DeepMind CEO calls for an independent standards body to regulate frontier AI

In an X post on Tuesday morning, Google DeepMind CEO Demis Hassabis called for the creation of a new regulatory body to oversee frontier model releases. Titled “A Framework for Frontier AI and the Dawning of a New Age,” the post makes the case for a “standards body” modeled after the Financial Industry Regulatory Authority (FINRA), which could test frontier models and develop best practices for their release.

“Initially, Frontier Labs would voluntarily share models with the Standards Body for review up to 30 days before release,” the post reads. “Once the assessment protocol is shown to be effective and robust, formalisation could quickly follow, meaning that Frontier Models would be required to pass it to be deployed in the US market. Labs would also work with the Standards Body to address any critical post-release vulnerabilities.”

The proposed system would build on the ad hoc reviews performed by the U.S. government on Anthropic’s Mythos and OpenAI’s Sol. Those reviews drew significant criticism for lack of technical expertise and opaque decision-making as to when a model could be released. Under Hassabis’ proposed regulator, those decisions could be handed off to a new organization, backed by the U.S. government but funded by the AI industry and operated independently.

The prospect of AI regulation remains controversial for both the tech industry and the Trump administration. Most recently, White House AI advisor and a16z general partner Sriram Krishnan discounted the possibility of an AI regulator within the executive branch, saying “there will not be an FDA for AI.”

Establishing the standards body as a self-regulatory organization like FINRA could be a way to address those concerns. Hassabis envisions the regulator being staffed by open source representatives and technical experts from within the industry, along with the financial backing from AI labs that would be necessary to retain them. They could even outsource some evaluations to the growing pool of AI safety groups that would be able to specialize in specific risks.

“The strength of this approach is it would be technically focused, while at the same time supporting innovation and incentivising responsible behaviour,” Hassabis argues. “It is designed to keep up with the field’s acceleration and adapt to the biggest risks as they are identified, and could be ratcheted up if the seriousness of the situation demands.”

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