Advancing AI Requires Major Data Center and Digital Infrastructure Upgrades

Amazon is planning an $11 billion investment for a data center complex in northern Indiana.¹² Google is set to construct a $2 billion data center in Fort Wayne, Indiana, and spend an additional $1 billion to improve existing facilities in Virginia.¹³ Google’s also investing globally, with $1.1 billion going to build a data center in Finland and over $5 billion for data center expansion in Singapore.¹⁴ Meta is building a two-building data center on a 328-acre campus in Davenport, Iowa, and it’s embarking on a multibillion-dollar data center project in Cheyenne, Wyoming.¹⁵

To process AI workloads, new data centers need a wide array of specialized hardware, which is creating unique tailwinds for specialized component providers. The most prominent example is Nvidia. In just the last 12 months, Nvidia sold nearly $66 billion worth of GPUs to data centers, helping it ascend to the rarefied air of a $3.3 trillion market cap.¹⁶ We expect spending on data center accelerators alone to grow at a nearly 30% annual growth rate to top $165 billion by 2030.¹⁷

Broadcom, one of the largest suppliers of AI networking solutions and application specific integrated chips (ASICs) for AI processing to companies like Meta Platforms and Alphabet, projects its AI segment revenues to grow by over 50% YoY to $11 billion in Fiscal Year 2024.¹⁸ Also, chipmakers Intel and AMD launched new products, including data center CPU chips, which are expected to be used widely for AI inferencing applications.¹⁹ Other prime examples of the infrastructure needed to support AI workloads include specialized cooling systems for data centers. High-capacity memory and fast storage solutions are also essential to handle the vast amounts of data processed by AI applications, such as those supplied by SK Hynix or Micron.²⁰

Another significant consideration with AI hardware growth is that it comes with severe power tradeoffs. An average ChatGPT query consumes 10 times more power than a regular Google search, resulting in an AI-first data center requiring nearly 2.5 times more electricity on average than a traditional data center.²¹ In primary U.S. data center markets, where new data center construction activity increased by 25% in first half of 2023, power supply demands increased 19.2% YoY.²²

Data centers currently account for 1–1.5% of global electricity use, but by the end of this decade it’s expected to exceed 5%.²³ Because conventional grids are unable to supply this increase, data center companies must resolve their power supply, which is a hurdle that can extend construction completion timelines by 24–72 months.²⁴ Potential solutions include the use of nuclear and renewable energies. For example, Amazon recently acquired a $650 million data center project from Talen Energy close to a nuclear power station in Pennsylvania.²⁵ Microsoft and Google joined hands for a partnership with Nucor to source geothermal, clean hydrogen, and other clean energy sources.²⁶

Existing mobile connectivity infrastructure is insufficient to handle the massive amounts of data AI systems generate. Seamless connectivity and data transmission to end users requires an extensive expansion and enhancement of cell towers, particularly in dense urban areas. A likely growth segment is small cell towers, which can increase service densification. U.S. cell tower leader Crown Castle expects demand for small cell towers to grow significantly in 2024, even as the telecom industry braces for a 10% drop in capital expenditure.²⁷ Crown Castle’s small cell tower business grew 6% YoY in 2023.²⁸

Edge data centers, along with content delivery networks (CDNs), are another segment that can play a crucial role in reducing latency and enabling AI systems to process data closer to end users and applications. Edge computing facilitates real-time processing and response that is vital for consumer applications and real-time connectivity.

Also, devices are likely to be optimized for AI. Local inference processing, where AI computations are done directly on the device, will become increasingly important. Smartphone giants like Apple are already moving in that direction. Apple’s latest announcements include the launch of Apple Intelligence, a device-based AI assistant capable of working only on Apple’s latest M-chip hardware and iOS 18.²⁹ This development signals the likelihood of a smartphone upgrade cycle, which could catalyze growth for a long list of component vendors and device manufacturers. Laptops, wearables, internet of things systems, and industrial automation setups will require upgrades, including specialized low-power chips and components to efficiently handle AI tasks.

Conclusion: Data Center & Digital Infrastructure Value Chain Appear Well-Positioned

We believe that the digital infrastructure value chain presents highly investable opportunities as corporate investments in AI continue to grow. Data center companies can leverage newly built and enhanced AI processing capacity to upcharge and boost their sales and earnings. Chip and component manufacturers can benefit from an upgrade cycle in processing hardware and devices, fueled by growing construction of new data centers. Cell tower companies, edge computing platforms, and device makers will provide critical infrastructure. In our view, exposure to these pillars of the AI ecosystem offers an attractive way to play AI’s proliferation.

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