DigitalBridge Group, Inc. (DBRG) Earnings Call Transcript & Summary

All right. Well, we'll keep moving here. So welcome, everyone, to our fireside chat on Power and AI and the emerging data center energy crunch. I'm Will Thompson. I'm a member of the thematic investment team here at Barclays. I've spent a lot of time running extensively on the Power and AI theme. So I'm really thrilled to be joined by Marc Ganzi, the CEO of DigitalBridge. Marc has decades of experience investing in digital infrastructure. Digital Edge, maybe I'd describe it as a relatively small asset manager, but a major investor in digital infrastructure that is pursuing somewhat of a differentiated grid-independent power solutions. So Marc, I think you've been pretty adamant about this topic. So maybe just a quick introduction to DigitalBridge for the audience.

Yes, sure. So DigitalBridge is a $106 billion AUM global asset manager. We focus on the development, ownership and management of digital infrastructure assets. Today, that portfolio is roughly about 40% data centers, roughly about 30% towers and then the rest would be fiber and other associated forms of infrastructure. As I like to say, we're the sort of accidental tourist in the power space. We sort of backed into being in the power space through our footprint, which is over 400 data centers. We have a power bank of about 22 gigawatts globally across 9 different data center businesses. And there's every form of power that this conference would know is tethered to one of our data centers. So we are every -- we're sort of in the vortex of all of this as it's evolving.

So Marc, I think you've quantified the AI opportunity of $7 trillion, while powering AI would require maybe $1.3 trillion to $1.5 trillion of new power infrastructure. The biggest question I'm getting today is when or if we will hit the power wall, does this force hyperscalers to actually cut back on CapEx? I maybe love you to dig into sort of DigitalBridge's secured power bank, which I think you just talked to, and its venture with ArcLight. Maybe give us a sense on how are you -- what's your vision for powering AI and general cloud needs?

Sure. And look, the energy crunch situation that's coming through data centers and through power, it's really just data, right? I'm just a big believer in data. Everything I've been doing for 32 years as the CEO is data-driven. We don't make decisions without really good data. What's happening is there is a trade and supply imbalance between power on our grid and how much leasing activity is happening in data center land. So there's a really interesting slide that a certain research firm put out, not named Barclays. But it was very telling in the sense that the sector has been kind of chugging along at -- you go back to 2022, the sector signed 3 gigawatts of leases. You go to 2023, that jumped to about 4 gigawatts. You jump to 2024, it was another 5.7 gigawatts. This is the first year the sector will sign 6 gigawatts of leases. The problem is the grid is only turning up about, on average, about 5 gigawatts of power per year of incremental power on the U.S.'s backbone infrastructure. And what's happening is leasing is going from 6 gigawatts to -- which will be this year's number, next year, somewhere around 7. The year after that, 8.6, then it jumps to like almost 10. And then by 2032, it goes to 20, okay, 20 gigawatts of new leasing. Meanwhile, the grid is chugging along at 5 to 6 gigawatts per year of new power. So each year, last year was the first year we had a trade and supply imbalance, which means the U.S. power sector didn't deliver about 900 megawatts against what was leased. So we started this year with a 900-megawatt deficit. This year, that deficit grows by 1.1 gigawatts. So if you're accumulating that deficit and you're just doing math, it's a 2-gigawatt deficit already, and we're in 2025. And as we look forward and you get out to that 2032 year, where the grid is turning up about 8 gigawatts and the sector is leasing 20, that's a 13 gigawatt deficit just in 1 year. Now the question is what AI number do you believe? And there's sort of -- there's 3 forecasts around the total amount of AI infrastructure needed to make this all work. There's the conservative guidance, which is 137 gigawatts. There's the midrange of that guidance, which is the number we're anchored on, which is about 196 gigawatts. And then there's the -- we call it the -- we always pick on [indiscernible] because he's such an optimist, but he believes that AI will consume about 300 gigawatts. Now if there's anything near kind of the AI Sam Altman number, which is the 300 number, we're in a world of trouble. But I don't think that number actually happens. I think to your point, CapEx does curtail. And why does it curtail? Because whether it was digital PCS, the Internet, cloud computing, I've been a CEO across all these thematics and all these things have a 7-year cycle, right? You get that steep slope in the first 3 to 4 years, which is we're sort of in the year 3 of AI infrastructure. It's going to go hard for another 2 more years, 4 to 5. It's going to taper and then it's going to fall off a little bit. And by the way, that's been every technology CapEx cycle for the last 30 years has followed that bell curve. So we think that bell curve lands us right at that 196 gigawatt number. Today, data center capacity in the U.S. is roughly about 60 gigawatts of compute. And again, with the sector adding 6 gigawatts this year of leases, you get to 66. And then if you follow that trajectory through 2032, you land right at about shy of about 190 gigawatts of power, which is getting through large language models, generative AI and inferencing. Inferencing is kind of the next big sort of leg up into compute. So again, I'm a realist. I'm not an alarmist. I've been around 4 or 5 different technology cycles. I really have a good feel for what our customers are doing in terms of CapEx. And we have a very good feeling for how fast these guys are getting the return on their investment in AI, which is faster than cloud. It took us just to frame cloud for a second. Let's go back in time. Let's go back to 2011 when the public cloud was formed. Public cloud to today, the cloud is essentially 14 years old. We're 80% built on public cloud. We're not finished yet. And so -- and remember, of that 60 gigawatts I just told you, AI is only 35% of that. The other 65% is public cloud, and we're just now starting to build private cloud, which is a whole new vertical. So everyone talks about AI, but one of the adjuncts of AI is data sovereignty and building private cloud in the sovereign cloud. Those are also building private large language models, which is its own language model for AI, which is outside of the hyperscalers. So I'd like to frame all this which is math and not to confuse people, but really to embrace it and to understand the gap. And so we saw this gap about 3 years ago, and we started going down the path of finding other forms of energy to our data centers. And it really started when we took Switch private about 2 years ago. Switch was really a very interesting company that really focuses on private cloud, but we weren't really so much focused on Rob Roy's passion for private cloud. I was more focused for his passion for these giga campuses and his ability to procure power outside of the grid. I thought that was really interesting. And so it was a public stock that we took private for $11 billion. The market really didn't understand it as a story. We've gone on to quadruple the size of the company. We've done a ton of bookings, and we've built new data center capacity. But as we built that capacity, we've been building power. And we've been building grid independent power across a series of microgrids. And those microgrids are sourced with -- we work with utilities. We have interconnection agreements in all of our microgrids. In some instances, we're building private lines from renewable power directly into the microgrid where we're leasing that infrastructure through the regulated utility company. But at the end of the day, we found a way to aggregate 4 or 5 or 6 different sources of power into a microgrid. And we've been able to create our own set of backup batteries where we store that power. And then we've optimized a 24-hour clock on how we use that power where we're constantly trading in and out of power with the regulated utility in that geography. A great example of that is we have a really positive relationship with Nevada Power & Light. And in fact, our 2 mega campuses, one in Vegas and in Reno, those 2 campuses together consume almost 3.4 gigawatts of power. So we've got a 1 gigawatt microgrid in Vegas. We now have a 1.8 microgrid in Reno, and we're expanding both of those microgrids now, supplementing them with LNG. Both those microgrids have 5 different sources of power, including hydro, wind, solar, LNG and grid connectivity. And I think when people ask us, why are you doing this? What's the purpose of this? And we say, look, we just can't be beholden to one source of power, it's just not feasible for what we're doing in these campuses, particularly when we're powering NVIDIA and CoreWeave and some of these really high-power density compute modules that we built out. So part of this has been necessity and survival and also our ability to embrace renewable power. The other microgrid we built, we have 2 small microgrids in Sao Paulo of all places. So Sao Paulo, Brazil is really interesting for us. There, we have a 500-megawatt microgrid. We have a 300-megawatt microgrid all in the city of Tambore. And there, we lease the transmission infrastructure, but we have 2 sources of hydro. So we're 100% hydro and across 14 different data centers. Today, we have excess power of about 300 megawatts. We sell all that power back into the São Paulo grid. And we also sell power to Digital Realty and we sell power to Equinix, our 2 competitors. Why? Well, we make money on it. And what we found is that building our own grid independent infrastructure has actually been a great return because we own the data center, we have the relationship with the customer. We figured out how to negotiate PPAs directly with them and the excess power we're trading in all day long. So we have found -- we've kind of turned a negative. I'm not going to tell you it's a positive yet because there's a lot of hard work to do. But we do have a really unique relationship with ArcLight. We have a fantastic relationship with them. We've combined up strategically. We have a pipeline of about 9 gigawatts of new power projects we're building with them. And it doesn't have to be a microgrid. It can be -- we may just build a solar farm and have an offtake agreement to Google. We may build an integrated solar farm and data center like in a place, for example, like Zaragoza, Spain. So we're coming up with unique ideas on how we build power generation adjacent to data centers or we're building our own microgrids, so we're sourcing the power and bringing it into our data centers. But in all instances, we're tethering that to a forward 10- to 15-year commitment with our customers who are looking for power and need power. And so we're taking kind of a negative and we're turning it into, we think, a positive, particularly for our portfolio companies.

And so there seems to be parts and sports that the U.S. seem to compete with China on AI, yet there's no consensus on how we're going to power it. And so to your point, it seems like a push for all of the above energy sources. Can you maybe talk about just the priorities now? Because it seems like speed to power is the priority. We've seen new policy measures in terms of something like Texas and Senate Bill 6. requiring some level of on-site power. But it seems like secondary measures have been emissions, capital costs, electricity prices, relative to speed to market. How is the data center industry prioritizing what's the requirements to...

Again, I'll try to keep it simple. I don't believe we are in an AI arms race with China. I maybe have a very different perspective on it, which is kind of a 12-year view of watching China build their state-controlled LLM. A lot was said about DeepSeek, not to take a tangent, but everyone asks me, they ask me my opinion what about DeepSeek? I say, look, it's really simple. DeepSeek doesn't exist without Meta. It's really simple. If Mark Zuckerberg's open source LLM doesn't exist, DeepSeek does not exist because if it did, it would just be another adjunct LLM that runs off of China's state-controlled LLM. Here in the U.S., we're building 7 large language models, 7 privately funded through a series of different hyperscalers that are highly sophisticated, incredible data gatherers and generally speaking, pretty secure in terms of the structure of that data. You sort of look at that in contrast to China, there's one LLM being built, which is the state's LLM, highly controlled, highly manipulated and doesn't have the funding and the capability that the 7 hyperscalers have. So -- but what China does have is they have a state regime that is very focused on power and making sure that China has an edge on power, no regulations in terms of the size of their LLM and where it goes and how it supports Tencent, Alibaba, ByteDance. Those are all companies that are supported by the Chinese sort of infrastructure. But DeepSeek only came to prominence by using a U.S. LLM, right? Not a Chinese LLM. They didn't use the state-owned apparatus. And so DeepSeek's first version came out, anyone know how accurate it was? About 63% accurate, their first version. Their second version, which is now being run on the state's LLM, has dropped to 53% accuracy. So that should tell you everything about the difference between China's LLM capabilities and the United States' capabilities. I'm betting on the U.S. Now our constraining factor is we don't have fields and fields of solar farms in the middle of nowhere, which China has done a very good job of doing. They've decided to weaponize their apparatus to go build as much renewable power to power AI. But ultimately, if you don't allow an LLM that sounds weird, it needs to grow. Large language models need to grow and learn and keep moving. If along that road, you're manipulating the data, you basically destroy the real sort of truth behind AI, which is that it has to get to that phase of inferencing, where it begins to think for itself. But if you have a model that's constantly being told, I got to help it think what it needs to think, you've sort of corrupted the whole concept of inferencing. Now will China get there? I don't know. It's not -- it's -- as I say, it's not my monkey, it's not my circus. But we keep our eye on China because it's -- some of those customers like Tencent and Alibaba and ByteDance are customers in my data centers in Asia and they're customers in my data centers in Europe. Our portfolio is a global portfolio. But coming back to the U.S., I think at the end of the day, -- as most everyone at this conference knows, what I've always been sort of looked as the sort of limiting factor to where we go is just our PUC structure. It's just very antiquated. You've got to go state by state. A lot of people in the data center space don't understand that. We've been building infrastructure for 30 years. So I know that building towers and fiber networks and data centers is a highly localized business. You then take that localization and you put the PUC on top of it and then you got FERC sitting on top of it. You have these layers of regulatory that I don't even think the White House fully understands or appreciates. At the end of the day, power is really the gatekeepers of the public utility commissions in each state. That is going to be the limiting factor. We can remove all of the red tape in Washington. But until you remove the bureaucracy at the state levels, each state is looking at their baseload and saying, okay, I'm concerned because as you said correctly, people are waking up to the fact this is going to hurt consumers. So what do we need to do? And we say, look, at the end of the day, if I can be a net contributor to the grid or if the microgrids we build or we aggregate power and I can sell power back into baseload, I turn from being an enemy into a friend at the PUC level. What I do worry about is that this weaponization of the cost to consumers is going to get politicized and it's going to slow us down. And I think that will be a real problem for the hyperscalers. Look, the last administration, our former Secretary of Energy, she -- I met with her twice. I think she's really smart. I really like her. But her answer was, well, we'll just wait around and let the hyperscalers pay for it. That's never the right answer. There has to be a solution that brings DC together, PUCs together, private investors like us and the hyperscalers to build some of these grid independent solutions, which is what we decided to just go do on our own, kind of we were left our own devices.

We often throw the different workloads in the same bucket in terms of data centers. Can you talk about what are the restrictions or requirements when we think about cloud, which can be hundreds of different cloud products and you talk about public and private cloud. And now we have AI inferencing AI workload. And there's different latency land requirements. There's potentially different power fluctuation requirements and then obviously, the five-nines often gets brought up in terms of reliability. Maybe just talk to the different considerations when we think about those different workloads.

Well, what's interesting is there are very distinct workloads now. And they tend to sit in different types of data centers, and they're using different types of GPUs. And ultimately, for AI inferencing and large language models, you want to use the highest powered GPU and get your hands on. So when we talk about NVIDIA's next-generation chips and you talk about the Blackwell chip, they're really expensive. But the hyperscalers want to get their hands on them because ultimately, that processing capability and the ability for that large language model to learn is much faster. Now the adjunct to that is there's -- as most of you know, power people that is more power density. So you're trying to squeeze more power into a smaller GPU, which is a smaller rack. If any of you ever get the chance to tour Switch in Las Vegas, it's actually where CoreWeave recorded their entire IPO roadshow, it's where they did the IPO. And you can go in a couple of those data halls and you hear these Blackwell chips and you've never heard a sound like this. The hissing, the sort of the pitch to that is defining. But what it is, it's power density. And those chips are no longer cooled with forced air. In fact, a Blackwell chip melts if you go through forced Air. The only way you can power NVIDIA's next-generation chip is through liquid cooling, which is what we're doing at Switch. And I think the other reason that Jensen and Mike at CoreWeave have chosen Switch is, to your point, they're the only Tier 5 operator out there. So it's not five-nines, it's 100% uptime. A Switch data center has never failed and people will pay for that. The customer will pay more rent to be there. And our liquid cooling system, our EVO system, which is our patented cooling system, is really revolutionary. We don't lose one drop of water, which I'm actually pretty excited about. I'm from Colorado. So we get a little excited when we talk about water because we're losing water every day. Once we're done talking about the degradation to the consumer in AI, people are going to move on to water next. That will be the next topic that we'll be talking about a year from now. But for right now, I've got to just solve the problem that's in front of me, which is how to convince public utility commissions that AI is not the devil, and it's not what's going to be driving up consumer prices, which right now it is. If you look at the data, the amount of power that's being consumed in the data centers is impacting baseload, which is impacting consumers. So I think some of that's going to have to come back to the state level, which unfortunately, those PUCs are going to have to be rethinking about rates. There's going to be so many rate cases in the next 12 months. You're going to see rate cases in every state, and I guarantee you're going to see very few rate cases where the rates are going down. My suspicion is rates will go up, and it will be segregated between AI rates and consumer rates. We will begin to see a parsing of how government charges the consumer versus how they charge data centers. Now for us, as an owner of data centers, that's a pass-through. We don't pay for the power. Now if we have our own microgrid and we're producing our own power and we're bringing it into the data center, then we do have an offtake agreement with our customer. So I do -- looking around corners, I worry a little bit about that.

And we get the sense that some of the utilities are driving these sort of data center-specific tariffs, right? We see in Ohio. I mean is that what you're talking about in terms of both the public commissions and at least recognizing that there is an inflationary effect to other retail and industrial.

Look, if I were running NextEra -- my friend, John runs NextEra, I really like him. He's a great CEO. If I was running a big public utility company, I would probably be doing that because I'd want to get out in front of that before I'm stuck in a rate case. Rate case is the 2 dirty words for them. So I think that the industry can stomach it. to a certain degree. I think at some point, there will be pushback and then the customers will seek their own solutions. But the reality is the solution set, if you're a hyperscaler and you're trying to build a 1 gigawatt data center, you don't have a lot of solutions. Where are you going to get the turbine for your LNG solution or CCG solution? Turbines right now are backed up 2 years. We have relationship with all the producers of turbines, and we have -- our forward log of turbines is we're set until the end of 2026. And then we even have a problem in terms of sourcing for our microgrids. But look at this thing is complicated. And like I said, the one thing we have learned in the last 3 years is, one, -- our friend is the public utility companies. We work with them. We're interconnected to them. And most of our power is bought from them across our global portfolio. And I think what we're trying to do is create solution sets that augment that and ultimately are a net contributor. I think if we stay in that swim lane and we keep going, I think we'll be pretty -- we should be successful.

And do you see a situation like we've seen in Texas with Senate Bill 6 is sort of forcing the hand to require microgrids that are interconnected or on-site power generation is back up just become when the grid does become constrained?

I think, look, the -- I think Abbott is smart. I think he knows his base, his base is the gas industry. There's an abundance of gas in Texas. We've proven that microgrids can work in Texas. The first Stargate at Crusoe is half grid, half ERCOT, half microgrid. We're working on a solution in Lancaster, Texas. It's very similar. It will probably be about 75% microgrid and about 25% ERCOT. And so Texas is pretty unique because it's ERCOT. It's one of those sort of for us, it's kind of a fish out of water. And so we've had to spend a lot of time with ERCOT trying to understand what the problem is. And by the way, ERCOT has its own problems, which is they're on a 10-year project to completely rebuild Texas. So anything, I think the governor is being smart because he knows you got to have supplemental power because the baseload on ERCOT right now is already fragile as we've seen in the last 3 years in the wintertime. So Texas is pretty unique. And most of our grid independent infrastructure is being built in Texas. 3 out of our first 5 microgrids are in Texas. And by the way, he's made it easy. I mean, he hasn't made it hard. He's been very clear about what Texas wants. And ERCOT has been also very clear, too. If you want interconnection to us, here's what we expect from you. I kind of like building data centers in Texas because the rules of the road are quite clear. You may not like the cost, you may not like the final outcome, but at least they make it very clear where you can go and where you can't go.

In the last 5 minutes here. Maybe you made some comments earlier about the digital infrastructure cycle. We often get a lot of questions on the ROI of AI and the sustainability hyperscaler CapEx. You suggested we're sort of inning 3 of a 7-inning game maybe in your viewpoint. But I get the question like how is this different than the telecom boom and bust of like the early -- earlier this decade.

Well, what's interesting is if you go back to the advent of the PC, the mobile phone, Internet, sort of mobile data and cloud computing, those are kind of sort of 5 tectonic shifts in technology. And there's a slope around adaptation and how long it took to adapt. So to get to widespread adaptation in the PC took 12 years. It took essentially less than a year to get almost 92% of Americans to touch AI. So adaptation and AI, the slope on that was the fastest we've seen in any sort of technology introduction. What's also interesting at that same time is the cost to produce AI or to reduce a token, which is a measure of AI, is radically falling, and it's fallen 40x since the inception of AI. So what's really interesting is cost per token is down, adaptation is like this, where you look at the PC, which was the curves were like this, adaptation came down like this and cost was like this. So sorry, adaptation like this and costs like this. And in between there were these other introductions of different technology. So what's interesting to me is the positive revenue impacts or the positive ROI in AI took 3 years. Public cloud took 5 to 6 years. So if you go back to the earnings of Microsoft, it really wasn't until 2016 that Azure was producing positive EBITDA and then it just ramped. And so what's happening right now, like with, for example, with Chat and you look at the earnings from Amazon, you look at earnings from Microsoft, these guys are all now producing positive net income from AI. We're 3 years in. It's early. And the use cases for AI in public cloud, there were 2 use cases. So in public cloud, you had public cloud, which is what we use for Internet and document storage. And then you have consumer, which are all the applications that sit on our phone. All of those run on the public cloud. When we go to AI, we actually have 5 different use cases for AI. So there's 3 new use cases in AI that didn't exist in public cloud. And so you've got -- obviously, you've got enterprise, you've got industrial applications. You've got consumer, which is the same as public cloud. You have enterprise, which is the same as public cloud. But there's 2 other new verticals that come out of that, which is data sovereignty, which is huge. And then the one that nobody talks about is machine-to-machine learning. So today, there's about 30 billion connected devices to the Internet of Things, to IoT. In the next 7 years, that goes to 60 billion devices. Remember, machine-to-machine connectivity means there's no human in between that. So you got one language, you got one model, talking to another model. One AI agent over here, one AI agent over here, going back and forth. And so it could be a public safety network talking to an autonomous vehicle. It could be a wireless electricity meter talking to your credit card company. So imagine a world where you've got 60 billion wireless devices, accelerating the conversation between 2 machines. That will be 80% of AI consumption will not involve a human being. That is staggering.

And is that -- because that's where part of my debate seems to be is that -- I mean my view is that we're now entering the agentic AI era. And that's time test scaling on steroids.

Correct.

And then you have this potential for Tesla humanoids, which is physical AI, which again would be time test scaling on steroids, and that's the surge we're seeing in maybe AI inference demand. Is that a fair way to think about it?

It is a fair way to think about it, but I think the Tesla robot for me is like an anomaly. It's like an outlier to a certain degree because there's robotics inside of that, that are less independent sitting on a factory floor that are a lot more productive than an Elon robot. Robotics is a part of that machine-to-machine learning, right? But the amount of data that will be consumed to deal with machine-to-machine learning there, we haven't even started the baseball game yet. We're like top of the first. The pitchers are out of the mount warming up. So -- and there's so many revenue models that pop out of that. And there's so many implications to fiber and to cell towers and to mobile infrastructure. The ecosystem is just getting warmed up. And again, to your point, we're in the third inning of a baseball game. So there's another 6 innings left of a lot of infrastructure spend coming.

All right. Well, obviously, I could talk to you for another half hour just to cover the topic, but we're out of time. I appreciate Marc coming, and thank you, everybody, for joining us.

Thank you for having me. Appreciate it.