Data Center Leaders on Building AI’s Infrastructure

Well, that was such a great set up by my colleague and Bloomberg Intelligence.

We don't need to speak anymore. No, it was fantastic.

Actually, I do want to say before we kick off our panel, the Segway was perfect from the prior speaker because he was just saying that if we're polite to these alarm models, it consumes more energy, becomes a lot more expensive.

So we've got a poll that should be coming up in a second.

Do you say thank you to your model? Yes.

No, I haven't used an AI model yet. So scan the code.

And while you do that, I'm going to get started with the panel.

It's really good to be talking to you. Naveen, I'm going to start with you, maybe just to set the stage for us, because, of course, there's so much hype around artificial intelligence and the two things sort of go hand in hand.

You need the data centers to train a lot of these models.

How do you see AI impacting data center infrastructure in the coming years?

Yeah. No good question.

But I think if you take a step back, you have to look at the whole demand for cloud storage to start with and then how A.I.

is having an impact on further increasing it.

Just while we were waiting backstage, I was just looking at the history of cloud, and since 2004, the word cloud storage did not exist.

Smartphone maximum memory was five gigabytes now is 100 gigabytes.

And just the amount of data storage created by the smartphones that we all have now is 246 billion gigabytes. Add to that Netflix, Amazon, Spotify teams, WhatsApp, Zoom, and you are and the fintech demands and you are at 3 trillion gigabytes. Add to that artificial intelligence.

trillion gigabytes. Add to that artificial intelligence.

Now the language learning models that you mentioned, and that's supposed to be 50% of cloud storage demand in the next few years.

So from our perspective, A.I. is, yes, enhancing the demand side.

But as we've been investing in data centers for the last ten years.

What we are focused is not so much on the demand side.

We are focused more on the capacity side, the supply side that do we have the capability and do we have the right partners to deliver these data centers that are required both for hyperscale, cloud storage and also for air?

And there are various challenges around that.

I'm going to come back to that point. I'd like to move on to you.

You have recently announced an expansion of your global data centers division online across pretty much everywhere, from what I can tell, North America, Europe, Asia. So it sounds like you see very strong

Europe, Asia. So it sounds like you see very strong demand as well, but not specific to any region everywhere.

Yeah. So.

We look at everything from a global perspective and we believe that there's going to be growth all across the globe right now.

We're seeing strong demand signals for the last couple of years in the U.S.,

Europe, Europe's been constrained because of power availability.

Some of those same issues exist within Asia.

And so we have a team of people that have literally scoured the earth to find ten plus properties, a little over two gigawatts of of of building load capable in those properties to build out. Because what we're seeing as a company is our pipeline has swelled over 25% year over

year. We are seeing tremendous offtake from

year. We are seeing tremendous offtake from both enterprise and cloud as well as I. And if you look at the stats on air and the projected growth rates. Jensen Huang talks about his belief that cloud that excuse me will contribute to over a trillion.

It'll be necessary to contribute over $1,000,000,000,000 worth of datacenters over the next four years just to meet the air demand.

And to put that in perspective. It's taken 15 years in the cloud environment to hit that $1 trillion datacenter number.

So it's about four x what you're looking at for AI.

So we're extremely bullish on the global platform and we're seeing different areas of the globe that we're investing in for different reasons.

In the U.S., India is mostly hyperscale and that's we're seeing most of our jobs or most of our offtake within Europe. There's a fair amount of retail and enterprise. And when they're in Asia, we're seeing a

enterprise. And when they're in Asia, we're seeing a tremendous amount of enterprise and we're starting to see some very large takedowns for AI and cloud. And so the properties we've built, we've purchased over the last year have been built on Frankfort, Hillsboro, Oregon.

Several in Tokyo. We've we've built we've purchased all across it. We're starting production on those and,

across it. We're starting production on those and, and frankly, on about half of those properties before we even put a shovel in the ground. We've already leased the properties out either partially or completely. So we're extremely bullish on the outlook of the industry. If you just go back a year ago, two years ago, the cargo was 39%. The project to Kagan now is 23%.

So, you know, there's not a lot of industries growing at that rate and we're very bullish. Yeah, the numbers are huge, though.

$1 trillion is a chart up there, you know, $230 billion.

Some of the big spenders last year that those five that are listed.

Mark, let me ask you, there is a lot of money going into this space.

Some there's skeptics out there, they're nervous about there potentially being a capital glut. And for all of this to come crashing

capital glut. And for all of this to come crashing down, are we setting up for another bubble?

Is this going to be another dot com bubble in the space it look to contextualize it, I think that we're in a very different environment.

From a demand perspective, I think you saw a lot of speculation in the dot com era where people laid fiber cabling with no customers on the other end of those cables. I think what's interesting, having been

cables. I think what's interesting, having been in the sector for 30 years and having watched some of these cycles, what is different about this cycle is we don't have a demand problem.

We have plenty of customers that require compute.

We certainly don't have a lack of land and we certainly don't have a lack of capital. What we do have is a lack of power and

capital. What we do have is a lack of power and we can certainly drill down into that. But on the capital side, it's actually interesting. I think most of the allocators globally

interesting. I think most of the allocators globally and I bet you could you could vouch for this.

I think investors have put a lot of capital to work in data centers, particularly in infrastructure funds. And a lot of these platforms that they've developed have reached a level of ten, 15, $20 Billion.

And ultimately MLPs are saying the same thing, which is, okay, this has been great. We've enjoyed writing this air wave, but

great. We've enjoyed writing this air wave, but at some point we need capital back. MM How do we create DPA and how do you recycle capital? Investors obviously want to invest in

recycle capital? Investors obviously want to invest in great ideas in AI. They want to be exposed to investment grade leases. But at the end of the day, we do have an

grade leases. But at the end of the day, we do have an obligation as a GP to return capital and to create returns.

So one of the things that I hear most recently in the last sort of six months of fundraising is what are you doing for us in terms of returning capital?

By the way, it was the same thing that happened in Cell Towers in the late nineties. It happened in Fiber in the early 2000.

nineties. It happened in Fiber in the early 2000.

It happened Doug with cloud computing in the last 10 to 15 years.

People want that capital back. And I think ultimately it's about where you sit in the capital structure. And if you think about the progression of how digital infrastructure has been built, it started almost as venture capital. It migrated to private equity and then

capital. It migrated to private equity and then ultimately it and now sits with infrastructure funds.

And now there's the next leg that's going to play out, which is 90 to 120 billion of stabilized data centers. That ultimately has to be in real estate vehicles, whether it's a publicly traded street or if it's a can.

Tuition fund or if it's really insurance capital that's coming into the sector for the first time, there is going to be a recycling of capital that is going to happen. And we're at that moment right now.

happen. And we're at that moment right now.

Would you say there is a funding gap, though, given given the needs and given your projections? I don't think there's a funding gap.

your projections? I don't think there's a funding gap.

I mean, we put we put out about 26 billion of CapEx last year, just a digital bridge loan. We have another 19 billion that's that's funded this year. And I think the way that we're funding those assets today are perhaps a little bit different from how we funded it three years ago. I think certainly the financing market has matured. We're talking about equity.

has matured. We're talking about equity.

We haven't even begun to talk about debt.

I think the way these assets are now being deployed in the securitisation market and the depth of that securitisation market, particularly through investment grade, is about as deep as you've ever seen it.

Just in the last 30 days alone, we had six data centre securitizations that got launched and closed across many different platforms, across many different GPUs. If you aggregate all of that securitized

different GPUs. If you aggregate all of that securitized capital, which is predominantly insurance companies or opportunistic debt investors, there was close to almost $12 billion of securitized paper that's traded hands in digital infrastructure in the last 30 days.

So there's a coming of age of this asset class that I think is really interesting. I've gotten to watch it because I

interesting. I've gotten to watch it because I started in the early nineties when I was backed by family offices in venture capital and now today we're an infrastructure fund.

But I think this progression of capital is really important and I think the movement of capital and the maturation of the debt markets for digital infrastructure is something that's really important.

So we don't have a funding gap. As long as capital keeps moving, capital has to keep moving in this market. Yeah, given one of the big events earlier this year was deep seek and for those in the space, it wasn't a massive surprise. But for the rest of us who are sort of

surprise. But for the rest of us who are sort of tourist tourists and the worlds of all of them did come as a surprise that China were becoming competitive and foundational alarms. How does that change the picture for China and for the broader Asia-Pacific region to have China really powering ahead with their own foundational models? Right.

models? Right.

I think historically we've always seen our China is actually very good at taking what's new technology and then adopting it, adapting it to create a actually better user experience quite often.

Or in this particular case, I think there's a lot of excitement.

In particular, what is the West APAC or China about insurance, right?

So when you go back to the supply demand question, you also also have to ask yourself because data center you have the old architecture I've done for us is co-location. Secondly is cloud.

co-location. Secondly is cloud.

And then thirdly, the new the new big used for A.I.

is training, which actually the Blackwater chips are what, 132 kilowatts demand going to 600 kilowatts a routine. But cloud architecture is only ten kilowatts, less than ten kilowatts. So we look at that and then you're going to ask you have to ask yourself, where's a supply demand imbalance?

Is it too much supply potentially at the hyperscale training site?

Right in China, we are the word is east side is for data, West side, it's for compute, because east saw is where is cooler, much cheaper power rate.

For example, China doesn't have a power constraint issue.

2% of their power today is consumed by data center, going to 4% by 2030, going to 2030. By 2035 is expected to be 10% of the

to 2030. By 2035 is expected to be 10% of the total power, and it has a much more modern power grid.

When you take the heart in the equation, then you realize in China you're seeing a little bit of how the world actually could play out because China is one big country. You have there are training being done

country. You have there are training being done on the east. You have compute being done in the West where it's closer to population. So latency matters, inference, it's actually where is driving the demand right now.

So the supply demand imbalance could very well go back to which part is the supply demand imbalance. Very well.

We could actually be completely under supply on the cloud architecture side by went across application address inference.

Well where do you see growth? What where do you see the fastest growth in the Asia-Pacific region right now? Certainly China is playing catch up with China this year. They're talking about 50 billion U.S.

dollars plus of Catholic spending. So China actually went to a very high CapEx spending during COVID because of stay at home and e-commerce adoption and so on so forth. And then went through a lot because of the chip war with U.S. so lack of ability to get chips and also the big players couldn't quite figure out is it going to be a winner takes all

on our AM So not all the biggest company were focused on creating or chasing because a business model is less clear on the web.

All right. So you have a bit of a lull and you've actually seem to have all of a sudden create urgency for everyone to play catch up. So the 50 billion USD in CapEx in 2025,

catch up. So the 50 billion USD in CapEx in 2025, it's a 50 not. Percent year on year increase from last year. And now three major players, Alibaba,

year. And now three major players, Alibaba, Tencent and Bytedance alone, are each spending over 10 billion USD.

Actually, 100 billion, remember? So 1,314 billion USD for company and then another three that are Baidu May Taiwan and show actually over 10 billion renminbi in spending alone in 2025. Well the numbers always has a big catch up. Okay.

up. Okay.

Let's bring up the results of the poll that I asked in the beginning.

If anyone is a polite, polite, the user of AI, most most of you were polite.

Okay. So about two thirds of the room say thank you to the model for this question is relevant because AI's is notorious for consuming a lot of power. And if you don't want to come back to you, you touched on this at the beginning and you're saying, you know, one of the considerations is the sheer power consumption that goes into powering these data centers. Is that the only consideration?

And that's certainly one of the main ones that Mark mentioned. The other one is just in terms of the supply chain dynamics with regards to fitting out a data has changed dramatically. We are looking at a data center right

dramatically. We are looking at a data center right now which is trying to do an LNG powered behind the meter supply for an hyperscale cloud center and they went to buy a gas turbine.

The only three companies in the world that make the gas turbine even for data centers, Siemens, GE in over and the waiting list is four years.

So even if they do get the power supply and the land bank suggested they have to wait for four years before they can kick it off in another transaction.

We are involved in the power generators coming from Caterpillar.

There's a nine month waiting list for that.

Right. There is also another constraint, which is human capital required to deliver these data centers the right specifications. You know, there are a lot of new players

specifications. You know, there are a lot of new players that have entered the space with traditionally real estate players and are trying to do data centers now. I mean, we are lucky to be partners with people like just the bridge and golf capital who have experience in this.

But finding the right capital, a human capital to deliver it is a challenge.

Yeah. I'd like to ask you about the heat that is generated by powering these data centers as well, because from what I understand, you also need to have effective cooling systems in place.

Is that acting as a constraint over how quickly you can build up capacity?

It's a huge constraint because if you think about the conversations in data centers, they often lead to power, which is our number one constraint as an industry to be able to find power. Land is extraordinarily difficult.

And when you look at air data science versus standard data centers, from my perspective, there's really three main areas where they differ.

First is on scale because an air data center generally is much larger.

Put that in perspective. The data centers that we've built for the last ten years have been around 36 megawatt buildings.

The new ones we're building are 100 megawatt, which sit on gigawatt campuses. To put that in perspective, a 100

campuses. To put that in perspective, a 100 megawatt building powers, a hundred thousand homes.

So you're using an extraordinary amount of power.

So the first issue scale is power scale and getting that power available.

The second issue is being able to have the capital, the scale, which Mark talked about earlier. And then the third issue is around innovation. And that innovation is all around liquid

innovation. And that innovation is all around liquid to the chip or what's called DLC, direct liquid to the chip cooling, which basically instead of using the technology we've used for 25 years for data centers, which is air cooled to remove the heat because power equates to heat and you have to remove that heat. We used direct liquid to the chip, which

is glycol device attached to the CPU, and it removes that heat.

That's the technology that allows air data centers.

If this were a data center hall and a very standard data center, you'd have ten racks of air sitting in the middle of it and a blank space.

In today's modern data centers that we're building, the entire floor would be filled with racks. And so that is really the gating issue that we've looked at for the last three years.

The claim to flame claim to fame for GDC is that we're the third largest global data center provider, not including the role of excuse me, market, but standalone data centers were the third largest.

We've got about $3 billion excuse me, $3 billion in revenue, 37 employees, 150 countries in 150 days as in 21 countries.

And our claim to fame is that we have 250 megawatts of production liquid, the chip cooling running in our data centers, which outside of the hyperscalers is the greatest amount. And I can tell you it's been an incredibly steep learning curve because for 25 years we did it, air cooled and now all of a sudden we use this new technology.

It's not proven, it's not standardized. And so that to me is probably the biggest challenge. Our industry is creating a standard so

biggest challenge. Our industry is creating a standard so that we can replicate these data centers in efficient, standardized manner and drive the cost out. Yeah, And just that can I just add something to that, which refers to your question earlier about whether there is enough capital available for data centers.

And I think for the real estate people who might be in the in the audience, one of the biggest challenge that real estate investors face when looking at data centers is what happens at the end of the lease.

So these data centers, when we invest in it, you know, they'll be rented by Google or WAC for 15 years. And like Doug mentioned, the technology is continuously changing, the efficiency is changing.

So so if I. Spent $1 billion on building an 85 megawatt data center in what will happen to it at the end of its life.

If I buy a hotel in Berkeley, in Mayfair, as realty investor, I know that 15 years later would be worth more than what I bought it for.

But with data centers, it's a slightly different calculation, slightly different returns, so that that's where the conflict comes in in terms of your core data thing. And it's interesting you say this because we had this debate in the early 2000s around cell towers.

People said, gee, you know, we're really concerned about the residual value of a cell tower. And what ended up happening was we found

cell tower. And what ended up happening was we found out that the location was really important.

And the interconnectivity of a mobile network is a part of an ecosystem that's daisy chained to many other cell sites. And what we're finding with data centers, as you think about the architecture of data gravity and where data goes, these data centers are highly interconnected, not only through their fiber optic networks, but the amount of investment that happens at the core of the network that sits in these very difficult to pick up $50 million of

infrastructure and move it across the street.

And so what we found is, again, we've we've been in the sector for over ten years. Doug, you've been in it longer than I

years. Doug, you've been in it longer than I have, but we've already gone through our first round of renewals of these leases.

And what we discovered is churn is less than 100 basis points across our global data center portfolio. We have 86 basis points of churn.

And the other key thing that we found out is the stickiness of these locations. Rental rates have moved up on average

locations. Rental rates have moved up on average about 22% and the renewal. Now, most of what we modeled as we thought rents would go down, we modeled 3% churn and we modeled 15% discount on rents. And what we found out is that rents were

rents. And what we found out is that rents were higher and the stickiness of these locations was really important.

And that location is critical. Right.

And I think a lot of the new investors and data centers might not realize the importance of Ashburn, Virginia, where 70% of Internet traffic passes by.

So absolutely right. If you want something there 15 years later, it will probably be more in demand.

But there are a lot of data centers popping up in locations related to air language learning models, where one just has to be cognizant of the fact that the location might not be as critical in 15 years as Virginia might be. Yeah, as the Mark aptly pointed out, the guy with the most gray hair up here, 25 years into this, I can tell you that we have recycled our capital through our older assets.

And to Mark's point, never had any issues at all.

It's been very smooth. We've we've lost, you know, less than 100 basis points on every asset that we've ever recycled.

It's been very productive and it's about how you maintain the infrastructure over time. So do you maintain the value of that

time. So do you maintain the value of that infrastructure? You know, and David, it's very

infrastructure? You know, and David, it's very interesting, right? As a risk investor, I started investing

interesting, right? As a risk investor, I started investing and at that time it wasn't even called data center.

When you were doing a cell tower, we were doing converting these office buildings in downtown L.A. and downtown San Francisco into telecom exchange on Wilshire when Wilshire, Right.

Those were the telecom exchanges, the predecessor of data center, in fact.

So we're always thought, what are the alternative uses of this asset when the lease runs out or this data center, get telecom exchanges, our data center get obsolete by, but they don't get obsolete because it's almost like 3G, 4G, 5G. Every time the pipe expand, there's more content to fill it right in in telecom terms. So in this data center, even some of these so-called obsolete legacy data

center there seem to be more and more actually uses because of the lack of latency. So there are more and more applications

latency. So there are more and more applications that are coming up that actually need the lack of latency and that interconnection at a data center. These talking about those legacy data centers are where those fiber optic cables come together and interconnection. And so as we watched the building of

interconnection. And so as we watched the building of cloud, public cloud the last 13 years, as Doug said, what was interesting is as the cloud developed and as the cloud moved and proliferated to the edge, these locations became more important because of that connectivity.

The same thing is going to happen in inference.

Inference will follow the same ball flight as cloud and cloud computing.

Why? 90% of it will sit on your mobile device. Inference by nature is going to be near

device. Inference by nature is going to be near the consumer and is going to be near the device, and that makes these locations as you start to build these language models and they proliferate into generative AI. Real estate becomes more important and

generative AI. Real estate becomes more important and real estate that's close to the connectivity, the customer, the enterprise and Iot devices. I just want to ask a question about the power generation. The assumption is that I guess most

power generation. The assumption is that I guess most people just assume that it will be generated from renewables somehow.

Is it going to be sufficient? Can renewables generate enough power based on your projections of how much demand is needed?

How much supply? I'll take a swing at this because we've been spending the last two years Where? On this.

I've been saying this. I think you and I talked down in Brazil about this. We don't have a generation problem.

about this. We don't have a generation problem.

Obviously, China has done a very good job with their generation and their transmission. The challenge in Europe, as we saw on

transmission. The challenge in Europe, as we saw on display in Portugal and Spain a couple of weeks ago, is that these grids are aging. These transmission grids were built

aging. These transmission grids were built after World War Two. They never contemplated building transmission infrastructure for air compute.

I can guarantee you that was not in the design 60 years ago.

And today, on a global basis, datacentres are consuming about 60 gigawatts of power. So imagine New York City consumes about five and a half gigawatts per day. So think about that.

That's almost 11 acts. New York City's happening every day and compute going to 300 gigawatts. So we know that there's 240 gigawatts of power that has to be created to make air work.

That's a big task. And we put that up against a current market construct where essentially the data center industry is turning on about 5 to 6 gigawatts per year. And over the next five years, we're going to have to light up 22 gigawatts per year.

So you have this massive trade and supply imbalance that's only growing, which is the demand is going from six gigawatts to seven to 9 to 22 per year.

And we can only turn on about six gigawatts per year.

So to your point, the traditional path of how we build power is completely insufficient. We have to throw everything at the

insufficient. We have to throw everything at the problem. It's solar, it's wind, it's hydro.

problem. It's solar, it's wind, it's hydro.

It's as the speaker before said, gas. Building micro-grids.

Building grid, independent infrastructure is critical to the future of AI. And there's not a one single bullet

of AI. And there's not a one single bullet solution. We've talked about nuclear power that 6

solution. We've talked about nuclear power that 6 to 8 years away. We have a real problem, which is how do we get the generation capabilities adjacent or near or into the data center. And this is the challenge that our

center. And this is the challenge that our industry has to deal with over the next 2 to 3 years.

Do you want to weigh in on that as well, just on the sources of power?

Sure. So what's interesting is we had a little debate in the green room about what the sources of power be.

I'm a big believer in smart, and I also am a realist.

So I understand that we're going to have to use gas and coal and other alternative forms until we get there. But as Smart have become so proven, they've been on nuclear ships and submarines for years.

They're very, very safe. And I think that we'd be naive to believe that we're going to be able to build the grids out without creating micro-grids that feed data centers. So I am a big believer and we are working down the path for integrating Mars into our portfolio over time.

I think they'll be controlled at the micro-grid level by the power providers, probably not the data and the providers. But we're getting ready because if you look at the other sources, they're diurnal.

You don't have sun at night. The wind doesn't blow 24 hours a day.

You look at what happened in Spain and Portugal and the issues that occurred there just recently. We've got to be very careful how we approach this issue, because today data centers are responsible for 2% of the global power usage. It'll be ten, 12% over time.

We don't know what those percentage would be, but it's going to increase dramatically. And so, again, to me, this is the

dramatically. And so, again, to me, this is the biggest issue facing our industry and we're spending a lot of time on this issue. Yeah.

issue. Yeah.

Naveed, I'm just going to end with you. I'm sure you get approached a lot as an allocator about these types of opportunities.

How do you distinguish? When you're looking at investments, what who the winners are versus the other pitches that come your way.

I think we were lucky that we started investing in data centers ten years ago, so we developed quite a few relationships early on.

But when we look at a new transaction today, the key factor is location and location and data center is all about latency and inference. So there are certain locations where

inference. So there are certain locations where there is Ashp in Virginia and London, Frankfurt, Amsterdam, Texas, where it's just having the Power Land bank available to build a data center makes the transaction attractive enough to underwrite.

Secondly, what the other change that has happened is we always did data centers that with good credit quality tenants like Amazon, Google, Microsoft, but now they have realized how powerful they are that when they assign a 15 year lease to our data center, they are creating value.

So it's quite important how the lease is structured, how much risk are they passing to us and how how much returns are we generating based on the risk we are taking? So it's a combination of factors, but

are taking? So it's a combination of factors, but it's essentially about credit quality of the tenants needs and location.

Okay. Thank you for the crisp answer.

I'm sure many people will be writing down notes in this room.

Panelists, thank you so much. That was really insightful and thank you for your participation in the call.