Predictive Analytics & Imagery for Environmental Monitoring

This project prototypes an embedded off-switch for AI accelerators. The security block requires periodic cryptographic authorization to operate: the chip generates a nonce, an external authority signs it, and the chip verifies the signature before granting time-limited permission. Without valid authorization, outputs are gated to zero. The design was implemented in HardCaml and validated in simulation.

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.