AI-Powered Cooling Systems Transform Data Center Efficiency

Data centers have quietly evolved into the backbone of modern digital infrastructure, supporting everything from financial systems to streaming platforms. While the concept of centralized computing facilities has been around for close to a century, the pace of technological progress in recent decades has transformed their role in the global economy—and the arrival of Artificial Intelligence (AI) is accelerating change like never before.

Though AI research has roots stretching back to the mid-20th century, its widespread adoption outside military and academic spheres is a much more recent development. Over the past two decades, AI’s capabilities have advanced to the point where they are now integral to optimizing critical operational functions within data centers—particularly in the area of cooling systems, a cornerstone of efficient and secure operations.

“Efficient cooling not only reduces energy usage and operational costs, but also enables smaller, more compact equipment designs,” explained Indian mechanical engineer Mohit Shrivastava, an industry veteran with nearly 13 years of experience. “This can yield significant environmental benefits by meeting sustainability standards in manufacturing and maintenance.”

AI-Specific Data Centers Demand a New Class of Infrastructure

As AI workloads such as model training, inferencing, and generative processing become core to business operations, a new type of data center is emerging—one that pushes the limits of thermal management, power distribution, and efficiency. Unlike traditional IT environments, these next-generation facilities are designed around extreme power densities, with some AI clusters consuming up to 100 kW per rack, compared to the 5–10 kW average seen in standard server deployments.

This density creates a thermal load that traditional air-cooled designs cannot handle effectively or economically. As a result, liquid cooling technologies—including direct-to-chip cold plates, rear-door heat exchangers, and full immersion tanks—are gaining traction as necessary tools for heat removal.

“AI data centers require fundamentally different infrastructure—not just more cooling, but smarter, adaptive, and ultra-efficient systems,” Shrivastava said. “Every watt saved on cooling directly increases the capacity for computation, which is essential in AI where time-to-result is everything.”

These systems also need high-voltage, high-resilience power architectures, often integrating on-site renewables or battery energy storage to handle peak AI training loads. AI models can also support these operations through predictive controls, continuously adjusting cooling and power delivery based on thermal, electrical, and workload data in real time.

The shift to AI-centric designs makes efficiency more critical than ever—not only to reduce operating costs but to avoid thermal throttling of GPUs, downtime, or carbon penalties in regulated environments.

Billions Invested in Upgrades

Recent figures illustrate the momentum in this field. In 2024 alone, Brazil—one of Latin America’s largest economies—saw a leading industry player commit approximately US$ 2 billion to upgrading its data center systems. Globally, the market approached US$ 10 trillion, marking a 10% increase from the previous year. These investments reflect not only physical infrastructure improvements but also consulting and strategic planning services aimed at enhancing security, efficiency, and service life.

According to Shrivastava, maintenance plays a central role in ensuring these investments deliver returns: “Proactive planning to anticipate and prevent issues is essential. It safeguards user trust and protects the credibility of the organizations operating these systems.”

Innovative Approaches Around the Globe

AI is unlocking new possibilities for cooling optimization at every stage—from initial design to real-time performance adjustments. In Northern Europe, for example, some facilities use cold seawater to regulate temperatures. AI systems determine precise operating parameters, ensuring maximum efficiency. Other projects are exploring renewable energy integration, with AI managing complex variables to maintain optimal performance.

In Asia, hyperscale campuses are experimenting with vertical integration of AI tools for power scheduling, predictive analytics, and facility-wide load shedding during demand response events. These strategies demonstrate how AI is not just a user of compute infrastructure—it’s increasingly the architect of its efficiency.

The technology also extends to workforce training, enabling on-site teams to work faster, safer, and more effectively. “We’re not just talking about hardware,” Shrivastava noted. “AI is enhancing human expertise, delivering new platforms and structures that streamline operations without compromising safety.”

Digital Twins and Predictive Modeling

Another rapidly growing trend is the use of AI-driven digital twins—virtual replicas of physical systems that allow engineers to simulate thousands of what-if scenarios. These models can anticipate cooling failures, identify efficiency bottlenecks, or test new layout configurations without interrupting operations.

“Digital twins make it possible to manage data centers like living ecosystems,” Shrivastava said. “We’re no longer limited to reactive maintenance. We can see problems before they arise, simulate changes, and train staff in a fully virtual environment.”

The predictive modeling capability is particularly valuable in AI-optimized environments, where GPU clusters are expensive and sensitive to thermal deviation. This capability ensures that performance isn’t sacrificed for sustainability—or vice versa.

A Career at the Intersection of Engineering and AI

Shrivastava currently serves as Director of Engineering Analytics at Switch, Inc., where he spearheads initiatives integrating AI, machine learning, predictive analytics, and digital engineering tools to boost data center performance. His career includes leadership roles at Amazon Web Services, where he developed global strategies for power and water efficiency across more than 200 hyperscale data centers, and founded the Data Center AI Research Group to incorporate AI-driven diagnostics into operational workflows.

Holding both bachelor’s and master’s degrees in Mechanical Engineering from Mississippi State University, Shrivastava is a licensed Professional Engineer in California and Oregon, and is pursuing ISO 50001 auditor certification. With credentials in advanced HVACR and pressure vessel design, his work bridges the physical and digital realms—delivering future-ready infrastructure that combines sustainability with cutting-edge technology.

The Future Is AI-First, Not Just AI-Enhanced

As industries continue to adopt generative AI, large language models, and intelligent automation, the demands on digital infrastructure will only intensify. In this context, data centers must evolve beyond traditional paradigms. They must become AI-first facilities, engineered from the ground up with intelligence, resilience, and extreme efficiency in mind.

“AI has brought data centers to an inflection point,” Shrivastava concluded. “We now have the tools to not just keep up—but to stay ahead. But success requires engineering discipline, innovation, and the courage to rethink everything from airflow to algorithms.”