Despite media reports, the idea of an "FDA for AI" that pre-approves models is not supported by key policy advisors. Insiders stress the goal is industry coordination to harden government systems against AI threats, not to create a Washington-based approval bottleneck that would kill innovation.

The traditional government model of setting a regulation and waiting years to assess it is obsolete for AI. A new approach is needed: a dynamic board of government, industry, and academic leaders collaborating to make and update rules in real-time.

The White House's proposed legislative framework explicitly recommends against creating a new, overarching federal body to regulate AI. Instead, it advocates for empowering existing agencies with subject-matter expertise (e.g., in finance or healthcare) to develop and enforce AI rules within their own domains, suggesting a decentralized approach to governance.

The Trump administration's consideration of an FDA-like review process for new AI models signals a trend towards "soft nationalization." This involves government agencies partnering with and overseeing top AI labs to mitigate catastrophic risks and maintain a national security advantage.

To pass a moratorium on state-level AI laws, the White House now acknowledges the need for a federal framework. Michael Kratsios expressed a desire for "regulatory certainty" and a willingness to work with Congress on a national policy covering areas like child safety and intellectual property.

The 'FDA for AI' analogy is flawed because the FDA's rigid, one-drug-one-disease model is ill-suited for a general-purpose technology. This structure struggles with modern personalized medicine, and a similar top-down regime for AI could embed faulty assumptions, stifling innovation and adaptability for a rapidly evolving field.

Contrary to their current stance, major AI labs will pivot to support national-level regulation. The motivation is strategic: a single, predictable federal framework is preferable to navigating an increasingly complex and contradictory patchwork of state-by-state AI laws, which stifles innovation and increases compliance costs.

An FDA-style regulatory model would force AI companies to make a quantitative safety case for their models before deployment. This shifts the burden of proof from regulators to creators, creating powerful financial incentives for labs to invest heavily in safety research, much like pharmaceutical companies invest in clinical trials.

The popular idea of a government 'sign-off' before an AI model's release is based on a false premise. Risk isn't a one-time event at launch; it's continuous, existing during model development, internal use, and post-release updates. Effective oversight must reflect this ongoing reality.