AI summary
Helen Toner, who helped fire Sam Altman, argues that AI CEOs remain untrustworthy, highlighting ongoing governance and safety concerns in the industry.
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The OpenAI board member who fired Sam Altman says we still can’t trust AI CEOs
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Today, I’m talking with Helen Toner, the executive director of the Center for Security and Emerging Technology at Georgetown University. This episode is part two of our series on AI in real life. Helen is a well-known figure in the AI safety and policy community, though she is perhaps best known for having served on the board of OpenAI, when she was one of four board members who voted to fire Sam Altman. That incident, now sometimes referred to as “the blip,” ended with Sam coming back as CEO, Helen resigning, and a new board of directors. OpenAI has since restructured entirely. But it’s worth remembering what Helen and the board were supposed to do: OpenAI’s nonprofit board existed specifically to make sure the company didn’t betray its mission to build safe AI by prioritizing profit and growth over safety. When the board actually tried to pull the lever, that accountability mechanism broke, and here we are. Verge subscribers, don’t forget you get exclusive access to ad-free Decoder wherever you get your podcasts. Head here. Not a subscriber? You can sign up here. Now, the question of AI safety is everywhere. This summer, AI models from OpenAI and Anthropic broke out of their testing environments and hacked real companies. The CEOs of these frontier labs have since called for a slowdown and asked Washington for help, but the White House’s answer was a voluntary accord — one that relies in part on the kind of board accountability that failed at OpenAI. Anthropic is also about to go public, which will put a whole new set of market pressures on OpenAI’s biggest competitor. Helen has seen up close what happens when safety commitments run headlong into commercial incentives. And as she put it to me, if our hopes for the future depend on any one person being trustworthy, we’re in big trouble. So we talked about what we might do instead: what the labs are actually trying to build, whether AI alignment is a problem that can be solved, and what a smarter approach to
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