Tokenomics refers to a resource allocation model centered on the token as the fundamental unit of measurement within the operation and application of generative artificial intelligence. This system encompasses model invocation, information processing, cost accounting, service pricing, and value transformation. Recently, China's National Data Bureau officially standardized the foundational concept of "Token" in natural language processing as "词元" (token). Tokens serve as the basic units for large AI models to process information such as text, code, and image descriptions, examples being a single Chinese character, a punctuation mark, or a word fragment.
With the sustained rapid growth in token invocation volume, a new economic paradigm—Tokenomics—is rapidly taking shape, defined by the measurement, invocation, accounting, and value transformation of tokens. By early 2024, China's average daily token invocation volume reached 100 billion. By March 2026, this figure had surged past 140 trillion, representing a growth of over a thousand times in just two years.
The accelerated formation of Tokenomics is primarily driven by the swift integration of generative AI into practical applications. Changes in the cost structure on the supply side, combined with the expansion of application scenarios on the demand side, provide a solid foundation for its development. The rapid 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 API calls, 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, the accelerated deployment of AI agent applications—such as in software development, deep research, and personal assistants—is extending AI use from simple Q&A to more complex task-processing scenarios. These scenarios typically involve longer context windows, more interaction rounds, and more intricate task decomposition, leading to significantly higher token consumption compared to general interactive scenarios and driving rapid growth in token invocation volume.
However, the development of Tokenomics does not depend solely on the expansion of invocation scale. More critically, it hinges on whether the consumption per token can be effectively translated into genuine service value and efficiency improvements. Essentially, the significance of Tokenomics extends beyond technical measurement; it holds substantial practical value for evaluating AI services, allocating resources, and enabling scalable applications.
First, it enables a more granular and flexible supply of intelligent services. Using tokens as a metric allows relatively abstract model capabilities to be transformed into quantifiable, comparable, and evaluable standard service forms. This refinement facilitates more precise 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.
Second, it effectively lowers the barrier to accessing intelligent services and broadens the scope of application. Token-based measurement enables more small and medium-sized enterprises and developers to flexibly invoke models according to their actual needs without bearing the high costs of bulk procurement, achieving true on-demand access. This not only helps extend intelligent services from a few high-threshold scenarios to broader application fields but also creates practical conditions for Tokenomics to take root in more industries and settings.
Third, it promotes synergistic optimization within the intelligent service ecosystem. From a macro perspective, the large-scale application of Tokenomics fosters a virtuous cycle involving the construction of high-quality datasets, optimized allocation of computing resources, and iterative improvement of model capabilities. The continuous growth in token invocation volume imposes higher demands on the quality of data supply, thereby driving the refinement of data factor markets and providing a more robust foundational support for model training and application deployment. Furthermore, the precision of token measurement shifts computing resource allocation from a coarse-grained approach to on-demand scheduling, thereby enhancing the overall utilization efficiency of computing resources.
Nevertheless, as an emerging form of intelligent economy still rapidly evolving, Tokenomics faces several significant challenges. The first challenge is pressure on computing power and energy supply. The large-scale development of Tokenomics implies massive model invocation and inference computations, placing high demands on computing infrastructure and energy security. Currently, shortcomings persist in areas such as the supply of high-quality data, guaranteed inference computing power, and the development of an open-source model ecosystem. As token invocation volume grows rapidly, computing power consumption and energy demands rise simultaneously. The ability of underlying resources to provide sustained and effective support will directly impact the expansion speed, operational efficiency, and cost structure of Tokenomics.
The second challenge is the immaturity of the industrial ecosystem and standard systems. For Tokenomics to mature, it requires not only demand traction and technical support but also compatible industrial infrastructure and regulatory frameworks. At present, supporting foundations such as the developer ecosystem and toolchain systems remain underdeveloped. Rules concerning token measurement, service pricing, quality evaluation, and settlement methods are still in the exploratory phase, and there is a lack of unified, transparent market rules across different models and modalities.
The third challenge involves data security and compliance. The high-frequency invocation and continuous interaction of tokens are built upon high-frequency, large-scale information processing and data flows, 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. There is an urgent need to accelerate the establishment of a comprehensive data security governance framework and cross-border regulatory collaboration mechanisms.
To promote the healthy and sustainable development of Tokenomics, concerted efforts are needed in three key areas. First, strengthen the underlying foundation to overcome resource constraints on scale. Accelerate the construction of computing infrastructure, particularly by enhancing computing power deployment tailored for large model inference needs, improving the efficiency of computing supply, and strengthening resource coordination capabilities. This will provide a more solid foundation for the large-scale development of Tokenomics. Continuously improve the supply level of high-quality datasets, support the development of the open-source model ecosystem, and consistently 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 enhance the stability and sustainability of the underlying supply system to better meet the demands driven by continuous growth in token invocation.
Second,健全 the industrial ecosystem and improve standard systems and market rules. Expedite the establishment of a foundational rule system covering token measurement, service pricing, quality evaluation, and settlement methods. Promote the formation of a unified standard system across models and modalities, ensuring comparability, connectivity, and verifiability across related links, 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 participants, and enhance the synergy and resilience of the Tokenomics ecosystem.
Third, fortify the security baseline and健全 the full-chain governance mechanism for data security. Quickly clarify data classification and grading protection requirements for high-frequency token interaction scenarios. Construct a security governance framework with clear responsibilities and technically controllable elements, focusing on strengthening the protection of personal privacy and commercial secrets. Establish and improve mechanisms for reviewing and tracing AI-generated content, clarify the "measurement tool" attribute of tokens, and prevent risks such as technological misuse and illicit financial speculation, thereby creating a secure and trustworthy environment for the standardized and orderly development of Tokenomics.
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