America needs to stop getting shocked by Chinese AI

Last week, two Chinese AI companies unveiled models they say can credibly compete with the best systems from OpenAI and Anthropic. The response was swift and predictable. Markets wobbled, commentators declared Silicon Valley shooketh, and policymakers reached for the familiar language of arms races and wake-up calls.

America needs to stop getting shocked by Chinese AI

Kimi K3 and Qwen3.8 should come as no surprise.

Kimi K3 and Qwen3.8 should come as no surprise.

In one headline, *The Associated Press *said a Chinese model had taken the “US tech industry by surprise.” Bloomberg described it as a “surprise breakthrough” that is “roiling markets” and sending global tech stocks tumbling over concerns it could force US firms to rethink their gargantuan spending on data centers, chips, and other AI infrastructure. Business Insider questioned whether the launch is “The next DeepSeek?”, referring to the Chinese model that blindsided the US AI industry last year. Xprize founder Peter Diamandis went as far to call the release America’s “AI Sputnik moment,” referring to the Soviet satellite launch at the height of the Cold War that encouraged significant US investment in its science and space programs. Of course, DeepSeek was also widely described as America’s AI Sputnik moment, a comparison that felt less gratuitous then as DeepSeek appeared to arrive with little warning, challenged the prevailing assumptions about the costs of frontier AI, and prompted immediate reactions across the technology and financial sectors.

What is actually surprising is that the model announcements were a surprise at all. For years, we have been warned that China was catching up in AI. Yet the world is shocked when it starts to look like the moment may have arrived.

US and Chinese companies train almost all of the world’s most-used AI models, and six of the top 10 AI tools on OpenRouter’s leaderboard tracking token consumption and benchmarks were Chinese. The performance gap has been narrowing for some time, with recent models from companies like Z.ai and DeepSeek seen as highly competitive with top-tier offerings from US labs like Anthropic and OpenAI. Chinese models are also significantly cheaper to use, and reports suggest US companies are increasingly turning to Chinese tools as the cost of using domestic providers surge.

Beijing has also been keen to support homegrown AI efforts, including incentivizing and funding innovation and cracking down on firms trying to shed their ties to China. Meanwhile, Washington’s AI strategy has often veered between heavy-handed intervention that has left allies questioning America’s reliability and a laissez-faire assumption that markets will see things right. It is a difficult approach to maintain against a competitor prepared to mobilize the full force of the state behind a single technological goal.

Beijing-based startup Moonshot AI, one of China’s leading AI model developers, unveiled a new flagship model on Friday, claiming it outperforms nearly every US model, trailing only OpenAI’s GPT-5.6 Sol and Anthropic’s Claude Fable 5. Moonshot is also pricing Kimi K3 aggressively, charging $15 per million output tokens, compared with roughly $30 for GPT-5.6 Sol and $50 for Fable 5. Demand was so strong after the launch that Moonshot, the company claimed, that it temporarily paused new subscriptions after the service was overwhelmed. The majority of responses mainly focus on this release.

Days later, Chinese tech titan Alibaba followed with a preview of Qwen3.8. It described the new model as “one of the most powerful model[s] available today” and “second only to Fable 5.” This only added to the uproar Kimi K3 had caused.

Crucially, both companies plan to make their new flagship models publicly available. Both Moonshot and Alibaba say they plan to release their models as open weight, which would allow developers to download, use, and modify the core values created during the AI’s training that shape its responses. It stands in stark contrast to the closed, proprietary approach to frontier models taken by most leading US AI labs, including OpenAI, Anthropic, and Google.

The economics deserve particularly close scrutiny. There’s the whole unsettled debate over whether, and to what degree, Chinese companies are — as American firms accuse — using US models to train their own, which could improve performance at a fraction of the cost. Tokens are not directly comparable between models, and token prices alone give an incomplete picture of how much it costs to use an AI system. A more expensive model may, for example, generate better responses with fewer tokens. Companies also routinely subsidize inference costs to win over customers. Cheaper, in other words, does not automatically mean better, or even less expensive overall.

Still, the possibility remains that Chinese labs may eventually produce models that are not merely cheap substitutes, but systems that could genuinely match or outperform their US rivals. Even companies that trail the frontier slightly could still have an enormous impact if their models are good enough, easier or cheaper to deploy, or available on more attractive terms. This could have direct consequences for US companies, the wider economy, and national security.

Anthropic and OpenAI are both gearing up for what could potentially be trillion dollar IPOs, valuations that in part depend on the expectation that they will dominate the global AI market. Capable Chinese models challenge that assumption, and could potentially draw away customers, squeeze margins, and weaken growth assumptions underpinning those valuations. Given how expensive American AI has become, some US startups are already reportedly turning to cheaper Chinese models. There is a wider market risk, too, reaching far beyond a handful of AI players. Tech stocks make up an outsized share of US markets, and much of that recent growth has been tied to expectations that AI demand will continue to soar. Companies have piled hundreds of billions of dollars into data centers, chips, energy, and other infrastructure that relies on the assumption American firms will continue to dominate. If Chinese labs can capture some of that demand, or show models that can be produced and operated for less, investors would inevitably question whether those costs are justified. Given the money involved, any reassessment on their part would have ripple effects throughout all of these industries, as well as the millions of people with savings or pensions exposed to them.

There are security considerations, too. Highly capable open Chinese models, even if trailing the US frontier, could make advanced AI systems available to a much wider range of users, notably in cases where US companies restrict access or impose stronger safeguards. When the US government demanded Anthropic limit access to its latest models, cybersecurity leaders warned that doing so would make it harder for defenders to find and fix vulnerabilities. Those restrictions are harder to justify if comparable models are available elsewhere. Organizations denied access to US models may feel compelled to rely on Chinese alternatives to secure their networks, or else accept the greater exposure to attackers able to use the same tools. Already, reports are starting to emerge where Kimi K3 identified and fixed cyber vulnerabilities that OpenAI’s Codex and Anthropic’s Fable would not touch due to safety guardrails. Even less broadly capable models can still pose a threat and some already appear to be doing so. In June, China’s Z.ai claimed its GLM-5.2 model could match Anthropic’s Mythos on cybersecurity tasks, even though it trailed in more general tasks.

As neither model has yet been fully released, it is still difficult to independently assess how capable either actually is, and companies’ benchmark claims should be treated with caution. Even so, there has been little public suggestion that the companies are fundamenta