Token economy emerges as core pillar of China’s AI-driven digital economy amid rapid scale expansion

Deeper integration of artificial intelligence across industrial sectors has elevated Token, or token unit, from a technical measurement tool into a core benchmark for intelligent economic growth. Serving as the fundamental unit for large model computing calls and intelligent application deployment, Token underpins a new economic paradigm that reshapes the competitive landscape of the global digital industry.

Industry operational data shows explosive growth in China’s Token consumption scale. The country’s average daily Token call volume stood at 100 billion at the start of 2024, before surging to 100 trillion by the end of 2025. The figure exceeded 140 trillion in March 2026, reflecting robust expansion of domestic AI application scenarios and computing demands. Despite rapid scale growth, the Token economy faces structural constraints including widespread cognitive confusion, lagging industrial measurement standards, incomplete industrial ecosystems and insufficient independent core technological capabilities.

Token consumption acts as a key observational indicator of AI activity and industrial transformation efficiency. Every interactive behaviour of large language models, including problem analysis, content generation, tool invocation and task execution, converts input and output information into standardised Token sequences. Visible user experience in terms of content quality and operational efficiency relies on underlying support from data resources, computing power, model performance and engineering scheduling systems. Continuous optimisation of Token consumption mechanisms drives coordinated upgrading of AI chips, servers, network infrastructure, storage facilities, inference frameworks and model platforms, forming essential infrastructure for the intelligent economy.

The Token economy operates via a cyclic industrial mechanism of production, circulation and consumption. On the supply side, intelligent service capacity is generated through AI chips, computing centres, inference frameworks and foundational large models. On the circulation side, cloud platforms, model application interfaces and intelligent agent systems distribute and deliver intelligent capabilities across industrial markets. On the demand side, industrial enterprises deploy AI technologies in research and development, production scheduling and knowledge service scenarios, translating continuous Token consumption into tangible industrial productivity. Expanding call scales, declining unit Token costs and diversified application scenarios create positive feedback loops between technological iteration and market demand.

Structural challenges continue to hinder high-quality development of the domestic Token economy. Mixed public understanding constitutes a primary obstacle. Long-term translation of the Token concept as blockchain tokens has led to widespread confusion between AI-based Token economic systems and encrypted asset tokenisation mechanisms among institutional investors, local industrial bodies and market enterprises. Such misunderstanding weakens industrial attention on standardised Token measurement, billing and system construction, and creates market loopholes for irregular speculative projects disguised as AI and blockchain integration businesses.

Unified industrial measurement standards remain absent, restricting refined industrial governance. Mainstream large model developers adopt inconsistent tokeniser algorithms, leading to substantial discrepancies in Token segmentation quantities for identical Chinese text across different platforms. No universally recognised equivalent conversion criteria exist for multimodal Token calculation covering text, image, audio and video content. The lack of unified statistical calibres, quality evaluation systems and transparent billing rules impedes macro-level monitoring of AI application penetration and micro-level corporate cost assessment and horizontal product comparison.

The industrial ecosystem retains obvious imperfections. Supply-side development is constrained by inadequate adaptation between domestic AI chips and mainstream inference frameworks, limiting large-scale, low-cost and stable Token output. On the demand side, large industrial enterprises in manufacturing, energy and transportation sectors primarily deploy large models in peripheral scenarios such as customer service and document retrieval, with limited integration into core production control and supply chain optimisation workflows, resulting in high-frequency yet low-value Token consumption. Small and medium-sized enterprises face operational barriers including elevated model invocation costs and shortages of professional talents specialised in prompt engineering and agent orchestration, restraining sustainable market demand growth.

Domestic core technological independence requires further consolidation. High-end training and inference chips, lossless cluster networking technologies and high-throughput low-latency inference acceleration frameworks still rely heavily on overseas technological ecosystems. Domestic computing products lag behind international mainstream platforms in operator library completeness, distributed parallel scheduling and video memory optimisation, leading to higher unit Token production costs and lower effective computing efficiency under equivalent power consumption levels. Further breakthroughs are also required in algorithm fields including long-context attention mechanisms, multimodal Token compression coding and Chinese-oriented tokeniser optimisation.

Systematic improvements covering policy design, industrial cultivation, standard formulation and technological innovation will drive healthy progress of the Token economy. National digital and artificial intelligence industrial development plans will incorporate Token economic development into overall strategic layout, establishing institutional frameworks for unified measurement, market supervision and industrial evaluation. Clear regulatory boundaries will be defined to distinguish AI Token service mechanisms from blockchain virtual asset applications, while industrial publicity and professional training will guide the whole industrial chain to prioritise practical application value, Token cost efficiency and business contribution over pure model parameter scale and ranking performance.

Synergetic optimisation of supply and demand will strengthen industrial ecological construction. Integrated national intelligent computing layout and coordinated computing-power and clean-energy development will support the construction of green low-carbon Token production bases in regions with abundant renewable energy resources. Joint technical optimisation between domestic chip developers and inference framework teams will improve underlying industrial support capabilities. Key industrial empowerment programmes will promote deep integration of large model technologies into core business processes across advanced manufacturing, energy, transportation and healthcare sectors to increase high-value Token application ratios.

Accelerated formulation of national Token standards will fill basic institutional gaps. Standardised specifications covering fundamental Token definitions, Chinese segmentation benchmarks, multimodal equivalent conversion coefficients and effective Token rate evaluation methods will be released to unify industrial implementation rules. National-level Token economic prosperity indexes will be established through pilot data collection and random inspection mechanisms, supporting regular industrial operation monitoring and analysis. Standardised disclosure of model segmentation rules, billing details and service level agreements, alongside third-party auditing and dispute arbitration mechanisms, will build a credible market operational environment.

Continuous breakthroughs in core independent technologies will consolidate industrial competitive advantages. Hardware innovation will focus on advanced chip microarchitecture, high-speed interconnection and cluster scheduling technologies. Software and algorithm research will target inference acceleration technologies and customised Chinese tokeniser systems to reduce Token redundancy and enhance semantic utilisation efficiency. Improved security evaluation benchmarks and real-time risk intervention mechanisms will standardise intelligent agent operational boundaries. Active participation in international AI standardisation and global governance dialogues will enhance industrial discourse power for domestic technological achievements and application practices.