The state of play of data center development

Summary

Podcast Summary: "The State of Play of Data Center Development"

Podcast Information:

• Title: Catalyst with Shayle Kann
• Host/Author: Latitude Media
• Episode: The State of Play of Data Center Development
• Release Date: June 12, 2025

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Introduction

In this episode of Catalyst, hosted by Shea Khan, Shayle Kann delves into the burgeoning world of data center development with a special guest, Chris Sharp, the Chief Technology Officer (CTO) of Digital Realty. With over 20 years of experience in developing, owning, and operating co-located data centers globally, Chris provides valuable insights into the current landscape and future trends of data center infrastructure, especially in the context of the increasing demand driven by artificial intelligence (AI) and other high-performance computing applications.

Geographic Dispersion of Data Centers

Shea Khan initiates the discussion by addressing the geographic dispersion of data centers in the United States. Historically concentrated in regions like Northern Virginia, the data center landscape has seen a significant spread due to the surge in AI and hyperscale data center planning.

Key Points:

• Workload Distribution: Chris Sharp emphasizes the distinction between training and inference workloads. While training large AI models initially drove broader regional deployment, current trends show that inference workloads are prompting more region-specific growth.

  “AI is an 'and' not an 'or' to cloud.” (05:12)

• Availability Zones: The concept of Availability Zones (AZs) remains pivotal, particularly in major markets like Northern Virginia, which continues to be a critical hub for AI growth due to its robust infrastructure and proximity to major city centers.

Power Constraints and Availability Zones

A significant portion of the conversation revolves around power availability and its impact on data center development within key regions.

Key Points:

• Power Saturation: Chris highlights that regions like Northern Virginia are approaching power saturation, with vacancy rates as low as 0.5%, indicating a near-maximum capacity for new data centers.

  “Some of the most recent stats, it has 0.5% vacancy rate, which is phenomenal.” (12:23)

• Utility Coordination: Effective collaboration with utility operators is crucial. Digital Realty invests in master planning and works closely with utilities to ensure they can meet the substantial power requirements of new data centers.

• Infrastructure Constraints: Long lead times for transformers and switchgear (over 50 weeks) present significant challenges, extending the timeline for data center development beyond the typical two-year window.

  “Transformer lead times are 50 plus weeks right now.” (15:51)

Timeline of Data Center Development

Shea Khan probes into the timelines involved in developing new data centers, contrasting current durations with historical benchmarks.

Key Points:

• Standard Timeline: Typically, the process from concept to operational data center spans approximately two years.

  “It takes about two years from concept to delivery to build out what I would say a versatile data center.” (14:39)

• Current Challenges: Due to power and equipment bottlenecks, timelines are elongating, sometimes surpassing four years, compared to the usual two.

  “It's elongating even beyond the 24 months that it would take us to pull that together.” (14:39)

Scale of Data Centers and Workload Requirements

The discussion shifts to the scale of data centers and how different workloads influence data center design and capacity.

Key Points:

• Diverse Needs: Not all data centers require massive scales. While some AI workloads necessitate gigawatt-scale centers, others operate efficiently with smaller capacities (e.g., 5-10 megawatts).

  “Not everybody needs 100 megawatt data centers.” (17:58)

• Workload Specificity: High-density GPU infrastructures are in demand for training, whereas inference workloads benefit from smaller, more flexible deployments embedded within existing data center portfolios.

  “Inferences can come in like 5-ish megawatt blocks.” (19:17)

Bridge Power and Utility Relations

A critical challenge in data center development is the interconnection to the power grid. Chris Sharp discusses strategies like bridge power to mitigate delays in grid interconnections.

Key Points:

• Bridge Power Usage: Although rare, some data centers employ bridge power solutions—using generators or on-site power sources temporarily until grid connections are established.

  “Bridge power is one tool among many that we're always looking at.” (26:32)

• Utility Collaboration: More common than bridge power is the proactive collaboration with utilities to negotiate interruptible tariffs or other agreements that facilitate faster power integration.

  “It's a negotiated sort of deal with the utility...” (29:14)

• Avoiding Grid Overreach: Digital Realty ensures that they are not contributing to overbuilding by aligning closely with utility operators and committing to long-term utilization of the infrastructure.

  “We form long term relationships with the utility operator...” (24:22)

Future of Data Center Infrastructure

Towards the end of the episode, Shea Khan and Chris Sharp explore the future advancements and innovations in data center technology.

Key Points:

• Efficiency Improvements: Innovations in liquid cooling are enhancing efficiency, as liquid can be 800 times denser than air, allowing for more compact and efficient cooling solutions.

  “Liquid's 800 times denser than air, so you can get more efficient.” (33:55)

• Advanced Hardware Designs: Collaboration with technology partners like NVIDIA is driving the development of next-generation AI hardware, improving performance and energy efficiency.

  “The ability of the token production against the watts associated with that is going to be phenomenal.” (33:55)

• Sustainable Practices: Emphasizing stewardship of power resources, Chris underscores the importance of sustainable practices in powering data centers to support both environmental goals and operational efficiency.

  “We want to be good stewards of the power we take from the grid.” (33:55)

Conclusion

The episode concludes with an optimistic outlook on the role of data centers in advancing AI and supporting climate-tech initiatives. Chris Sharp expresses enthusiasm for projects like Gephion in Copenhagen, which leverage large-scale data centers to drive significant advancements in fields such as pharmaceuticals.

“The amount of pharmaceutical work associated with that one deployment is just what it's going to be able to do for humanity is very exciting to me.” (35:18)

Shea Khan thanks Chris Sharp for his insights, wrapping up a comprehensive discussion on the current state and future trajectory of data center development.

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Notable Quotes:

• “AI is an 'and' not an 'or' to cloud.” – Chris Sharp (05:12)
• “Some of the most recent stats, it has 0.5% vacancy rate, which is phenomenal.” – Chris Sharp (12:23)
• “Transformer lead times are 50 plus weeks right now.” – Chris Sharp (15:51)
• “It takes about two years from concept to delivery to build out what I would say a versatile data center.” – Chris Sharp (14:39)
• “Not everybody needs 100 megawatt data centers.” – Chris Sharp (17:58)
• “Inferences can come in like 5-ish megawatt blocks.” – Chris Sharp (19:17)
• “Bridge power is one tool among many that we're always looking at.” – Chris Sharp (26:32)
• “We form long term relationships with the utility operator...” – Chris Sharp (24:22)
• “Liquid's 800 times denser than air, so you can get more efficient.” – Chris Sharp (33:55)
• “The amount of pharmaceutical work associated with that one deployment is just what it's going to be able to do for humanity is very exciting to me.” – Chris Sharp (35:18)

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Additional Information:

For more insights and episodes on climate tech and energy transitions, visit Latitude Media.

Transcript

[Latitude Media]

Latitude Media covering the new frontiers of the energy transition.

[Shea Khan]

I'm Shel Khan and this is Catalyst.

[Chris Sharp]

There is a lot of noise like one of the things we've been joking about is a lot of bragawatts. Oh I have a gigawatt. I have a gigawatt coming up.

[Shea Khan]

What's more insatiable power demand from data centers or my appetite to talk about it?

[Energy Hub]

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[Latitude Media]

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[Shea Khan]

I'm Shea Khan. I invest in early stage companies at Energy Impact Partners. Welcome. So we've of course spent a lot of time on this podcast talking about the Energy Data center nexus. Too much time? Who's to say? Objectively, it's the biggest thing happening right now. So buzz off haters. Anyway, one thing we haven't done amidst all that discussion is talking to somebody who's actually building data centers and has been for a long time for that matter, so that seems dumb. Fortunately, there's Chris Sharp. Chris is the CTO of Digital Realty, which has been around for 20 years, developing, owning and operating co located data centers all over the world. Chris is just very insightful about what's going on in the space and what's coming next, so I brought him on to Talk about what the hell's happening in data center world and. And a fair bit about what's happening at the data center power nexus. Here's Chris. Chris, welcome.

[Chris Sharp]

Thank you. Thanks for having me. Looking forward to it.

[Shea Khan]

Excited to talk about the state of data centers in the world, I think particularly in the United States. Let's restrain ourselves to something reasonable to talk about because there's a lot to talk about even here. You've been in data center world how long now? Like 15 years or more?

[Chris Sharp]

Yeah, 15 plus years. Longer than I care to admit. Believe me, when I started it wasn't cool and it wasn't at the forefront of every headlines.

[Shea Khan]

So lots change. Congrats on finally being cool.

[Chris Sharp]

Thank you. I wouldn't go that far, but okay.

[Shea Khan]

Yeah, sure. I want to start by talking about geography a little bit again. Maybe we'll focus primarily on the U.S. i mean, my perception of how this world has evolved is that historically the data center development activity and operation activity was very concentrated in a pretty small number of regions. Northern Virginia being the one everybody probably knows the most about. But then there were like some other tier 2 regions behind that. And that part of what has happened in this new wave of excitement and AI and hyperscale data centers getting planned everywhere is that there's been a big geographic dispersion. And so I guess one question for you is, is that true or is it still really regions that drive the majority of the growth?

[Chris Sharp]

Yeah. So I think you have to take a step back and look at the problem from two lenses. Right. The first lens is what are the workloads coming to market? And I think that lens is interesting. Right. Where the most simplistic terminology people hear about training and inference. I think training has forced a broader regional deployment, but that's for, you know, training these kind of frontier models. I would say that there's been a lot of growth in that, but we see that kind of leveling out where we really see the consumption of AI or inference that's driving that regional specific growth going forward. And I think that's where it's more embedded in a lot of the existing, if you will, follow the clouds with availability zones. That's where it's really starting to be, that investment growing and evolving quite quickly. And I think you brought it up with Northern Virginia, that Nova Market has been one of the critical availability zones, which is now represented as a critical kind of AI growth sector going forward as well as when you say availability.

[Shea Khan]

Zone, what's the promise? What's the availability promise? That's being made because this is what's driving its regions for this reason. Right. It is a promise of a certain level of availability.

[Chris Sharp]

Yeah. And so I think I always go one step further on that training inference. But it's monetization. Those Availability Zones were foundational and set up gravity, if you will, of what was driving that is SLAs and consumption to the enterprise. And I think that's where you see a lot of these capability and infrastructure being invested in these zones all around the globe. Where there's a major city center, it's usually closer to the CBD because there's proximate requirements with throughput and latency associated with it. But those Availability Zones are what has built that kind of, if you will, first wave of cloud infrastructure coming to market. And Availability Zones are slowly evolved. They're not everywhere, they're not in these tier 2, tier 3 markets. But we're seeing a lot of AI applications being embedded inside of those Availability zones. And the last piece I'd leave you with is that AI is an and and not an or to cloud. Right. I want people to get really comfortable with that. Is that a lot of these AI capabilities are being embedded in the cloud services you're consuming today, like Copilot, in some of the early capabilities coming to market. But that's how we really see a lot of these markets maturing over time.

[Shea Khan]

You mentioned latency there. So my layperson's understanding here of what you were describing is, okay, training a model you can kind of do anywhere, but the models are getting bigger and bigger and bigger. And so we need bigger and bigger data centers, but they're not as geographically constrained. And thus you can put a training focused data center maybe in the middle of nowhere, assuming you have all the other things that you need. You have power, you have labor, you have water, et cetera, et cetera. But then inference latency matters more and thus you want high, not only latency, but I guess availability as well, because these are time sensitive requests. And so that's why you want to be clustered in a region and so on. I've heard some people this will get to the energy data center nexus a little bit, speculating that you could bifurcate even the inference workloads into things that are latency sensitive and things that are not. And the ones that are not. Maybe you go take advantage of cheap, clean power, maybe even intermittent power out in the middle of nowhere. Right. Which definitely exists, but is not where the rest of the data centers are getting cited. Do you view that as a viable approach given the actual workloads.

[Chris Sharp]

It is, it is. And you know, it's great that we're going through the workloads, right? Like that workload is what depicts the infrastructure required to making it successful. And I think latency and throughput mean many different things. So I always try to double click on it a little bit. The amount of throughput required is what's challenging. Right. And latency, as long as it's consistent for a lot of the workloads we see, they can operate fine. But it's that throughput, the amount of data that's required for delivering kind of an inference type of solution is something that is again proximate not only to the consumer, but proximate to an ecosystem. So I'll hit on your second point where yeah, we see a lot of text to text scenarios where that workload can be deployed in two or three markets throughout North America and service the entire market. And so that's a very simplistic kind of scenario where we're in the early innings of AI and the complexities hasn't really come to fruition for the broader market. But as you see bimodal some of these more advanced reasoning models where a token isn't just generated against a prompt and then you consume it and it's done. A token may be generated inside of an AI world and go through multiple models to ensure that it's not hallucinating or that it has a mixture of experts or a depth expertise in that outcome of that token. So that's where these ecosystems of other AI infrastructure being proximate to itself, not only just the data, but I would be remiss not to hit that AI is only as smart as the data sets you feed it. And so those training you were able to feed that monolithic set of data in the middle of nowhere, but now we're more real time micro learning inference, it's starting to become more proximate to where the data oceans and that data gravity, which we've produced a report a long time ago, is happening around these availability zones and these epicenters of these tier one markets.

[Shea Khan]

So it sounds like what you're saying, if I'm interpreting it right, is that this notion of let's go where this just all else equals, just go where the stranded power is, it probably has some validity because there are some workloads text to text, for example, as you said, that can handle that where the throughput requirements are not so high and the latency requirements are not so high. But it also sounds like you're saying the direction of travel is in the opposite direction because actually the workloads are becoming more sophisticated, leveraging multiple models and the throughput requirements are getting higher. And so that set of opportunities just go wherever the cheap available power is probably dries up. Or at least the relative share of like how much you can build in that use case versus how much you could build if you actually have a cluster and it's all regional and it's near all the other models, like that's going to be a much bigger opportunity. Do I have that about right?

[Chris Sharp]

Yeah, no, you're spot on. And there's a confluence of events that are happening there. It's not just power. The expense to stand this infrastructure up is these chips are not cheap. Right. And so being able to utilize that over a longer horizon of workload and driving that utilization is also a form factor of if I have it installed for training. And training can be very. A spiky workload that I can embed inference in that capability to get higher utilization out of that investment. Because the ROIC is real on this. And so you see a lot driving that direction as well. But no, as we see these higher value kind of aggregators, if you will, of multiple models. Right. Multiple capabilities that's really starting to become more proximate to one another. And again, data is everything to a lot of these environments, be it hyperscalers or be it enterprise, which we focus on both having the ability to embed algorithms or this accelerated compute infrastructure in close proximity to their existing data oceans or constant data creation models is everything to our customers.

[Shea Khan]

Okay, so assuming that the majority of the growth will continue to occur in regions, maybe not all in today's tier one region, but that it's still going to be sort of a regionally driven market, I guess the question is how quickly do we tap out these regions from a power perspective in particular? Because unless you tell me otherwise, I think that tends to be the thing that maxes out first. Unless you tell me, maybe it's labor. But let's take. We talked about nova, right? Northern Virginia. How close to tapped out are we there? How much more can we possibly build in that region?

[Chris Sharp]

Yeah, so you bring up great points, right? Where tapping out is the right word, where in a lot of these markets power has been tapped out? Phenomenal market. I mean, some of the most recent stats, it has 0.5% vacancy rate, which is phenomenal. I mean, it's a multi gigawatt market. I think one of the Things that differentiates how we view these markets is coming in and master planning, not only with the entitlements and making sure you have access and the rights to the land, but that master planning arc is sometimes five plus years. And a critical element of that is working with the utility operator so that they know that when we say we're going to need a gigawatt, like with what we're building right now right next to the Dulles Airport, that they have an understanding of that power requirement. And in a lot of the cases they're able to meet that. But in certain cases, particularly in Northern Virginia, which has been wildly publicized, is that some of the, not necessarily generation, but the distribution of the grid has been challenged. And so we're always working with different solutions to overcome those shortcomings in the short term, but then ultimately working with that utility operator so that they get an understanding of the future growth associated with these markets. Because again, this infrastructure, and by saying this, this AI kind of secondary wave to cloud wants to be proximate to existing infrastructure. So being able to tie those things together and the power has been challenging. Right. And I think it's challenging not only throughout the globe, but in a lot of these markets where you need to be working with utility operators. Which is why I love talking to the market and educating not only the end consumer around what's happening in AI and the workload, but ultimately the broader infrastructure like the utility operators and some of the other technology coming to market in solving for that power constraint.

[Shea Khan]

Yeah, you mentioned timelines. I wanted to ask you about that. So it's obviously location specific, but can you talk to me particularly relative to your history in this sector from, I don't know, from the beginning of development of a new site to operations of that site. What does that timeline look like? What's the range of timelines that that looks like today and how does that compare to history?

[Chris Sharp]

Yeah, so it is a challenging scenario where all things being equal, it takes about two years from concept to delivery to build out what I would say a versatile data center. And by versatility, I mean comprehensive portfolio of solving for the hyperscaler needs, but also solving for the enterprise customer. So that's a 24 month window. But with the backdrop that we're experiencing, particularly with the power and the grid and just the overall equipment bottlenecks, I mean utilities are requesting aggressive kind of four year ramp projection that when you start to take that power down, you need to utilize it, which we've been very good stewards in a lot of these markets that when we do that master planning, we project that we are going to need 500 megawatts, we take down that 500 megawatts and operate that over a longer period of time. But some of these other interconnects are definitely. No two markets are alike, but they're elongating even beyond the 24 months that it would take us to pull that together. So there's a lot of challenges there. And I referenced that at a high level. But some of the broader infrastructure constraints are transformer lead times are 50 plus weeks right now.

[Shea Khan]

Well, I was going to ask you about. That's challenging, right, because transformer lead times and switchgear and stuff like that, that has been a challenge in all sorts of areas of the power sector. But I wonder whether because you have the added constraint of really long interconnection lead times, does it just mean that the interconnection is the long pole and the tent and so you sort of, you have enough time that the transformer thing doesn't actually. Or twitch gear or whatever doesn't actually delay projects because you happen to have another thing that takes even longer? Or is it its own constraint?

[Chris Sharp]

Yeah, there's two high levels. It is a constraint. Don't get me wrong, there's two high level elements that we've been doing at Digital for I've been here 10 years, the company's been around 20 years is vendor managed inventory. So not only understanding, hey, here's our portfolio in a single market, but really operating at a point where we're buying that switch gear, buying that infrastructure ahead of time where we can alleviate some of the bottlenecks. But yeah, that secondary constraint, and this is why I referenced earlier the master plan, it showing and signaling to the utility operators and being a good steward of having top tier customers and credit worthy customers in our portfolio which want to operate with us 10 plus years in that asset, balancing that together is everything. And so that interconnect from the utility has become constrained. And some markets were always investigating different types of solutions via gas turbines and even those are backlogged. Plus 20, 29. Right. That's an extensive background as well.

[Shea Khan]

Yeah, we're definitely going to talk about bridge power because that is super interesting. Before we do though, one other question I have for you about sort of how the market has developed is about scale of data centers. And we sort of alluded to this when we talked about, okay, that the training models need really big scale data centers. But you know, over history, right, like you guys probably were developing 20 megawatt data centers 10 years ago. Right. And now it's hundreds of megawatts. Or you mentioned a gigaw lot. What does the demand picture look like for you? Does everybody want the biggest possible data center that you can build them or like what is the nuance to that?

[Chris Sharp]

Yeah, it's a good piece to dig into right where there is a lot of noise. Like one of the things we've been joking about is a lot of bragawatts. Oh, I have a gigawatt. I have a gigawatt. And there's just so much noise out there that you really want to get underneath the workload and the durability of the company behind the workload. And that's where being a publicly traded operator, we're constantly watching that and not everybody needs 100 megawatt data haul. And there's certain use cases where a contiguous set of GPU infrastructure which requires a very discrete capability, which we have some of the strongest heritage of engineering talent within digital that have been solving this for the clouds and now it has grown, but they want a contiguous 100 megawatt GPU array. So it's not just about the total capacity block, but then it's the densification of that capacity within the asset. And so we're always watching that. But what we're really seeing is inference can come in in like 5ish megawatt blocks. And you can solve for it a bit differently now the densification is still there and then the private AI pieces, there's hotspots where it can be a couple of megawatts as well, but they want to be embedded in their existing portfolio of assets. And balancing those two things is something we're always eyes wide open.

[Energy Hub]

Catalyst is brought to you by Energy Hub. Energy Hub helps utilities build next generation virtual power plants that unlock reliable flexibility at every level of the grid. The Energy Hub platform takes the guesswork out of balancing energy supply and demand. It uses machine learning to control customer owned distributed energy resources like EVs, home batteries and smart thermostats to precisely shape load profiles for grid flexibility and reliability. As the industry leader, Energy Hub helps more than 80 utilities manage 1.7 million devices, more than any other edge derms on the market. Click the link in the show notes to learn more or go to energyhub.com Catalyst is brought to you by Antenna Group, the OGs of PR and marketing for climate tech. Is your brand a leader or challenger? Are you looking to win the hearts and minds of customers, partners or investors? If you're a startup investor, enterprise or innovation ecosystem that's helping drive climate's aid of adoption, Antenna Group is ready to power your impact. Visit antennagroup.com to learn more.

[Latitude Media]

Do you have questions about how potential policy changes or tariff adjustments could impact development and procurement plans? ANSA can help companies move fast, stay informed, and make better procurement decisions. With in depth supplier relationships and 20 plus years of industry experience, ANSA's team can help buyers rapidly execute procurement strategies that hedge against trade and policy risk. ANSA offers the industry's most comprehensive platform for supplier product pricing and availability data, plus several gigawatts of US Inventory ready for purchase. Whether you are looking to evaluate risk exposure, move quickly on inventory, or simply gain better visibility into market options, Anza is here to help. Learn [e-mail] Latitude.

[Shea Khan]

But so this gets to an interesting question I've been wondering about, right? Which is mostly what you hear about these days is 100 plus megawatt data centers getting built mostly hundreds of megawatts, if not gigawatts. And that's what all the news is about. But if the individual inference workloads need to be they can be 5 megawatt chunks. Albeit you want some degree of densification, could you employ a strategy where you go build 120 megawatt data centers all in a region? Is that a viable approach? Because from a power perspective, my suspicion is that in the regions with a lot of data center activity already, the scales might soon tip where it could actually be easier to build 120 megawatt things than a single 2 gigawatt thing. Or a single two 21 gigawatt things.

[Chris Sharp]

Yeah. One of the most challenging things represented by AI is the ambiguity in the workload. Right? Like there may be one workload, like yep, okay, I can take five or 51 megawatts and I don't care where they are. That's less than that's the outlier of what we're seeing is that to operationalize they would really like it to be more of a contiguous scenario where they do logically think about them as 5 megawatt chunks and they want a bit of resiliency. You had said it earlier, reliability becomes increasingly important with inference because that's the consumption of that capability. What we're seeing is having a 100 megawatt hall where a bunch of 5 megawatt deployments could come in and be represented as inference. That has a higher viability for a lot of the hyperscalers plugging in a multitude of capabilities because it's not one AI workload. It could be a bit of. I always am challenged not to reference a specific workload that I'm working on with a specific customer. But just some of the stuff spoken about publicly. But you look at some of the most recent announcements from Google and Gemini and VO3, those are probably compromised. Coming to market as a composition of multiple inference capabilities. Coming to market to meet that customer demand. And you have to solve for the peak. Right. Like people always forget, like we learn this in the web scale hyperscale.

[Shea Khan]

It's like the grid. It's the exact situation as the grid.

[Chris Sharp]

Right, Exactly.

[Shea Khan]

We build the grid for the peak. We also build data centers.

[Chris Sharp]

You have to.

[Shea Khan]

You can't not deliver.

[Chris Sharp]

Absolutely.

[Shea Khan]

You mentioned the Bragawatt thing. I mean that's the other thing that feels to me like we're clearly in a moment. Like two things can be true at the same time. There can be explosive actual demand growth for computer leading to actual need for lots of gigawatts of new data centers. And also it can be true that the volume of data centers in development and certainly the volume of load interconnection requests going to utilities is like an order of magnitude more than is actually going to happen. Like both of those things can be true. But I wonder the degree to which that presents a challenge for folks like you. Because you need to get stuff built. But on the other side of the table from you is a utility who's inundated with load interconnection requests and needs to figure out which things are real and which things are not. And I imagine that sort of gums up the works a little bit.

[Chris Sharp]

Yeah, no, you're spot on. Right. And I view that as three elements. Right. Where the power, the amount of power and even the amount of financing required to meet these upper end projections, it doesn't exist. Right. And so you can't solve it all even if you wanted to. But then double clicking on aligning to your customer. Right. And not all customers are equal and really understanding what their goals are and what they're trying to achieve. That's the heritage of digital reality. Right. And that's where we've been doing that in pretty much every theater on earth over multiple cycles. Right. So AI represents a new cycle in a new wave that's bigger and faster than we've ever seen before. But it takes partnerships to really pull that off correctly. And I think I couldn't say it better in that some of the works that we've been doing together collectively and also with the utility operators having a communication with them to show that they won't overbuild unless they have a level of comfort that you'll take and utilize that infrastructure they brought to market. So that ramp is everything to them. Working with that customer to show them that we have the right customers, we have the understanding to support the workload is everything. Because there's going to be some probably written about very big challenges and failures where they wanted a total capacity block but they couldn't support the densification or they were just building for the spike. It was very spiky. But the longer term utilization is much lower than anybody had projected. That will have very negative impacts on them to operate in a longer term horizon. So we're always focused on right types of customers, right types of partnerships to meet that peak load demand and the financing required to hit it.

[Shea Khan]

Okay, so you mentioned bridge power. I want to hear how that is playing out in the market. We hear a little bit about it, right? There are folks who are saying, I mean, famously the GROK data center employed this substantially where you just say like, okay, the grid interconnection timeline is too long. And so I'm going to throw a bunch of generators on site and operate off of the generators as a bridge until the grid comes along for me. How common is that actually?

[Chris Sharp]

Yeah, I think there's some outliers. Very few of it gets covered in the press because nobody wants to really go onto the market where the grid can't meet the customer ramp demands today. I mean full stop. And I think one of the things that we're always looking at, and I keep harping on this, is that customer ramp requirements are an absolute key driver. But bridge power is one tool among many that we're always looking at. Right. And natural gas, which is what you were referencing, referencing earlier, I think it has a solution in a shorter term horizon. But we're always looking at what are the longer term power generation capabilities that could potentially coming online and working with the utility operators on if it is grid constrained and they couldn't get the resiliency in the grid, or if it's a generation challenge. We're always looking at how do you hit that peak demand with some of the batteries and some of the other technology that we've been seeing come to market. But yeah, it is an outlier. I think a lot of the utility operators are starting to understand that hey, this is real demand and all aligned to it. They're not chasing the noise because Nobody wants to invest in a bubble, right? Like I'll go on record to saying that we're always looking at to ensure that we're not aligned to a bubble and that long term durable workload is there. But yeah, we too investigate NAT gas turbines. We investigate all options within the grid to overcome some of those shortcomings so that we can service our customers. Because I think it's often missed, I talk to the utility operators that if our data center goes dark and is dormant, it doesn't allow our customers to grow and hit that next capability they need represented as AI or not, or even cloud services. They have to be able to generate revenue out of that very expensive infrastructure going forward. So they're looking for that long term master planned alignment to the utility operators.

[Shea Khan]

My sense is that there's kind of two different things you can do if you think about bringing assets beyond the assets you would normally bring, right? You're always going to have backup power or whatever. But if you're going to think about bringing anything beyond that alongside behind the meter at a data center, you can either do the pure bridge power thing, which is we will supply our own generation and operate the data center off grid or partially off grid until the interconnection arrives. Or there's this other thing which is, okay, we will proactively strike a deal with the utility wherein we will bring our own generation or we'll bring our own batteries or whatever it might be, and we'll have an interruptible tariff or something like that. And in so doing we will get faster time to power. But it's a negotiated sort of deal with the utility as opposed to a bridge to utility. My sense is that the latter version is more common, more prevalent than the former. Is that right?

[Chris Sharp]

Absolutely, yeah, absolutely. And it's because like we don't want to be all things to everyone because we won't be good at anything, right? Where you want to invest in the utility to allow them to do what they were good at and get over this challenge of the spike in demand. But that longer term environment should be with the utilities. And that's why we form long term relationships with the utility operator and act as a good customer to them on behalf of our customers, it's that, it's that chain of value that we're always watching because doing behind the meter, becoming a power generation capability, I think is a short term gap. Nobody would be investigating that if we didn't have the constraint. And so that in itself tells you that a lot of individuals want to stay to their core stitching of hey, what are we good at? What is our core capabilities we're bringing to market. But I think there's a short term, let's meet the demand and then longer term who would be better at managing and operating these things going forward.

[Shea Khan]

The way I think about the archetype of like what, what are the energy resources at a data center historically was you would have a UPS system and you would have backup power and those are basically you had a diesel generator and EPS system and that like every data center had those things. Do you think that'll change? It's like there, is there a new archetype?

[Chris Sharp]

I was hopeful early on, but I've never seen one built with the right type of sla, so said a bit differently. Yeah, I wanted a straight in facility where the software would have resiliency to fail over where I didn't have to invest in all of the diesel generators or some backup system if the utility failed. I haven't seen one come to market. But we've always been watching that because there is a lot of, I mean believe me, I don't like to admit the secondary piece, which maybe a lot of the listeners already recognize, we operate almost 3 gigawatt of diesel generators today. And so finding that balance and you know, we do utilize some of those for, for peak loads and peak shaving and things like that. But I would love to build an environment where for certain workloads we can build a different type of data center. But just because of the SLAs, because of the requirements associated with these chips where they're liquid cooled, right? And that liquid cooling you want almost 3 in worth of reliability where if that pump goes down that those hot set of infrastructure that accelerated compute continually has the right type of liquid for a certain period of time where it doesn't damage that infrastructure. And we're talking billions and billions of dollars, you know, for 30, 35 megawatt build up to 50 megawatts. It's very expensive infrastructure that we're watching. So high hopes but it never really came to fruition.

[Shea Khan]

That's a really interesting point, one that I hadn't fully appreciated. That liquid cooled actually possibly increases the need for reliability. If anything that thermal doesn't go away. So even if you had a workload that didn't need it, right. Because the promise, the thing that people talk about sometimes it feels like it's this ethereal concept that never occurs really is like oh, but there are some workloads that don't need three, nines of reliability or five nines of reliability. It's like, okay, right, we're tagging. The cloud example used to be like, we're tagging photos for Google images or whatever. You should be able to operate those in a different manner. You shouldn't need the diesel generator. You should be able to place it wherever you want it. We should relieve all these constraints. And I think there was this concept that, look, if the grid is as big a bottleneck as we think it will be, and it gets just harder and harder to find sites to build data centers at the scale that we need, then naturally the market is going to start to separate out those workloads and put them in the places that you can still build. But there are all these other constraints. It's not just about the workload though, that is challenging for the reasons you said before. It's also like you don't want to fry the chips basically. And you've invested a lot of capex in those chips.

[Chris Sharp]

Yeah. And you can double click on the infrastructure. There's probably some components that could go in and have a little bit more resiliency. But if you're liquid cooled, the thermal doesn't just displace itself, so you have to continually run liquid through that so you don't damage the chips. But, but to your other point, the availability zones have grown, right, because of the constraint. Lack of land, lack of entitlements. They've grown, but they're still proximate to that kind of CBD within these critical markets that we see evolving quite in the foreseeable future. We just see so much demand with as more customers start to utilize AI and the complexity of these models come to market, we only see that increasing.

[Shea Khan]

All right, final question for you, Chris. What are you most excited about? Like, what's the coolest thing that might be coming on the horizon in data center technology world?

[Chris Sharp]

Yeah. So I think some of the newer designs we see and some of the efficiency factors. What's awesome is having two small children myself. We want to be good stewards of the power we take from the grid. So the PUE is increasing, shifting to liquid. Liquid's 800 times denser than air, so you can get more efficient. I think that's going to be a net positive. And then I'm a technologist at heart. Some of the designs of the hardware coming to market are just phenomenal. Working with our partners across the broad spectrum, watching and working with Nvidia, Vladimir Troy, who runs R and D there, we spend a lot of time with them, not just on the current generation that the public gets to see, but what are the two and three generations out. The ability of the token production against the watts associated with that is is going to be phenomenal and hopefully everybody, all of our listeners here today, they understand the value of not only the data center but these tokens and AI. I'm a very pro AI kind of individual. There's always going to be some negative associated with it, but what AI is going to be able to do for us not only as individuals but as a society. I mean, I'm pretty excited about some of the use cases and workloads that we've seen. One case I would leave you with is Gephion, a project we did out in Copenhagen. It's one of the largest DGX pods for Novo Nordisk. Just the amount of pharmaceutical work associated with that one deployment is just what it's going to be able to do for humanity is very exciting to me.

[Shea Khan]

All right, Chris, this is a really fun conversation. Appreciate the time as always, appreciate it.

[Chris Sharp]

Thanks for the opportunity. Stay safe out there.

[Energy Hub]

Catalyst is brought to you by Energy Hub Energy Hub helps utilities build next generation virtual power plants that unlock reliable flexibility at every level of the grid. The Energy Hub platform takes the guesswork out of balancing energy supply and demand. It uses machine learning to control customer owned distributed energy resources like EVs, home batteries and smart thermostats to precisely shape load profiles for grid flexibility and reliability. As the industry leader, Energy Hub helps more than 80 utilities manage 1.7 million devices, more than any other edge derms on the market. Click the link in the show Notes to learn more or go to energyhub.com.

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[Shea Khan]

Chris Sharp is the CTO of Digital Realty. This show is a production of Latitude Media. You can head over to latitudemedia.com for links to today's topics. Latitude is supported by Prelude Ventures Prelude backs visionaries accelerating climate innovation that will reshape the global economy for the betterment of people and planet. Learn [e-mail] this episode is produced by Daniel Waldorf. Mixing and theme song by Sean Marquand. Stephen Lacy is our Executive editor. I'm Shayl Khan and this is Catalyst.