Gandu, chang, bin
With artificial intelligence depths capable of doing everything, token, a core measurement unit for large model computing, smart applications, has been upgraded from a technical professional concept to a core ruler for intelligent economic development, creating a new token economic shape, which is the key base for determining competitive patterns in the digital industry. According to the data, in early 2024, the daily average number of token calls rose to 100 trillion by the end of 2025, reaching 1. 4 trillion by march 2026, but token's economic development is still faced with short-sets such as cognitive deviations, delayed measurement standards, inadequate industrial ecology and control over core technological autonomy. Building on the new phase, mastery of the core content of the token economy, completion of the development of the short board, sound institutional systems and enhanced technological innovation are key to the healthy and orderly development of our artificial intelligence industry and the construction of new competitive advantages in the digital economy。
Token economy
Each time a large model understands a question, generates an answer, calls a tool, performs a task, it converts the input and output into a token sequence. Users are aware of content and efficiency, and the system consumes data, computing, modelling and engineering movement capabilities behind it. Token consumption, although not the only indicator, can provide an important window for observing artificial intelligence application activity and industry transformation levels, becoming a `bottle nose' that evolves in tandem with a towed chip, server, network, storage, reasoning framework and modelling platform. The development of the token economy is essentially a new infrastructure and institutional framework for the intelligent economy。
In terms of industry operations, the token economy presents a cycle of “production-circulation-consumption”: supply-side production of intelligent capabilities based on chips, calculator centers, reasoning frameworks and basic models; circulation-end distribution of intelligent capabilities through cloud services, model interfaces, intelligent platform; and demand-end continuous mobilization of intelligent capabilities in the context of r & d design, production scheduling, knowledge services, etc., and conversion of them into real productivity through token consumption. When the scale of calls is growing, unit token costs are falling, and the ai application landscape is growing, there may be a virtuous and mutually reinforcing flyer effect between technological advances and market demand。
Recognizing token's economic development as a prominent issue
The development of the token economy in our country has a better realistic basis, but the problems are equally prominent, mainly in the following areas。
One is cognitive deviation. Because the word token has long been translated into “coin” in the area of block chains, some local governments, investment agencies and even individual enterprises have confused the concepts of the token economy with the virtual currency, the encrypted asset-licensing economy. This perception bias tends to lead to a lack of attention to real token measurement, billing and industrial system building, and even leaves room for arbitrage in the “ai+ block chain” of counterfeit projects and disrupts market order。
Second is the lag in measurement. The terminological algorithms currently used by bma are different, with text in the same paragraph likely to be divided into several times different token numbers under different large models, and the token conversion method for cross-temperature input is more of a case-by-line, with no recognized equivalent conversion benchmark. The lack of uniform token call calibration, quality evaluation standards and billing publicity rules prevents accurate monitoring of ai application penetration at the macro level, as well as the difficulty for micro-level enterprises in horizontal pricing and accounting for intelligently modified inputs。
Thirdly, industrial ecology is inadequate. At the supply end, the adequacy of the national production of ai chips and the mainstream reasoning framework needs to be enhanced to limit the large-scale stabilization of the supply of low-cost token. On the demand side, large enterprises in industries such as industry, energy and transport mostly use large models for outside scenarios such as passenger calls, document retrieval, less embedded in core business streams such as production process control, supply chain dynamic optimization, and token consumption is characterized by “high but low-value” consumption. Small and medium-sized enterprises face problems such as the high costs of modeling, the lack of expertise in engineering and agent programming, which constrains the formation of a robust and sustainable demand for token consumption。
Fourthly, the core technology autonomy controls the existence of a short board. High-end training and reasoning ai chips, cluster non-destructive group network technology, high-input low-delayed reasoning acceleration framework, etc. Are still heavily dependent on foreign products and ecology. Nationally produced computing products still differ from the cuda ecology in terms of the completeness of the algorithms, distributed parallel strategies, visual optimization, etc., resulting in high costs of generating the same amount of energy as token and low efficiency conversion. In addition, algorithmic layers such as the long context attention mechanism, the efficient compression of multi-mode characters, and the optimization of terminologists in the chinese context also need to be strengthened。
Promotion of token's economic health
Token's economic well-being needs to be driven by a combination of technological innovation, industrial development and institutional supply, centred on the following four areas。
One is to strengthen top-level design and public opinion. Token's economy as an important component of the intelligent economy is integrated into national digital industry development, artificial intelligence development programmes, etc., accelerate the construction of an institutional framework for coverage measurement, regulation and evaluation, clarify the policy positioning of token as a measurement unit for intelligent services, and clarify its regulatory boundaries with block chains, virtual currency. At the same time, it has improved the dissemination mechanism for the general public and established a long-term mechanism for professional training of practitioners, leading the entire chain of “production studies” from mere cross-model parameters, rankings and consumption to the examination of actual application penetration rates, unit task token costs, effective output ratio and business value contributions, and to a healthy industrial development orientation。
Second, industrial ecology is built on both the supply and demand sides. The supply side will optimize the national integrated intellectual architecture, promote synergy between east and west counts and computing, support the construction of a green, low-carbon token production base in clean energy-rich areas, and focus on the joint fine-tuning of the national production of ai chip manufacturers and the framework team for reasoning, and the refinement of algorithms, compilers and distributed reasoning support. Token enabling works in key industries at the demand end, selecting poles in areas such as high-end manufacturing, intelligent energy, intelligent transport, modern medicine, educational and scientific research, and continuously promoting the sinking of large models from auxiliary questions and answers to core decision-making and control processes to raise the high value token ratio。
Third is the refinement of the system of basic standards and rules of the system. Accelerate the development of token national standards to clarify the token base definition, the chinese terminological reference frame (including industry terminological adaptation), the text/code/image/spoken multimodular token equivalence conversion factor, the measurement method for valid wordrate (non-duplicate/non-filled token ratio) and the token energy consumption accounting norms in units. Pilot collection and sampling of token consumption data for selected priority sectors and areas, construction of a national token economic climate index, regular publication of token call scale, structural distribution and value density analysis reports. Promote the model service provider's public terminological rule, billing and sla commitment to establish a third-party token audit and dispute arbitration mechanism to provide a credible basis for business selection, cost accounting and policy assessment。
Fourthly, capacity-building for innovation through enhanced core technology ownership. At the hardware level, emphasis is placed on breaking through the smart chip microstructure, high-speed interconnectivity between the chips, smart cloud operating systems and cluster error control techniques. At the software and algorithm level, support has been given to scientific institutions and leading companies in joint efforts to speed up reasoning techniques such as low-intensity, grouping, decodering, dynamic batch processing, construction of ad hoc chinese-language syllables and field-adaptation dictionaries, compression of redundant characters and increasing semantic density. At the level of security governance, fine-tuned large model security assessment baselines, content marking traceability requirements and smart body behaviour norms under the hf token call scene, and established mechanisms for real-time call interruption and incident reporting. At the same time, scientific institutions and leading science and technology enterprises are encouraged to participate deeply in the international organization of artificial intelligence standards and the global ai governance dialogue to gain a voice in rule-making。
Zhang bin, associate professor, school of economic management, beijing post and telecommunications university
