For a global industry convinced that US large language models (LLMs) were unassailable—built by companies with oceans of compute, elite talent, large capital infusions, and company valuations the size of small-nation GDP—DeepSeek upended assumptions. The Chinese model emerged at a time that China’s AI sector seemed beleaguered by chip bans and a slowing economy and against a backdrop in which private sector investment into the US sector was twelve times China’s and twenty-four times the UK’s. Analysts found that DeepSeek’s parent, High-Flyer, hadn’t spent its way into contention; instead, it had been crafty: focusing on targeted domains, lean training, and aggressive energy efficiency. While critics whispered about secret Nvidia chips, covert ChatGPT usage, or hidden costs, the signal was clear: US companies wouldn’t hold a monopoly on advanced LLMs.

This “DeepSeek moment” ignited a debate: “Are Chinese companies catching up in AI?” That framing misses the point. The AI stack is broad—spanning energy, infrastructure, chips, foundational research, and application deployment—and across that landscape the US and China possess different strengths. The real picture is that the world’s two dominant AI markets aren’t competing in the same race; they’re running parallel ones shaped by different economic constraints, cost structures, and market demands—and that divergence will shape global AI over the next decade.

“If you look at the future of deep tech, it’s clear that the US and China are like the two sides of [the traditional Chinese martial arts practice] tai chi, with its black-and-white yin and yang symbol—each with unique strengths, each pushing the other forward,” says Soul Capital’s Herry Han, “It’s not just competition. It’s a dynamic balance.” In the US, AI development follows a capital-intensive path: massive data centers, soaring talent costs, and billion-dollar frontier science bets. Well-funded US leaders will keep pushing toward artificial general intelligence (AGI) and increasingly sophisticated agentic systems designed to pursue goals through multi-step planning and self-directed action.

Chinese companies face a different competitive reality: relatively tighter capital, more limited access to high-end computing power, and a smaller domestic profit pool. Its companies respond by leaning hard into open-source, cost efficiency, fast application layer innovation, and global markets for monetization.

China’s AI story isn’t about catching up or winning the frontier model race but rather by industrializing the use of AI. Chinese AI firms are building an alternative ecosystem that’s leaner by necessity, more open by design, and working to wire into the economy. This is prompting its adoption across start-ups, universities, and mid-market firms worldwide. As global industry leaders continue a preference for keeping their most capable models “closed-source”—which keeps source code proprietary or secret—Chinese models are diffusing through easy access and utility, quietly shaping global AI practices and creating a new, technical form of soft power.

China’s AI Efficiency Machine: Low Costs and Constraints

An equivalent dollar in China goes farther—a fact that reframes the AI investment picture. While the US dominates total AI funding by a wide margin, Chinese firms often produce more output per unit of capital. 

This efficiency begins with input costs. AI engineers earn about 402,000 RMB (about $57,000) per year, far below US salary norms. China also trains one-and-a-half to two times as many AI-relevant PhDs as the US, and many trained researchers are returning home, creating a large, affordable talent pipeline. Data centers benefit from cheaper electricity, discounted land, and aggressive local subsidies—reinforced by national policies that treat large-scale computing as strategic infrastructure. In some provinces, electricity costs are halved for facilities using chips.

These advantages are further sharpened by the constraints that characterize China’s AI ecosystem. Foreign direct investment into China has fallen more than two-thirds since 2019, access to Western markets has tightened, high-end domestic GPUs lag behind imported ones, and regional grids are straining under data center loads. Moreover, China’s business-to-business (B&B) market is characterized by a low willingness to pay for services; firms routinely build software tools in-house rather than buy them. The result is a domestic software market roughly one-quarter the size of the US’s $237 billion industry, making it difficult for many AI start-ups to reach scale or charge premium prices. In addition, large corporation culture—especially state-owned enterprises—prizes headcount and incremental change, resists automation, and blunts AI’s potential impact.

Angel investor Jun Xu captures a core monetization constraint: AI’s total addressable market—the revenues available for a company’s product or service—tracks the cost of white-collar labor. “And that pool is simply much larger in the US and other developed markets than in China because salaries are much higher,” Jun says. “China’s AI problem isn’t chips or models or supply—it’s demand. Demand is cheaper and smaller.” These pressures have pushed Chinese firms toward efficiency and ingenuity.

Nvidia’s Jensen Huang said in 2025 that restricting US chip sales to China would only accelerate China’s domestic push. “Local companies are very, very talented and very determined,” he said, “and the export control gave them the spirit, the energy, and the government support to accelerate their development.” Altogether, the ecosystem has created a set of Chinese open-source LLMs operating along the “efficient frontier,” delivering leaner architectures and stronger reasoning at modest compute levels. By late 2025, DeepSeek’s parent announced that one million units of output could be had for about 3 RMB—that’s about fifty cents and a twentieth the cost of ChatGPT at the time.

This combination of high efficiency, thin margins, and a smaller domestic monetization pool shapes how Chinese AI companies scale. Many are becoming what we call “skinny athletes”—lean, fast, and relentlessly efficient—which not only affects how they compete at home but also how and where they grow. For many, that increasingly means serving customers abroad, and the use of generative AI means that language, localization, and customer support are no longer decisive obstacles to global expansion.

“All AI start-ups have an international strategy from day one in China, and Southeast Asia is top of the list,” says Cindy Chow, CEO of the Alibaba Hong Kong Entrepreneurs Fund, which recently launched a new fund dedicated to AI application start-ups. “In fact, many Southeast Asian conglomerates and financial institutions are keen to invest with us, as they believe that the region won’t catch up in AI development on their own, and that accessing advancements from China is key to staying competitive.”

The Chinese AI unicorn 01.AI shows what global expansion looks like when delivering value is the organizing principle. Founded by Kai-Fu Lee, an ex-Google China chief and president of leading tech VC Sinovation Ventures, the company started as a pure LLM developer in 2023 but quickly shifted toward enterprise AI agents, or what they call “super-employees” designed for functions such as insurance brokering, procurement, and logistics optimization. “Monetization challenges force AI to accelerate faster,” says Ning Ning, 01.AI’s vice president of international business and AI consulting. “The future of enterprise AI is not about selling technology but rather making AI accountable for business outcomes.”

In practice, delivering value means embedding “24/7 digital specialists” directly into enterprise operations. In one deployment with a Perth, Australia-based mining company, 01.AI’s engineers positioned its AI agents as “teammates” alongside employees: a logistics scheduler to optimize rail and port traffic; a procurement agent that reads vendor emails and generates purchase orders; an operations planner to juggle trucks and crews. Each agent retrains itself as conditions change, says Ning, continuously improving its performance.

China’s Open-Source Bet: Diffusion and Collaboration

China’s open-source culture has become an accelerant. Hundreds of teams now openly release their model architectures and weights—the blueprints and parameters that a model acquires through training—creating a shared infrastructure anyone can examine, build upon, and improve.

Chinese AI firms are pragmatic, says Chloe Fang, a founder in the text-to-image/video space. They aim for AI that delivers value and often use open-source “as a global hook to attract global users,” she says. “They start building brand equity and word of mouth—and then release better closed-source models later on.” Chinese models now lead in several key generative media categories, accounting for five of the top ten image-to-video models globally and three of the top ten text-to-video and image-editing models.

This open-source approach aligns with national priorities emphasizing “open ecosystems,” deep integration with all industries and sectors of the economy and society, and “increased global cooperation”Chinese open-source LLMs—led by Qwen, MiniMax, and DeepSeek—now account for one-third of global LLM usage, up from virtually nothing in late 2024.

Start-ups and companies from Silicon Valley to Africa and Southeast Asia now run Chinese models because they’re accessible, transparent, and far cheaper to run than many US alternatives. You can remove anything you want, add anything you need. That flexibility and openness actually gains trust,” says Sinovation Ventures founder Kai-Fu Lee, an ex-Google China chief and an influential AI voice. He points out many US institutions, students, and researchers are using Chinese models “not because they’re Chinese but because they’re open.”

Jian Wang, founder of Alibaba Cloud, argues that this moment in AI echoes the late 1990s: Netscape made its browser free and its code publicly available—an open-source “watershed” that helped catalyze the commercial internet. Today’s AI parallel is similar, he says: foundational tools are now open and collectively improvable, so the constraint is no longer whether source code itself is accessible. Rather, the industry is shifting toward what Jian calls “ ‘open resources,’ especially model weights, data, and computing resources, which are indispensable to advancing this industry.” As more of these resources become available, the more developers can skip over the massive costs of reproducing work already done by others—a key variable in accelerating the spread of AI, he says.

That said, models must also be “differentiable and sufficiently capable,” researchers from Andreesen Horowitz find. In this fast-moving industry, users who find a model unreliable or a poor fit will quickly migrate, “finding value in a wider array of options, rather than defaulting to one ‘best’ choice.

We expect the next wave of momentum to emerge at the application layer, with gains to society realized only as organizations and

individuals adapt how they work. The potential of AI is universal; the challenge of capturing it remains distinctly human.

Adapted from The Next China Is Still China: An Insider’s Playbook for Winning in the New Era by Joe Ngai and Nick Leung. Copyright © 2026 by Joe Ngai and Nick Leung. Reprinted by permission of Scribner, an imprint of Simon & Schuster, LLC.