Produced in partnership with Blue Owl

Global data centre spending is on track to exceed $1 trillion by 2029, according to Dell’Oro Group, driven by an insatiable demand for hyperscalers to fuel AI and machine learning technologies.

Robert Hartog, who is a senior member of Blue Owl Capital’s digital infrastructure team, believes the hype is justified, with global data centre capacity up more than 200 per cent in the past decade and McKinsey predicting that usage could climb from 60 gigawatts today to up to 298 gigawatts by 2030.

Meeting this demand will require more land, capital and expertise to build more data centres and ancillary digital infrastructure, with the cost estimated to be north of $6.7 trillion globally, based on analysis by McKinsey.

Hartog describes the demand and supply dynamic as a “generational market opportunity.”

“Multi-trillion-dollar investment is needed for real estate, construction, IT equipment and power so the opportunity set is just massive,” he says.

“If you focus on cloud-computing alone, the growth in that market has been 20-30 per cent year on year, which is enough to build a very strong investment thesis, but then add to that AI and it significantly increases the scale of the opportunity set.”

“Hyperscaler facilities provide critical infrastructure for the roll out of cloud and AI, which is essential for society and communities to function.”

With Blue Owl’s digital infrastructure team having close to 10 years of data centre investing experience, the group is well-positioned to take advantage of the scale and capital required, and with asset owners to develop and build the digital infrastructure for the future. Blue Owl’s digital infrastructure strategy has $14 billion in assets under management across the entire digital infrastructure opportunity set from real estate and infrastructure development to credit and stabilised assets. Critically, Hartog says the ability to execute is crucial, as projects continue getting bigger, more complex and more expensive.

“If you look at the capex profiles of hyperscalers, including Microsoft, Amazon, Oracle, Meta and Google, they just keep expanding globally,” he says.

“There is a massive roll out of new and ongoing projects right now and they’re just getting larger and larger.”

“Data centre development is not easy and the larger these projects get, the bigger the constraints.”

Hartog says there are constraints on capital, power, land, supply chains and construction with other considerations including backlogged utility queues, delayed transmission upgrades, and complex regulatory conditions, all of which require an experienced partner with subject matter expertise, global scale, and local presence.

 

From asset owner to asset manager

An M&A lawyer by background, Hartog joined Blue Owl in 2024 through the firm’s acquisition of global alternatives manager, IPI Partners.

During his time as managing director of IPI Partners, the group grew to become one of the largest private data centre investors.

Prior to that, Hartog was the head of communication infrastructure investments at the €251 billion Dutch pension fund, PGGM and he describes the transition from asset owner to asset manager as an “extension of activities.”

“At PGGM my role was to find suitable digital infrastructure assets around the world, including telecommunications towers, data centres and fibre networks, and my work at Blue Owl builds on that,” he says.

“The data centre industry, especially the hyperscale data centre industry, is very interesting for institutional asset owners because of the long-term contracts and stable cashflows.”

Institutional clients have different participant pools, liabilities and time horizons, making it difficult to make a blanket statement about the ideal allocation to digital infrastructure, Hartog says.

“For long-term institutional investors, digital infrastructure and hyperscalers, in particular, can offer the benefits of risk-adjusted returns and capital preservation. We focus on resilient, long-lived facilities with long-term contracts and high credit rated tenants, which underpins stable, predictable cashflows,” he says.

 

Not all data centre developers are created equal

But no matter how favourable the macro thematic tailwinds, hyperscaler investments – like any investment – carry risks, including counterparty risk and development risk.

To help manage risk, investors can work with a manager with a track record of delivering large-scale projects.

“Ultimately, we are developing hyperscale facilities together with our hyperscale tenants, and they are relying on our capability to deliver on schedule, on budget, as per the specs,” he says.

“That means having the required land and permits and ensuring our supply chains can deliver all the necessary equipment and power. Scale is critical to be able to purchase equipment and items ahead of time, and engage large contractors, because there is a limited amount of time to deliver these projects.”

For established players with capital and experience, these challenges spell opportunity, according to Hartog.

“Projects are flowing to new markets that can offer energy resources and infrastructure advantages, which creates jobs and spreads economic development,” he says.

“Over the last decade, we’ve built a business focused on meeting the needs of hyperscalers. Our team is dedicated to solving problems for our tenants and providing them with the infrastructure and real estate they need for the roll-out of their product to the end-users. We’ve built a vertically integrated operating platform with specialised resources in local markets across the world to help address the challenges they are facing.”

These value-added services include site selection, securing alternative power solutions, coordinating land and power permits to drive developments, and engaging with the communities and regulators where we are developing.

“This doesn’t happen overnight. It has taken the better part of a decade to be this specialized and to build and acquire inhouse capabilities to address the specific challenges of delivering purpose-built hyperscale facilities.”

Blue Owl employs a range of specialists in areas like power, land acquisition and property development.

“We continue to be a strategic investor in the space and in order to deliver we need to make sure that we have the right capability sets inhouse, which is why we have power specialists who really understand the grid and the transmission and distribution challenges,” Hartog says.

“We have people who specialise in land acquisition strategies and developing sites into data centres. These are skill sets that are needed to continue to deploy assets in this space.”

While the opportunity for data centres and digital infrastructure may indeed be generational, Hartog believes that managers with the right skill set, expertise, hyperscaler relationships, community presence, scale, and proven ability to execute will be positioned to deliver.

Source: Bloomberg New Energy Finance (BNEF), “Data Center Market Overview”, June 2025.

Red-hot demand for data centres and a lack of infrastructure capital is creating a rare market dynamic in which lower-risk credit can yield more than equity investments in the sector, according to investors speaking at the Fiduciary Investors Symposium at Stanford University.

“Given the lack of debt capital relative to equity capital, we can generate some nice premiums,” said Jennifer Hartviksen, managing director and head of global credit at Canada’s Investment Management Corporation of Ontario (IMCO), citing high single-digit to low double-digit returns for single-B to BB- risk.

“Twelve months ago, when spreads were wider, we were seeing mid-teens for those types of paper that have really great structural downside protections with very nice returns relative to what we get in other markets and, in some cases, relative to what the equity below us can be getting.”

She said there were probably only 20 infrastructure credit funds targeting below investment grade that were lending to data centres, with the sector requiring a completely different skill set than investing in the far more popular direct middle market loan space.

“There’s just not a lot of folks doing that type of lending, and there’s really only two or three that do big funds that are multi-billion funds, so there’s a lot less capital there. And as a result of that, the spreads have come in less.”

Patrick Lawler, portfolio manager and head of core acquisitions on Blue Owl Capital’s digital infrastructure team, said some equity deals were closing with negative leverage, where the year-one cap rate is below the cost of debt. Those investors were betting on mark-to-market rent increases over the next few years.

“It can still be an attractive risk-adjusted return over a seven- to 10-year hold… given the immense rental growth we’ve seen in top markets.”

Data centres sit at the intersection of power and communications infrastructure, and years of investment in established tier-1 hubs such as Ashburn, Virginia, have created powerful network effects. The flip side, Lawler warned, is that “all the top markets are effectively out of space and power,” forcing developers and investors toward tertiary or even frontier markets to secure capacity.

Tammi Fisher, managing director, real assets, at Australia’s Future Fund said refinancing risk and rental renewal risk hadn’t yet been tested through a full cycle.

“We’ll talk to our partners and some of them are definitely more cautious about that, like how do you price that risk that’s maybe 10-15 years out in data centre locations that are highly customised, super-spec’d for a specific tenant. How do you think about that end-of-life risk, if there is end of life? You’ve got others who are much more optimistic about that, and, you know, and there’s so much growth in it,”

The booming energy needs of data centres is also transforming once “sleepy” infrastructure holdings, which now require large capex programs and different operating skill sets from management.

AI builds on existing cloud demand

While AI is attracting significant attention, hyperscale cloud computing remains the underlying demand driver for data centres, Lawler said. For example, Amazon Web Services generated more than $100 billion in revenue last year with about 30 per cent operating margins.

“You have this existing wave of cloud computing demand, and then you have a second entirely independent wave of demand coming from AI. They’re independent, and very importantly, they’re not cannibalizing each other.”

However, one way to de-risk investments in data centres was to structure the deal so that returns were not dependent on the unpredictable future path of AI. Hartviksen said the loans they were making were typically five-to seven-year maturities, with a lot of customisation in the space allowing for enhanced terms.

“We don’t need to think about the technology risk or what these data centres are going to look like beyond the term of our loans, which is another reason we find it very attractive.”

Lawler said Blue Owl owns the building, including the mechanical, electrical and plumbing infrastructure that will support a data centre’s use cases, but it does not own servers, networking equipment, or GPU chips.

“I don’t think anyone here knows exactly what AI is going to look like in seven to 10 years, but what we want to be able to do is look at our investment and say over this 15-year lease term, even if the residual value is effectively zero – so an enormously draconian scenario just by virtue of the lease payments and all the different structure that we’ve built in – we can still generate a positive and attractive, risk-adjusted return for our partners.”

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While AI might be the topic du jour, Philippe de Weck, chief investment officer for equities at Pictet Asset Management, thinks there’s another AI that investors need to reckon with: the adaptation imperative. 

While much of the attention has been focused on mitigating climate change by investing in energy sources that reduce the carbon dioxide intensity of the energy mix, the reality is that “climate change is occurring and impacting assets”, and it’s necessary to make them resilient against it.

“Corporates do acknowledge that climate has an impact on their business, but they’re not sure how to respond,” de Weck told the Top1000funds.com Fiduciary Investors Symposium at Stanford. “But we believe that, as events happen, corporates will start investing more and that gap will close, and that’s what we seek to benefit from.”

Pictet has made investments in companies like Xylem, which is a holding in its water investment strategy, and offers solutions that can help to manage large volumes of water and enable its efficient recycling at venues like sports stadiums. Then there’s Bentley Systems, which provides software for digitally modelling and inspecting infrastructure assets like dams to determine where stresses are occurring.

“The most important thing is to avoid losses,” de Weck said. “To avoid losses, you need to make assets more resilient.

“I think there’s a perception that investments in climate mitigation or adaptation are charity, but that’s not the case. These things have drivers behind then, and they’re supposed to achieve market-beating returns because of megatrends that mean these businesses will be favoured.”

The main reason that the $556 billion CalPERS invests in climate is to “generate alpha”, Peter Cashion, its managing director for sustainable investments, told the same session.

“We see climate as one of the megatrends, alongside AI and deglobalisation, and we see strong parallels with the technology revolution started 30-plus years ago,” Cashion said.

“Even though Silicon Valley is right there, [California pensions] weren’t early investors in that. Unfortunately, we’d probably be closer to fully funded at this point if we were.

“So in this climate revolution we want to be taking a leadership position in terms of identifying opportunities that we might otherwise miss, and that’s why we have a dedicated focus on it.”

CalPERS wants to have $100 billion invested in climate solutions by 2030. It’s coming up on $60 billion after starting from a base of about $47 billion just two years ago and sees potential climate investments as fitting into three categories: renewable energy, adaptation and transition.

“We’ve invested recently in a fire mitigation company that uses drones and AI to detect wildfires, as well as heat resistant crops… I would say that about 10 per cent of our investments in our portfolio are adaptation, and we expect that to grow,” Cashion said.

“We’re actually looking at some public equity strategies where the main focus is companies that are doing adaptation, because we anticipate higher growth there.”

Elizabeth Steiner, Treasurer of Oregon, said that, because the $100 billion Oregon Public Employees Retirement System (OPERS) represents almost the entirety of the state’s employees – and that the state’s political leaning can be best described as “purple” – there’s lots of different views.

“When we were thinking about this issue around climate resilience and climate positive investing, we had to take an approach that addressed the concerns of all of those constituencies. And that means, first of all, broad stakeholder engagement in the conversations,” she said.

OPERS’s stakeholders include six major public employee unions that “run the gamut” in terms of their interest in the topic. Steiner said some members would prefer the fund divest immediately and completely from fossil fuels. Others think the fund “shouldn’t even be talking about climate positive investments”.

“So we have to navigate through that, and we have to navigate through the legislature, and we have to navigate through the investment council, many of whom are concerned that if they go too far in one direction or another, there’s going to be litigation. So that’s a lot of navigation that’s required,” she said.

“[But] our conviction in Oregon is that to talk about environmental, social and governance investing is no different from talking about almost any other constraints that’s going to affect a company’s bottom line, right?” she said.

“If corporations are not taking into consideration the impact of climate change, for example, on their business model, then they are failing in their fiduciary responsibility.”  

Making sense of an increasingly complex and chaotic world is testing the capabilities of even the most seasoned investors as they try to determine whether the established orthodoxy of causality in markets and economies still holds true.

Bridgewater Associates has found success employing a proprietary AI, which it calls AIA (short for Artificial Investment Associate, and pronounced “eye-a”) to help it make sense of this shifting landscape and to manage portfolios. The Fiduciary Investors Symposium at Stanford University heard that while there are strong use cases for AI, it also has limitations.

Alex Smith, partner and senior portfolio strategist at Bridgewater, said the firm’s ambition is to use AI to help it “understand the world, and importantly not in a black box way, [but] in a causal way, a fundamental way”.

“When we think about how to understand the world… predicting the future is really hard, and so our approach has always been to take fundamental understanding, and by that, I just mean we’re obsessed with causality,” he said.

“We want to know why something works. If we’re going to put capital behind it, we think the world works like a machine. If you understand that machine, measure conditions, you can better anticipate outcomes.”

Smith said that once the fundamentals of something are understood, they can be systemised. And that system can be tested “across every historical case you can find, across every country you trade, to test its reliability”.

If it proves to be reliable, it’s turned into an algorithm, which can be applied at scale.

Recent events – geopolitical, macro-economic and market-related – might appear chaotic or random, and have certainly tested investors’ mettle, but Smith says that “all the causal linkages that have always mattered”, including growth, inflation, monetary policy, commodity prices, portfolio allocation, still matter.

But two unique challenges stand out today. One is that understanding causality relies on being able to manage unstructured data “that doesn’t fit neatly into a numerical time series”.

“It’s always important, but it’s really important today, because in this mercantilist world, so much policy is government-driven,” he said.

“And related to that, many of these events [have] very few historical precedents. The challenge is that with unstructured data that’s hard to measure and systemise. You can do it, but it’s hard, and because there’s few historical precedents, there aren’t that many analogues that you can stress-test against, which underscores the need to be causal, the need to be fundamental.

“And up until recently, that had been a unique ability that we humans have.”

Generational technological progress

Nina Lozinski, co-head of artificial intelligence and machine learning investment strategy at Bridgewater, said these unprecedented geopolitical and economic events are “happening against the backdrop of generational technological progress”.

Lozinski said the pace of progress is accelerating: it took 15 to 20 years for technology to progress to the point of being able to understand handwriting – and even then the only organisation really excited about that development was the Post Office – but the advent of speech recognition, then visual reasoning and image recognition, happened much more quickly. Then, “all of a sudden, now we have another kind of intelligence that’s able to process language”, and that opens up vast new possibilities.

“The goal in creating AIA for us was to create something that would be good, that we could have confidence in, that still understood causal linkages, that was stress-tested, that was diversified, but that also would be different, that would lean into the kinds of things that machines are really good at, that humans may not be as good at,” Lozinski said.

“Things like, how do you think in many different dimensions at once? For us, we’ve got two dimensions, we’re pretty good at three dimensions, but past that is more difficult to achieve.”

Using AIA to make decisions on how to invest has resulted in portfolios that look different from portfolios constructed by human investors.

“It’s been good, performance has been solid; but it’s also been different. We’ve had different positions, we’ve had different periods of being up and down, and that’s been the experience so far,” she said.

Something that looks like reasoning

Lozinski said that it appears AI is capable of doing something that looks like reasoning, which leads to questions such as how to use that reasoning ability to help build portfolios.

“Of course, there’s very real weaknesses, and so you may have experienced some of these if you’ve tried to experiment with AI,” she said.

“Some of the ones I’d highlight are AI systems today are not yet very good at complex analytical tasks – they lack number sense. You shouldn’t use them to be a calculator. They can hallucinate. They can make up facts that aren’t in the source materials.”

AI systems also have embedded knowledge, Lozinski said, which raises the issue of working out if a system is genuinely reasoning or just relying on what it has, in essence, remembered.

“In many ways, these are just different ways of saying there’s really two problems. The first is that predicting market returns directly is still too hard, and that’s a very hard problem, and an AI is not ready to do that just as a magic off-the-shelf tool yet,” she said.

“And the second thing is that AI will answer any question you give it, but most of those answers are going to be bad. So how do you know if what you’re going to get back from AI is any good? And those are some of the things I think about a lot.”

Smith and Lozinski said that despite current limitations, there are real opportunities to use AI to address the increasingly complex issues facing investors.

A live, on-stage demonstration of how Bridgewater deploys AIA to process unstructured data and form views about how the world is likely to play out revealed the complexity of the processes it goes through in response to an inquiry.

“It went through a process just now of searching what’s going on in the internet, thinking about what it found, searching again if it needs new information, and coming up with its own independent forecast,” Lozinski said.  “We’ll actually have a panel of 10 forecasts here.

“The step that it’s doing now, which is the second phase of this process, is reconciling them. So now, we have another agent that is currently going through each of those 10 trying to understand, well, if they agreed, why did they agree; and if they disagreed, why did they disagree? And what can we do to suss out those questions and try and do a second step which is, on the areas of disagreement, should I be looking up anything? Should I be thinking?”

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Producing 50 grams of protein in the form of beef produces about the same amount of carbon dioxide – roughly 19 kilograms – as 617,000 Gemini AI queries. Producing 50g of protein in the form of chicken produces an equivalent amount of CO2 to about 95,000 AI queries. And producing 50g of protein in the form of peas produces the equivalent of about 6700 queries.

The point of the analysis, presented at the Fiduciary Investors Symposium by Viktoras Kulionas, an investment manager and senior environmental economist at Pictet Asset Management, was not to spark a dietary rethink but to put AI’s environmental impact into perspective.

Kulionis said a comprehensive life cycle assessment of AI must weigh both its footprint – the impact of building and running AI systems – and its handprint, the potential positive effects of AI in cutting other industries’ emissions.

The power consumption of AI, for example, is routinely described as “obscene” or “shocking”. Some estimates suggest that if every Google search became an AI chatbot interaction, the company would consume as much electricity as Ireland. The capital investment needed to support that volume of AI interactions is estimated at $100 billion, making such a shift unlikely.

Nevertheless, such analyses “usually give an impression that AI will have a substantially negative environmental impact because it uses a lot of energy and water and also emits a lot of greenhouse gas emissions,” Kulionis told FIS.

“However, when you start to look at these numbers in a bit more detail, you start to see a different picture, a picture that is a bit more positive.”

Kulionis said the take-up of AI and AI-enabled processes can be seen as a continuation of a decoupling of GDP growth and energy consumption dating back to around 1860 and the introduction of the steam engine.

“During that period, what happened is that energy use was growing in parallel with GDP. Those two were very, very closely linked together,” he said.

“Then that relationship started to change during the Second Industrial Revolution. What happened then was that GDP continued to grow. Energy use continued to grow, but… GDP grew faster than energy use.

“That relationship changed once again around 1970s with the introduction of semiconductors, and during that time what happened is that there were even weaker links between energy use and GDP – GDP continues to grow, but energy use levels out. It still increased, but it didn’t increase as much as before.”

We are now entering potentially a fourth industrial revolution, Kulionis said, where GDP growth and energy consumption will decouple even further, and “AI is that kind of catalyst that can bring us those big changes”.

“And the question is, what will be the impact of AI? Will it have this positive impact on energy use, where it will lead to increasing energy use; or will it have this negative impact on energy use, where it will help us to reduce energy use, because it helps to achieve certain efficiencies?”

“AI is only part of – is a subset of – that entire data centre energy usage, and it’s relatively difficult to estimate,” Kulionis said.

“Different literature sources suggest that today it’s between 15 to 25 per cent, so here I assume that it is 25 per cent and if we do the numbers, then AI-related energy use would account for about 0.09 per cent of total final energy consumption globally. So it’s not a huge number, it’s not small number, but it’s not also as dramatic as some of the headlines suggest.”

Kulionis said energy demand from data centres will undoubtedly continue to increase but “it will not be the key driver for energy demand growth” – there are other categories such as electrification of industry, air conditioning and electric vehicles that will contribute significantly more to that growth in energy demand.

The handprint of AI can be very significant, Kulionis said. For example, the aviation industry is responsible for about 3 per cent of global climate change through aeroplane emissions and through contrails that can trap heat and prevent it from escaping the atmosphere.

“If you change the flight path of your plane, you can reduce these contrails,” Kulionis said. An analysis of flight paths suggested that “about 54 per cent of these contrails could be reduced, if you slightly modify that route”.

“You would be able to reduce that climate change impact by about 0.57 per cent and… this application alone, if it would be successful, would be more than enough to offset entire data centre emissions,” he said.

“So it’s quite substantial, and that’s only one application.”

The key insight, Kulionis said, is that “AI has a footprint, but in a grand scale of things, that footprint remains modest and not large”.

“It’s also likely to remain small because of grid decarbonisation,” he said.

“But there are also some things that are important to keep in mind and monitor, and one of those things is water usage, especially water usage in water-stressed regions.

“And if we put everything together the key message or insight that we get here is that the downside associated with AI, the downside on the environment, seems to be limited, but the potential upside can be huge because of all these positive impacts.”

Be part of the conversation next year – register now for the Fiduciary Investors Symposium Stanford 2026. Asset owners only. 

Our next event is at the University of Oxford, November 4-6 2025. Register today – asset owners only.