As the artificial intelligence (AI) arms race intensifies, corporate and institutional investors are shifting their focus beyond the usual suspects like NVIDIA or OpenAI. They’re looking deeper into the hidden, but indispensable, backbone of the AI revolution: data center infrastructure. From power management to cooling solutions, fiber optics to semiconductor memory, an entirely new class of AI-enabling businesses is gaining investor spotlight. A recent move by CNBC’s Jim Cramer to initiate a position in Eaton Corporation underscores the sharply rising interest in these unsung heroes of the AI economy (CNBC, 2025).
Key Drivers of AI Data Center Investment Growth
The exponential demand for AI model training and inference—particularly for large language models (LLMs) like OpenAI’s GPT-4 and Google DeepMind’s Gemini—has made infrastructure scalability a frontline concern. According to McKinsey Global Institute, demand for AI-relevant compute is projected to grow tenfold between 2023 and 2030, driven heavily by hyperscalers such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud.
Modern AI models require vast computational horsepower. OpenAI CEO Sam Altman confirmed that GPT-5 training will be “significantly more compute-intensive” than its predecessors, requiring state-of-the-art power delivery systems and thermal management solutions (OpenAI Blog, 2024). Beyond GPUs like NVIDIA’s H100, the real bottlenecks lie in the infrastructure: electricity substations, transformers, and cooling facilities that prevent thermal overload in AI clusters running at near-maximum capacity.
Moreover, AI models rely heavily on real-time, high-bandwidth data movement. This elevates the importance of low-latency interconnects and photonic transmission systems, most often powered by companies in the optical networking space. Companies building fiber-optic networks, voltage regulation, and power distribution equipment are now essential players in the AI gold rush.
Notable Investment Targets in AI Infrastructure
Below is a table summarizing companies that have emerged as strategic investment targets due to their indispensable role in powering AI data centers.
| Company | Sector | Relevance to AI Infrastructure |
|---|---|---|
| Eaton Corporation (ETN) | Power Management | Manufactures transformers and energy systems critical for data center reliability and uptime. |
| Vertiv Holdings (VRT) | Cooling Solutions | Produces advanced cooling systems like liquid cooling and thermal monitoring for AI workloads. |
| Arista Networks (ANET) | Networking | Provides high-performance network switches that facilitate hyperscale AI data transfer. |
| Broadcom (AVGO) | Semiconductors | Delivers custom silicon and accelerators that support AI hardware layers, particularly in memory and connectivity. |
| Corning (GLW) | Fiber Optics | Provides fiber-optic cables essential for high-bandwidth AI data streaming and low-latency communication. |
Companies like Eaton and Vertiv may lack the tech glamour of NVIDIA, but they serve as the power grid of the AI economy—indispensable yet often overlooked. According to a recent Motley Fool analysis (The Motley Fool, 2024), Eaton’s earnings have steadily risen thanks to surging demand from data center projects in North America and Europe. Vertiv Holdings grew net sales by 18% year-over-year in Q4 2024 due to increased orders from hyperscalers such as Microsoft and Meta (VentureBeat AI, 2024).
AI Infrastructure: A Capital-Intensive Opportunity
Building next-gen AI data centers is a capital-expenditure-heavy endeavor. Microsoft recently disclosed plans to spend over $100 billion on AI-specific data centers over the next few years (CNBC Markets, 2024). Google and Amazon are set to follow suit. These investments don’t go toward GPUs alone—they fund backup power systems, HVAC cooling, advanced routers, and custom silicon deployment.
The cost to layout a single hyperscale AI data center often ranges from $600 million to over $1.5 billion depending on compute demand, regional grid availability, and government subsidies. According to Deloitte’s AI Infrastructure research (Deloitte Insights, 2024), power delivery alone can consume up to 30% of a data center’s total budget. As such, suppliers with scalable, modular, and power-efficient solutions see a long runway of growth ahead.
A tangible example is Eaton’s Power Xpert™ system technology, which helps datacenters monitor energy consumption and load balances in real time, optimizing uptime and reducing energy waste amid growing environmental and ESG constraints. Additionally, Vertiv’s Liebert® cooling systems are becoming the standard for AI environments operating beyond 400 watts per square foot.
Strategic M&As and Partnerships Fuel Sector Evolution
With competition intensifying, companies are aligning through mergers, acquisitions, and strategic collaborations to increase vertical integration and R&D capabilities. For example, in 2024, Broadcom completed its $61 billion acquisition of VMware to enhance its AI networking stack and hybrid cloud infrastructure offerings (MarketWatch, 2024).
Similarly, Arista Networks and Microsoft have been collaborating to build AI-optimized data center fabrics based on Ethernet and NVLink to support LLM federations. Corning recently entered into a strategic alliance with Hyperoptic to deploy ultra-fast fiber networks targeting urban data zones. These partnerships underscore the value chain logic now shifting to integrated AI infrastructure ecosystems.
Risks and Challenges to Consider
While the long-term thesis appears strong, investors must also evaluate near-term risks. These include fluctuations in interest rates impacting capital expenditure, geopolitical supply chain disruptions—particularly for semiconductors and rare-earth power components—and regulatory bottlenecks in environmental zoning and energy usage.
Moreover, as AI regulation expands globally, infrastructure developers could soon face carbon compliance measures, further pushing up costs. In line with recent reports by the FTC and the EU’s Digital Markets Act, tighter oversight over data processing and energy efficiency is expected, especially in hyperscaling projects involving consumer data.
Supply chain dependencies on Asia-based component manufacturers also expose companies like Broadcom and Corning to systemic geopolitical risks. Lastly, a potential oversupply due to misprojected AI demands could result in underutilized infrastructure and fixed-cost saturation—similar to what occurred in previous tech buildout cycles of the early 2000s.
Outlook and Long-Term Investment Strategy
Despite these challenges, future demand fundamentals remain robust. For investors with a medium to long horizon, positions in critical infrastructure players offer attractive upside and diversification from traditional tech ETFs or megacap AI stocks. In fact, ETF managers are beginning to include firms like Vertiv and Eaton within their thematic AI-driven portfolios, signaling broader institutional uptake (Investopedia, 2024).
Looking ahead, innovations in edge computing, LLM-as-a-service models, and decentralized inference architectures will only increase the need for robust infrastructure layers. AI hardware will inevitably evolve, but no matter how efficient these systems become, they will still require reliable, scalable, and sustainable support systems—from energy conversion to thermal management and optical transmission.
Simply put, without data center infrastructure, there is no AI. And for investors comfortable venturing beyond software-centric strategies, this space represents a once-in-a-generation opportunity to capitalize on the physical foundation of the next digital age.
by Alphonse G
This article is based on and inspired by CNBC’s coverage of Eaton Corporation and its role in AI infrastructure: Original Source
References
- CNBC. (2025). We’re initiating a position in a company that is critical to powering AI data centers. Retrieved from https://www.cnbc.com/2025/05/13/were-initiating-a-position-in-a-company-that-is-critical-to-powering-ai-data-centers.html
- OpenAI Blog. (2024). The Road to GPT-5. Retrieved from https://openai.com/blog/
- VentureBeat AI. (2024). Infrastructure demand surges amid LLM boom. Retrieved from https://venturebeat.com/category/ai/
- The Motley Fool. (2024). Vertiv earnings analysis. Retrieved from https://www.fool.com/
- Deloitte Insights. (2024). Future of AI Infrastructure. Retrieved from https://www2.deloitte.com/global/en/insights/topics/future-of-work.html
- MarketWatch. (2024). Broadcom’s VMware Acquisition Completed. Retrieved from https://www.marketwatch.com/
- Investopedia. (2024). AI ETFs broaden exposure to infrastructure players. Retrieved from https://www.investopedia.com/
- McKinsey Global Institute. (2024). AI Infrastructure Roadmap. Retrieved from https://www.mckinsey.com/mgi
- FTC. (2024). AI and energy policy intersect. Retrieved from https://www.ftc.gov/news-events/news/press-releases
- MIT Technology Review. (2024). AI’s appetite for compute. Retrieved from https://www.technologyreview.com/topic/artificial-intelligence/
Note that some references may no longer be available at the time of your reading due to page moves or expirations of source articles.