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Alphabet Inc, Google’s parent company, is locking down the essentials for data centers that fuel AI scaling: cooling systems.
The company might be snapping up Chinese firm Envicool and is in discussions with others too, per media reports.
The next frontier in AI growth is not writing the perfect prompt or sentience. We’re far from it today. The immediate need for AI growth is thermal infrastructure.
AI workloads aren’t your typical home computing tasks. These generate far more heat than traditional computing, and the servers need fresh water to cool them in a safe way. This is pushing a shift from conventional air cooling to liquid cooling technologies, which are more efficient for high-density servers.
Liquid cooling, offered by companies such as Envicool, are for denser, more powerful AI workloads and improve cost and energy efficiency. Don’t just look at chips and semiconductors, think backend — cooling systems, power grids and efficient data center design.
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AI scaling is fast becoming an infrastructure problem, not just a computing one. Even if companies secure enough chips, they cannot deploy them without adequate thermal management.
And this is where China comes in. Chinese companies like Envicool are embedding themselves within the physical infrastructure component of AI, making them essential players in the backend. While American companies fight over models and chips, Chinese companies like Envicool, Sugon Data Innovation, Shenling Environment, and Yimikang Technology are focused on building precision cooling systems and thermal management solutions for high-performance computing.
If these names are hard to remember, we also have the usual suspects like Huawei and Lenovo Technologies, which are beefing up the infrastructure required for hyperscaling AI. But unlike chip manufacturing, where monopoly is the goal, several competitors in the cooling space can coexist. Cooling systems involve multiple components suppliers and integrators. Some make the parts, some assemble them.
For now, the future is Asia in terms of AI infrastructure.
Chinese firms are at it hard building out and testing massive data centers, working on vertically integrating AI hardware with a faster churn out. And they are doing it with far less money, which is what consumer tech companies want. To fail faster, and figure it out quicker.
But demand at this scale, comes with environmental and resource implications. Liquid cooling systems are often more energy-efficient and can reduce water usage, but not at the scale data centers are expanding. As AI infrastructure scales globally, balancing performance with energy and water constraints is a reality we’re already dealing with.
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