Both sides would have liked to deescalate. But for decades, neither could trust that the other would comply with any arms reduction treaty. By the 1980s, however, scientists had developed seismographs, satellites, and tamper-proof cameras. Now that each side could monitor the other, disarmament took hold. A Russian proverb, “trust, but verify,” came to define the approach that helped end the Cold War and avert nuclear holocaust.
The result is a strategic stalemate. Neither the U.S. nor China wants a catastrophe, but there is no way to trust that slowing down would do anything other than cede victory to the other. With the AI race reaching a critical inflection point on cybersecurity, the leaders of major U.S. AI labs have indicated that they might support a slowdown—if only one were possible to achieve.
Also in June, OpenAI CEO Sam Altman wrote that the world needs a new global organization “to make it possible for the world to take coordinated action, including slowing frontier [AI] development when needed.”
A pause, however, is only seen as impossible because the technology does not yet exist to verify that all sides are complying with it. The seismographs and satellites of the AI age, in other words, don’t yet exist. But what if they did?
Just as it was crucial for Cold War verification systems to not allow either side to steal the other’s nuclear secrets, a crucial part of AI verification will be in allowing oversight while not risking the industry secrets of AI companies, proponents say.
The idea is that a special piece of software could sit inside these trusted environments, where it could examine the AI and check whether it complies with a given rule. For example, it could confirm that a specific model is being run, or determine whether chips are being used in the training of a new model, which might be outlawed.
Kristian Rönn, the CEO of Lucid Computing, says this approach is designed to avoid a future theorized in 2019 by the philosopher Nick Bostrom, who imagined that the risks of super-powerful AI might one day incentivize states to impose totalitarian-style surveillance in order to prevent the end of the world.
Rönn says Lucid is currently testing its technology, and has discussed it with many U.S. government agencies and the leading AI companies. But he says it is not mature enough yet to help guarantee any international treaty.
“We want [verification technology] to exist, we want it to be red-teamed with nation-state actors and the labs, and ready to go,” he says. “We don't want to be too late. Being too late here has real world consequences.”
Verify what, exactly?
Across the Atlantic, a British engineering firm called Amodo Design is taking a different approach. Known as recomputation, this works by re-running portions of a company's AI workload and examining the results—letting an outside inspector confirm that a data center is, say, running an agreed-upon AI model rather than quietly training a more powerful one.
Both approaches carry some limitations. For example, neither Amodo nor Lucid’s methods of examining known chips could confirm that an adversary wasn’t hiding a secret data center under a mountain somewhere.
To get around this problem, a paper from researchers at the think tank RAND argues, at least six different types of verification may be necessary. As well as software monitoring systems, these could include built-in security features in AI hardware, monitoring the internet networks of data centers, plus more traditional measures like whistleblower protection programs, personnel interviews, and surveillance by intelligence agencies.
It’s less clear whether China would support a pause, given that it’s playing catch-up to U.S. AI companies. And to be sure, much talk of a pause by American companies might just be bluster—easy statements for leaders to make in the knowledge that a pause is a political non-starter.
That problem is that nobody has yet agreed on what exactly they are trying to verify. Slowing down in AI sounds simple in theory, but it is very hard to specify restrictions that wouldn’t leave glaring loopholes in practice. There are all kinds of different ways of measuring the capabilities and behaviors of AI systems. These measures are frequently subjective, and they become outdated rapidly.
Solving AI governance might therefore be a thorny policy problem, Heim says, more than a tractable technical one.
“This is a beautiful property,” Heim says. “AI is not that. Not by any means.”
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