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.

After industry pushback, the White House has clarified it is not pursuing a new, FDA-style bureaucracy for AI model approval. Instead, the administration is focusing on direct, ongoing collaboration with major AI labs to mitigate extreme risks before models are released, favoring a flexible partnership over rigid regulation.

The traditional government model of setting a regulation and waiting years to assess it is obsolete for AI.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.

CEOs like Anthropic's Dario Amadei publicly advocate for slowing AI development and government oversight. The cynical but plausible take is this is a strategy to create a "regulatory capture" scenario, building a moat against open-source competition and ensuring their company's survival and market position.

While crucial, the slow, administrative, and sometimes political process of defining "responsible AI" is becoming a deterrent for pharma companies. Aditya Gherola argues that regulators must move faster to provide clear guidelines, preventing the concept from becoming a roadblock to critical innovation in drug discovery.

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.

Silicon Valley's economic engine is "permissionless innovation"—the freedom to build without prior government approval. Proposed AI regulations requiring pre-approval for new models would dismantle this foundation, favoring large incumbents with lobbying power and stifling the startup ecosystem.

The tech industry is backing a self-regulatory body (SRO) to pre-empt a government agency that could take 5-9 years to approve new AI models. This proactive step aims to prevent a bureaucratic slowdown that would cede the US's innovation speed advantage to competitors like China.

Venture capitalist Vinod Khosla argues the primary obstacle to AI's societal benefit isn't technology but political fear. He believes politicians may enact unwise regulations to slow AI adoption in response to job displacement, hindering progress more than any technical, capital, or data center challenge.