The National Data Bureau recently standardized the fundamental concept of "Token" from natural language processing, naming it "词元" in Chinese. A token serves as the basic unit for large AI models to process information such as text, code, and image descriptions, which can be a single character, a punctuation mark, or a word fragment. With the rapid and sustained growth in token usage volume, a new economic framework centered around token measurement, invocation, accounting, and value conversion—termed the "token economy"—is accelerating its formation.
The token economy refers to a method of resource allocation developed during the operation and application of generative artificial intelligence, where the token acts as the fundamental unit of measurement. This system encompasses model invocation, information processing, cost accounting, service pricing, and value transformation. In early 2024, China's average daily token usage was 100 billion; by March 2026, this figure had surpassed 140 trillion, representing a growth of over a thousandfold in just two years.
The rapid emergence of the token economy is primarily driven by the accelerated real-world application of generative AI. Changes in the cost structure on the supply side, coupled with the expansion of application scenarios on the demand side, provide a solid foundation for its development. Currently, the swift advancement of generative AI is profoundly altering methods of knowledge production, content generation, industrial organization, and value realization.
From the supply perspective, as large model capabilities are increasingly embedded into specific business scenarios through APIs and other interfaces, the method of supplying intelligent services is also evolving. On-demand access, usage-based billing, and dynamic optimization are becoming significant trends. On the demand side, applications like intelligent agents for software development, in-depth research, and personal assistants are being deployed more quickly, extending AI use from simple Q&A to more complex task-processing scenarios. These scenarios typically feature longer context, more interaction rounds, and more complex task decomposition, leading to significantly higher token consumption compared to general interactive situations and driving the rapid increase in token usage.
Of course, the development of the token economy does not rely solely on the expansion of usage volume. More importantly, it depends on whether the consumption per token can be effectively converted into genuine service value and yield efficiency improvements. Essentially, the significance of the token economy extends beyond technical measurement; it holds substantial practical value for evaluating AI services, allocating resources, and enabling large-scale application.
Firstly, it enables a more precise and flexible supply of intelligent services. Using tokens as a metric, relatively abstract model capabilities can be transformed into quantifiable, comparable, and evaluable standard service forms. This facilitates more detailed cost accounting, service pricing, and resource allocation for intelligent services, making the supply method more adaptable to diverse and differentiated application needs. For users, this allows for personalized selection and configuration of intelligent services based on invocation efficiency, service quality, and cost control.
Secondly, it effectively lowers the barrier to accessing intelligent services and broadens the scope of application. Token-based measurement allows more small and medium-sized enterprises and developers to flexibly invoke models according to their actual needs without bearing the high costs of entire package procurement, enabling pay-as-you-go access. This not only helps extend intelligent services from a few high-barrier scenarios to a wider range of applications but also creates practical conditions for the token economy to take root in more industries and settings.
Thirdly, it promotes synergistic optimization within the intelligent service ecosystem. On a broader level, the large-scale application of the token economy drives a virtuous cycle where the construction of high-quality datasets, optimal allocation of computing resources, and iterative improvement of model capabilities reinforce each other. The sustained growth in token usage imposes higher demands on the quality of data supply, thereby driving continuous improvement in the data element market and providing a more solid foundation for model training and application deployment. Furthermore, the granularity of token measurement shifts computing resource allocation from a coarse-grained approach to on-demand scheduling, thereby enhancing the overall efficiency of computing resource utilization.
However, it is important to recognize that the token economy, as a new form of intelligent economy still rapidly evolving, faces several significant challenges. First, computing power and energy supply are under pressure. The large-scale development of the token economy implies massive model invocation and inference computations, placing high demands on computing infrastructure and energy security. In reality, shortcomings remain in areas such as the supply of high-quality data, guarantees for inference computing power, and the development of an open-source model ecosystem. As token usage grows rapidly, computing consumption and energy demands rise simultaneously. Whether the underlying resource supply can provide sustained and effective support will directly impact the expansion speed, operational efficiency, and cost level of the token economy.
Second, the industrial ecosystem and standard system are still immature. For the token economy to mature, it requires not only demand pull and technological support but also compatible industrial配套设施 and institutional norms. Currently, foundational supports like the developer ecosystem and toolchain systems are still insufficiently developed. Rules concerning token measurement, service pricing, quality assessment, and settlement methods are still in the exploratory stage, with a lack of unified, transparent market rules across different models and modalities.
Third, it faces challenges related to data security and compliance. The high-frequency invocation and continuous interaction of tokens are based on high-frequency, large-scale information processing and data flow, increasing the pressure to protect personal privacy, commercial secrets, and important data. Particularly as cross-regional and even cross-border invocations increase, defining the boundaries of data flow, usage, and compliance responsibilities becomes more complex, urgently necessitating the accelerated establishment of a robust data security governance framework and cross-border regulatory collaboration mechanisms.
To promote the healthy and sustainable development of the token economy, concerted efforts are needed in three key areas. First, solidify the underlying foundation to overcome resource constraints on scale development. Accelerate the construction of computing infrastructure, especially deployment tailored for large model inference needs, to enhance computing supply efficiency and resource coordination capabilities, providing a more solid base for the large-scale development of the token economy. Continuously improve the supply level of high-quality datasets, support the development of the open-source model ecosystem, and steadily enhance capabilities in model training, inference optimization, and intelligent service supply. Coordinate the synergistic保障 of key resources like computing power and energy, optimize resource allocation methods, and strengthen the stability and sustainability of the underlying supply system to better meet the demands arising from continuous growth in token usage.
Second,健全 the industrial ecosystem and improve the standard system and market rules. Accelerate the establishment of a foundational rule system covering token measurement, service pricing, quality assessment, and settlement methods. Promote the formation of a unified standard system across models and modalities, ensuring comparability, connectivity, and verifiability across related processes, and gradually build a unified, transparent, standardized, and orderly market environment. Simultaneously, better leverage market mechanisms and industry collaboration, improve the developer ecosystem and toolchain systems, reduce access and innovation costs for market entities, and enhance the synergy and resilience of the token economy ecosystem.
Third, reinforce the security baseline and健全 a full-chain governance mechanism for data security. Expedite the clarification of data classification and grading protection requirements for high-frequency token interaction scenarios. Construct a security governance framework with clear responsibilities and technically controllable measures, focusing on strengthening the protection of personal privacy and commercial secrets. Establish and健全 mechanisms for reviewing and tracing AI-generated content, clarify the "measurement tool" attribute of tokens, prevent risks such as technological misuse and illicit financial speculation, and create a secure and trustworthy environment for the standardized and orderly development of the token economy.
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