The rise of the AI ‘token economy’ and China’s big bet on scale

The rise of the AI ‘token economy’ and China’s big bet on scale

In the first of a two-part series, CNA looks at the rise of AI tokens and how China’s rapidly expanding token economy is reshaping the costs, scale and security risks of the next era of artificial intelligence.

SINGAPORE: The future of artificial intelligence may hinge on something surprisingly small.

Not huge data centres or the latest wave of AI agents, but tiny units of data called tokens, which AI models use to process information and generate responses.

Just like how electricity is measured in kilowatt-hours and mobile data in gigabytes, tokens are increasingly becoming the unit used to measure and price AI services.

And as AI adoption accelerates worldwide, governments and tech companies have been paying closer attention.

China, in particular, has been rapidly expanding its AI infrastructure to support rising token consumption, observers say - underscoring how its AI sector is evolving into a full-fledged “token economy”.

“There is a rising emphasis on the token economy in China,” said Qian Zilan, a research associate at the Oxford China Policy Lab - noting that Chinese state telecom giants have launched token services and subscriptions for both everyday users and developers.

In the first of a two-part series, CNA explores the rise of tokens and how their explosive growth is shaping China’s AI future and creating new business opportunities, as well as security risks, for the world’s second-largest economy and beyond.

THE BUILDING BLOCKS OF AI

Behind every chatbot response, AI-generated summary and image prompt are tokens.

In simple terms, a token is a small unit of data that AI models process when reading, interpreting or generating information.

A short word such as “dog” may count as a single token, while a longer one like “thunderstorm” would be broken down into multiple tokens.

Tokens are also not limited to words. Anything that can be typed on a keyboard - including numbers, spaces, punctuation marks, symbols and even emojis - can be converted into tokens for AI systems to process.

“They are the operational unit of generative AI and determine how much input a model can process, how fast it responds, how long an answer can be and how much computing power is used,” said James Pang, an analytics and operations professor at the National University of Singapore (NUS) and director of the NUS Business Analytics Centre.

“Think of them as Lego bricks or building blocks that an AI platform uses to read and write.”

And the more tokens an AI model has to process, the more resources it consumes, Pang said, which can have significant implications for computing costs and infrastructure demand.

The way tokens are counted also differs across languages and AI systems - a distinction experts said can significantly affect computing costs as businesses scale up AI usage across millions of queries and interactions.

Chinese, for instance, is often more semantically dense than English, meaning fewer characters can convey the same idea, said Pang.

A four-character Chinese idiom, for example, may require far fewer characters than an entire English sentence expressing similar meaning.

But he cautioned against oversimplifying the comparison.

“Different AI models use different tokenisation methods,” Pang said, noting that Chinese-optimised AI models may process Chinese text more efficiently, while English-centric models would split the same text into a larger number of tokens.

Wong Qi Han, an independent AI researcher and builder, told CNA that token efficiency depends heavily on how a model is trained.

“If a model is trained predominantly on English, its tokeniser compresses English text efficiently. But for other languages, the model breaks text into smaller, less efficient pieces,” Wong said.

“A model optimised for Chinese or English won’t necessarily tokenise Tamil, Bahasa Indonesia or Vietnamese efficiently,” he added.

On a larger scale, small differences in token usage would translate to significant differences in computing costs.

Overall AI costs still depend on factors such as model size, inference architecture, caching, batching, output length and task complexity, Pang said.

“So fewer tokens do not automatically mean better reasoning or better business value.”

CHINA’S BET ON THE “TOKEN ECONOMY”

China’s use of AI has rapidly surged, with more than 600 million people using generative tools as of December 2025.

Younger and more educated users accounted for the bulk, according to an October 2025 report by the China Internet Network Information Center (CNNIC) - with those under 40 comprising more than 74 per cent of users and 37.5 per cent holding higher education qualifications.

And as AI becomes increasingly embedded into daily routines, powering everything from ordering food to transport, travel and shopping, token consumption has also risen exponentially.

Daily token usage surpassed 140 trillion in March, according to China’s National Data Administration (NDA) - more than 40 percent higher than 100 trillion recorded at the end of last year.

China’s AI industry is “evolving from basic chat functions to more sophisticated systems capable of decision-making and task execution”, NDA chief Liu Liehong said during a Mar 25 press briefing, describing tokens as both a key indicator of AI growth and a potential new export frontier.

The growing importance of tokens was further underscored that month when China formally adopted the term ciyuan for AI tokens - combining the Chinese translation for “word” with the country’s currency unit, the yuan.

Observers said the naming reflects how tokens are increasingly being viewed not just as a technical measure of AI activity, but as a unit of economic value within China’s emerging AI economy.

Liu said the next step would be the creation of a “national computing network” - aimed at turning AI infrastructure into a public utility as token consumption continues to surge.

Just another day in Shenzhen - grabb