Keep Government Out of AI: Y/N? The $7 Trillion Question

Stephen Moore's latest op-ed arguing for hands-off government approach to AI regulation has reignited one of the most consequential policy debates of our time. With Goldman Sachs projecting $7 trillion in global AI-generated wealth in the next decade and China's state-directed AI investments potentially exceeding U.S. federal spending, the stakes couldn't be higher. But Moore's binary framing—government interference bad, private sector good—oversimplifies a complex strategic calculation that could determine American technological supremacy for the next century.

Let's examine both sides of this trillion-dollar question.

The Case for Hands-Off: Silicon Valley's Proven Formula

Moore's argument taps into a powerful historical precedent. The internet boom of the 1990s provides a compelling case study in regulatory restraint. While Europe pursued heavy-handed industrial policy, subsidizing and then taxing tech companies into mediocrity, America's "Wild West" approach created the Magnificent Seven—companies now worth more than every European company combined.

The numbers are staggering. AI-related federal contracts increased by almost 1,200% from $355 million in August 2022 to $4.6 billion in August 2023, but that's still peanuts compared to private investment. Microsoft alone is on track to invest approximately $80 billion in FY 2025, while Trump announced a private sector investment of up to $500 billion to fund infrastructure for artificial intelligence.

The regulatory burden is already crushing. More than 1,000 pieces of AI-related legislation have been introduced in states just this year. Sam Altman warned that complying with "50 different sets of regulation" would be "quite bad" for U.S. competitiveness, slowing companies down "at a time where I don't think it's in anyone's interest for us to slow down."

Near-term implications of the hands-off approach:

• Faster AI development and deployment cycles
• Massive private investment continues unimpeded
• American companies maintain first-mover advantages
• Innovation clusters develop organically without bureaucratic interference

Long-term benefits:

• Market-driven solutions emerge for AI safety and ethics
• American AI dominance solidifies globally
• Economic productivity gains materialize faster
• Competitive pressure drives responsible development

The Case for Strategic Government Involvement: China's Challenge Changes Everything

But Moore's framework ignores a crucial reality: we're not in the 1990s anymore. China's overall government-led funding likely exceeds investment by US federal and state governments, and Beijing has been systematically building AI capabilities since 2017. This isn't about regulatory capture—it's about strategic competition with an authoritarian regime that views AI as a zero-sum geopolitical weapon.

The current approach already shows dangerous gaps. Over the past five years, the US government has committed just over $12 billion to AI-related obligations—what Microsoft spends in roughly two months. Meanwhile, Chinese generative AI models likely lag behind US competitors by approximately three to six months, and that gap is narrowing rapidly.

China's regulatory approach offers a sobering counterpoint to Moore's thesis. China's CAC went in the direction of licensing but strategically exempted research and development, suggesting they "prioritized innovation and speed over bureaucratic process" while maintaining oversight of public-facing deployment.

Near-term implications of strategic government involvement:

• Coordinated national AI infrastructure development
• Security standards for AI systems handling sensitive data
• Research funding for breakthrough technologies private sector won't pursue
• International coordination on AI governance frameworks

Long-term strategic benefits:

• Prevents authoritarian AI systems from dominating global markets
• Ensures AI development serves democratic values and human rights
• Maintains Western technological leadership through allied coordination
• Creates framework for responsible AI scaling without stifling innovation

The Nuanced Reality: Neither Laissez-Faire Nor Central Planning

The most sophisticated analysis suggests the optimal approach lies between Moore's extremes. President Trump has signaled a permissive approach to AI regulation, issuing an Executive Order for Removing Barriers to American Leadership in AI, while companies will need systematic, transparent approaches to confirming sustained value from their AI investments.

The key insight: government's role should be infrastructure enabler, not innovation inhibitor. AI investment could approach $100 billion in the U.S. and $200 billion globally by 2025, but this requires massive energy grid upgrades, skilled workforce development, and international coordination—areas where government involvement is essential, not harmful.

Smart regulation focuses on outcomes, not processes. Rather than prescriptive rules about how AI systems should be built, effective policy creates frameworks for accountability, transparency, and safety without micromanaging innovation cycles.

The synthesis approach:

• Government invests in foundational infrastructure (energy, chips, talent)
• Private sector leads in model development and application
• Regulatory frameworks focus on high-risk, high-impact deployments
• International coordination prevents authoritarian AI dominance

Sounds... reasonable. TBD.