The Cost of Second Place in the AI Race (w/ Jamil Jaffer)

For years, American labs held an uncontested lead in artificial intelligence. That is no longer obviously true: Chinese open-weight models have closed much of the gap, and by some measures have pulled ahead. The stakes are not merely commercial. Whoever leads sets the terms for what the world runs on, who gets access to it, and what is built into the models themselves, including (in the Chinese case) political censorship and reported evidence that these systems may hand certain users deliberately weakened code. Yet American firms are adopting them anyway, sometimes for cost, sometimes because our own models are constrained from doing the work. So what would losing this race actually cost us? Why are American companies turning to Chinese models to defend their own infrastructure? And can Washington regulate a technology that outpaces the law faster than the ink can dry?

In this episode, Paul Saunders speaks with Jamil Jaffer, the founder and executive director of the National Security Institute at the Antonin Scalia Law School at George Mason University. Jaffer also serves as a venture partner with Paladin Capital Group and is a contributing editor at The National Interest. He has previously served on multiple Congressional committees and as a White House counsel during the George W. Bush administration.

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Editor’s note: This episode was recorded before OpenAI’s July 21 statement on the Hugging Face security incident discussed by our guest, which attributes the intrusion to OpenAI’s own models running with cyber safeguards disabled for internal evaluation. Listeners can find that statement here.