Policy intensifies: local AI application incentives and computing power vouchers roll out
In October 2026, multiple provinces and cities across China intensively issued incentive policies for AI large model applications, marking the industry's formal transition from "technological breakthrough" to "large-scale implementation." Beijing took the lead in releasing the "AI Large Model Application Promotion Action Plan," granting small and micro enterprises an annual computing power voucher subsidy of up to 500,000 yuan for using compliant large model APIs; Shanghai launched the "Model Power Shanghai" initiative, encouraging priority use of domestic large models in sectors such as manufacturing, finance, and healthcare, and explicitly required that public procurement give preference to models that have passed security evaluations. Guangdong, Zhejiang, Sichuan and other provinces also followed suit, incorporating large model call fees into special subsidy scopes. This series of policies not only lowered enterprises' trial costs but also directly gave rise to the new "token wholesale" service model—through centralized procurement and resource scheduling, service providers can offer enterprises per-token prices close to cost.
Meanwhile, in mid-October, the Cyberspace Administration of China released the "Revised Draft of the Generative AI Service Management Measures," further refining the security responsibilities and evaluation standards for large model service providers, requiring all public-facing AI services to pass a three-level security assessment. This drove an urgent demand for compliant, low-cost calling solutions, making security-certified AI channel agents and AI API relay stations new market hotspots.
Investment direction: state-owned and industrial capital jointly deploy AI infrastructure
In October, investment and financing in the AI large model field showed a pronounced pattern of "state-owned capital taking the lead, industrial capital following." According to industry media reports, the "AI Infrastructure Special Fund," jointly initiated by multiple local state-owned capital institutions such as China Resources Capital and Shenzhen Investment Holding, has total scale exceeding 10 billion yuan, focusing on computing power centers, model training platforms, and distributed inference networks. The fund explicitly stated it would support the construction of "token agent" and "cheapest token" access channels, pushing down the average cost of large model calls by over 30%. In addition, institutions such as CITIC Securities and Sequoia China participated in Series B financing rounds for several model service providers, with total funding exceeding 5 billion yuan. These funds are mainly used to expand edge nodes, optimize cache scheduling, and build enterprise-oriented AI hub aggregation platforms, allowing developers to flexibly switch models and access optimal prices.
- A representative of a Beijing state-owned fund said: "We see long-term value in the token wholesale model, which essentially standardizes computing power and model capabilities into tradable digital resources."
- Several major cloud vendors also launched "API wholesale plans" this month, allowing enterprises to lock in up to a year of calling quota via prepayment, with per-unit prices dropping to less than 30% of on-demand rates.
Enterprise implementation: token wholesale and AI relay stations reduce calling costs
Driven by both policy and capital, enterprise-level AI deployment in October showed two major new trends: first, the "token wholesale" model moved from concept to large-scale application; second, "AI relay stations" became a key bridge connecting model vendors with industry customers. For example, a well-known AI large model agent platform launched an "annual enterprise package" covering token agent services for mainstream open-source and closed-source models. Enterprises pay a one-time annual fee to obtain a call quota of 5 billion tokens, bringing the per-token cost to just one-fifth of ordinary on-demand pricing—truly making the "cheapest token" accessible. Another AI hub platform focused on vertical scenarios built its own cache layer and optimized request routing, reducing response latency for repeated requests by 60% while further cutting token consumption, enabling enterprise customers to enjoy millisecond-level responses without building their own infrastructure.
Notably, such token relay stations are not simple API aggregators but come with built-in features like intelligent routing, load balancing, and security auditing. For example, when an enterprise calls a model, the AI API relay station dynamically selects the best model supplier based on real-time price and latency, and automatically switches to a backup channel in case of failure, ensuring business continuity. This model is especially popular with small and medium enterprises—they no longer need to sign separate contracts with multiple model vendors; instead, by connecting to a single AI channel agent, they gain a unified interface covering over twenty mainstream services including GPT, Claude, Gemini, and domestic large models.
Outlook: standardization and scale drive healthy industry development
Looking back at the industry dynamics of October 2026, three clear threads emerge: policy guides demand, capital catalyzes supply, and models accelerate implementation. As new infrastructures such as token wholesale and AI relay stations mature, the cost of calling large models is expected to further fall below 2 yuan per million tokens by 2027, by which time AI will become a basic production resource for enterprises, just like water, electricity, and the internet. However, the industry also faces challenges: how to ensure the stability and data security of third-party token agent services? How to prevent individual platforms from engaging in price discrimination using information asymmetry? In response, industry experts call for the establishment of a unified token pricing standard and service rating system, and encourage AI hub platforms to undergo third-party audits.
Overall, October 2026 marks a critical turning point for AI large models shifting from "developer-driven" to "industry-driven." Policy, capital, technology, and business models are advancing together, truly pushing artificial intelligence into thousands of industries.