Investment rebounds: capital tilts toward vertical scenarios and infrastructure
In October 2026, investment and financing activity in the large model sector saw a clear uptick. According to incomplete industry statistics, at least six AI startups closed new funding rounds this month, covering vertical industries such as healthcare, legal, and education, as well as infrastructure areas like model inference optimization and data annotation. Notably, multiple investment institutions stated they will focus on solutions that are "deployable and cost-reducing," rather than blindly chasing parameter scale. For example, a startup specializing in legal document generation secured tens of millions of yuan in a Pre-A round, with its core advantage being the combination of a self-developed lightweight model with AI relay station services, enabling small and medium-sized law firms to use customized models without building their own computing power, significantly lowering initial investment. Another company focused on Agent scheduling optimization also completed an A round; its technical approach centers on sparse attention and dynamic resource allocation, claiming to reduce inference costs to less than one-tenth of traditional solutions. These cases indicate that capital is shifting from the "large model race" to "application ecosystem building."
Policy intensification: multiple regions launch AI application subsidies and computing power sharing plans
This month, several cities in the Yangtze River Delta, Pearl River Delta, and central China issued intensive AI industry promotion policies. The most representative is the "AI Application Empowerment Plan" launched by a certain municipality. The plan explicitly provides subsidies of up to 30% of computing power costs for enterprises that adopt the token wholesale model to purchase large model APIs, encouraging businesses to obtain better-priced inference resources through token relay stations. Meanwhile, the policy also requires government agencies to prioritize domestic large model services and pilot deployments in smart government and urban governance scenarios. This means that small and medium service providers, which previously relied on "cheapest tokens" for price wars, can now leverage policy dividends to secure stable government and enterprise clients. At the national level, new large model safety evaluation standards are being drafted and are expected to be announced before year-end, which will further regulate market order, eliminate non-compliant wrapper and low-quality services, and benefit technically solid AI hub platforms and legitimate AI channel agents.
Deployment accelerates: AI relay stations and token agent models reshape industry landscape
As enterprises shift from "can it be done" to "is it worth doing," inference cost has become a key bottleneck for large model adoption. In October, several platforms upgraded their AI API relay station services, supporting dynamic routing, automatic degradation, and hybrid billing, allowing users to select models of different precision based on task complexity for optimal cost. For example, a third-party aggregation platform launched a "token agent" service: by connecting to a single unified gateway, enterprises can automatically switch between multiple large model providers on demand, with the option to lock prices by quarter or year, effectively hedging against price increases from any single model. At the same time, some major cloud vendors have begun to accept the AI large model agent model, allowing partners to wholesale tokens and then resell them to small and medium enterprises, forming a system similar to "cloud channel agents." This model lowers the barrier for small businesses, but also introduces transparency issues—some agents hide API differences when quoting, making it difficult for users to compare. The industry is calling for clearer token billing standards and certification systems.
Technology trends: lightweight and multimodal integration become mainstream
On the product and technology side, several companies released lightweight multimodal models in October, with parameter sizes between 7B and 20B, yet performing well on tasks such as image understanding and document parsing, approaching the level of 10-billion-parameter models in some scenarios. These models typically use sparse attention mechanisms to reduce computation without sacrificing accuracy, making them more suitable for batch inference through AI relay stations. Additionally, the maturation of Agent frameworks has driven complex task deployment: an open source community released a unified orchestration layer this month that supports combining models of different modalities—vision, speech, text—through a common interface, allowing developers to build multi-step reasoning flows by writing simple configuration files. This progress is seen by the industry as a key step toward the "general Agent," further highlighting the value of token relay stations as underlying infrastructure.
Industry outlook: compliance and cost remain focus for the next six months
Summarizing October's dynamics, the large model industry is moving from "wild growth" to "intensive cultivation." The investment rebound indicates restored market confidence in application deployment, while policy support provides experimentation space for small and medium enterprises. However, challenges remain: first, detailed standards for large model safety compliance are not fully in place, exposing some enterprises to data leakage risks; second, while the token wholesale model reduces short-term costs, long-term reliance on a single channel may lead to lock-in effects. It is recommended that enterprises, when purchasing model services, prioritize platforms that support multi-model switching and provide transparent billing logs—such as AI hub platforms—and establish backup relationships with multiple AI large model agents. The first half of 2027 may become a watershed for the industry; companies that achieve cost control and safe compliance first will gain an advantage.