The Rise of Tokens in AI

In early 2026, a set of data sparked intense discussions in the global AI industry. OpenRouter, the largest AI model API aggregation platform, revealed that from February 9 to 15, the token usage of Chinese large models reached 41.2 trillion, surpassing the US models’ 29.4 trillion for the first time in history. This trend continued for several weeks, with usage exceeding 73 trillion by mid to late March, and four out of the top five models globally were from China.

This data is not just a comparison of numbers; it signifies a quiet revolution in the basic measurement unit of the AI industry—tokens are becoming the “kilowatt-hour” of the intelligent era. The six dimensions of models, computing power, data, applications, industry, and governance are profoundly reshaped by the establishment of this measurement unit. Understanding AI in 2026 begins with understanding tokens.

Sixfold Reconstruction from a Measurement Unit

The measurement unit of the Industrial Revolution was the “kilowatt-hour,” allowing energy to be precisely measured, priced, and transported across domains. The measurement unit of the Information Revolution was “bits” and “bandwidth,” enabling information to be packaged, transmitted, and billed for the first time. The measurement unit of the Intelligent Revolution is “tokens,” allowing intelligence to be segmented, measured, priced, and traded for the first time.

The popularization of the token concept and its rapid growth in usage are gradually pushing intelligence towards industrialization, marketization, and circulation.

Models: From “Training as Asset” to “Inference as Production”

The economic value of large models is shifting from one-time training costs to long-term inference outputs. Model vendors are no longer merely “selling capabilities” but are directly “selling tokens”—pricing based on millions of tokens for input and output has become a global industry norm. The asset attribute of models is transitioning from “weight files” to “the ability to continuously produce tokens.”

Computing Power: From “Training Computing Power” to “Inference Computing Power”

Training computing power is pulsed and centralized, while inference computing power is continuous and distributed, posing new demands for latency, energy efficiency, and geographical distribution. The collaboration of cloud-edge-end computing, inference-specific chips, silicon photonics, and computing networks is becoming the new focus of infrastructure. JPMorgan predicts that China’s inference token consumption will grow by more than two orders of magnitude by 2030 compared to 2025.

Data: From “Raw Data” to “Tokenized Corpora”

Just as raw coal must be processed into standard fuel for power generation, data must be cleaned, labeled, and tokenized before entering large models. In long-tail scenarios such as autonomous driving, robot training, and scientific discovery, synthetic data generated through simulation has achieved large-scale application. The construction of a data factor market has also entered a substantial phase, where “trainability” and “token output density”—rather than mere data scale—are becoming new metrics for data asset pricing. This shift is significant: the valuation of data is starting to be linked to its actual contribution in the token production chain, providing a more solid economic basis for the market allocation of data factors.

Applications: From “Function Delivery” to “Token Consumption”

Traditional software charges based on seats or functions; today, applications charge based on token usage and business outcomes. Intelligent agents are becoming the main consumers of tokens, with complex tasks potentially consuming hundreds of thousands or even millions of tokens. The “intelligent agent as a service” market is rapidly expanding, with performance-based billing models being implemented at scale in customer service, marketing, compliance, and programming scenarios. The essence of applications is shifting from “delivering functions” to “consuming intelligence.”

Industry: From “Software Industry Chain” to “Token Industry Chain”

A new industry chain is forming around the production (models and computing power), distribution (inference networks, APIs, intelligent agent protocols), consumption (applications and intelligent agents), and measurement (evaluation standards, auditing, and trusted validation) of tokens. The boundaries between model layers, inference service layers, intelligent agent middleware layers, and industry application layers are becoming increasingly clear, with industry-specific intelligent agents becoming mainstream investments. Model vendors, cloud vendors, chip manufacturers, green power operators, and content distribution network vendors are collaboratively forming the ecosystem of the token industry chain. According to the China Academy of Information and Communications Technology, the scale of China’s core AI industry is expected to exceed 1.2 trillion yuan by 2026, and the collaborative effects across the entire industry chain are becoming evident.

Governance: From “Algorithm Governance” to “Token Full-Chain Governance”

As the AI industry has developed, the governance focus has expanded from “algorithms and code” to the full chain of token production, circulation, consumption, and cross-border flow: traceability of tokens, synthetic content identification, cross-border token flow, constraints on computing power and energy consumption, and trusted evaluation and benchmarks—these new issues call for new governance tools and rules. The year 2026 may become a key year for the concentrated implementation of global AI governance rules.

China’s Position in the Global Token Wave

In the global wave of tokens, China is forming a unique position with multiple supports.

On the production side of tokens, a cluster of domestic models is rising. Models such as MiniMax, Dark Side of the Moon, Deep Quest, Zhipu, Alibaba Qianwen, and Byte Bean have leveraged mixed expert architectures and extreme engineering optimizations to continuously improve performance while reducing inference prices to a fraction of comparable global models. On the OpenRouter platform, 47% of users are from the US, while only about 6% are from China, yet the usage is led by Chinese models—this is a recognition derived from global developers voting with their feet.

On the consumption side, applications are penetrating deeper than ever, with tokens entering daily life at an unprecedented speed. A general practitioner in a county hospital can use AI to identify nodules and provide differential diagnosis suggestions from a suspicious lung CT in just a few seconds and thousands of tokens, compressing what used to take two weeks into a single outpatient visit. A farmer in Shouguang, Shandong, can take a picture of a curled cucumber with his phone, and a smart agriculture app uses tokenized agricultural knowledge to inform him whether it’s thrips or a viral disease and which medication to use. An elderly person living alone can tell a smart speaker in their dialect, “I feel tight in my chest,” and after a conversation of several thousand tokens, their children’s phones receive a warning and location sharing for emergency services. Delivery riders no longer hear mechanical instructions like “turn right ahead” but receive route planning based on real-time traffic and elevator wait times. AI assistants in government service halls respond 24/7 to inquiries about medical insurance transfers and property registration, replacing “people running errands” with “tokens running errands”… Tokens are becoming the “invisible labor force” across various industries.

On the industry chain level, a full-stack collaborative ecosystem is rapidly taking shape. From domestic chips like Ascend, Cambricon, and Hygon to inference service platforms like Volcano Engine, Alibaba Cloud, and Tencent Cloud, along with a range of open-source middleware and industry-specific intelligent agents, the entire industry chain covering chips, computing power, models, middleware, and applications is quickly improving. The “East Data West Computing” project provides low-cost computing power, and green electricity directly supplies data centers, solidifying the energy foundation.

However, it is essential to recognize that there is still significant room for improvement in areas such as originality of cutting-edge models, high-end computing power foundations, cross-language and cross-cultural ecological influence, and participation in global rules.

The second half of the token wave is not about “having already won” but rather “just beginning.” In the global landscape unfolded by small tokens, China is not only a massive market but also an active builder and responsible co-governor. Understanding tokens means understanding the next phase of artificial intelligence.