China's 140T token surge forges new AI distribution market

A new intermediary market for AI model APIs is emerging, driven by a 1,000x surge in China's daily token call volume to over 140 trillion and creating a "wholesale-to-retail" network for artificial intelligence.

The commercialization of artificial intelligence is creating a new class of middleman, a distribution layer where the basic units of machine intelligence are bought, routed, and resold like a commodity. This market for "tokenomics" is being driven by a more than 1,000-fold explosion in China's daily AI model API calls, which surged from 100 billion at the start of 2024 to over 140 trillion by March 2026, according to a report from Huayuan Securities. This new layer connects upstream model makers like ByteDance and Alibaba with a fragmented downstream of developers and companies, creating a liquidity infrastructure for the global flow of AI tokens.

"Token运营正在形成一个新的中间层市场，即探索Token分销模式，连接上游大模型厂商与下游开发者、企业和个人，本质是全球Token的批发到零售网络的流动性基础设施 (Token operation is forming a new intermediary market, exploring a token distribution model that connects upstream large-model manufacturers with downstream developers, enterprises, and individuals. It is essentially a liquidity infrastructure for the global wholesale-to-retail network of tokens)," Huayuan Securities analyst Chen Liangdong said in the report.

The growth is fueled by the sheer scale of AI consumption and the rising competitiveness of Chinese models. In the first quarter of 2026, Chinese models surpassed their U.S. counterparts in weekly call volume on OpenRouter, a popular routing platform, for the first time. Between February 16 and 22, Chinese models accounted for four of the top five models by call volume, contributing 85.7% of the total. Platforms like OpenRouter, which has seen its annualized revenue grow five-fold to over $50 million since October 2025, and the domestic Silicon Mobility (硅基流动) are building the core infrastructure for this trade, offering unified APIs that allow developers to access hundreds of different models through a single key.

This new token economy mirrors the evolution of other technology stacks, from cloud computing to crypto payments, where infrastructure and distribution ultimately capture significant value. As the AI race shifts from the capital-intensive process of training models to the operational challenge of running them for billions of users—a process known as inference—the new competitive moat becomes cost per answer. With Chinese models like MiniMax's M2.5 offering input costs of just $0.30 per million tokens compared to $5 for a model like Claude 4.6, the economic incentive to route workloads efficiently is creating a multi-billion dollar opportunity for these new AI brokers.

The Utility Layer for Intelligence

The business of token distribution is not merely about reselling API access at a markup. While a base reseller margin, like OpenRouter's 5.5% premium, provides a foundational business model, the real value is being created further up the stack. The most sophisticated players are developing proprietary inference acceleration engines to lower the actual cost of running a model. Silicon Mobility, for instance, claims its SiliconLLM and OneDiff technologies can improve language model inference speed by 10 times, allowing it to offer API calls at what it says is one-tenth of the industry standard cost.

This focus on the unit economics of inference is critical as the industry moves toward "agentic AI"—autonomous, always-on systems that could be 60 to 130 times more energy-intensive than current AI tools, according to Goldman Sachs research. These agents, which will handle tasks from enterprise workflows to managing smart devices, will drive a persistent, utility-like consumption of AI tokens. The platforms that can reliably and cheaply route, meter, and bill for this consumption are positioning themselves as the essential utilities for the AI economy. This parallels the infrastructure battle in cryptocurrency, where firms like Circle and Coinbase are competing not just on issuing a stablecoin, but on building the payment and settlement rails (like Arc and Base) that control its flow.

Risks and Rewards in the AI Supply Chain

For investors, the emergence of this token distribution layer opens up new avenues beyond simply backing the high-profile model builders. The Huayuan report identifies two primary investment theses: backing the companies with superior model capabilities like Alibaba, Tencent, and ByteDance, and backing the companies with strong client relationships and high-consumption scenarios, particularly in marketing, gaming, and e-commerce. Companies like yodo (易点天下) and BlueFocus (蓝色光标) are highlighted for their potential to embed token-based AI services directly into their existing client workflows.

However, the model is not without risks. The low technical barrier to entry for basic API reselling invites intense competition, which can compress margins. Distributors also face significant capital pressures, as they often need to pre-purchase capacity from model providers while offering more flexible payment terms to downstream clients, creating exposure to bad debt. The most significant risk is the dependency on the upstream model providers, who control pricing and access. A sudden policy change by a major provider could instantly undermine a distributor's business, making this a high-growth but potentially volatile segment of the AI value chain.

This article is for informational purposes only and does not constitute investment advice.