Artificial intelligence is driving an unprecedented wave of data center development. While the largest projects tend to command the headlines, the buildout is also creating a broad set of opportunities for middle-market companies and investors.
BGL Managing Director Brian Thom and Director Ryan Gillis, recently joined ACG’s Middle Market Growth Podcast to discuss the investment themes and technology M&A opportunities emerging across digital infrastructure, infrastructure software, and specialized services. Their conversation with ACG explored how middle-market investors are positioning themselves to capitalize on growing demand for AI infrastructure, the challenges shaping development, and the characteristics that make businesses attractive acquisition targets.
Below includes highlights from their conversation on the podcast.
What middle market trends are you watching in the areas within towers and data centers?
Ryan Gillis: There are two parts to the story. The first involves legacy hard assets. Trillions of dollars are flowing into data centers. These multibillion-dollar megaprojects aren’t especially relevant for the private equity middle-market investors we work with because the return profiles and investment horizons aren’t a natural fit. What they are targeting are smaller, phased developments of modular data centers that can be developed and brought online faster. Instead of focusing on hyperscalers, those projects often serve enterprise customers, regional markets, specialized cloud providers, and neocloud providers. The investment case begins with demand, and we’re seeing demand from those customers.
While many investors focus on hyperscalers and megaprojects, we see significant potential and interest in this other segment. Beyond the hard assets, we’re spending a lot of time on services. The data center boom of the past decade has created business lines that largely didn’t exist more than 10 years ago. These include engineering and commissioning work, cooling systems, flushing and filtering those systems, maintenance, retrofitting, testing, and inspection. At BGL, we view these businesses through the picks-and-shovels analogy. You can mine for gold and perhaps strike it rich, but many miners go bust. By serving those miners, you can be insulated from some end-market risk while still benefiting from the data center and AI tailwinds. The more complex these facilities become, the more advanced their service and engineering needs become. This work also extends well beyond initial construction. Facilities need retrofits, ongoing maintenance, testing, inspections, and equipment upgrades as workloads become more demanding. We see substantial opportunity throughout the data center life cycle.
What middle market trends are you seeing in the Infrastructure Software sector?
Brian Thom: First, we should agree that the AI data center buildout is happening right now. From a hardware and services perspective, there are investable themes today. Software comes after the hardware is installed because software can only run on infrastructure that is operating. Some of the investable themes I’m watching have always existed, but they now have an AI angle. One is observability. Once a data center is running and applications are being built within its workloads, developers and management teams want to understand what’s happening inside those workloads. Companies addressing that need have already been built and continue to develop. Another ecosystem I’m watching is FinOps, which is essentially cost management for software and AI usage. Cloud usage, OpenAI usage, Gemini usage, and similar tools are expanding rapidly within enterprises. FinOps software helps rein in those costs. These companies have always existed, but historically they focused on cloud, SaaS, and other software expenses rather than AI-specific costs. The third area is cybersecurity. Attack surfaces are expanding, and attackers are using AI to target enterprises and governments. Many cybersecurity companies are creating solutions to protect personally identifiable information, websites, enterprises, and other critical assets.
Those investable themes exist in the middle market, but AI is currently the hot sector, so those companies are generally growing rapidly. From a middle-market perspective, it may be harder to find the perfect fast-growing company at the right check size, but it’s easier to find companies growing rapidly on AI tailwinds. There’s some give and take. Firms that implement cybersecurity for enterprises, SMBs, and middle-market organizations remain attractive to middle-market investors. The same applies to companies implementing AI within enterprises and helping businesses become more productive. Services that maintain and support data centers and AI data centers overlap with Ryan’s coverage and are also attracting middle-market interest. To capitalize on these themes, investing in picks and shovels rather than the gold mine is a strong approach.
Where do middle market investors and dealmakers fit into the investment wave around hyperscalers?
Ryan Gillis: There are limited ways for a middle-market investor to participate directly in hard assets, but opportunities do exist. Some real estate-oriented middle-market investors that might not historically have fallen under my coverage are moving toward data centers because of the sector’s demand and returns. They may participate in one part of the development life cycle. For example, an investor may take land it already owns in an attractive geography near water and utility infrastructure, then seek an interconnection agreement. By investing a smaller amount of capital early, building the necessary infrastructure, and securing power, the investor can remove lead time for the next buyer and create significant value. It’s also a shorter-term project. The timeline doesn’t have to match the longer horizon of a traditional infrastructure investor, and it doesn’t require hundreds of millions or billions of dollars on day one. It can support a timely exit because there are many potential offtakers once the site is powered. That said, only so much land and so many sites are attractive for this strategy.
We’re spending more time on services within BGL’s digital infrastructure team, especially for middle-market investors. One strategy is to acquire an established service provider that operates in a limited geography, then provide financial backing to scale the company and expand geographically. Cooling is one example. There are different technologies for cooling GPUs and chips in next-generation data centers, from single-phase direct-to-chip systems to immersion cooling. The constant is that legacy air cooling is insufficient for today’s chips and facilities. An investor might acquire a company focused on one cooling technology and help it become a one-stop cooling provider. Because different data centers have different cooling needs, that expansion can increase the company’s capabilities and address more of the market.
Investors can also help a company expand geographically, deepen customer relationships, and move along the data center life cycle into engineering, maintenance, and retrofitting. The opportunity is to begin with a provider embedded in key relationships, then expand wallet share. We’re also seeing joint ventures and SPVs. An investor may not participate in data center development nationwide but can invest in a unique project. These are often smaller edge, enterprise, or neocloud sites rather than campuses dedicated to AWS or another hyperscaler. They may be colocated among multiple tenants or serve one smaller tenant. Sites in the five- to 50-megawatt range can be accessible to middle-market investors. Middle-market investors can serve the largest campuses and hyperscalers through service businesses, participate in hard assets at a smaller scale, and invest on a timeline and with a check size that fit their funds. The investors we speak with find the thesis compelling. When we present a data center or infrastructure services business, they’re eager to learn more. Appetite is strong.
What are the most compelling areas in infrastructure software that are benefiting from AI?
Brian Thom: One area that comes directly to mind is software that gets as close to the chip as possible. People have heard of CoreWeave, Nebius, neoclouds, and hyperscalers investing significant capital. What receives less attention is the software that wraps around chips and hardware, turning raw compute infrastructure into a platform that can run AI workloads. Companies have been created, and continue to be created, around GPU orchestration and AI orchestration. If an organization wants a sovereign cloud without relying on neoclouds or hyperscalers, it can create an on-premises GPU cloud. That gives it more control over sensitive and proprietary data. The software effectively allows a company to create a lighter version of AWS or Azure without relying on third parties to host its data. We’re seeing this particularly among large conglomerates and companies outside the United States.
My prediction is that more middle-market and smaller companies will care about where their data resides because they view that data as proprietary and don’t want AI companies controlling it. Investors should watch the software that wraps around chips and transforms raw hardware from Nvidia, HPE, Cisco, Dell, and others into intelligent infrastructure. Another area is data. An AI model is only as good as the data it receives. Data can be broken, incomplete, or unclean. Companies that host data, improve data quality, and make it accessible can help models perform better. These businesses have always existed, but because data is foundational to AI, they are becoming far more important. Investors also need to consider whether data warehouses and data lakes can support AI workloads and make data accessible to models quickly and accurately. We all care about speed and quality. Models need to retrieve data in a way that minimizes hallucinations and incorrect answers. AI models are probabilistic rather than deterministic. They may provide an answer with 95% confidence rather than absolute certainty, and that remaining 5% can make a significant difference to users who need the correct answer.
How are power constraints creating opportunities for Infrastructure Services?
Ryan Gillis: The data center boom has experienced shifting bottlenecks, and the milestones investors need to achieve are constantly moving. Most recently, power generation and availability have become key constraints and critical components of bringing sites online. The first consideration is the timeline to power. There is significant permitting and regulatory work, and power may not be available near the intended site. The constraint could involve generation, transmission, substation upgrades, or unavailable equipment.
One recent theme is the repurposing of turbines and equipment from unrelated industries because the lead time for newly manufactured equipment can be five to seven years. By then, a proposed site may no longer be relevant. Some developers are walking away from projects because they can’t obtain the equipment needed to generate power. We’re also seeing more behind-the-meter generation. Power produced at the facility can supply the site directly, often through natural gas systems, and bridge the gap while developers wait for grid power. If utilities can’t provide power for a project, developers increasingly look behind the meter while pursuing a longer-term supply. Behind-the-meter plans still require dependable fuel, permitting, specialized equipment, and a design suited to the site. Those complications have created an emerging subsector. Some developers are building behind-the-meter grids and hoping to sell powered sites.
For middle-market investors, this creates opportunities in electrical engineering, microgrid integration, installation, testing, inspection, and maintenance. Unlike infrastructure projects that require a large upfront investment and then produce stable cash flows, many of these service businesses are asset-light and generate recurring revenue. That’s exactly what many middle-market investors want. The power bottleneck has created another opportunity for investors seeking asset-light businesses with reliable, repeat business.
What deal activity are you seeing in the broader infrastructure software ecosystem?
Brian Thom: In my area, significant middle-market private equity activity is occurring in services, including AI implementation, cybersecurity, and IT services. As AI data centers and digital infrastructure are built, those services become necessary. There are many regional operators in Texas, Alabama, the South, and across the United States. National providers exist, but so do many regional companies, which creates middle-market activity. Software is different. In my opinion, there is currently a pause in middle-market software activity as private equity firms, including larger sponsors, assess what will happen after this phase of the AI buildout. One key question is how lenders view these businesses. The typical private equity model involves raising debt, acquiring a company, and contributing only part of the purchase price in cash. Lenders are also trying to determine how sustainable a software business is in light of AI disruption. I’m seeing many services transactions but less private equity activity in software. I think that will evolve, and activity should increase in 2027, 2028, and beyond. Strategic acquirers, including Cisco, Dell, CrowdStrike, and Palo Alto Networks, are active.
Companies described as AI-native, meaning they were created after AI became mainstream, are being evaluated by large, diversified strategic technology companies. Those acquirers are filling product gaps through acquisitions rather than building everything internally because many emerging companies are on the leading edge of technology and would take time to replicate. There’s significant activity, but it’s bifurcated between private equity and large-cap strategic buyers.
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