How to Govern AI When You Can't Predict the Future
Charlie Bullock discusses radical optionality, a framework for governing advanced AI under uncertainty. The episode covers transparency, evaluations, cybersecurity, technical hiring, and tensions between private and government oversight.
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Show Notes
Charlie Bullock is a Senior Research Fellow at the Institute for Law and AI. He joins the podcast to discuss radical optionality: how governments can prepare for very advanced AI without locking in premature rules. The conversation covers why law often trails technology, and how transparency, reporting, evaluations, cybersecurity standards, and expanded technical hiring could help. We also discuss private oversight, state versus federal rules, and the risk of concentrating power in companies or government.
LINKS:
CHAPTERS:
(00:00) Episode Preview
(01:04) The pacing problem
(06:18) Defining radical optionality
(11:03) Assumptions under uncertainty
(16:00) Industry convenience concerns
(20:41) Political will realities
(26:48) Private governance limits
(30:28) Government misuse risks
(36:29) Balancing institutional power
(42:25) Transparency and reporting
(49:35) Evaluations, security, talent
(58:26) State law preemption
(01:04:20) Historical nuclear analogies
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