americanization” of chip supply chains, and risks alienating commercial AI developers and partner nations. Far-reaching controls are also hard to enforce: AI chip smuggling is already happening today and could significantly grow in volume over the coming years.[1](https://www.cnas.org/publications/reports/secure-governable-chips#fn1)

The unique challenges of AI governance and the opportunities afforded by modern security technologies suggest alternative approaches are both necessary and possible. What if policies concerning AI chips could be implemented directly on the chips themselves? What if updates to export regulations could be deployed through a simple software update, backed by secure hardware? This report introduces the concept of “on-chip governance mechanisms”: secure physical mechanisms built directly into chips or associated hardware that could provide a platform for _adaptive governance_. Its key findings are as follows.

**On-chip governance mechanisms could help safeguard the development and deployment of broadly capable AI and supercomputing systems in a way that is complementary to American technology leadership.** One especially promising near-term application is export control enforcement, where on-chip mechanisms could prevent or place boundaries around unauthorized actors’ use of export-controlled AI chips. Implemented well, this would greatly aid enforcement, and reduce the need for top-down export controls that harm the competitiveness of the U.S. chip industry, instead enabling more surgical end-use/end-user–focused controls if desired. Later applications include enforcing the terms of future international agreements or other regulations that govern the large-scale training and deployment of AI models. Here, on-chip mechanisms could widen the space of possible agreements and policies by providing a trustworthy verification platform. For example, on-chip governance mechanisms could allow AI developers to credibly report “training runs” that exceed certain computation thresholds, as called for by a recent White House Executive Order.[2](https://www.cnas.org/publications/reports/secure-governable-chips#fn2) The existence of these mechanisms could allow for flexible and efficient international governance regimes for AI, allowing policymakers to think beyond the limitations of slow and complex structures such as the Intern

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