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Competing Interests, Common Narrative: How Industry Shapes the AI Race

Different firms in the AI value chain invoke the AI race narrative, but with different policy preferences in mind.

A recent Wall Street Journal article warned that the “[c]lampdown on top U.S. artificial intelligence is fueling concern that Washington is handing Beijing a cyberwarfare advantage.” It went on to note that China’s release of Z.ai by Zhipu AI is on par with Anthropic’s Mythos model in detecting software vulnerabilities. The article, along with a growing body of recent commentary, taps into the prevailing China-U.S. AI race narrative at a time when the United States is deliberating a unified AI policy and reportedly considering banning Chinese open weight models. 

The AI race narrative holds that the United States and China are engaged in a zero-sum contest to develop and deploy frontier AI so as to accrue lasting geoeconomic advantage. In a May 2026 article for the Transformer newsletter, Yi-Ling Liu outlined how Silicon Valley’s major companies have themselves pushed this AI race narrative, potentially overstating the extent of the competition. However, the interests of these companies are not uniform, nor are the policies that they advocate. Different firms across the AI value chain have continued to invoke the AI race narrative to advance policy preferences that align with their commercial interests.  

Given the concentrated nature of the global AI supply chain, a select few companies have immense stakes in how policies and regulations on AI evolve and, consequently, strong incentives to shape them. Sitting atop the hardware segment of the AI supply chain is Nvidia, which accounts for over 80 percent of global AI chip sales. Nvidia’s GPUs form the backbone of the compute infrastructure required to train and develop generative AI models. This market position, while enviable, has exposed Nvidia to the broader geoeconomic disruptions that have emanated from the AI race with China. 

In April 2025, building on Biden-era export controls, the Trump administration introduced measures that would require Nvidia to secure additional export licenses for the sale of its H20 chips to China, which would result in a $5.5 billion accounting charge. In response, Nvidia CEO Jensen Huang extensively lobbied the administration to reverse this policy and reconsider its broader approach toward the export of AI chips to China. 

As part of this effort, Nvidia skillfully invoked the AI race narrative to achieve its objective. In a report presented to the government, the company argued that existing chip restrictions would only accelerate Huawei’s production of indigenous alternatives, increase its market share, and pave the way for the company to eventually become a competitive threat, thus eroding U.S. technological leadership in AI. While the basis for these claims is contested, it proved to be effective in convincing the Trump administration to relax its export control policy, enabling Nvidia to resume sales to China. The Trump administration’s decision to permit H20 exports was reportedly linked to a trade deal for rare earths from China. The subsequent policy shift allowing for the export of more advanced H200 chips, in limited quantities, can be understood as part of Nvidia’s broader lobbying campaign.

Beyond hardware, American companies also currently lead the development and deployment of closed-source proprietary AI models. In contrast, Chinese companies have developed and deployed open-weight models that are fast closing this performance gap and offering lower cost alternatives. This progress is driven by a range of factors, including AI model distillation – a method that involves transferring knowledge from larger AI models to smaller, faster, and cheaper models. Companies such as Anthropic and OpenAI have accused Chinese AI developers of using distillation to reproduce the capabilities of proprietary models. In a recent press release, Anthropic claimed that three Chinese AI labs were involved in an industrial scale campaign to extract capabilities from Claude, necessitating a coordinated response from “industry players, policy makers, and the global AI community.” More notably, Anthropic highlighted its consistent support of “export controls to help maintain America’s lead in AI” and argued that such distillation attempts by Chinese labs “reinforce the rationale for export controls.” 

While the legality of distillation is beyond the scope of this article, it is pertinent to examine Anthropic’s framing of the incident. By portraying distillation attempts by Chinese AI laboratories as akin to industrial intellectual property theft, Anthropic has positioned its AI models as strategic assets in the ongoing AI race that require regulatory protection and, if necessary, punitive enforcement by the U.S. government. As Craig Smith aptly noted, “Anthropic’s larger project is to persuade Washington to define the rules of the frontier so that model extraction becomes a sanctionable offense.” 

Recent developments in Washington indicate that Anthropic’s efforts for such a policy shift may be gaining traction. In a recent tweet, Director of the White House Office of Science and Technology Policy Michael Krastios referenced Chinese AI Lab Moonshot’s efforts to distill from Anthropic’s Fable to develop its K3 model using sophisticated means. He also reiterated the U.S. commitment to fostering a competitive ecosystem for AI development, while stressing that “large-scale, covert industrial distillation aimed at stealing proprietary U.S.........

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