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Ran Guo

At the end of March, China inaugurated the World Data Organisation in Beijing, a body with a stated mission of “bridging the data divide, unlocking data’s value and powering the digital economy”.
The move is the latest signal of a broader trend: over the past several years, Beijing has developed a distinct data governance strategy to drive artificial intelligence (AI) development as it reshapes the terms of technological competition.
Since late 2025, Beijing has pursued an aggressive AI adoption strategy across industries through its “AI-plus” initiative. At the core of this shift is data, which Chinese policymakers see as the “new oil” powering AI. But, like oil, the supply of usable data is finite. In 2024, former OpenAI chief scientist Ilya Sutskever warned that the world had effectively exhausted human-generated data for AI training – a constraint China has taken seriously.
Rather than accepting this as a given, Beijing has reformed its data governance strategy to address this shortage. Over the past three years, China has reorganised its data-sharing regime to pool and channel the vast reservoirs of data generated by large-scale digitisation, feeding this data into specialised models poised to drive the next stage of AI development.
There are, broadly speaking, two types of AI models. The first are general-purpose frontier models like the ChatGPTs and Claudes, which are headline-grabbing giants trained on staggering volumes of data.
The second type is less glamorous but more economically consequential: specialised models. These sector-specific systems power many things like telemedicine diagnostics, financial fraud detection and transport planning. Rather than scraping data from the entire internet, they rely on highly specific data sets such as medical records, financial transactions and logistics flows.

Much of the current debate about data scarcity centres on frontier models. But data availability presents an even greater constraint for specialised AI, for they require large volumes of high-quality, real-world data that cannot simply be improvised on demand. In many cases, the data exists, but the problem is that it remains locked inside firms, held as corporate intellectual property in the private network of individual hospitals, factories and banks.
China is trying to integrate these discrete data islands into a national data market, through incentives rather than coercion. Beijing has launched a trio of policy experiments to address this: data assetisation, state-backed data exchanges and public data opening and franchising.
Beijing seeks to incentivise corporate data collection and usage by recognising data as a type of asset on balance sheets – the first attempt at formal assetisation in the world. The financial implications are real: China’s three largest mobile operators alone have collectively listed roughly 1.6 billion yuan (US$233 million) in data assets on their balance sheets.
At the market level, China has attempted to expand multiple channels to boost data flow. The government has doubled down on its support for “data exchanges” where data products are displayed and sold. These regulated online marketplaces exist throughout China and offer AI developers a place to acquire specialised data sets on a massive scale.

Another enormous reservoir for specialised AI development lies in public-sector data. Governments collect vast quantities of information, covering critical sectors ranging from administrative records and tax filings to weather monitoring, transport flows and public health databases.
Such data sets are deeply embedded in real-world systems and can be especially valuable for applied AI. Access to government data has been a key factor behind the global success of China’s facial recognition AI firms.
Beyond open government data platforms, China has developed an alternative economic model: public data franchising. Local governments authorise selected firms to process, develop or commercialise government-held data sets, often receiving financial returns in the process. This allows local governments to claim a stake in the data economy by turning public data into government revenues.
China’s framework of data assetisation, state-backed exchanges and public data franchising is a unique approach to solving a data shortage problem. As these policies unfold domestically, early signs of adoption are emerging. Through the national “Data Elements X” competition organised by the National Data Administration, firms are designing specialised AI applications in sectors from mineral exploration to pharmaceutical research.
China’s effort to build up its reserves of “digital oil” is a tale of state-led development and strategic adaptation in the AI era, one that we might also witness elsewhere as time goes on. A state-led data economy can be particularly attractive to emerging digital powers, as the governments strive to lead digital transformation without strong capital markets.


Western governments are not absent from this terrain. In tech development, norm diffusion and geopolitical rivalry are not mutually exclusive practices and can occur simultaneously. Some of China’s data asset pricing and management measures, for example, have been accepted as international standards of best practice. The US and the European Union are also expanding public data usage and encouraging data sharing, albeit in less participatory or interventionist ways, through antitrust laws, integrated digital markets and open data government platforms.
Historically, data governance regimes evolved through learning from each other: “the Brussels effect” describes how the EU’s data privacy law has led to a global surge in data privacy legislation; in the same way, China’s norm of data localisation [begin cut for print](cross-border data flow restriction)[end cut for print] also found its way in American policies. [begin cut for print]While it remains too early to tell, it is possible that[end cut for print] we may find ourselves “in a very Chinese time” of data governance.


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Beijing’s distinct approach to AI development and its digital economy strategy could help overcome the apparently finite nature of human-generated data


Beijing’s distinct approach to AI development and its digital economy strategy could help overcome the apparently finite nature of human-generated data.


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World may find itself ‘in a very Chinese time’ of data governance

.css-1c6uqr6{color:inherit;font-weight:inherit;font-size:inherit;font-family:inherit;line-height:inherit;overflow-wrap:break-word;}Beijing’s distinct approach to AI development and its digital economy strategy could help overcome the apparently finite nature of human-generated data

Beijing’s distinct approach to AI development and its digital economy strategy could help overcome the apparently finite nature of human-generated data