80,000 Hours Podcast: Amodo CEO on Verifying a US-China AI Training Pause

If the US and China agreed to pause frontier AI training, how would either side confirm compliance? In Episode 257 of the 80,000 Hours podcast, Tom Milton, CEO of UK hardware lab Amodo Design, argues that technical verification of compute usage is the key. Milton notes that while shutting down data centres entirely is the simplest fallback, it is extremely costly because it cuts off all existing AI services. Thus, the core value of verification technology lies in reducing compliance costs, allowing parties to continue serving inference even while pausing training.

In the episode, Milton introduces several specific verification mechanisms Amodo is developing. The first is "inference recomputation," which involves installing network taps in a data centre to copy traffic to an independent verifier server that reruns the model to check for output consistency, thereby detecting undeclared training workloads. Second, he discusses using GPU power fluctuations as "side-channel" information, since training and inference exhibit distinguishable power consumption patterns. Additionally, he mentions solutions based on zero-knowledge proofs, designed to complete verification without revealing model weights.

Although these technologies are conceptually tractable, Milton admits the field is currently very small, with only about 50 engineers worldwide working on compute verification. He emphasizes that custom verification hardware has long lead times, requiring security testing, red-teaming, and scaled-up production. If a rushed US-China deal were struck in the coming months, existing tools might not be ready for deployment, potentially forcing extreme measures like data centre shutdowns. Therefore, he calls on AI companies, governments, and funders to scale up investment in this area now.