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.

Scott Galloway says export restrictions are the tell: the administration wants leverage over AI companies, not safety. He calls it trade policy replacing regulatory policy meant to prevent a tragedy of the commons. Officials are floating export restrictions to pressure companies into compliance; he says that is not safety.

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.

Many companies have formed AI governance committees, but these groups lack the deep technical expertise to ask probing questions. They tend to accept superficial answers from vendors, creating a false sense of security and failing to mitigate real risks.

The US government's delayed AI safety framework is being developed behind closed doors. By sharing physical drafts in briefings without allowing companies to keep them and keeping the benchmarking process classified, the White House is creating uncertainty and forcing companies to self-regulate in a vacuum.

The Trump administration's key AI decision-makers largely lack hands-on experience in building or evaluating frontier AI systems. Policy is being shaped by individuals with political and business backgrounds, creating a critical expertise gap in governing this complex technology.

High-profile government accords—such as the White House Accord on Super Intelligence or the 2023 voluntary commitments—consist of non-binding pledges that replicate existing industry promises without legal enforcement mechanisms. Rather than instituting actionable regulatory guardrails, these events serve primarily as political loyalty tests and photo opportunities where tech leaders publicly defer to administration narratives to protect regulatory leverage.

Vance argues that AI companies creating potentially dangerous models have a responsibility to build and release defensive countermeasures. He views their calls for government regulation as an attempt to shirk this responsibility, rather than a genuine safety effort.

The concept of embedding independent auditors in AI labs is plagued by practical issues. Key challenges include finding trusted, qualified talent, securing unbiased funding (government vs. industry), and ensuring their recommendations can be enforced against powerful tech companies.