Building verification mechanisms for high-stakes coordination over AI development and deployment.
No single lab, evaluator, or government can make frontier AI safe on its own. As capabilities grow, safety increasingly depends on coordination, and coordination depends on trust. Our engineering team works on the technical mechanisms that underlie high-stakes coordination.
Our goal is to make it inevitable that verification mechanisms are built. We don’t have to be the ones who develop the final systems, but we do need to make sure someone — possibly ourselves — does the work. As such, we work in two ways.
Verification is a process, not a one-shot solution. A ladder of technical mechanisms can build trust towards high-stakes coordination over AI — and nuclear verification history shows how the early rungs get climbed.
Verification technologies might be useful even if you're not shooting for an international agreement. Here are a few reasons why.
Verifying what a compute cluster is doing might involve "tapping" the network — copying network packets for recomputation. These notes explore what it might take to do that.
We mapped the power delivery systems of AI data centers. Our diagram explores the data available at each stage, measurement techniques and signals of interest for AI security.
A quick test installation of a 10G network tap for AI verification — passive vs active taps, fibre types, and what scales to production speeds.
We used a DPU as a tray-level bandwidth limiter — an extra layer of security for protecting model weights, achieving near line-rate encryption with hardware-offloaded IPsec.
Free security and verification research infrastructure.