As AI workloads become more sophisticated and widespread, data centers face a growing challenge that threatens to limit AI’s potential: heat. With the rise of AI supercomputing — facilities designed to support exponentially larger workloads — coming online in 2026, this challenge will only intensify, forcing changes to the way data centers operate.
Next-generation AI chips could draw as much as 15,000 watts each, and facilities are deploying thousands of them. Traditional air-cooling systems simply cannot dissipate this level of thermal output while maintaining the reliability AI workloads demand. Liquid cooling paired with predictive maintenance offers the only viable path forward.
What Is Liquid Cooling? How Does It Make Data Centers More Efficient?
Liquid cooling technologies acts to efficiently remove more heat and ensure the productivity of high-density computing systems, leading to higher performance and reliability. By targeting specific central processing units (CPUs) and graphics processing units (GPUs), data center operators use the technology to enhance overall thermal management and reduce the space needed for cooling infrastructure.
AI Is Pushing Data Centers to Their Limits
The thermal challenge created by AI workloads is more than just an annoyance; it can hinder innovation and result in costly downtime when things go wrong. In 2023, data centers accounted for roughly 1-1.3 percent of global electricity consumption. With AI integration accelerating, that figure could increase by 50 percent by 2027 and 165 percent by 2030.
That’s not just an energy problem. It’s a thermal management crisis. Modern GPU clusters generate heat densities that far exceed the capabilities of most existing facilities. When systems consistently operate above recommended temperature ranges, you’re likely to encounter hardware degradation, increased maintenance costs and a shortened equipment lifespan. For companies investing millions in AI infrastructure, this poses significant risks to the way their organizations operate.
The Case for Liquid Cooling in the AI Era
Liquid cooling systems work because they address AI’s heat challenge by delivering cooling directly at the chip level rather than trying to manage it after it radiates into the surrounding environment. This approach isn’t just marginally better than air cooling; it’s fundamentally more effective. In fact, liquid-cooled data centers achieve energy savings of 30.6 percent under variable environmental temperatures and 42.7 percent under typical variable loads.
Liquid cooling systems don’t just treat heat as a liability, however. They can actually transform it into a potential asset. Rather than expelling thermal energy into the atmosphere at great expense, liquid cooling systems can capture and redirect heat for secondary applications, such as industrial processes. These systems are already operating in specific regions around the world. In Northern Europe, for instance, thermal energy captured within data centers is helping to heat nearby residential and commercial buildings.
Furthermore, for enterprises running AI above 30-40kW rack densities, investments in liquid cooling systems pay for themselves through operational savings alone, often within months of deployment. This savings is mainly because this type of cooling helps data centers remain online and protects expensive equipment from malfunction. With data center downtime costing operators up to $9,000 per minute, the investment is worth it.
The Importance of Predictive Maintenance
There are many benefits to liquid cooling, but data center operators must be prepared to manage the additional maintenance and workforce costs required to keep it operational. Liquid cooling requires specialized, routine maintenance, and failure to do so can result in leaks and sensor failures, all of which can harm data center equipment.
Additionally, the current workforce lacks the necessary skills to maintain liquid cooling systems effectively, with 53 percent of IT leaders reporting significant skills gaps or staffing shortages related to managing high-density AI infrastructure. Expertise in traditional data centers does not translate to liquid cooling, which requires specialized knowledge in thermal dynamics, fluid mechanics and advanced monitoring protocols.
These gaps make predictive maintenance platforms essential for maintaining liquid cooling systems. According to SimpleBi, implementing preventive maintenance measures, such as routine inspections, fluid level checks and water quality measurements, can reduce unplanned downtime for enterprises by up to 30 percent. These proactive steps help identify potential issues before they escalate, ensuring that cooling systems operate efficiently and reliably.
By planning for routine, predictive maintenance, data center operators can significantly enhance their operational efficiency and return on investment. Moreover, this proactive approach will lead to more reliable operations and significant cost savings.
Cooling Is the New Constraint — and the New Advantage
For companies investing in AI infrastructure, the cooling strategy can’t be an afterthought. The computational demands of AI have made cooling a first-order constraint on what’s technically feasible. This requires involving thermal management experts early in the design process, budgeting appropriately for both capital and operational costs and building their capabilities to operate these systems effectively.
Data centers that take this needed step will stand out as clear winners, as they will be able to deploy more powerful AI systems, operate them more efficiently and scale them more quickly than competitors still struggling with thermal constraints.
Liquid cooling technology will continue to evolve, resulting in improvements in efficiency, reliability and ease of deployment. But the fundamental shift to this type of cooling is already here, and the computing power AI demands simply can’t exist without it.
