The traditional government model of setting a regulation and waiting years to assess it is obsolete for AI.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

Formal regulations are struggling to keep up with the breakneck speed of AI innovation. Consequently, the actual standards for AI governance will emerge organically from industry best practices, born from incident responses and cutting-edge research. These practical solutions will be adopted long before they are codified into law.

A responsible, iterative approach to AI regulation begins not with new frameworks, but by auditing existing laws. Domain experts should update current rules for professions like medicine or finance to ensure they explicitly cover actions performed by or with AI, addressing immediate gaps without stifling future innovation.

The European Union's strategy for leading in AI focuses on establishing comprehensive regulations from Brussels. This approach contrasts sharply with the U.S. model, which prioritizes private sector innovation and views excessive regulation as a competitive disadvantage that stifles growth.

Robert Wright argues the US, as the leading AI power, should redefine its national mission. Instead of a breakneck race with China, its goal should be to guide the world toward a stable, coordinated international framework for AI. This reframes leadership from dominance to stewardship for humanity's collective benefit.

Direct, detailed government regulation of AI in the U.S. is unlikely to be effective. A better model is a self-regulatory organization like FINRA, where the government sets broad risk tolerance levels, and an industry body creates and enforces specific technical rules.

The AI competition is not a race to develop the most powerful technology, but a race to see which nation is better at steering and governing that power. Developing an uncontrollable 'AI bazooka' first is not a win; true advantage comes from creating systems that strengthen, rather than weaken, one's own society.

Global consensus on AI safety is unlikely soon. The most practical approach is a bilateral agreement between the US and China, the two dominant players. This can begin with informal 'track two' conversations between their respective AI labs before escalating to a formal government pact.