AI Data Center Infrastructure: Assessing the Long-Term Viability and Investment Opportunities Amid Rising Upgrades

The rapid evolution of artificial intelligence (AI) has transformed data centers into the backbone of modern technological progress. However, this transformation is not without its challenges. Central to these challenges is the lifecycle of AI hardware-specifically, the tension between accounting estimates and real-world durability-and its cascading effects on power and cooling infrastructure. As the demand for AI workloads surges, so too does the need for robust, scalable infrastructure to support it. This analysis evaluates the implications of AI hardware upgrade cycles for power and cooling suppliers, identifying key investment opportunities in a sector poised for significant growth.

The Hardware Lifecycle: A Mismatch Between Accounting and Reality

The useful life of AI hardware, such as GPUs and TPUs, is a critical determinant of infrastructure demand. While companies like Google, Oracle, and Microsoft often use a six-year depreciation schedule for accounting purposes according to a CNBC report, technical assessments suggest a far shorter lifespan. A high-ranking Alphabet specialist noted that data center GPUs may last only one to three years under typical utilization rates of 60–70%, while a Google architect estimated survival rates of one to two years, with three years as a rare upper limit according to a Princeton research blog. This discrepancy-where actual hardware lifespans are at least half of accounting assumptions-creates a financial mismatch and accelerates the need for infrastructure upgrades.

The implications are profound. If AI hardware must be replaced every 1–3 years, power and cooling systems must be designed for frequent scaling or replacement. This dynamic is already reshaping energy demand. Goldman Sachs Research projects that global power demand from data centers will increase by 165% by 2030, with AI accounting for 27% of total demand by 2027. The International Energy Agency (IEA) further notes that AI-driven electricity demand could double between 2022 and 2026 according to Goldman Sachs insights, driven by the energy intensity of high-performance processors and the cooling systems required to manage them.

Market Dynamics: Power and Cooling as Growth Levers

The surge in AI workloads has pushed data center rack densities to unprecedented levels. AI-specialized facilities now operate at 120–132 kilowatts (kW) per rack, far exceeding the capabilities of traditional air cooling systems. This has necessitated a shift to advanced cooling technologies, such as direct-to-chip liquid cooling, which can achieve power usage effectiveness (PUE) of 1.10–1.35 compared to air cooling's 1.55–1.67. The transition is accelerating: liquid cooling adoption is growing at 25–40% annually, with hybrid approaches (70% liquid, 30% air) becoming common for retrofitting existing facilities.

This shift creates a clear tailwind for suppliers of power and cooling infrastructure. For instance, NVIDIA's Blackwell GPUs, which generate up to 1,000 watts per chip, have forced hyperscalers to adopt liquid cooling solutions. Vertiv Holdings Co. (VRTX) has emerged as a pure-play leader in this space, co-developing reference architectures for NVIDIA's GB200 NVL72 systems and securing a $9.5 billion backlog. Similarly, Modine Manufacturing (MOD) is capitalizing on its AI cooling expertise, while SPX Technologies (SPXC) is expanding its HVAC capabilities through strategic acquisitions.

The market's growth trajectory is equally compelling. The global AI data center market is projected to expand from $236.44 billion in 2025 to $933.76 billion by 2030, growing at a compound annual growth rate (CAGR) of 31.6%. Hyperscalers, which already account for 60% of global data center capacity according to Markets and Markets, are expected to dominate further, with their share rising to 70% by 2030. This concentration of demand among a few players-Amazon, Google, and Microsoft-creates a predictable revenue stream for infrastructure suppliers, particularly those aligned with hyperscalers' sustainability goals.

Investment Considerations: Sustainability and Scalability

While the growth of AI data centers is undeniable, investors must also consider the environmental and operational challenges. The IEA highlights that AI model training exacerbates energy consumption and water usage, particularly in regions with water scarcity according to Penn State research. Additionally, the short lifespan of AI hardware contributes to electronic waste and resource extraction demands according to Penn State research. To mitigate these risks, companies are increasingly adopting sustainable practices, such as aligning workloads with renewable energy availability and optimizing AI models for energy efficiency according to Penn State research.

For power and cooling suppliers, the path forward lies in innovation and scalability. Companies that can offer modular, energy-efficient solutions-such as Vertiv's PurgeRite acquisition for thermal management or SPX's expanded HVAC capabilities-are well-positioned to capture market share. Moreover, the integration of renewable energy sources, such as nuclear and solar, into data center operations, will further drive demand for infrastructure that supports hybrid power systems.

Conclusion: A Sector Defined by Urgency and Opportunity

The AI data center infrastructure market is at an inflection point. The mismatch between hardware lifespans and accounting estimates, coupled with the energy demands of AI workloads, is accelerating the need for advanced power and cooling solutions. While challenges such as supply chain constraints and permitting delays persist according to Deloitte insights, the long-term growth trajectory is clear. For investors, the key lies in identifying suppliers that can scale with the sector while addressing sustainability concerns. As AI continues to redefine global computing, the companies that enable its infrastructure will play a pivotal role in shaping the next industrial era.