Yinlun Shares: Large Cooling Modules for High-Horsepower Generator Sets Used in Data Centers Are in Mass Production

As the world focuses on the parameters and capability leaps of large models, another dimension of the AI competition—the "arms race" for infrastructure—has quietly reached a feverish stage. The latest two news items clearly outline both the grand scale and intricate details of this global AI infrastructure landscape.

SoftBank's announcement to invest up to €75 billion in France to build Europe's largest data center cluster is undoubtedly a heavy punch in this round of competition. This is not only SoftBank's largest AI investment outside the United States, but also signals a subtle yet profound shift in the global distribution of AI computing power. What Masayoshi Son is betting on is not just France's computing market, but also Europe's independent path in data sovereignty and AI regulation. The planned total computing power of up to 5.1 gigawatts will directly rival that of American hyperscale data centers, aiming to establish a strategic foothold for Europe in an era where "computing power is power." However, this is not a reckless gamble, but a consideration based on practical benefits: France's relatively low electricity prices, stable nuclear power supply, and the Macron administration's proactive attitude toward investment attraction together form a magnet for top-tier investments. Behind Son's swift decision lies SoftBank's ambition to transform from a financial investor to an industry enabler, seeking to embed another critical chip in the global AI supply chain at the hardware level.

Corresponding to the grand national blueprint outlined by SoftBank is a specific, tangible victory in mass production that penetrates deep into the capillaries of the industry chain. The production information disclosed by Yinlun Co. on its interactive platform may seem unremarkable, but it is actually highly significant. It provides another footnote for China's AI industry participating in global high-end manufacturing: this company, known for its heat exchangers, has successfully mass-produced large-scale cooling modules for high-horsepower generator sets used in data centers and will soon deliver gas power exhaust treatment systems to international clients. These two businesses precisely target the two most core and energy-intensive aspects of AI data centers: continuous and stable backup power supply and the immense cooling demands generated by massive computation. Without efficient thermal management, even the strongest chips cannot sustain computing power output; without reliable power保障, intelligence in the cloud is like a tree without roots.

The case of Yinlun reveals a often-overlooked truth: the competitiveness of AI does not only exist in open-source models and cool applications, but is also deeply rooted in the seemingly traditional "hard technology" supply chains of power, cooling, and structures. When international giants compete to plan campuses of hundreds of megawatts, who will provide efficient, reliable, and cost-controlled infrastructure components? This is precisely where the system-level engineering capabilities and rapid mass production ramp-up capabilities accumulated by Chinese manufacturing over decades come into play. Yinlun is not an isolated case; from liquid cooling systems to backup power supplies, a group of Chinese companies is quietly embedding themselves into the backbone network of global AI infrastructure.

Placing these two news items side by side, a clear map of industry chain division emerges. Capital heavyweights and tech giants like SoftBank act as "architects" and "general contractors," designing and investing in grand computing blueprints; while companies like Yinlun are indispensable "core equipment suppliers," translating these blueprints into solid and reliable physical foundations. The rapid advancement of global AI relies precisely on this precise coupling of "top-level design" and "grassroots manufacturing."

Therefore, SoftBank's €75 billion investment and Yinlun Co.'s mass production announcement together point to a more solid (perhaps also heavier) underlying logic of the AI era: The height of intelligence will ultimately be determined by the density of energy, the efficiency of heat dissipation, and the reliability of infrastructure. When we marvel at the emergent capabilities of large models, we should not forget that supporting all of this is the surging electric current underground, the humming cooling systems in server rooms, and the silent manufacturing force that assembles steel, copper pipes, and chips together. The future AI hegemony may be half in the clouds of algorithms and the other half in these solid and reliable industrial foundations.