Two Visions of Global Governance in the Age of AI

Since June, artificial intelligence has become the latest focal point of U.S.-China strategic competition—not because of another breakthrough model, but because of a growing dispute over who should have access to advanced AI and how that access should be governed. The latest controversy surrounding Anthropic illustrates this shift. Reports revealed that the company had experimented with identifying China-based users of its coding assistant Claude Code while simultaneously accusing several Chinese AI companies of using “model distillation” to narrow the technological gap with leading American systems. What might once have been viewed as a commercial dispute over intellectual property has increasingly been framed as a matter of national security.

What makes the episode particularly noteworthy is that neither of the underlying issues is actually new. Model distillation has long been a common engineering practice throughout the AI industry, allowing smaller models to learn from more capable ones. American companies have also relied on distillation techniques, and OpenAI itself has released tools that make the process easier for developers. Likewise, companies have always sought to protect proprietary models from unauthorized use. What has changed is not the technology itself, but the strategic context in which these practices are now viewed. As AI competition between the United States and China intensifies, commercial concerns are increasingly being redefined as strategic ones. If this sounds familiar, it should. Similar transitions have already transformed debates surrounding Huawei, TikTok, connected vehicles, and a growing list of emerging technologies. The technologies of concern change, but the policy logic remains remarkably consistent.

This shift is becoming increasingly evident across U.S. AI policy. The Trump administration’s AI strategy initially focused on accelerating innovation, expanding computing infrastructure, and maintaining America’s technological leadership. Over recent months, however, that emphasis has gradually broadened. Executive actions have elevated frontier AI models to national security assets. Restrictions on foreign access have become tighter. Leading AI companies have worked more closely with government agencies on protecting advanced models. Treasury Secretary Scott Bessent recently summarized this evolving mindset bluntly, arguing that “the biggest risk to AI is China getting ahead of us.” In other words, AI policy is no longer primarily about promoting innovation. It is increasingly about preserving strategic advantage.

There is also a certain irony in Washington’s changing relationship with its own leading AI firms. For years, U.S. trade policy has criticized China’s state-owned enterprises and state-directed industrial policies for distorting markets and blurring the boundary between commercial competition and government strategy. USTR’s annual China WTO compliance reports, for example, have repeatedly described China’s economic model as being founded on state intervention and non-market policies. Yet in the AI sector, public ownership and government equity stakes are no longer fringe ideas in Washington. OpenAI has reportedly discussed giving the U.S. government a 5 percent stake, while broader proposals for public ownership in AI companies have entered mainstream political debate. The United States is not becoming China, but strategic competition is clearly changing what forms of state involvement American policymakers are willing to consider.

Viewed from this perspective, recent attention over model access and distillation are not isolated incidents but logical consequences of a broader policy evolution. Once AI becomes a strategic technology comparable to advanced semiconductors or critical infrastructure, controlling its diffusion naturally becomes part of national security policy. Access itself becomes a policy instrument. Questions that were previously decided by markets—who can use a model, who can train on it, who can collaborate with developers—are increasingly becoming questions of strategic governance.

China, meanwhile, has responded through a noticeably different narrative. Rather than focusing primarily on restricting access, Beijing has continued to emphasize international AI cooperation, capacity building, and broader participation by developing countries. Recent proposals for a global AI cooperation organization, together with continued support for multilateral AI governance and technological cooperation with the Global South, reflect an effort to position AI as a driver of development rather than primarily as a strategic asset requiring containment. This does not mean China is indifferent to security, control, or technological leverage.  Rather, it suggests that Beijing is seeking to frame technological leadership through a different model of international engagement.

This approach is also reflected in China’s domestic AI development strategy. While Chinese companies continue to compete aggressively at the technological frontier, much of the country’s AI ecosystem has placed greater emphasis on open-weight models, lower-cost deployment, and wider commercial adoption. Rather than allowing market demand alone to determine AI development, Beijing has consistently framed artificial intelligence as a strategic capability that should serve broader development objectives—including industrial upgrading, productivity growth, digital transformation, and economic modernization. In this sense, China’s AI strategy has been development-driven from the outset, with commercial innovation expected to support national development priorities rather than define them. 

By contrast, the United States has struggled to generate comparable economy-wide demand outside the private technology sector; as a result, government procurement, defense applications, and national security use cases have become more important channels for sustaining and directing AI development. This reliance on hard demand is reinforced by domestic political constraints. In the United States, AI adoption is increasingly accompanied by concerns over job displacement, inequality, and local resistance to the infrastructure needed to support it. Together, these dynamics further reinforce the tendency to frame AI development through the lens of national security rather than economic transformation.

The resulting contrast is therefore larger than a disagreement over AI regulation. It reflects two increasingly distinct approaches to global governance. The emerging U.S. approach begins with security. It assumes that the most advanced technologies create strategic vulnerabilities and that international cooperation should therefore be organized through trusted networks, managed access, and selective participation. The Chinese approach begins with development. It emphasizes technological diffusion, international capacity building, and broader participation as essential components of global economic modernization. Both approaches are rooted in their respective strategic interests. Neither is simply a technological policy; both increasingly represent competing visions of how international cooperation itself should be organized.

This divergence is unlikely to remain confined to artificial intelligence. Similar patterns have already emerged across investment screening, industrial policy, export controls, supply chain security, and market access regulations. Over the past decade, Western economic policymaking has gradually evolved from prioritizing innovation, to securitizing economic interdependence, and now increasingly toward governing access. AI has simply become the latest—and perhaps clearest—manifestation of this broader transformation. Questions that once centered on how countries could innovate together are increasingly being replaced by questions about who should be permitted to participate in critical technological ecosystems in the first place.

This broader evolution also helps explain why many forms of international economic cooperation are becoming more difficult despite their obvious commercial benefits. Chinese overseas investment, for example, is frequently presented by Beijing as contributing to local industrial development, infrastructure, employment, and economic modernization. Yet policymakers in Washington and parts of Europe increasingly evaluate many of these same activities through a different lens—one focused on ownership structures, technology transfer, strategic dependence, and long-term security implications. The disagreement is therefore no longer confined to individual investment projects or specific technologies. It increasingly reflects competing assumptions about the purpose of international economic engagement itself.

For many countries, however, neither of these visions fully captures their own priorities. Most developing economies are less interested in joining technological blocs than in gaining access to affordable AI tools, digital infrastructure, investment, and innovation that support their own development strategies. Their objective is not to choose between Washington and Beijing, but to preserve sufficient policy flexibility to engage with both where doing so serves their national interests.

The emerging competition over AI therefore raises a question that extends far beyond artificial intelligence. The central issue is no longer simply who will build the world’s most capable AI systems. It is whether the governance of these technologies will gradually evolve into competing international systems that redefine the rules of cooperation itself. If AI becomes another arena for geopolitical bloc formation rather than collective problem-solving, the greatest consequence may not be technological fragmentation, but the fragmentation of global governance.