Tom Milton on how we could verify a US–China deal on AI

If the US and China agreed to pause frontier AI training, how would either side confirm the other was complying? Today, Tom Milton argues, the most likely answer would be to shut down data centres entirely. That would throw away everything else those chips do, and make any agreement far more costly to accept.

Tom is CEO of Amodo Design, a UK hardware lab building prototype tools to verify how AI chips are being used. He argues that verification is what makes a slowdown affordable: if a cluster can be shown to be serving only existing models rather than training new ones, most of AI’s benefits can continue during a pause.

Amodo’s pilot reruns a cluster’s inference on a separate verifier server and checks that all of its network traffic can be explained by the agreed model. AI companies are developing related tools: in its October 2026 Frontier Safety Roadmap update, Anthropic reported a prototype that runs on production inference and checks that outputs come from a specific set of model weights.

The field is small. Tom estimates that only about 50 engineers worldwide work on compute verification, compared with the billions of dollars the US spent on Project Vela to detect Cold War nuclear tests. And custom verification hardware has long lead times: it has to be designed, security-tested, red-teamed by both sides, manufactured, and trusted before anyone can rely on it.

If a deal is struck in a hurry, Tom expects it to be assembled from whatever tools already exist. Were that to happen next month, he says, the only option would be to shut data centres down entirely. That is why he argues the work needs to scale up now, with more people and resources from AI companies, governments, and funders, so that better options are ready as soon as a deal is needed.

Tom and host Tom Reed also discuss how an AI company might evade verification, lessons from nuclear and chemical arms control, and how AI is changing hardware engineering.

Learn more about Amodo’s work at ai-verification.com.

Links, video, and full transcript: https://80k.info/tm

This episode was recorded on September 29, 2026.

Chapters:

• Cold open: Can we verify a US–China AI deal? (00:00:00)
• Why compute is the best place to verify an AI slowdown (00:01:04)
• Can you prove a lab isn’t cheating? (00:06:08)
• Live demo: how to spot a cheating GPU (00:13:22)
• Could China hide a data centre? (00:19:50)
• Can verification tools be hacked? (00:23:35)
• Is anyone at OpenAI working on this? (00:29:13)
• Chinese inspectors in US data centres?? (00:37:49)
• The 20-day panic deal (00:46:07)
• What would a US–China AI deal look like? (00:51:27)
• Why lab researchers should work on verification (00:55:38)
• What if we never build this? (00:58:45)
• Can verification protect against power grabs? (01:02:06)
• How good is AI at hardware design? (01:07:20)
• What’s holding AI back in hardware? (01:18:32)
• Risks of automating hardware R&D (01:22:44)
• The DARPA model for AI safety (01:25:41)
• What’s left for engineers after AI? (01:32:26)
• Personal gadgets, designed by Fable? (01:40:07)
• What if the US stops sharing its AI? (01:41:56)

Our production team includes:

• Video editors: Josh Alward, Dominic Armstrong, Jasper Luithlen, Milo McGuire, Luke Monsour, and Simon Monsour
• Producers: Elizabeth Cox and Nick Stockton
• Coordination and support: Katy Moore and Lou Moran
• Camera operator: Dominic Armstrong

Music: CORBIT