GenAI buildouts spur tech infrastructure boom

Key takeaways

• Ambitious capital spending on generative AI projects by hyperscalers, which we view as a once-in-a-decade cycle, represents a meaningful tailwind for technology infrastructure providers supplying the picks and shovels to run large language models.
• Cloud infrastructure software companies play a critical role in enabling AI workloads by providing specialized computing, data management and workload monitoring services to enterprise customers.
• The processing needs of inference models like the leading chatbots require more specialized chips than traditional enterprise applications, leading to a renaissance in custom silicon development by semiconductor makers.

Hyperscaler capex growing steadily

Since the first public availability of ChatGPT three years ago, generative artificial intelligence (Gen AI) has expanded rapidly across consumer and business markets, both among public traded companies and privately held firms. Cloud hyperscalers were among the first providers of GenAI services and, judging by their capital spending commitments, they should remain the drivers of GenAI adoption. While use cases, commercial monetization and potential disintermediation of certain parts of the economy remain at an early stage, large language models (LLMs) are proliferating, and we believe industries providing the picks and shovels to power GenAI workloads offer compelling investment opportunities today.

We broadly describe these industries as technology infrastructure providers, as they bring together the data and processing power needed to make LLMs function. More specifically, we see cloud infrastructure software companies and developers of application-specific integrated circuits (ASICs)—custom silicon solutions—as near-to medium-term beneficiaries of ambitious GenAI capex. Both industries are seeing improving fundamentals and new growth avenues thanks to GenAI—a trend that appears to be in its early innings.

In 2025, the major hyperscalers, which provide cloud computing services at scale through large networks of data centers, are forecast to spend a combined $378 billion,¹ up 65% compared to 2024 and a meaningful increase from the start of the year (Exhibit 2), while projections for 2026 indicate steady spending growth. Not included in these totals are multibillion budgets by rising cloud platforms like Oracle, CoreWeave and privately held names. Such commitments, which we view as a once-in-a-decade capex cycle, represent a meaningful tailwind for technology infrastructure providers. And the fact that the capex is coming from these companies’ own cash flows rather than through new debt issuance gives us confidence in Gen AI as a secular evolution in computing.

Infrastructure software recovering

Cloud infrastructure companies play a critical role in enabling AI workloads by providing the foundational computing, storage and networking resources needed to train, deploy and scale LLMs. Many enterprises initially attempted to build AI capabilities in-house with ad-hoc tools, but the complexity and high failure rates of these DIY efforts have driven a shift back toward proven platforms from specialized vendors. The expertise and ecosystems developed by cloud infrastructure firms for running AI at scale have become preferable to “home grown” solutions, especially as companies realize that reliable performance and security are paramount for AI projects.

Rapid AI adoption is turbocharging digital transformation as companies modernize IT systems and migrate more workloads from on-premise data centers to the more AI-applicable cloud. This wave of cloud migration creates knock-on demand for infrastructure software and services that can manage complex, distributed applications. The result is a rising tide for both the major hyperscale clouds and a cadre of “vertical” best-of-breed infrastructure software players that focus on specific needs. Many enterprises now use a combination of both—leveraging hyperscalers for base cloud services and supplementing them with independent software vendors for specialized functions like data analytics or monitoring. In fact, the average large business today uses hundreds of different software applications to get work done, highlighting the complexity of modern tech stacks.

Many infrastructure software makers feature consumption-based business models as opposed to subscription models offered by most application software vendors. Consumption models enable customers to only pay for what they use, providing flexibility and enabling prioritization in IT budgets. After a soft period in 2024 when many enterprises were optimizing costs and digesting their cloud spend, consumption-driven providers are now seeing re-accelerating growth in their quarterly results, boosted by strong reception to new AI product offerings and increased usage.

Infrastructure software: Data warehousing

Collecting, analyzing and protecting data are critical functions in enterprise IT operations run in the cloud. Quality data is also an essential input in training LLMs. Data warehousing software makers fill this need with cloud-based architectures that enable customers to organize their data for the purpose of advanced analytics and GenAI use.

Infrastructure software: Monitoring and observability

Where a corporate IT department may have managed a handful of third-party vendors in the past, today the number of specialized platforms and applications can run into the hundreds. The complexity of enterprise technology stacks and the unique configurations of software applications and hardware that work together to manage operations underscore the need for comprehensive oversight of all these functions. Monitoring and observability software providers offer that oversight, helping customers monitor and analyze IT performance as well as identify issues and threats. Observability is an underpenetrated market, and we see continued growth as organizations increasingly reliant on digital infrastructure expand the number of applications monitored. We believe LLM observability, a rapidly growing market due to the acceleration of GenAI workloads, creates a new vector for growth for these stocks not reflected in fundamental estimates. While competition is high due to the presence of lower-priced, open-source data monitoring vendors and some enterprise customers insourcing some observability functions, we believe companies with the end-to-end platforms to consolidate multiple observability vendors remain well positioned (Exhibit 3). In our view, larger software companies that host customers on their own cloud infrastructure are also well-suited in the near-to-intermediate term for an ongoing capex cycle.

Custom silicon to power next wave of AI buildouts

The unprecedented compute demands of Gen AI have not only supercharged cloud capex, they have also upended the semiconductor landscape. Serving millions of intelligent queries through inference models like ChatGPT and Google Gemini requires far more specialized chips than traditional enterprise applications. This has led to a renaissance in custom silicon development, marked by the design of “XPUs,” where X can stand for any accelerated processor (GPU, CPU, etc.) tailored to AI. While Nvidia’s GPUs and its complete ecosystem of chips and software have led the company to maintain a large market share lead, other semiconductor makers also play critical roles in developing custom silicon as a complement or alternative to Nvidia. The result is a wave of new AI chips designed for specific needs—some targeting training giant models, but the majority optimized for inference models already in use and many integrating novel approaches to boost performance or efficiency.

These ASICs are purpose-built by the hyperscalers themselves, often in conjunction with dedicated chipmakers. This strategy of partnering rather than competing with the hyperscalers has proved fruitful for custom silicon makers. We believe the custom silicon total addressable market is large and growing rapidly enough to support multiple players.

As AI models become larger and more distributed, we believe networking is becoming as important as processing speed. Importantly, these custom silicon developers are targeting an inference market that now represents the bulk of new net AI spending and is growing at a much faster pace than the training market.

Custom silicon developers also make networking chips, switches and interconnect devices to meet the higher-bandwidth data movement and massive storage requirements of AI workloads. These companies partner with a number of supporting players in the semiconductor capital equipment, electronic design automation and contract manufacturing areas that supply the high-performance silicon wafers and design tools to meet the increased chip complexity in the GenAI era.

Conclusion

The development and adoption of GenAI is a secular growth trend still in its early innings. It remains to be seen which parts of the technology universe will ultimately benefit or be challenged by Gen AI, but we believe consistent capex growth by hyperscalers creates a positive demand environment for both infrastructure software and custom silicon developers. Earnings and free cash flow growth rates for mega cap hyperscalers remain healthy, which should continue to support the growth outlook for more vertically focused companies supporting GenAI buildouts.