AI’s Expansion Runs on Smaller Companies

• AI’s expansion is not solely a mega cap story — it is a multiyear infrastructure cycle supported by smaller companies building, powering and equipping the data center ecosystem.
• In mechanical construction, distributed power generation and semiconductor and electrical componentry, smaller cap specialists are supplying the essential inputs enabling hyperscaler AI deployment.
• Investing beyond the headline-grabbing model developers can provide differentiated exposure to sustained AI-driven capital expenditure across construction, energy and enabling technologies without having to bet on a singular technology, developer or architecture.

Artificial intelligence (AI) has become closely associated with a small group of mega cap technology companies and large language model (LLM) developers. While headlines and capital markets have thus far been driven by hyperscalers and chip designers, the scaling of AI capabilities depends on a far broader ecosystem of companies.

The expansion of compute capacity to fuel LLM development requires sustained investment across a number of different areas, including physical plant construction, power generation, heating and cooling solutions, and increasingly complex electrical and semiconductor systems. As record-breaking capital expenditure continues to surpass expectations, those investments are being spent by and with companies operating well outside the mega cap universe.

The most visible evidence of the AI investment cycle is the rapid construction and retrofitting of advanced data center facilities. This is a critical and technically demanding task due to higher-performance servers requiring effective cooling of their significant thermal loads, not to mention the networking capabilities needed to support high data bandwidth and the ample electricity to run power-hungry GPUs. As a result, mechanical and electrical contractors with specialized and scarce expertise are central to the data center build-out.

As data centers grow in number, scale and complexity, smaller, more specialized companies such as Comfort Systems, which provides HVAC and mechanical systems installation and maintenance, and Vertiv, best known for its liquid cooling solutions to optimize server performance, will likely find themselves able to exercise selectivity, maintain pricing discipline and expand margins.

While construction services, particularly in the mechanical, HVAC and electrical realms are in short supply, securing adequate power for these new or revamped AI data centers is proving to be equally challenging. AI training and inference workloads are materially increasing in power demand and intensity. As hyperscalers expand the number of data centers along with their regional footprints (Exhibit 1), electricity availability is becoming a gating factor in deployment timelines, with grid interconnection delays and transmission bottlenecks already presenting increasingly visible constraints.

Exhibit 1: Data Centers, Power Demand Expected to Rise Rapidly

Generative AI’s insatiable demand for computing capacity is also reshaping the energy landscape that supports it. Given the challenges in securing traditional grid interconnection, smaller cap companies developing new and innovative alternative energy sources (fuel cells, gas turbines, nuclear) are becoming key partners that provide structural solutions to these challenges. For example, Bloom Energy’s on-site solid oxide fuel cell systems provide reliable and clean energy directly at the point of consumption, reducing reliance on traditional grid expansion. In high-demand environments, this ability to provide power locally with fast start-up times can accelerate data center deployment.

At the same time, broader investment in nuclear and advanced generation technologies reflects the rising recognition that a long-duration baseload power system will be required to support structural electricity growth. Hyperscalers Microsoft, Amazon and Meta Platforms have all signed long-term agreements with nuclear energy producers to secure carbon-free power for their new data centers and are also investing to develop small modular reactors (SMRs). This positions companies such as BWX Technologies, with its long track record of producing nuclear components and services (since the 1950s) and emerging business lines in managing complex reactor operations and security, at the forefront of this longer-term shift toward a modernized and scalable power mix.

The rapid scaling of AI is also driving system complexity, opening opportunities for providers of specialized chips, connectivity solutions and electrical systems.

These high-performance computing environments require increasingly precise power management, signal integrity and connectivity solutions — a dynamic benefiting a range of analog and control-oriented semiconductor companies not initially viewed as direct AI participants. This includes companies like Lattice Semiconductor and Allegro MicroSystems, both of which provide chips that address rising power density and control requirements in data center and high-performance applications. Through long-standing investment in innovative technology solutions, these companies have begun to win share in next-generation compute architectures.

Similarly, companies such as Regal Rexnord, which supplies engineered power transmission and motion control technologies, are participating in broader electrification and industrial automation trends that intersect with AI-driven capital deployment.

We believe some of the most compelling AI-related opportunities reside in companies supplying enabling technologies rather than those developing models. As compute architectures grow more sophisticated, so grows the demand for components and systems from “picks and shovels” providers — often smaller cap companies.

While the AI narrative remains dominated by hyperscalers and private LLMs, the underlying capital investment supporting this growth is far more distributed. Data center construction, power generation expansion and semiconductor complexity all represent multiyear structural themes where smaller companies are exceptionally well-positioned as key partners and contributors to the AI ecosystem’s success.

Applying this broader infrastructure lens may offer investors more diversified and differentiated exposure to the AI investment cycle than capitalization weighted indexes. Rather than relying solely on application-layer adoption or model leadership, investing in these smaller, picks and shovels companies captures businesses with durable competitive advantages positioned within sustained capital expenditure cycles.