Alex Yang and Angela Huyue Zhang explain why China's approach to scaling AI could determine who wins the global tech race

By Alex Yang and Angela Huyue Zhang*

China’s just-concluded “Two Sessions” – the annual meetings of the National People’s Congress and the Chinese People’s Political Consultative Conference – delivered a clear message: technology will be the primary engine of the country’s economic future.

In its 15th Five-Year Plan, the government again highlighted the development of “new quality productive forces,” setting an ambitious goal of positioning China as a global leader in areas like AI, quantum technology, biotech, clean energy, drones, and 6G.

Making sense of China’s 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 – from electric vehicles (EVs) and batteries to solar and AI – 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 “excessive competition.”

A more accurate way to describe China’s innovation system is as a state-orchestrated platform. Rather than replacing the market, the government structures it – 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.

China’s 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.

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’s international competitiveness. BYD illustrates the pattern, having recently overtaken Tesla as the world’s largest EV manufacturer despite a domestic price war that has put pressure on its profitability.

The solar industry offers another example. Many firms are struggling financially, yet China now controls more than 80% of the solar-panel supply chain, with intense domestic competition helping to consolidate production and strengthen the country’s global advantage.

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.

At the same time, the Chinese government is shoring up the broader ecosystem. The National Data Bureau has issued guidelines to promote high-quality training datasets, while the Ministry of Industry and Information Technology is advancing a nationwide computing grid to connect and standardize architectures, thereby easing capacity constraints and mitigating the impact of chip shortages.

With physical AI – including autonomous vehicles, robots, and drones – 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 recent paper, China’s advantage lies in its ability to build a “data flywheel,” whereby deployment generates continuous data streams that, in turn, improve performance and reliability.

Government-backed pilot programs play a key role in this effort, accelerating adoption while facilitating data collection. In 2022, Baidu received China’s first permits for fully driverless commercial robotaxi services. Within three years, Chinese robotaxi firms expanded to more than 20 cities across the country, as well as Middle Eastern and European markets.

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 more than 120 companies, academic institutions, and industry groups to develop common standards.

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 stricter controls, making it harder for foreign firms to use Chinese data for model training. These rules are already having tangible effects, as Tesla’s rollout of its advanced driver-assistance systems has been delayed despite its large market presence in China.

None of this is to suggest that China’s innovation model is inherently superior. The United States remains more open, more pluralistic, and often more effective at producing technological breakthroughs. China’s approach also carries serious risks, including misguided investments, chronic overcapacity, and widespread inefficiencies.

But the platform power of the Chinese state should not be underestimated. If the ultimate prize is artificial general intelligence – potentially delivering outsize rewards to whoever develops it first – 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.

*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 High Wire: How China Regulates Big Tech and Governs Its Economy (Oxford University Press, 2024) and Chinese Antitrust Exceptionalism: How the Rise of China Challenges Global Regulation (Oxford University Press, 2021). Copyright 2026 Project Syndicate. It is here with permission.