A recent joint directive from the State Administration for Market Regulation and the National Development and Reform Commission has laid out a systematic plan to build China's artificial intelligence measurement capabilities. The "Guidance on the Construction of Artificial Intelligence Measurement System and Capabilities (2026 Edition)" focuses on six key areas, aiming to bridge the gap between laboratory innovation and real-world industry application. This policy directly addresses two core pain points in the current AI industry: the "unmeasurable" nature of algorithms and the "data scarcity" in specific scenarios. It explicitly promotes the establishment of measurement standards that make AI technical performance "measurable, comparable, and traceable," and calls for the construction of test datasets with the highest metrological characteristics. Resonance between policy and industry is accelerating.

Just days before this policy release, Xunce (03317), known as the "first Token stock," officially launched the world's first TokenOS operating system—TokenONE. Its core proposition aligns closely with the policy direction: making large model outputs measurable. Notably, the timing is striking; Xunce's "measurable" TokenOS technology landed just as the relevant policy emerged. This synchronization suggests not just fortunate timing but a deeper, systemic synergy.

The guidance specifically targets industry challenges like the "black box" nature of large model algorithms and poor decision interpretability, deploying key technological research for monitoring and characterizing AI system internal states. Xunce's TokenONE is not merely a cost-statistics dashboard but a complete system that reconstructs the credibility of AI cost governance from the ground up. It redefines each model call not as mere computational consumption but as quantifiable, traceable business value output, allowing each Token to directly drive business decisions. TokenONE also pioneers the world's first business model that charges "based on Token invocation effectiveness," transforming enterprise private data into measurable, priciable, and exchangeable scenario Tokens, eliminating ambiguity in commercial settlements.

Another major pain point for enterprise AI implementation is the structural shortage of high-quality scenario data. Generic Tokens often have low information density and high无效消耗, struggling to support critical scenarios like financial risk control or energy dispatch that demand extreme accuracy. The guidance explicitly proposes building standard reference and test datasets to break down industry data barriers. TokenONE's core mission is precisely to solve this supply bottleneck. The operating system, leveraging its three core capabilities of "refinement, delivery, and decision-making," transforms an enterprise's scattered, heterogeneous raw data into high-purity, high-value "scenario Tokens" through an industrial process, turning dormant data into core productive materials that drive precise large model operations.

The issuance of the guidance marks a transition in China's AI industry from "building computing power and expanding scale" to "improving quality and strengthening foundations." The two focal points of Xunce's TokenONE—standardized measurement of core AI productive materials and the industrialized supply of scenario data—resonate strategically with this policy direction. This systemic linkage, from technological implementation to policy follow-through, further validates Xunce's foresight and strategic positioning in the AI infrastructure domain. As the TokenOS operating system moves towards large-scale commercial application, Xunce is steadily transitioning from a "solution provider" to a leader in "AI infrastructure," garnering increasing attention from the capital markets for its long-term value and growth potential.