The Limits of AI as an Economic Weapon

Since World War II, Washington has leveraged the global presence of U.S. companies to advance its national security interests. AI models are the latest arena where U.S. and Chinese technology firms compete for market share. The U.S. government controls where Silicon Valley companies can export the best chips; intelligence agencies create backdoors on digital platforms to spy on adversaries worldwide. The world relies on these same technology firms, from Nvidia to Meta, which also grant Washington significant power. Many American technologists and policymakers hope AI technology could strengthen this tech advantage. However, changes in the AI sector might make this playbook stall before it starts, regardless of whether China truly “catches up” in its AI model capabilities.

The two AI superpowers fight for market share with distinctly different approaches. OpenAI and Anthropic keep their best models “closed,” not revealing the neural net weights, the numerical parameters that generate answers and form the core of modern large language models (LLMs), or the proprietary training techniques used. Silicon Valley labs then charge users for access to these proprietary models. Yet, this business model is not the only one. Leading Chinese firms Moonshot, Alibaba, Zhipu, and DeepSeek make the weights of many of their LLMs publicly available, allowing users to download the entire model and run it on an owned or rented graphics processing unit (GPU) cluster. Beyond selling AI usage, these Chinese AI companies earn revenue indirectly, through consumer-facing applications, hosting their models in the cloud, offering consulting, and granting licensing agreements.

Today, any user with access to enough computing power can download an LLM from the open internet, watch tutorials on how to “tune” its weights, and run the model themself, bypassing the guardrails that closed models impose. If current trends continue, global demand for AI products will likely fragment into a “divided market” split between a larger commoditized pool and a smaller premium echelon of offerings. That weakens the power of the inventing state. Currently, common tasks are still sent to expensive models, like simple advice, web research, and writing, but they will become table stakes for any future off-the-shelf model. Pricier models will dominate a smaller sliver of complex, innovative AI applications unknown today. Washington has in the past used critical Silicon Valley technology to coerce other states, but a divided AI market filled with free, open-weight models undermines future U.S. ability to use AI models as an economic weapon.

Such divided markets exist elsewhere today. Take medicine: 90% of all prescriptions are generic drugs but make up only 12% of spending; brands capture only 10% of prescriptions but take home 88% of all spending. The commoditized tier holds most of the volume while the smaller premium tier captures profit. Buyers will pay a premium when the gap in capabilities is worth it, but below that, they decide based on cost. Like in the drug market, we are headed towards a familiar future where the products split between commoditized and premium AI offerings.

Are these open models as capable as closed models? No, but they’re good enough. Benchmarks like Epoch and METR suggest even the best Chinese open models continue to trail the American frontier, but they don’t have to surpass it when cost is the concern. A divided market just needs the commoditized pool to satisfy basic demands, not rival the tier above it. Companies that use cheap open-weight models for routine tasks can still selectively pay for Claude when needed for tougher jobs.

Motivated businesses and savvy consumers are already waking up to the advantages in splitting their use between open and closed AI models, and open models are flooding the market from Chinese firms. Looking at “gateways”, companies that route LLM usage as a service, analysts can compare spending between open Chinese models and closed domestic AIs. Vercel’s September 2026 report showed the market dividing the same way drugs do: “Open-weight models processed 56% of August’s gateway tokens and accounted for 14% of spend.” In July, a CNBC report found that the share of tokens used by U.S. companies on Chinese models through OpenRouter, which lets developers access different models, has sat above 30% since February and risen to as high as 46%. Downloads tell a similar story. Hugging Face, a popular website to download model weights, found the majority of its 2025 trending models were “either developed in China or derivative of a model developed in China.” Sophisticated buyers reserve expensive models for the demanding work that requires them and route everything else toward the cheapest capable models, themselves predominantly Chinese.

Measuring AI usage is inherently difficult: tech reporter Neil Woodford pointed out that “OpenAI’s API alone moves roughly six and a half times as many tokens each month as every model on OpenRouter combined”. Finding accurate industry-wide numbers is near impossible. Adding to the difficulty, when companies and employees do choose to disclose how they use AI, it can come back to bite them. As open models compete for a higher share of tasks, firms want to stay quiet about it: revealing AI usage can disclose sensitive aspects of corporate strategy. And politically, with an administration obsessed with technological competition, employees holding the AI checkbook in the U.S. gain little goodwill if they reveal they are using models controlled by Beijing.

The stakes are high for a market transformation arriving unannounced. Divided markets such as U.S. pharmaceuticals didn’t imply the risk of losing a war. AI does. In his Palladium article “American AI May Not Survive Chinese Open-Source,” Daniel Francis frames the flood of Chinese open models as an “economic attack on U.S. military development.” These military concerns should be front of mind when considering national security, but there are economic weapons at stake too. Francis argues that as AI capabilities improve exponentially, AI will become “a permanent strategic advantage technology that must be pursued at nearly any cost.” If the commoditized layer in the private sector continues to grow, this starves the frontier labs of crucial profits they rely upon to further develop the best models for the military.

Francis is right to focus on the U.S. military. When policymakers consider how to secure America, “national security” concerns often lead them to prioritize military might. But since the Second World War, policymakers have also realized that thanks to the United States’ lead in the global economy, they wield tools capable of coercing other states. After a long run of U.S. technology dominance, a dividing AI market weakens the next U.S. economic weapon on the tech frontier.

Throughout the Cold War and after, the United States has played a delicate balancing act of leveraging new waves of technology to serve the national interest. Foreign policy makers in Washington benefit each time Silicon Valley invents anew for the world—each wave of innovation centers Bay Area businesses at nodes of webs that span global manufacturing chains. Through desktops in the 1970s, operating systems in the 1980s, the internet and e-commerce over the 1990s, search engines, social networks, and smartphones in the 2000s, and streaming and cloud computing in the 2010s, U.S. firms have gone abroad with Washington behind them. Technology companies seek to internationalize their businesses to improve profit margins and reach new customers, but the U.S. mandate to protect national security follows behind them.

When technology firms go abroad, they do not set out to create ways to coerce geopolitical adversaries, but they bring home economic weapons for Washington all the same. When U.S. policymakers became concerned about the Chinese telecommunications giant Huawei’s international expansion, Washington cut Huawei off from Google’s Android in 2019 and TSMC’s chips in 2020. Subsequently, Huawei’s global smartphone market share collapsed from 20% in 2020 to virtually nothing outside mainland China by 2024. The webs that U.S.-aligned technology companies wove to maximize profit end up making even foreign champions reliant on them. Henry Farrell and Abraham Newman describe this underground empire as a world of “weaponized interdependence.” Firms chasing profit “inadvertently provide states, which are concerned with political as well as economic considerations, with the necessary levers to extend their influence across borders.” Washington gains policy levers with its ability to force technology companies to coerce others on its behalf.

In the late 20th century, Cisco found itself in this web of weaponized interdependence. Over the course of the 1990s, Cisco commercialized an array of profitable products that built the backbone of the internet and provided a node that Washington could control. Its routers trafficked most of the world’s online data. In fiscal year 1998, Cisco sold $8.5 billion of equipment that cost it $2.9 billion to make. Cisco sold network infrastructure to make money, not to spy. But so much of the internet ran through Cisco hardware that a means of U.S. control emerged. Glenn Greenwald’s No Place to Hide revealed photos of National Security Agency (NSA) employees planting “beacons” into Cisco equipment.

For this strategy to be successful, it requires control of an economic “chokepoint.” Former Treasury official and international sanctions expert Edward Fishman defines these chokepoints by three conditions: “a single country or coalition of close allies possesses a dominant, concentrated market share… substitutes are unavailable in the short term… and the country or coalition can weaponize its position in ways that impose asymmetric pressure, inflicting substantial pain on the target while suffering minimal self-harm.” Washington remains hopeful: U.S. AI frontier labs are leading innovation and chasing profits that pose to create a new chokepoint for coercion. Fishman himself hypothesizes that “if the United States can sustain its lead in artificial intelligence, the models that Silicon Valley firms produce could become the backbone of global business, handing Washington another chokepoint of extraordinary reach.”

Yet, a divided AI market denies Washington the use of AI as a chokepoint, crippling its viability as an economic weapon for U.S. policymakers. Although labs in Silicon Valley may lead the frontier, a plethora of open-weight Chinese good-enough alternatives spreads the world’s usage across models Washington cannot control. Substitutes exist, are free to download, and will continue to improve in the future. Because of these factors, there’s little chance for asymmetric pain to be inflicted by U.S. control of AI models. Even worse, trying to use AI as a chokepoint could backfire: sanctioning Chinese models could spur more open-source development within China, Europe, or even the United States itself. Fishman warns of this feedback loop, in which “every time Washington weaponizes a chokepoint, other countries take steps to insulate themselves from it”.

Today, both Washington and Silicon Valley are facing the economic pressure of China’s AI industry. Washington cannot restrict access to open-source models without upsetting thrifty players at home and abroad. If companies utilizing Chinese models were put on some sort of entity list, it would, as Fishman warns, needlessly disrupt U.S. firms and weaken their influence. A ban would also spook U.S. allies into pushing for their own sovereign models, probably themselves chasing open-weight Chinese models that could be run locally.

In Silicon Valley, labs are locked into a drive for profits that disincentivize them from diverting resources to open, unprofitable models. Unlike their more public-spirited Chinese counterparts, U.S. lab