Investing in Data Centers: Trends, Risks and Strategies for a Fast-Growing Asset Class

This article summarises insights from the Forvis Mazars webinar: Investing in Data Centers, hosted in collaboration with Aurora Energy Research.

---

Moderated by Neethu Ram with panellists Anton Yunussov (Cybersecurity Practice Lead, UK), Alice Strevens (Director, Sustainability Consulting), Marten Ford (Project Leader, Aurora Energy Research) and Ryan Humphrey (Partner, Financial Modelling).

Data centers have moved well beyond their origins as tech infrastructure. They now sit firmly in the world of infrastructure investment, drawing capital from institutional funds, development finance institutions and project finance lenders alongside the hyperscalers and colocation operators that have traditionally driven the sector.

The numbers behind that shift are striking. Today’s connected world generates roughly 3 trillion gigabytes of data through smartphones, digital platforms and fintech applications. Add AI workloads to the picture, and demand is expected to nearly triple in the next five years. For investors, the question is no longer whether data centers are investable. It is how to navigate the risks that come with an asset class that is evolving faster than the infrastructure around it can keep up.

How AI and cloud adoption are driving data center demand

The growth in data center demand is being driven by three forces. The first and most visible is the explosive growth of artificial intelligence workloads. Training large language models, running advanced analytics and delivering AI-powered services all require immense processing power. AI workloads are expected to account for 50% of cloud demand in the coming years.

The second driver is the broader wave of cloud adoption and digital transformation. Organisations across every sector are migrating to cloud infrastructure, and that migration requires compute capacity at scale.

The third is regulatory. Data sovereignty requirements under frameworks like GDPR are driving demand for localised data center capacity, meaning operators cannot simply consolidate into a handful of global locations.

“AI training, large language models, advanced analytics require immense processing power. That’s the reason why we’re seeing the data center space exploding in terms of demand.”

Anton Yunussov

But as Marten Ford observed, it is not just the growth in use cases that matters. It is the fact that those use cases are now being monetised and turned into investable, long-term revenue streams, which is really critical at driving some of these really big pieces of infrastructure.

Where capital is flowing in global data center investment

The mature markets for data center investment remain North America and Europe, but the pipeline is genuinely global. As Ryan Humphrey noted, the Forvis Mazars team has been involved in approximately 20 data center transactions in the last couple of years, spanning not only the US and Europe but also Africa, APAC and Latin America.

Within those regions, the micro-level question of where to build is increasingly determined by access to power. The traditional European clusters, often referred to as the FLAP-D markets (Frankfurt, London, Amsterdam, Paris and Dublin), are leading the way but are also increasingly constrained by grid capacity. Developers are moving further afield to access the power connections they need to deploy rapidly.

“Having easy access to power means that you can build and deploy rapidly and meet that exponential growth in demand for AI services as quickly as possible.”

Marten Ford

The investment model itself has also shifted. What started as venture capital and equity investments now sits firmly in infrastructure funds, which has turned data centers into real estate assets rather than just tech hubs. Finding land or capital is no longer the constraint. The real constraint is power generation and, more specifically, power transmission.

Energy strategy and grid constraints for data center projects

Power is the single largest operational cost for a data center. As Ryan Humphrey put it bluntly, roughly half of the OPEX is power, probably more. That makes energy strategy central to the investment case, not peripheral.

Marten Ford outlined three dimensions of the energy challenge. The first is access to power, which in most markets means securing a grid connection. The second is optimising the energy procurement strategy, which is a significant determinant of long-term operational costs. The third is managing the wider system implications that a data center has on the grid around it.

The grid challenge is particularly acute because data centers represent a step change in demand. In many Western countries, overall power demand has been falling for decades, and grids have not had to accommodate new power-intensive industries at this scale. Networks need to reorientate themselves to support data center connections, and that takes time.

“Transmission is the key short-term issue. That is what is constraining projects today.”

Marten Ford

In the medium term, the coordination challenge between government-led generation investment and market-driven data center demand becomes critical. Operators need to be proactively engaged with policymakers, not waiting for the grid to catch up.

Practical responses include behind-the-meter power solutions, bridging power arrangements while grid upgrades are completed, and flexibility in power demand. Innovative power purchase agreements, such as the recently announced Google and Shell deal in the UK, are early examples of how operators are trying to solve these challenges commercially.

The question of whether nuclear power could play a meaningful role drew a cautious response. Extending the life of existing nuclear assets through PPAs, as seen in several US transactions, is a near-term opportunity. But new nuclear build, including small modular reactors, is a medium-term aspiration that needs to demonstrate construction delivery before investors should place significant weight on it.

For modellers working through these energy assumptions, our tutorials on CFADS and the features of a cash flow waterfall in project finance cover the mechanics that translate power costs into the cash flow metrics lenders and investors scrutinise.

Cybersecurity risks investors must assess before acquiring data centers

Data centers are high-value targets for cyber adversaries, and the risk profile spikes during acquisitions and expansions. Anton Yunussov identified three categories of risk that investors need to understand.

The first is resilience. Data centers face sustained denial-of-service attacks, and the ability to withstand them is a fundamental requirement. The second is hidden vulnerabilities. When acquiring or merging data center operations, unknown security gaps must be identified and remediated before the deal closes. The third is the expanded attack surface that comes from integrating IT systems from different environments, which increases complexity and the number of attack vectors.

“When acquiring data centers, please conduct robust and in-depth cyber due diligence before the deal closes, so that any hidden vulnerabilities, resilience gaps and compliance gaps are known before the deal can go through.”

Anton Yunussov

Pre-deal cyber due diligence typically involves two workstreams: a cyber risk assessment against industry standards applicable to data centers, and penetration testing, often including red teaming simulations that mimic real-world attacker techniques to test controls in practice.

Beyond the deal itself, operators face ongoing challenges around technology obsolescence, talent shortages, and regulatory compliance. The EU’s NIS2 regulations now classify data centers as critical infrastructure providers, requiring continuous evidence of compliance.

ESG and sustainability considerations for data center investments

The regulatory landscape for ESG is in flux. Deregulation is on the cards across parts of Europe and at the US federal level, while at the same time sustainability requirements are tightening at the state and national level. This creates uncertainty, but it does not reduce the importance of building sustainability into investment decisions from the outset.

As Alice Strevens noted, the risk is that if you focus on minimum compliance requirements only, there is a potential that you do not build in sustainability principles into the planning and investment decisions, and that means you may not be able to safeguard your investments for the longer term.

Data centers are among the most resource-intensive asset classes. Investors are increasingly looking for low carbon footprints, renewable energy sourcing where possible, efficient water consumption and transparent reporting. The direction of travel for ESG is moving from voluntary to mandatory, driven through local compliance requirements and disclosures.

Social licence to operate is an emerging risk that deserves more attention than it typically receives. The Dublin moratorium on new data center grid connections illustrated what happens when data center growth crowds out other uses of grid capacity, including housing. Communities may initially expect broad economic benefits from data center construction, only to find a disconnect between those expectations and the reality of a facility that, once operational, creates relatively few local jobs.

“Engaging with communities to understand what’s needed within that local context could help to enhance your local community engagement and help you tackle risks with communities as they arise.”

Alice Strevens

Practical community engagement initiatives include funding local skills development and training, enhancing broadband connectivity for local institutions, and reusing waste heat for homes in the surrounding area.

On greenwashing specifically, investors should scrutinise unsubstantiated claims of carbon neutrality or 100% renewable energy that lack third-party audits. Recognised certifications such as LEED, BREEAM or ISO 14001 should be verified for authenticity, and an over-reliance on carbon offsets should be treated as a red flag.

Financial modelling challenges for data center transactions

From a financial modelling perspective, data center transactions present a mix of familiar infrastructure modelling challenges and sector-specific considerations. Ryan Humphrey observed that many of the financial risks are consistent with other asset classes: uncontracted revenues are riskier than contracted revenues, construction costs are significant, and supply chain risks are real.

The sector-specific elements, such as PUE calculations, power cost modelling and churn assumptions, are often the areas that concern investors most. But the modelling of those tends to be quite straightforward. It is effectively a price times volume calculation.

“Where we’ve seen issues and actually found problems in models is because they haven’t been planned and designed well, or they’ve tried to use a previous model as their business has evolved.”

Ryan Humphrey

The more common problems the team encounters are structural. Aggregating multiple data centers in a portfolio, splitting a single location by service type, and accommodating different revenue mechanics across sites all introduce complexity. These are well-understood risks from a financial modelling perspective, but combined they become challenging, particularly for investors making their first data center transaction.

The recommendation is to invest more time in model planning and design. Ryan Humphrey suggested that the standard guidance of spending 10 to 20% of the time planning a model should be higher for data centers, precisely because the calculations are not the hard part. The structure is. For teams exploring how AI tools can support model review and documentation, our AI in Project Finance Modelling course covers practical workflows for using AI across the modelling lifecycle, from data extraction and integrity checks through to model documentation.

Flexibility is equally important. Many operators use the same model across multiple transactions, evolving from equity-funded greenfield builds to refinancings as cash flows mature. Building in the ability to disaggregate and restructure the model from the outset avoids the common trap of retrofitting a model that was not designed for the next stage. Our Financial Modelling for Digital Infrastructure course addresses these structural challenges directly, covering portfolio aggregation, revenue mechanic design and the flexibility required as data center businesses evolve through successive financing stages.

For teams building sensitivity analysis, data centers introduce some distinctive scenarios. Run-off cases that assume no customer growth, testing existing cash flows adjusted for churn, are a relatively new type of sensitivity that the team is seeing more frequently. Construction delay and capex sensitivities remain relevant for greenfield projects, consistent with any infrastructure rollout.

Our tutorials on average DSCR, the loan life coverage ratio (LLCR) and cash sweep analysis cover the credit metrics and debt mechanics that lenders apply across infrastructure asset classes, including data centers.

Is the data center market a bubble?

The question came directly from the audience, and the panel addressed it head-on. Ryan Humphrey’s view was that the interest is underwritten by real money from big tech, governments and lenders, at the same time as monetisable AI use cases are growing. That combination suggests it is not a bubble, even if the volume of investment discourse creates that impression.

Marten Ford added two qualifying points. First, the rapid growth may be naturally constrained by power availability, which could temper some of the very large proposed investment figures. Second, advances in chip design are reducing power requirements per unit of compute, meaning the relationship between data demand and physical infrastructure is not linear.

—

To explore data center modelling, energy strategy and infrastructure investment in more detail, Forvis Mazars offers a range of financial modelling training courses and advisory services. Our best practice project finance modelling and advanced project finance modelling courses cover the financial statement construction, debt sizing mechanics and scenario analysis relevant to infrastructure transactions.