Introduction

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

This data is not merely 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 implications of this measurement unit profoundly reshape the six dimensions of models, computing power, data, applications, industry, and governance. Understanding AI in 2026 begins with understanding tokens.

Six Transformations Driven by a Measurement Unit

The measurement unit of the Industrial Revolution was the “kilowatt-hour,” enabling energy to be precisely measured, priced, and transported across domains. The Information Revolution’s units were “bits” and “bandwidth,” allowing information to be packaged, transmitted, and billed for the first time. The measurement unit of the Intelligent Revolution is the “token,” which allows 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

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

Computing Power

The focus is shifting from “training computing power” to “inference computing power.” Training computing power is pulse-based and centralized, while inference computing power is continuous and distributed, posing new requirements for latency, energy efficiency, and geographic distribution. The collaborative tri-level computing power of “cloud-edge-end,” inference-specific chips, silicon photonics interconnection, and computing power networks are 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

Data must be processed into standardized fuel before it can be used for power generation; similarly, data entering large models needs to be cleaned, labeled, and tokenized. 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, with “trainability” and “token output density”—rather than mere data volume—becoming new metrics for pricing data assets. This shift is significant: the assessment of data value is now linked to its actual contribution in the token production chain, providing a more solid economic foundation for the market allocation of data factors.

Applications

The focus is moving from “function delivery” to “token consumption.” Traditional software charges based on seats or functionalities; today, applications are billed based on token usage and business results. Intelligent agents are becoming the primary consumers of tokens, with a complex task 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

The industry is transitioning from a “software industry chain” to a “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 benchmarks, auditing, and trusted verification) 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 delivery network providers 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, with the synergistic effect of the entire industry chain becoming evident.

Governance

The governance focus is shifting from “algorithm governance” to “full-chain governance of tokens.” As the AI industry has developed, the governance objects have expanded from “algorithms and code” to the entire chain of token production, circulation, consumption, and cross-border movement: token traceability, synthetic content identification, cross-border token flow, computing power and energy consumption constraints, and trusted evaluation and benchmarks—all 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 supported by multiple factors.

On the production side, domestic model clusters are rising. A number of domestic models, such as MiniMax, Dark Side of the Moon, Deep Quest, Zhipu, Alibaba Qianwen, and ByteDance Doubao, have leveraged mixed expert architectures and extreme engineering optimization to continuously enhance performance while reducing inference prices to a fraction of comparable global models. On the OpenRouter platform, U.S. users account for 47%, while Chinese users make up about 6%, yet the usage volume is led by Chinese models—this is a recognition determined by global developers voting with their feet.

On the consumption side, applications are penetrating deeper into daily life at an unprecedented pace. A general practitioner in a county hospital, faced with a suspicious lung CT, receives AI’s identification of nodules and differential diagnosis suggestions in just a few seconds and tens of thousands of tokens, compressing what used to take two weeks into a single outpatient visit. A farmer in Shouguang, Shandong, uses a smart agriculture app to identify whether his curling cucumbers are affected by thrips or viral diseases and what medication to use. An elderly person living alone speaks to a smart speaker in dialect, and after a few thousand tokens of dialogue, their children’s phones receive alerts and location sharing for emergency services. Delivery riders now hear route instructions that consider real-time traffic and elevator wait times instead of mechanical commands. AI assistants in government service halls respond to inquiries about medical insurance transfers and property registrations, replacing the need for citizens to run errands with “token errands.” Tokens are becoming the “invisible labor force” across various industries.

At the industry chain level, a full-stack collaborative ecosystem is rapidly taking shape. From domestic chips like Ascend, Cambricon, and Haiguang 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 maturing. The “Eastern Data and Western Computing” project provides low-cost computing power, while 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 the originality of cutting-edge models, high-end computing power foundations, cross-language and cross-cultural ecological influence, and participation in global rule-making.

The second half of the token wave is not about having “already won,” but rather that it is “just beginning.” In the global landscape unfolding from small tokens, China is not only a vast market but also a proactive builder and responsible co-governor. Understanding tokens is key to understanding the next phase of artificial intelligence.