The White House Floated AI Model Vetting, Then Backed Off in 72 Hours

The 72-Hour Whiplash on White House AI Vetting

The segment opens with a mini-drama: reports say the Trump administration is considering an executive order to create a federal review process for powerful AI models before public release, with Anthropic, Google, and OpenAI already in the room. Then Kevin Hassett goes on Fox Business and likens it to FDA drug testing — and that’s when the blowback hits.

From “FDA for AI” to “No, No, Just Partnership”

By that night, chief of staff Susie Wiles posts that the White House is not in the business of “picking winners and losers,” and officials start insisting Hassett was taken out of context. By Friday, Bloomberg reports the administration is instead preparing agencies to partner with AI companies on cyber defense, notably stopping short of requiring government approval for frontier models.

Why the Hosts Think the Administration Is Internally Split

Paul says the back-and-forth feels like the administration testing messages in public: Tuesday it sounds like model approval, by Thursday it’s suddenly draft language and collaboration. His read is that some advisers are genuinely alarmed by frontier-model misuse — cyberattacks, infrastructure threats, hostile nations — while others see regulation as a direct threat to innovation and U.S. competitiveness.

The Bigger Problem: Lawmakers Don’t Really Get Frontier AI Yet

He’s especially worried about Congress operating at what he calls a “beginner level” understanding of AI — maybe they’ve used ChatGPT, but they don’t grasp agentic systems, reasoning models, or self-improvement. That matters because they’re being asked to make national-security and economic decisions about systems they can’t realistically project even one or two years forward.

The Vaccine Study Example as a Warning Shot

Paul then pivots to a New York Times report that the FDA blocked publication of studies showing serious side effects from COVID and shingles vaccines were very rare, despite the work involving millions of patient records and millions in public funds. His point isn’t to relitigate vaccines; it’s that if even relatively objective science can get politically filtered, AI model evaluations — full of judgment calls about bias, outputs, and ideology — would be far easier to manipulate.

Dean Ball’s Case for Catastrophic-Risk Regulation Only

The most substantial section is a close read of Dean Ball’s essay “Before Leviathan Wakes,” which Paul recommends even though Ball starts from an aggressively anti-regulation position. Ball says he opposes nearly all AI policy ideas — from algorithmic discrimination rules to pauses and bans — but makes one exception: state action to manage catastrophic risks like cyberattacks on hospitals, banks, power plants, and potentially biological threats.

The “Intermediary Layer” Instead of Soft Nationalization

What Ball supports, and what the hosts find useful, is not direct nationalization but “sustainable methods of perpetual interference”: private institutions positioned between the state and frontier labs. The idea is to give government some control and the feeling of control, without letting politics decide which CEO or model gets approved.

Why They Think the Government Will Move Either Way

The conversation ends on a blunt note: Mike says he doesn’t see a plausible path where nationalization pressures don’t increase, and Paul says his baseline assumption is that the government is already building — or will build — its own models. They cite DARPA history, the book “The Pentagon’s Brain,” In-Q-Tel, and even Intel to make the point that none of this is conspiratorial; the national security apparatus has been playing this game for decades.