By Alex Yang and\u00a0Angela Huyue Zhang*<\/p>\n
China\u2019s just-concluded \u201cTwo Sessions\u201d \u2013 the annual meetings of the National People\u2019s Congress and the Chinese People\u2019s Political Consultative Conference \u2013 delivered a clear message: technology will be the primary engine of the country\u2019s economic future.<\/p>\n
In its 15th Five-Year Plan, the government again\u00a0highlighted<\/a>\u00a0the development of \u201cnew quality productive forces,\u201d setting an ambitious goal of positioning China as a global leader in areas like AI, quantum technology, biotech, clean energy, drones, and 6G.<\/p>\n Making sense of China\u2019s technological ambitions, however, requires moving beyond the common perception of its innovation system as driven entirely by top-down state planning. In many of its most dynamic sectors \u2013 from electric vehicles (EVs) and batteries to solar and AI \u2013 the leading firms are privately owned, with only limited state involvement. Market competition is often so intense, and margins are so thin, that the government has at times intervened to curb what it describes as \u201cexcessive competition.\u201d<\/p>\n A more accurate way to describe China\u2019s innovation system is as a state-orchestrated platform. Rather than replacing the market, the government structures it \u2013 setting strategic direction, creating experimentation zones, facilitating procurement, coordinating standards, and providing regulatory support. Firms then enter these markets and compete intensely, driving rapid improvement and large-scale deployment.<\/p>\n China\u2019s approach resembles that of a platform company like Nvidia or Apple: the state builds the architecture within which firms compete while also governing interactions across the broader ecosystem. In this model, firm-level profitability is not the primary measure of success. Instead, value accrues at the system level through lower costs, integrated supply chains, faster learning cycles, and the adoption of new technologies, all of which strengthen national capabilities.<\/p>\n This dynamic is already evident in sectors where China has become globally dominant. In solar panels and EVs, fierce competition has often eroded profit margins while bolstering China\u2019s international competitiveness. BYD illustrates the pattern, having recently overtaken Tesla as the\u00a0world\u2019s largest EV manufacturer<\/a>\u00a0despite a domestic price war that has put pressure on its profitability.<\/p>\n The solar industry offers another example. Many firms are struggling financially, yet China now controls\u00a0more than 80%<\/a>\u00a0of the solar-panel supply chain, with intense domestic competition helping to consolidate production and strengthen the country\u2019s global advantage.<\/p>\n AI now appears to be following a similar trajectory. Chinese AI companies, many of which have embraced open-source models, are under significant financial pressure. But open-sourcing also serves a strategic purpose by accelerating the diffusion of AI capabilities and narrowing the gap with leading US labs.<\/p>\n At the same time, the Chinese government is shoring up the broader ecosystem. The National Data Bureau has\u00a0<\/a>issued guidelines<\/a>\u00a0to promote high-quality training datasets, while the Ministry of Industry and Information Technology is advancing a\u00a0nationwide computing grid<\/a>\u00a0to connect and standardize architectures, thereby easing capacity constraints and mitigating the impact of chip shortages.<\/p>\n With physical AI \u2013 including autonomous vehicles, robots, and drones \u2013 moving from development to deployment, the next phase may prove even more consequential. Because these systems require extensive real-world interaction, data has emerged as the primary bottleneck. As one of us (Zhang) argues in a\u00a0recent paper<\/a>, China\u2019s advantage lies in its ability to build a \u201cdata flywheel,\u201d whereby deployment generates continuous data streams that, in turn, improve performance and reliability.<\/p>\n Government-backed pilot programs play a key role in this effort, accelerating adoption while facilitating data collection. In 2022, Baidu received China\u2019s first permits for fully driverless commercial robotaxi services. Within three years, Chinese robotaxi firms expanded to\u00a0more than 20 cities<\/a>\u00a0across the country, as well as Middle Eastern and European markets.<\/p>\n Standard-setting has also become an important tool for reducing costs and enabling data interoperability. To coordinate the complex supply chains required for humanoid robots, Chinese authorities have brought together\u00a0more than 120 companies<\/a>, academic institutions, and industry groups to develop common standards.<\/p>\n Just as companies like Meta and Alphabet rely on proprietary data as a competitive moat, China is increasingly treating data generated by physical AI as a strategic national asset. New regulations on cross-border transfers of automotive data, finalized earlier this year, impose\u00a0stricter controls<\/a>, making it harder for foreign firms to use Chinese data for model training. These rules are already having tangible effects, as Tesla\u2019s rollout of its advanced driver-assistance systems has been\u00a0delayed<\/a>\u00a0despite its large market presence in China.<\/p>\n None of this is to suggest that China\u2019s innovation model is inherently superior. The United States remains more open, more pluralistic, and often more effective at producing technological breakthroughs. China\u2019s approach also carries serious risks, including misguided investments, chronic overcapacity, and widespread inefficiencies.<\/p>\n But the platform power of the Chinese state should not be underestimated. If the ultimate prize is artificial general intelligence \u2013 potentially delivering outsize rewards to whoever develops it first \u2013 the US may well retain the lead. If value is instead distributed through low-cost deployment, industrial integration, and ecosystem-wide learning, America could end up building the most advanced models and still lose the race to shape the industrial revolution that AI is set to unleash.<\/p>\n *S. Alex Yang is Professor of Management Science and Operations at London Business School. Angela Huyue Zhang, Professor of Law at the University of Southern California, is the author of\u00a0High Wire: How China Regulates Big Tech and Governs Its Economy<\/a>\u00a0(Oxford University Press, 2024) and\u00a0Chinese Antitrust Exceptionalism: How the Rise of China Challenges Global Regulation<\/a>\u00a0(Oxford University Press, 2021).\u00a0Copyright 2026 Project Syndicate. 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