Prototyping an Embedded Off-Switch for AI Compute

We design and simulate a "border patrol" device for generating cryptographic evidence of data traffic entering and leaving an AI cluster, while eliminating the specific analog and steganographic side-channels that post-hoc verification can not close. The device eliminates the need for any mutually trusted logic, while still meeting the security needs of the prover and verifier.

This paper develops a stylized game-theoretic model of cross-border AI training projects in which multiple states jointly train frontier models while retaining national control over compute resources. We focus on decentralized coordination regimes, where actors publicly pledge compute contributions but privately choose actual delivery, creating incentives to free-ride on a shared public good. To address this, the model introduces explicit verification mechanisms, represented as a continuous monitoring intensity that improves the precision of noisy signals about each actor's true compute contribution. Our findings suggest that policymakers designing international AI governance institutions face a commitment problem: half-measures in verification are counterproductive, and effective regimes require either accepting some free-riding or investing substantially in monitoring infrastructure.

The AI safety and governance community is making progress on defining red lines around existential risk from advanced AI systems, and building verification infrastructure to support this objective. However, this is only half the battle. Implementing these red lines requires unprecedented international coordination, and enforcing them requires credible commitments from national governments, all during a period of increased geopolitical tensions.

We present a whitepaper that makes the case that:

Implementation and enforcement of red lines is as much a political question as it is a technical one

While studies exist showing general public support for AI regulation, the depth of domestic political support among major powers for costly international AI regulation and willingness to make real sacrifices to enforce these red lines is understudied

Rigorous policy-credible surveys of public opinion in this area would both enhance the AI safety community’s efforts to prioritize scarce resources to the most effective action areas and provide advocates a valuable source of evidence to point to when lobbying national governments and international organizations

And therefore proposes and develops the methodology for AIRAS.