Favorable policy! "Modular resonance" presses the fast forward key for AI industrialization, XUNCE Technology (03317) wins with Token conversion efficiency.

On April 28th, the Ministry of Industry and Information Technology, in conjunction with the National Bureau of Statistics, issued a notice on the joint implementation of the "Modular Resonance" action in 2026, focusing on more than twenty industries including steel, petrochemicals, non-ferrous metals, electric power equipment, information communications, and network security, to systematically promote the benign mutual promotion cycle of "data-model-scenario application". The notice requires each industry to identify no less than 5 high-quality general data sets, construct no less than 1 specialized data set for each scenario, and create no less than 1 dedicated model or specialized intelligent body for each scenario. This means that more than twenty key industries in China will, under the guidance of policies, complete a round of structural construction of industry-specific data assets. Thousands of specialized data sets and specialized intelligent bodies are expected to go online in bulk within the next year - and the amount of Token they consume daily, ultimately translating into how much real business decision-making value, will directly determine the success or failure of this round of AI industrialization. The policy points to the core issue of "Token conversion efficiency". XUNCE Technology (03317), with over ten years of deep cultivation in high-barrier industries, has formed a specialized data barrier that is becoming a key variable in the ROI of industrializing large models. After the universal availability of AI, what are companies really competing for? A paradox is forming regarding the profits of implementing large models: the cost of calling general large models approaches zero, almost all companies can equally utilize AI, but more and more companies are finding - they've spent money, bought Tokens, but AI hasn't delivered the expected returns. The reason is simple. General large models rely on public data, but when AI enters core business scenarios such as healthcare, manufacturing, and energy, the "noise ratio" of general data increases sharply. Companies using general models to process data are far less accurate than using industry-specific models trained on data from a decade. For example, a manufacturing company using a general model to analyze equipment failures is much less accurate than a specialized system that integrates real industrial control logs. Huatai pointed out that AI pricing power is shifting from the level of computing power to the level of scenarios, and the premium ability of high-value specialized Tokens is continuously strengthening. Data from the Tencent Research Institute shows: daily casual conversation Tokens cost only 0.01 USD per million calls, while legal document review costs up to 1000 USD - a value difference of one hundred thousand times. This means that "Token conversion efficiency" is becoming the core variable for the ROI of enterprise AI deployment. Companies that cannot build high-quality industry-specific data assets will see their AI investment devolve into an inefficient Token consumption battle. In the race for "Token conversion efficiency," XUNCE's barrier is not a fraction of a second difference in algorithms, but the industry-specific data assets developed over more than a decade - an essential infrastructure that large models entering real business scenarios cannot bypass. Its advantages manifest in three dimensions: First, the barrier of data scarcity. XUNCE's industry-specific Tokens are priced at 10-100 USD per million Tokens, over ten times that of Anthropic. Deeply embedded in customer business processes, the moat is extraordinarily deep. Second, the shortest conversion path. A general Token may require 100 calls to produce accurate results, while an industry-specific Token from XUNCE hits the mark with just one call. Customers are looking at the bottom line - it may be ten times more expensive, but they save on 99 ineffective calls, reducing overall costs. Third, continuous scale effect. China's daily Token call volume has exceeded 140 trillion, a growth of over a thousand times in two years; JPMorgan Chase forecasts a 370-fold increase in AI reasoning Token consumption by 2030; IDC predicts a 139% five-year growth in global AI Agent deployments. Each run of an intelligent body is expanding the application scale of industry-specific Tokens. XUNCE's competitive advantage lies in its ability to make each Token burn with the highest business value, not in minimizing the price per Token. In the battle of Token consumption, the true winner isn't the one driving the price down the most, but the one maximizing the business value of each Token burned. As the "Modular Resonance" policy forcibly initiates the data refinement cycle for more than twenty industries, and as thousands of specialized data sets and intelligent bodies go online in bulk, the key variable determining the success of failure of this AI industrialization effort is no longer "who has computing power" or "who has models," but "whose Token conversion efficiency is the highest." XUNCEARR has seen a quarterly increase of 300%, making it the clearest signal that in the era of efficiency, scarce assets are being revalued.