The unseen engine of artificial intelligence is humming within sprawling data centers, the digital equivalent of bustling marketplaces where powerful computing resources are leased. Juan Font, CEO of CoreSite, likens his company’s facilities to these retail hubs, offering shared infrastructure—power, cooling, and connectivity—instead of each enterprise building its own costly data center. This model is gaining significant traction as technology titans grapple with an insatiable demand for high-performance computing to fuel their AI ambitions.
These AI-optimized data centers are the physical bedrock enabling the seamless digital experiences we rely on daily, from our smartphones and personal computers to wearable tech and connected vehicles. The leading technology conglomerates, including Amazon, Alphabet, Meta Platforms, and Microsoft, have already committed astronomical sums, investing billions in expanding their data center footprints. This year alone, these four giants are on track to collectively spend at least $608 billion in a fierce AI arms race. The final tally could climb even higher as they report their latest earnings. Adding to this frenetic pace are emerging AI powerhouses like OpenAI and Anthropic, reportedly spending at breakneck speeds in anticipation of potential initial public offerings (IPOs) later this year.
The operational integrity of these complex, energy-intensive data centers hinges on a sophisticated interplay of interconnected technologies. This surge in demand for AI infrastructure is creating a wealth of investment opportunities, particularly for companies embedded in critical components of these facilities. To gain a deeper understanding of how these digital fortresses are constructed and the roles played by leading players in areas like chip manufacturing and energy generation, an in-depth examination of a CoreSite facility offers crucial insights.
**The Unyielding Demand for Power**
At the core of this AI revolution lies a fundamental resource: power. “Power is the innermost loop of this intelligence revolution,” emphasizes CoreSite’s Font. Data center developers prioritize securing adequate electricity before any technology is even installed, a crucial first step in the intricate development process. This underscores the foundational role of energy, a concept echoed by Nvidia CEO Jensen Huang’s “five-layer cake” model for AI, where energy forms the base.
Just a few years ago, clients would typically inquire about power capacities in the range of 10 to 30 megawatts, enough to service a sizeable town. Today, the conversation has escalated dramatically, with clients demanding hundreds of megawatts, and for the largest facilities, multiple gigawatts. This exponential increase in power requirements is a direct consequence of the computational demands of AI.
The sheer scale of data center operations is often measured by these energy increments, as power availability acts as the primary constraint on deliverable computing capacity. The high-performance chips essential for training and running AI models, coupled with the extensive infrastructure needed for their operation, connectivity, and cooling, are placing unprecedented strain on existing public energy grids. Many grids are reportedly operating at their maximum capacity, necessitating data centers to either locate in areas with existing grid surplus or incorporate on-site power generation capabilities.
This supply-demand imbalance is compelling companies to explore alternative and supplementary power sources, including natural gas, fuel cells, solar, and wind solutions, to ensure project continuity. GE Vernova, a provider of critical equipment such as natural gas turbines, is a prominent player in this sector. The company’s stock has experienced significant growth, reflecting the robust demand for energy products powering data centers. GE Vernova recently reported strong first-quarter results, with revenue increasing 16% year-over-year to $9.3 billion, driven by substantial equipment orders across all segments. The company added $13 billion to its backlog within 90 days, projecting a backlog of $200 billion by 2027.
Ensuring the efficient operation of these power-hungry data centers also relies on advanced power management solutions. Companies like Eaton are integral to this ecosystem, supplying essential components such as power distribution units (PDUs) and remote power panels, which are critical before power reaches customer racks. Eaton’s recent financial disclosures highlighted an all-time record high for its Electrical Americas order backlog, a testament to the unprecedented demand in this sector. The company’s stock has also shown considerable strength, trading near its all-time closing high.
**The Brains of the Operation: High-Performance Chips**
The immense power consumption fuels the heart of the data center: the server rooms, filled with racks housing high-performance chips. The leading players in this domain are Nvidia, with its industry-leading AI chips, and Broadcom, which is increasingly supplying custom chip solutions. Nvidia’s stock, after a period of consolidation, has seen a significant resurgence, driven by the sustained demand for its AI accelerators. The company remains a pivotal force in the AI landscape, a sentiment echoed by market observers who highlight its indispensable role.
In parallel, Broadcom is making significant inroads, particularly with its custom chip designs. The company co-designs Google’s Tensor Processing Units (TPUs) and has recently forged a strategic partnership with Meta to support its custom AI accelerator chips through 2029. This trend toward custom silicon signals a growing sophistication among hyperscalers, aiming to optimize performance and cost for specific AI workloads and potentially challenging Nvidia’s current dominance. While hyperscale data centers built by giants like Amazon are on a much grander scale than facilities like CoreSite’s, the underlying reliance on advanced chip technology remains constant. Amazon, for instance, designs its own chips for various applications, alongside its substantial procurement of Nvidia’s offerings. The chips deployed in these facilities are crucial for “inference,” the process of using pre-trained AI models to generate responses, as exemplified by services like ChatGPT and Gemini. Although inference demands less computational power than model training, it still necessitates dense clusters of GPUs and substantial power delivery.
**The Arteries of Data: Optical Fiber and Networking**
As data flows through these high-performance systems, efficient connectivity is paramount. Thousands of optical fiber connections, capable of transmitting vast amounts of data at exceptionally high speeds, form the digital arteries of data centers. Companies like Corning are at the forefront of this critical infrastructure, supplying the fiber optics that enable rapid data movement between servers, cloud providers, and external networks.
The escalating complexity and scale of AI workloads are driving an ever-increasing demand for more fiber and faster connections. Fiber optics, with their superior speed and lower heat generation compared to copper, offer significant advantages in terms of power efficiency and reduced cooling loads. Corning’s strong performance this year, particularly in its optical communications segment, reflects this trend. The company has secured significant long-term supply agreements for fiber-optic cables, including a substantial deal with Meta for its AI data centers, and is expanding its manufacturing capabilities to meet the growing demand from major tech players.
Complementing optical fiber is the realm of networking equipment, which directs data traffic both within and beyond the data center’s perimeter. Network switches and routers are essential for managing data flow and ensuring timely delivery. Broadcom plays a role here as well, manufacturing network switches that are integral to data traffic management. The company has reported substantial growth in its networking revenue, driven by AI-specific applications. Nvidia, through its strategic acquisition of Mellanox, also commands a significant presence in the networking space, offering advanced technologies that support both scale-up and scale-out architectures. The demand for these networking solutions has reached record levels, fueled by the widespread adoption of technologies like NVLink and InfiniBand.
**Taming the Heat: The Challenge of Cooling**
The immense processing power and data flow within data centers generate substantial heat. Managing this thermal output has become a critical operational challenge, with some server racks producing heat equivalent to hundreds of hair dryers running simultaneously. Traditional air-cooling methods are increasingly insufficient for modern, high-density AI environments. Consequently, data centers are transitioning towards liquid-cooling systems, where coolant is directly delivered to servers or even individual chips to prevent overheating.
“Probably the biggest innovation that AI has brought to data center design is liquid cooling capabilities,” states CoreSite’s Font. This shift necessitates specialized equipment, and companies like Eaton and Dover are providing essential components, including cooling systems, power management equipment, and thermal connectors. Eaton’s recent acquisition of a leading liquid cooling provider is a strategic move to capitalize on this burgeoning market. Dover, a manufacturer of thermal connectors used in liquid-cooling solutions, anticipates significant revenue growth this year from applications linked to artificial intelligence and power generation infrastructure. The company’s CEO highlighted the increasing density of thermal requirements in data centers as a direct driver for its connector and heat exchanger businesses.
**The Bottom Line: A Multifaceted Investment Opportunity**
From the foundational need for power to the intricate layers of chips, optical fiber, networking, and cooling, the current investment landscape offers exposure to companies integral to the AI-driven data center boom. Technology firms are channeling substantial capital into building data centers to meet the escalating demand for computing power, a trend that Nvidia’s CEO has characterized as the fourth industrial revolution. This AI surge is not merely reshaping our daily lives; it is profoundly transforming the fortunes of a wide spectrum of companies, from consumer-facing giants to the critical behind-the-scenes enablers. Investors are presented with a dual opportunity: to participate in the transformative power of AI and to capitalize on the economic expansion driven by the companies building its physical infrastructure.
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