Today, Qiushi published an article titled “Driving High-Quality Development of the Intelligent Economy with Tokens as the Engine,” authored by Tang Jie, co-founder of Zhipu AI and professor in the Department of Computer Science and Technology at Tsinghua University.
The article’s core argument is that tokens should not be treated merely as a technical unit through which large models process information, but should become the foundational unit for measuring the production, exchange, and value creation of intelligent services. The development of the intelligent economy depends on three factors: model intelligence level, which determines the intrinsic worth of each individual token; API call volume, which determines the market scale of intelligent services; and intelligent conversion efficiency, which determines whether tokens can actually translate into productivity gains, enterprise revenue, and economic growth.
The author argues that China has built meaningful advantages through domestically developed open-source models, low-cost services, and sheer volume of API calls, but that a large proportion of token consumption remains concentrated in low-value scenarios such as chat and image generation, while fundamental model innovation and application in high-value domains like industrial manufacturing and scientific research remain insufficient. The next step should not be to pursue call volume growth alone, but to simultaneously raise model capability and the rate of conversion into the real economy, while working to establish measurement, pricing, trading, and governance rules for tokens. That said, tokens from different models are not fully comparable, and growth in call volume does not necessarily represent growth in economic value — which is precisely why intelligent conversion efficiency matters more than raw token counts.
Full translation of the article is available:
Recently, Qiushi published an article by Tang Jie, professor in the Department of Computer Science and Technology at Tsinghua University, titled “Driving High-Quality Development of the Intelligent Economy with Tokens as the Engine.” “Tsinghua Humanities and Social Sciences” hereby reposts the article for readers.
Driving High-Quality Development of the Intelligent Economy with Tokens as the Engine
Tang Jie
On July 17, General Secretary Xi Jinping delivered a keynote speech at the opening ceremony of the 2026 World Artificial Intelligence Conference and High-Level Meeting on Global AI Governance. He stressed the need to “comprehensively promote scientific and technological innovation, industrial development, and scenario-based application of artificial intelligence,” and to “empower all industries with artificial intelligence.” At present, the intelligent economy, mainly supported by large models and agents, is rapidly reshaping the global economic structure. Tokens, as the basic unit of AI-generated content, have gone beyond being a purely technical concept and are becoming a new type of data element that promotes the development of China’s intelligent economy. They are not only bringing changes to the business model of the AI industry, but also opening up new growth space for future industries characterized by intelligence. We should uphold innovative thinking and a systems perspective, better leverage the multiplier effects of model intelligence level, application programming interface (API) call volume, and intelligent conversion efficiency, allow tokens to generate richer economic value, and better promote the high-quality development of China’s intelligent economy.
I. What Are Tokens?
Tokens originate from natural language processing and are currently the smallest unit through which large models process information. As a bridge connecting human natural language with machine intelligence computation, tokens are becoming a core indicator for measuring the transaction volume of intelligent services and a micro-level carrier for value creation in the intelligent economy. They have clear attributes of productivity.
First, tokens are measurable, priceable, and tradable, thereby restructuring the mechanism that connects technology supply with business demand. As the statistical unit for the scale of large model calls, tokens precisely quantify the consumption of intelligent services. As a billing unit, they push the software business model to shift from traditional licensing and subscription toward on-demand calling, making intelligent services available like water and electricity, and providing a clear quantitative basis and transaction possibility for the large-scale implementation of the AI industry.
Second, tokens are dynamically generated, high-density data elements. Unlike traditional data, which serve as static records and production factors, tokens represent knowledge that is being generated and activated by models. They include not only textual semantic slices, but also vectorized expressions of multimodal information such as images, audio, and video. They contain the model’s reasoning, generation, and decision-making processes in specific tasks, and are an intelligent form created after data elements are processed by large models, which can directly serve production and daily life.
Third, tokens have long-term stability and technological neutrality, and possess the attributes of a value anchor and measurement language in the intelligent era. Tokens are not tied to a specific technological route or a single vendor, and are highly universal. Just as the “barrel” in the oil era and the “kilowatt-hour” in the electricity era served as key units, mastering the token as a unit of measurement is essentially equivalent to mastering the right to define and measure the infrastructure of the intelligent economy. Establishing a digital economy foundation based on tokens is of great significance for China to gain standard-setting power and pricing leadership in global intelligent economy competition.
II. Three Dimensions of Tokens as an Engine for Intelligent Economy Development
Tokens promote the vigorous development of the intelligent economy through their full penetration into production, life, industry, and commerce. They not only give rise to AI-native new business forms such as agent services and “one-person companies,” but also create tokenized value increments as AI empowers existing industries, and restructure the value chains and collaboration-distribution methods of existing industries. The effectiveness of tokens’ contribution to the development of the intelligent economy is mainly determined by three factors: model intelligence level, API call volume, and intelligent conversion efficiency.
The model intelligence level determines the intelligence density and value content carried by a single token. The higher the model intelligence level, the stronger the logic, accuracy, and creativity of the generated tokens, the more significant their ability to solve complex problems and assist scientific decision-making, and the higher the use value of the generated content. Model intelligence level can be measured by performance in benchmarks covering comprehensive knowledge, complex reasoning, code engineering, multimodal processing, and autonomous execution by agents. Since last year, the iteration speed of mainstream large models worldwide has accelerated, continuously refreshing benchmark scores. These test results provide an important reference for observing the relative intelligence levels of different models.
API call volume is the scale foundation of the intelligent economy. It measures model computing power consumption and data throughput, and reflects the popularity of large models and the activity level of the AI industry. At present, major model vendors around the world all provide API services billed by call volume and actively open them to industry. Through APIs, tokens flow across different industries, regions, and organizations, becoming the main way for intelligent elements to be allocated across scenarios. Overseas users can also remotely call models and computing power within China through APIs to obtain inference services and pay according to token consumption, thereby realizing the “export of tokens.”
Intelligent conversion efficiency reflects the rate of value realization in the intelligent economy. It measures the conversion effectiveness between token generation and economic value creation, and is closely related to factors such as the penetration rate of model applications in industries and their adaptability in scenarios. The higher a model’s intelligence level, the deeper its integration with industries, and the stronger users’ human-machine collaboration capabilities, the more economic value can be produced with less token consumption, achieving higher intelligent conversion efficiency. Higher intelligent conversion efficiency means that tokens can more accurately match industrial needs, deeply integrate with a wide range of industries, promote expansion, cost reduction, and efficiency improvement, more fully drive productivity enhancement and economic benefit growth, and complete the value loop of the intelligent economy.
The three factors above — model intelligence level, API call volume, and intelligent conversion efficiency — shape the activity characteristics of workers using AI for production and economic value creation from three dimensions: tool quality, behavioral scale, and output effectiveness. When large models are deeply integrated with industrial scenarios, tokens can transform from digital costs into intelligent assets. Their growth-driving effect will be reflected in the intelligent economy through improved production efficiency, better service quality, and the emergence of new business forms, opening broader, more practical, and more sustainable growth space for future industries.
III. New Characteristics of China’s Intelligent Economy Development Driven by Tokens
At present, the token-driven development of China’s intelligent economy already has notable advantages such as leading overall scale and rich application scenarios. It is showing clear characteristics including volume explosion, deeper application, structural optimization, and model innovation, driving China’s intelligent economy to accelerate from a stage of concept popularization into a stage of large-scale value creation.
The comprehensive competitiveness of domestic open-source models continues to make new breakthroughs, and their ability to serve the intelligent economy through token supply has significantly strengthened. In April this year, a domestic large model ranked first globally in a professional software engineering benchmark, surpassing U.S. closed-source flagship models from the same period. In June, on a front-end development evaluation system involving blind testing by one million users worldwide, a domestic large model again ranked first among globally available models. Unlike the U.S. government’s ban on foreign entities accessing its leading closed-source models, domestic large models have firmly upheld openness and open source, providing high-quality, accessible, and trustworthy large model services to global users. At the same time, the service prices of domestic open-source models also have cross-order-of-magnitude advantages compared with the flagship versions of U.S. closed-source models. The narrowing technological gap, extreme cost-performance advantage, and stable openness together form the core support for the comprehensive competitiveness of domestic models.
API call volume has exploded exponentially, making China the world’s largest token consumption market. Over the past two years, China’s large model applications have rapidly become widespread, the agent ecosystem has accelerated in development, and token call volume has grown explosively. This is a direct reflection of China’s ultra-large-scale market advantage in the intelligent economy. At the beginning of 2024, China’s average daily token call volume was 100 billion; by the end of 2025, it had jumped to 100 trillion; and in March 2026, it exceeded 140 trillion, representing growth of more than a thousandfold in two years. According to statistics from OpenRouter, a global large model aggregation platform, since late April 2026, the weekly token call volume of Chinese large models has exceeded that of the United States for three consecutive months and remained first globally. This explosive growth marks the formation of a massive intelligent consumption market in China, making China one of the most active and promising token consumption centers in the global intelligent economy.
Application scenarios are moving from conversational interaction into production processes, and the path of intelligent conversion is becoming increasingly clear. By being embedded into specific production, service, and trade processes, tokens are converted into visible and real economic increments. This reflects the evolution of the intelligent economy from being pushed unidirectionally by the technology supply side toward being pulled by value validation on the demand side. In enterprise-level applications, new application forms such as intelligent R&D assistants and intelligent operations hubs continue to emerge, and token consumption is accelerating its embedding into core business processes and high-value links. Some analysis reports show that in the second half of 2025, China’s average daily token call volume for enterprise-level large models rose to 3.6 times the level of the first half of the year. Since 2025, the revenue of China’s major model companies has increased substantially, also showing that model-as-a-service platforms can directly convert token consumption into measurable business revenue through API call pricing, and leading models are achieving both volume and price growth.
At present, although China’s intelligent economy is developing rapidly, using tokens as the engine to promote high-quality development of the intelligent economy still faces some constraints. First, improvement in model intelligence level faces bottlenecks, with an emphasis on engineering optimization but insufficient breakthroughs in principles. There remain gaps with international leading levels in autonomous reasoning, long-range planning, multimodal integration, safety alignment, and other areas, while innovation in basic theories, underlying technologies, and core algorithms remains insufficient. Second, token calls face structural contradictions. Consumer-level applications such as chat and text-to-image generation have large call volumes but low commercial returns. Although the market for small and medium-sized enterprise-level applications is growing rapidly, its overall penetration rate still needs improvement. A large amount of high-value calls remains inside closed enterprise systems and has not been converted into dividends for the open platform API ecosystem. Third, the conversion rate from token output to real-economy value is not high, especially in high-value-added fields such as industrial manufacturing and scientific research innovation, where penetration remains low. Only by properly solving these problems can tokens further play their role as an engine and inject stronger driving force into the development of China’s intelligent economy.
IV. Fully Leveraging the Role of Tokens as an Engine for Driving High-Quality Development of the Intelligent Economy
The “15th Five-Year Plan” period is an important strategic window for China’s AI development to shift from technological explosion to mature application. The endogenous growth momentum of the intelligent economy comes from the positive feedback loop of “intelligence level — API call volume — intelligent conversion efficiency.” We should closely follow the engine function of tokens, raise the intelligence ceiling for token value release, expand the usage scale of generated token content, improve the integration level between tokens and the real economy, and build a governance system suited to this process, so as to promote the transformation and upgrading of China’s intelligent economy from scale leadership to quality leadership.
Raise the intelligence ceiling and improve the model intelligence level. Intelligence level is essentially a reflection of the accumulation of general-purpose technological capital. Investment in intelligence level is the accumulation of intelligent capital stock, and its returns have the characteristic of long-term increase. More attention should be paid to the leap from perceptual intelligence to cognitive intelligence, vigorously promoting original innovation in model architectures and algorithms, and seeking breakthroughs in long-range task capabilities of large models, autonomous agent systems, self-evolution, and autonomous training. We should accelerate the construction of high-quality, multimodal Chinese corpora and knowledge bases covering key industries, and explore safe and compliant data sharing and “data elements ×” models. Policy guidance and institutional supply should be strengthened, universities, research institutes, and leading enterprises should be supported in jointly carrying out frontier basic research, funding support for large model R&D, computing power construction, and data element markets should be increased, the intellectual property system should be improved, and innovation vitality should be stimulated.
Innovate service models and expand API call volume. API call volume is essentially the demand scale for the new consumer good of “intelligence as a service.” An increase in user numbers will accelerate model improvement, and model improvement will in turn attract more users, forming demand-side economies of scale. The cost of model access and switching should be reduced, differentiated pricing should be implemented, and a more mature API market should be cultivated. An independently controllable “model-chip-energy integrated” AI infrastructure should be built, and the construction and intensive, efficient use of a nationwide integrated computing power network should be advanced. An open-source and open high-performance computing power service platform should be built, the API service ecosystem should be continuously optimized, and inclusive computing power support should be provided to a wide range of small and medium-sized enterprises. Agents should be vigorously developed, and large-scale application of agents in various vertical fields should be encouraged, so that agent clusters capable of autonomous driving, collaborative operations, and round-the-clock functioning become a new industrial form, truly moving from “intelligent assistants” to “digital employees.” Token measurement and trading markets should be improved, unified token measurement standards and trading rules should be established more quickly, and the production, circulation, and consumption of tokens should be further activated.
Strengthen scenario guidance and improve intelligent conversion efficiency. As tokens penetrate from the consumer side into the production side, they are concentrated in the early stage in standardized tasks that are easy to mass-produce, such as copywriting generation and code completion. In the later stage, they need to solve “last-mile” adaptability problems involving industry knowledge, business processes, and regulatory constraints. The key to improving intelligent conversion efficiency lies in designing a “token-task” matching mechanism, so that token production is as close as possible to value creation scenarios. With advanced manufacturing clusters, national-level economic and technological development zones, and pilot free trade zones as key areas, and modern agricultural industrial parks and modern service industry clusters as broader extensions, industrial and enterprise business processes should be systematically reviewed. Token-driven solutions should be developed for pain points and bottlenecks, a number of benchmark scenarios for the intelligent economy should be created, and intelligent conversion efficiency should be improved. An evaluation system for intelligent economy conversion efficiency should be built, and efforts should be made to explore incorporating new indicators such as the GDP-driving effect per unit of token consumption and AI penetration rates in key industries into the national statistical system.
Strengthen governance support and build an ecosystem for the sustained and healthy development of the intelligent economy. Focusing on property rights, transactions, and risks, a token governance system that is incentive-compatible, balances public and private interests, and has clear rights and responsibilities should be built more quickly. Alignment capabilities suited to model intelligence levels should be built in parallel. National laws and regulations, social norms, morality, and ethics should be written into models’ value functions as underlying axioms, ensuring that AI technology promotes the intelligent economy toward good. A sound algorithm security regulatory system should be established to prevent risks such as algorithmic discrimination, privacy leaks, and the spread of false information. Openness and cooperation should be upheld, active participation in the formulation of global AI governance rules should be pursued, and an open, fair, and non-discriminatory international environment for intelligent economy development should be promoted.
This gets more interesting if you put it next to the Bank of China story about lending against token burn. You have a lab founder saying tokens are the engine of the intelligent economy, and a bank starting to treat token usage as a credit signal. Same unit of account, from two very different places.
The question is whether that metric lasts. Or whether it ends up like GMV: useful for showing activity, less useful for showing value. What would convince you it is durable?