{"id":1565,"date":"2026-06-16T22:11:21","date_gmt":"2026-06-16T22:11:21","guid":{"rendered":"https:\/\/crosscountrymovingteams.com\/?p=1565"},"modified":"2026-06-16T22:11:21","modified_gmt":"2026-06-16T22:11:21","slug":"china-isnt-trying-to-beat-the-u-s-at-ai-its-playing-a-completely-different-game","status":"publish","type":"post","link":"https:\/\/crosscountrymovingteams.com\/?p=1565","title":{"rendered":"China isn\u2019t trying to beat the U.S. at AI \u2014 it\u2019s playing a completely different game"},"content":{"rendered":"
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For a global industry convinced that US large language models (LLMs) were unassailable\u2014built by companies with oceans of compute, elite talent, large capital infusions, and company valuations the size of small-nation GDP\u2014DeepSeek upended assumptions. The Chinese model emerged at a time that China\u2019s 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\u2019s and twenty-four times the UK\u2019s. Analysts found that DeepSeek\u2019s parent, High-Flyer, hadn\u2019t 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\u2019t hold a monopoly on advanced LLMs.<\/p>\n

Read more AI won\u2019t transform your business\u2014until you redesign work itself<\/a><\/p>\n

This \u201cDeepSeek moment\u201d ignited a debate: \u201cAre Chinese companies catching up in AI?\u201d That framing misses the point. The AI stack is broad\u2014spanning energy, infrastructure, chips, foundational research, and application deployment\u2014and across that landscape the US and China possess different strengths. The real picture is that the world\u2019s two dominant AI markets aren\u2019t competing in the same race; they\u2019re running parallel ones shaped by different economic constraints, cost structures, and market demands\u2014and that divergence will shape global AI over the next decade.<\/p>\n

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\u201cIf you look at the future of deep tech, it\u2019s 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\u2014each with unique strengths, each pushing the other forward,\u201d says Soul Capital\u2019s Herry Han, \u201cIt\u2019s not just competition. It\u2019s a dynamic balance.\u201d 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.<\/p>\n

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.<\/p>\n

China\u2019s AI story isn\u2019t 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\u2019s 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 \u201cclosed-source\u201d\u2014which keeps source code proprietary or secret\u2014Chinese models are diffusing through easy access and utility, quietly shaping global AI practices and creating a new, technical form of soft power.<\/p>\n

An equivalent dollar in China goes farther\u2014a 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.\u00a0<\/p>\n

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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\u2014reinforced by national policies that treat large-scale computing as strategic infrastructure. In some provinces, electricity costs are halved for facilities using chips.<\/p>\n

These advantages are further sharpened by the constraints that characterize China\u2019s 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\u2019s 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\u2019s $237 billion industry, making it difficult for many AI start-ups to reach scale or charge premium prices. In addition, large corporation culture\u2014especially state-owned enterprises\u2014prizes headcount and incremental change, resists automation, and blunts AI\u2019s potential impact.<\/p>\n

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Angel investor Jun Xu captures a core monetization constraint: AI\u2019s total addressable market\u2014the revenues available for a company\u2019s product or service\u2014tracks the cost of white-collar labor. \u201cAnd that pool is simply much larger in the US and other developed markets than in China because salaries are much higher,\u201d Jun says. \u201cChina\u2019s AI problem isn\u2019t chips or models or supply\u2014it\u2019s demand. Demand is cheaper and smaller.\u201d These pressures have pushed Chinese firms toward efficiency and ingenuity.<\/p>\n

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Nvidia\u2019s Jensen Huang said in 2025 that restricting US chip sales to China would only accelerate China\u2019s domestic push. \u201cLocal companies are very, very talented and very determined,\u201d he said, \u201cand the export control gave them the spirit, the energy, and the government support to accelerate their development.\u201d Altogether, the ecosystem has created a set of Chinese open-source LLMs operating along the \u201cefficient frontier,\u201d delivering leaner architectures and stronger reasoning at modest compute levels. By late 2025, DeepSeek\u2019s parent announced that one million units of output could be had for about 3 RMB\u2014that\u2019s about fifty cents and a twentieth the cost of ChatGPT at the time.<\/p>\n

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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 \u201cskinny athletes\u201d\u2014lean, fast, and relentlessly efficient\u2014which 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.<\/p>\n

\u201cAll AI start-ups have an international strategy from day one in China, and Southeast Asia is top of the list,\u201d says Cindy Chow, CEO of the Alibaba Hong Kong Entrepreneurs Fund, which recently launched a new fund dedicated to AI application start-ups. \u201cIn fact, many Southeast Asian conglomerates and financial institutions are keen to invest with us, as they believe that the region won\u2019t catch up in AI development on their own, and that accessing advancements from China is key to staying competitive.\u201d<\/p>\n

Read more Robinhood announces it will reduce headcount by 10%, CEO seeks to avoid \u2018heavily-layered\u2019 organization<\/a><\/p>\n

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 \u201csuper-employees\u201d designed for functions such as insurance brokering, procurement, and logistics optimization. \u201cMonetization challenges force AI to accelerate faster,\u201d says Ning Ning, 01.AI\u2019s vice president of international business and AI consulting. \u201cThe future of enterprise AI is not about selling technology but rather making AI accountable for business outcomes.\u201d<\/p>\n

In practice, delivering value means embedding \u201c24\/7 digital specialists\u201d directly into enterprise operations. In one deployment with a Perth, Australia-based mining company, 01.AI\u2019s engineers positioned its AI agents as \u201cteammates\u201d 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.<\/p>\n

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China\u2019s open-source culture has become an accelerant. Hundreds of teams now openly release their model architectures and weights\u2014the blueprints and parameters that a model acquires through training\u2014creating a shared infrastructure anyone can examine, build upon, and improve.<\/p>\n

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 \u201cas a global hook to attract global users,\u201d she says. \u201cThey start building brand equity and word of mouth\u2014and then release better closed-source models later on.\u201d 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.<\/p>\n

This open-source approach aligns with national priorities emphasizing \u201copen ecosystems,\u201d deep integration with all industries and sectors of the economy and society, and \u201cincreased global cooperation\u201dChinese open-source LLMs\u2014led by Qwen, MiniMax, and DeepSeek\u2014now account for one-third of global LLM usage, up from virtually nothing in late 2024.<\/p>\n

Start-ups and companies from Silicon Valley to Africa and Southeast Asia now run Chinese models because they\u2019re 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,\u201d 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 \u201cnot because they\u2019re Chinese but because they\u2019re open.\u201d<\/p>\n

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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\u2014an open-source \u201cwatershed\u201d that helped catalyze the commercial internet. Today\u2019s 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 \u201c \u2018open resources,\u2019 especially model weights, data, and computing resources, which are indispensable to advancing this industry.\u201d As more of these resources become available, the more developers can skip over the massive costs of reproducing work already done by others\u2014a key variable in accelerating the spread of AI, he says.<\/p>\n

That said, models must also be \u201cdifferentiable and sufficiently capable,\u201d researchers from Andreesen Horowitz find. In this fast-moving industry, users who find a model unreliable or a poor fit will quickly migrate, \u201cfinding value in a wider array of options, rather than defaulting to one \u2018best\u2019 choice.<\/p>\n

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We expect the next wave of momentum to emerge at the application layer, with gains to society realized only as organizations and<\/p>\n

individuals adapt how they work. The potential of AI is universal; the challenge of capturing it remains distinctly human.<\/p>\n

Read more Meloni quit smoking; notorious bike-hater Trump gets gifted a bike; Macron loses his watch: Hot mic moments from the G7 summit<\/a><\/p>\n

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While Silicon Valley bets billions on frontier models and AGI, China is industrializing AI from the ground up.<\/p>\n","protected":false},"author":1,"featured_media":1564,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[282],"tags":[],"class_list":["post-1565","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-book-excerpt"],"yoast_head":"\nChina isn\u2019t trying to beat the U.S. at AI \u2014 it\u2019s playing a completely different game - Cross Country Moving Team<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/crosscountrymovingteams.com\/?p=1565\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"China isn\u2019t trying to beat the U.S. at AI \u2014 it\u2019s playing a completely different game - 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