A new front has opened in the U.S.-China competition in artificial intelligence: open-weight, local AI models. Until recently, the most capable AI models were too big and too costly to run anywhere but in giant data centers packed with expensive, specialized chips. But now these systems are rapidly migrating from the cloud to consumer hardware—including laptops and mobile devices—where they can answer questions, write code, and take actions on a user’s behalf without sending data to a remote server. Thanks to technological advances in both AI models and chips, the so-called open-weight AI models that increasingly underpin most local AI deployments are smarter and smaller than their predecessors and can be freely downloaded from the internet, modified, and deployed without a centralized provider.

These rapid local AI systems promise to democratize access to powerful technology, reduce costs, and give users more control over the tools they use daily. But they also risks entrenching a profound asymmetry. The most capable open-weight models now flowing onto local devices are disproportionately Chinese. And if current conditions continue, China will retain this powerful edge. Moreover, many of the best open-weight models have been built by Chinese companies that systematically extract the capabilities of frontier American systems by applying a process called distillation—in which a smaller, more efficient AI model is trained to mimic a more sophisticated one—at an industrial scale. It is an approach that U.S. firms, constrained by contracts and legal norms, cannot follow because the terms of service of every major AI provider prohibit using model outputs to train competing systems.

Given these dynamics, the central contest in AI is no longer limited to development. It has expanded to include distribution—that is, to determining which country’s models, chips, and software frameworks will become the default on billions of devices. At present, U.S. firms design and sell the best chips and frontier AI models. But Chinese firms are distilling these models, compressing them to run inexpensively on cheap hardware, and shipping the results to the world. Indeed, in many cases, they are shipping the results back to the United States. The resulting Chinese advantage distorts the market and leaves U.S. firms and global users that need to build on open-weight AI with the unenviable choice of using Chinese models or falling behind.

It now seems possible that the United States could win the AI training battle and lose the distribution war—not because it failed on technical merit, but because it failed to ensure a level playing field. Fortunately, there is a way to respond. Over the past decade, Washington has honed a playbook for dealing with China’s anticompetitive practices. It must now adapt those strategies to promote U.S. progress and leadership in open-weight AI while penalizing actions by Chinese entities that cross the boundary between common research practice and economic warfare. This strategy carries a risk: if executed too broadly, it could damage U.S. AI leadership by constricting access to the very talent and models Washington needs to succeed. Executed precisely, however, this strategy could complement private-sector efforts to combat unauthorized distillation and ensure that the United States’ AI advantage continues into the next decade.

Until recently, ordinary users struggled to run AI locally. The chips inside everyday devices, including laptops and phones, had too little memory to fit capable models and too little computing power to run them at usable speeds. And the software that enabled these models to reliably perform multistep tasks (such as reading a document, drafting a reply, and saving it to the right folder) was not yet mature.

Today, that is no longer the case. Hardware has become much more efficient, as have local models themselves. A recent study by researchers at Stanford showed that the share of queries that local models can accurately answer rose from 23 percent in 2023 to 71 percent in 2025. This means that a person can now download an open-weight AI model directly onto a laptop or smartphone and run it without an internet connection, with queries answered by the device’s own chip rather than by a remote data center.

Demand for local AI systems is increasing for a variety of reasons. Developers want models they can download, modify, and deploy independently. Businesses want to keep sensitive data on their own infrastructure, and AI companies need models that........

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