In the middle of the twentieth century, the existential threat posed by nuclear weapons seemed inevitable. The number of countries with nukes was climbing rapidly, and the idea of stopping the nuclear arms race seemed like a pipe dream. 

But that’s exactly what happened. Over the course of 60 years, nations around the world agreed to nuclear red lines, slowdowns, and even disarmament. How did this happen? Largely because of technology. 

The biggest obstacle to agreeing on nuclear red lines was that adversaries couldn't trust any promise the other made. They needed to know the number of warheads, the amount of enriched uranium, or whether a nuclear device was for a weapon or a power plant. None of that was possible until we built the tech needed to verify those things. 

Today, we're in a similar situation with AI. For adversaries like the United States and China to agree on reasonable AI red lines on issues like bioweapons, cyber hacking, or the risk of recursive self-improvement, they first need to be able to trust each other. We urgently need to build the verification technology that would make that trust possible. 

In this episode, Tristan sits down with two experts in this field to discuss the kinds of verification technology we need for AI, the challenges of building it, and the world it could unlock if we do. Tim Fist is the Director of Emerging Technology Policy at the Institute for Progress, and Janet Egan is Senior Fellow and Deputy Director for the Technology and National Security Program at the Center for New American Security.

Anthropic’s open letter warning about recursive self-improvement and calling for a pause in development.

The website for the Center for AI Standards and Innovation (CAISI)

Further reading on the different mechanisms of verification for international AI governance.

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Correction: Tim referred to the CargoScan technology as being jointly developed by the US and the USSR. It was actually developed solely in the US and administered in Soviet nuclear facilities. 

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