In August, the Trump administration reversed course on Nvidia’s H20 AI chips, lifting previous restrictions under a 15 percent revenue-share condition, and is now considering allowing exports of a slightly downgraded version of Nvidia’s Blackwell — America’s most advanced AI chip. Proponents argue it will keep Chinese developers “addicted to the American technology stack” because Nvidia’s technology is hard to replace or replicate. The evidence suggests otherwise: in the long term, selling U.S. AI chips to China is unlikely to create lasting dependencies on the American tech ecosystem.
At the heart of the “addiction” theory lies the belief that using American chips creates vendor lock-in, compelling foreign AI developers to remain within U.S. tech platforms and to keep buying American chips. These arguments have some merit. Nvidia’s proprietary networking equipment and stack strongly incentivizes engineers to stay within its ecosystem, having refined its CUDA software platform over nearly two decades into a “strategic moat.” With its vast libraries of pre-written code and supporting tools, CUDA allows developers to leverage the parallel computing capabilities of Nvidia chips. When AI companies build on and develop expertise in Nvidia’s hardware and software ecosystems, it creates a degree of path dependence. This lock-in makes it more likely that each additional dollar of AI investment flows to and strengthens the American — rather than Chinese — AI ecosystem, reinforcing the U.S. lead. Proponents use this argument to justify selling AI chips to China: get the country’s tech sector “hooked” on Nvidia to capture greater market share and redirect Chinese investment toward U.S. AI innovation.
But making China dependent on U.S. technology is not that straightforward. AI chips are more akin to generators than utility companies. Generators are necessary to produce power, but once the generator is running, the manufacturer does not control what is powered with it. Generators can be swapped out for others, combined with local power sources, or integrated into hybrid energy systems. Unlike a utility company that maintains persistent control over electricity supply and pricing, chips are a one-off input — value-neutral hardware that runs whatever code developers choose. Developers may use CUDA today, but they can layer domestic software and tools on American hardware tomorrow. They can gradually integrate these systems with local infrastructure as their domestic ecosystem matures. Beijing has a well-funded national strategy to indigenize chip production, and access to U.S. chips will not meaningfully diminish these efforts. Rather than creating lasting dependence, exporting U.S. chips will simply expedite China’s AI progress as it scales its indigenous chip manufacturing capacity.
The United States’ own AI labs show that reducing reliance on Nvidia’s proprietary ecosystem is not only possible, but already happening. Anthropic originally relied on Nvidia graphics processing units (GPUs) to train its flagship model, Claude, but it has since shifted most of its computing needs to other ecosystems. Today, Anthropic optimizes for training on AWS’s Trainium hardware, while also using a mix of Google tensor processing units (TPUs) and Nvidia GPUs. Google DeepMind followed a similar trajectory. The company once depended on Nvidia GPUs for early breakthroughs, but it has since moved beyond CUDA and now trains its capable Gemini models on Google TPUs. Both shifts highlight a crucial lesson: Nvidia’s chips are less of an addiction than a gateway drug. There is clear proof that the Nvidia moat is surmountable. If U.S. AI labs with no political mandate to leave the Nvidia stack can accomplish this transition, then Chinese labs facing clear national incentives can likely do the same.
Switching costs are a significant, but surmountable, engineering challenge that Chinese developers are already planning for. Beijing has signaled that reliance on American AI chips is risky through energy efficiency rules that discourage the use of certain U.S. chips, along with public warnings about their security risks. Similarly, U.S. export controls have already undercut the Chinese AI industry’s perception of U.S. firms as trusted suppliers. Chinese firms will act accordingly to avoid long-term lock-in through investment in indigenization and diversification.
These shifts are already underway. DeepSeek, one of China’s top AI developers, is both dependent on U.S. chips for AI training and adapting its software and processes to deploy its models on Chinese AI chips. Likewise, Huawei is investing in technology to make CUDA work with non-Nvidia chips. These challenges will not be overcome overnight – China will face setbacks along the way. But once refined, hardware-agnostic platforms have the potential to harness the power of American and Chinese chips alike, overcoming software moats. These efforts are not afterthoughts; they align with China’s national strategy and chip investment fund, and guard against volatile U.S. export controls.
U.S. policy should not rest on the illusion that selling chips can trap China inside the American tech ecosystem. That advantage is fleeting. What ultimately matters is how much computing power China can marshal. If Washington wants lasting leverage, it should look higher up the stack — renting cloud-based compute, or exporting American AI models and applications — services that generate revenue while retaining influence. Selling chips alone will not create a lasting “addiction,” but it will provide China with the building blocks for AI competitiveness.
