By Dov Hertz
The rapid advancements in artificial intelligence (AI), particularly with the emergence of DeepSeek and other next-generation models, have sparked discussions about potential impact on the demand for data centers. While some argue that increased AI efficiency will lead to reduced infrastructure needs, industry reports and expert analyses currently suggest the opposite: AI innovation is set to accelerate data center expansion rather than diminish it.
The AI Boom and Its Infrastructure Demands
AI-powered applications, from large language models (LLMs) to generative AI, require immense computing power. The rise of DeepSeek and similar technologies underscores the insatiable demand for high-performance computing (HPC) environments. As Barron’s recently noted, “AI models are becoming exponentially more complex, requiring larger and more sophisticated data center networks to support them.”
The infrastructure behind these models relies on massive power consumption and cooling capabilities, key components that modern data centers must continuously upgrade. As the Wall Street Journal reported, data center investments are surging due to AI workloads, with hyperscale operators leading the charge in expanding their footprint across the U.S. and globally.
Power and Sustainability Challenges
One of the primary challenges in AI-driven data center expansion is power consumption. The latest AI models require thousands of specialized GPUs, each drawing significant energy. According to the Q4 earnings report from Eaton, a supplier of electrical equipment, power demand from data centers is expected to grow at an annual rate of 15% over the next five years, with hyperscalers driving much of this increase.
“AI is fundamentally changing the power profile of data centers,” the Eaton report states. “The shift toward more energy-intensive workloads requires new strategies for efficiency and sustainability.”
This growing demand has prompted data center operators to invest in alternative energy sources, including solar, wind, and battery storage solutions. Even with efficiency gains in AI model training and deployment, the overall power draw from AI applications will likely continue to rise, reinforcing the need for additional infrastructure.
Data center power use currently competes with the power demands of other businesses, including logistics warehouses, which have adopted extensive sustainability measures, but which likely will require greater power as automation continues. In many locations, electrical girds are near capacity now, and this has raised concerns among many developers and investors.
Hyperscale and Colocation Growth
Hyperscale data centers—facilities designed to support large-scale computing operations—are seeing unprecedented expansion. Companies like Google, Amazon Web Services (AWS), and Microsoft Azure are investing billions in new facilities to accommodate AI-driven workloads. Meanwhile, colocation providers, which lease data center space to enterprises, are experiencing surging demand.
According to The New York Times, the rapid expansion of AI infrastructure is creating a shortage of available data center space in key markets such as Northern Virginia, Dallas, and Phoenix. “Leasing activity for colocation providers has hit record levels as enterprises seek flexible solutions to scale their AI operations,” the report notes.
Cooling Innovations for AI Hardware
As AI-driven workloads grow, cooling has become a major concern. Traditional air-cooled data centers struggle to manage the heat generated by AI processors, leading to the adoption of liquid cooling technologies.
As The Wall Street Journal reported, “Data center operators are rapidly investing in liquid cooling solutions to accommodate AI clusters, as traditional methods are proving inadequate.” Liquid cooling not only enhances efficiency but also allows for denser AI workloads within the same physical footprint, reducing the need for excess land usage while maintaining performance.
The Edge Computing Factor
Another trend driving data center demand is edge computing. While AI applications require massive cloud-based training environments, real-time AI processing increasingly occurs at the edge—closer to end users. This has led to a rise in regional and micro data centers to support AI-powered applications such as autonomous vehicles, smart cities, and industrial automation.
As noted by Commercial Property Executive, “AI-driven edge computing is forcing data center operators to expand into secondary and tertiary markets, increasing the overall infrastructure footprint rather than reducing it.”
Regulatory and Investment Landscape
Governments and enterprises are recognizing the critical role of AI infrastructure. The Biden administration had proposed new initiatives to support data center development, ensuring national competitiveness in AI research and deployment. Meanwhile, investors continue to pour capital into data center real estate investment trusts (REITs), reflecting long-term confidence in the sector.
As Barron’s reported, “Institutional investors view AI and data centers as one of the most resilient and high-growth asset classes for the next decade.” This optimism underscores the industry’s commitment to expansion rather than contraction.
AI Growth Reinforces Data Center Expansion
Despite speculation that AI efficiency could reduce data center needs, the reality seems clear: the continued advancement of AI models like DeepSeek will drive unprecedented demand for computing infrastructure. Increased power requirements, the need for cutting-edge cooling solutions, the rise of hyperscale and colocation facilities, and the expansion of edge computing all point to a sustained data center boom. Investors, developers, and policymakers must recognize this trend and prepare for continued expansion, ensuring that data center capacity keeps pace with AI’s relentless evolution.