Market Perspective – Navigating the Digital Frontier with Applied Digital

Jarrett, thank you so much for joining us today. We're really excited to get your feedback on some important aspects of what's going on in this industry. First, would you mind describing your background and experience for Thanks, Aaron, for inviting me. Sorry I couldn't be there live. I'm a senior advisor. Now I run an advisory business. For the digital infrastructure space. Started out in the network world, you know, thirty years ago, got into data centers about fifteen years ago. Most straight recently, I was chief operating officer for Digital Rility. I left there about five years ago after fifteen years in the industry. And, I joined up now. I'm a senior advisor to the Blackstone group, and I work closely with emerging companies like Applied Digital. So, excited to be here today. Awesome. If you wouldn't mind sharing, how did you first learn about Applied? Well, I've been working with the Nvidia ecosystem for the last four or five years with some of my clients and was really excited to see the emergence of digital infrastructure players who had GPUs and were doing new products and solutions like a bare metal offering and and such in the market I actually support Blackstone on their diligence on Core Weave. And I looked around and said, who's got a pretty interesting model. So I reached out to, West the CEO and really had a discussion with him and what your strategy was and what you might be doing. Perfect. And as a very obvious expert in the space, you alluded to a little bit, maybe I could get you to expand a little bit more about what was it, about applied specifically that made you wanna connect? Well, I think, there's a couple key trends in our in our ecosystem right now. One, is the impact of AI machine learning on data center campus designs and a new product. So a whole new structural change in how buildings are being built to support the AI machine learning workload. So I thought that was super interesting. Particularly starting with the North Dakota campus and what's going on up there. Second, this emerging services. Particularly bare metal and GPUs and getting control of that pipeline. It's a very scarce offering, capability. And I think Applied has some great capabilities and services they can offer. And then finally, the level of investment in the partnerships are very intriguing. I've been working with the hyperscalers for twenty years, and they have a heavy reliance on these types of services, and it's super interesting to see the partnerships, including the Nvidia Elite partnership, that you have in place. Well, maybe let's take a step back a little bit and first talk about the overall data center marketplace. What is high performance computing from an equipment and technical requirement stand point? Well, I think we've seen a an evolution of workloads from enterprise, solutions that really had a a high dependency on networking and power densities were you know, in the three to four kilowatts of cabinet range. We saw this evolution of cloud availability zones, in which are large deployments, you know, typically in the eighteen to thirty six megawatt deployments, power densities, tripled or quadrupled to eight to ten kilowatts a cap. But in today's world, it's about cooling and delivering high power density solutions, that could easily be in the forty to fifty kilowatts of cab or in some cases, even a hundred and twenty or more, we've seen. So the HPC AI world is the next generation. This is why the biggest structural shifts that I've seen in my thirty years in the industry. And I think the folks who were in the leading edge, like Applied Digital of creating a product and buildings and cooling solutions that can support this generation are gonna be winners in the marketplace. And who is it that needs GPUs? And what are most of the high end GPUs being used for today? Well, the ones we read about them in the press all the time from the hyperscalers who've quickly pivoted in the last year. You've even seen some announcements where they had to stop you know, their data center development programs for some time to retool the design architecture and supply chains to support it. So clearly, the hyperscalers all need these types of solutions and we'll partner up, but enterprises need it as well. It's It's this is a disruptor, and it can really support everything from financial services, health care, pharma, pretty much every industry will have some type of AI machine learning dependencies. I think the first generation is we're seeing a lot of the training workloads, which can be further away from city centers But the real value, I think, is dual purpose where you're closer in. You can support businesses nearby and enterprise, and, channel partners, managed service, and channel partners. Got it. And what are some of the ways, you know, the data center requirements differ from these hyperscalers we're speaking of to enterprise data centers? I think one is scale. I mean, the enormous scale of hyperscalers, you can actually see campuses. From a hyperscaler. I was at a conference recently and they go, I don't get out of bed for under a hundred megawatts anymore. That's not what an enterprise would look. They yeah. They want a room or cabinet, you know, type of solution, but now you're seeing whole buildings And you're even seeing campuses that are five hundred megawatts or a gigawatt even in today's market. And the real estate's important fiber is important from a site selection standpoint, but power costs and total cost to ownership and the ability to cool. Is really a differentiator in today's market. And what would you say is the pace of change in the equipment and, like, where are all these technical requirements going? Well, I think it's we're early days. We're in early innings of the, call hype curve, and you see a lot going on. But I don't think everyone's at least the the clients and, that we're working with around the world don't have their final solution yet. That's why this transformation so important, but they're they're experimenting. They're executing and delivering solutions to see what's gonna happen, and it it'll take years to optimize the supply chain, the buildings that are built, the products and solutions, and and how to maximize it. So it's an exciting opportunity in the industry. And, yeah, I I didn't see the scale a year ago. I just was at, again, another conference in a report. I think there's, like, seven gigawatts of data center development going on right now to be delivered by, you know, twenty twenty six. So that type of scale, and we we've not seen ever really. It's just a very structural change. So it applied as building and designing one of the world's largest GP work clusters with keeping latency at the forefront. Can you provide some feedback on this new design? Yeah. I what what's intriguing to me and is the importance of latency and the network neural network in the middle of all this. We saw campuses start out in the availability zones. They were single story spread out pretty significantly in very large buildings. To go to market quickly, and then people started stacking that design. In today's world, that it's good potentially, they'd be vertical. And so seeing applied solution that it's vertically stacked to reduce latency to improve information transfer, and the other piece of it is it has to be always on in terms of you're running these GPU chips continuously and try to be efficient until you've maintained. And that's a really different it's an all out type of deployment, which is very heavily dependent on the the network in the system, the performance of the GPU chips, and the cooling technology that all have to play together. I'm finding this the purpose filled applied design super interesting in in this in this first campus. And I think that's one of the new things we're gonna see Early movers kinda use the existing data center and colocation space, but you're gonna need purpose built. AI machine learning, data centers kinda going forward. You don't know all the answers, but it's good to be in front of it and testing these in the market. And as we've been talking about, the industry is moving and changing really fast. So that's obviously creating many hurdles to meeting this demand, you know, that is ever flowing The first that everyone's generally aware of is obviously GPU supplies. A second bottleneck is likely much more a longer term hurdle and that's power availability. Can you provide some commentary on where applied fits into this? I think number one, we don't know the importance of latency and distance away from where the internet is. So the internet lives in key peering hubs around the world. And so when the cloud kinda started out, there were distance limitations how far your way. So for certain AI workloads, we can really test that. That's where North Dakota comes into play because it has cheap power because you know, it's a cooler environment, and you can build very, very large scale. We're gonna see people go there. And, you know, I worked on a project a year and a half ago in in a middle of Pennsylvania, and it was because it was near nuclear power. And when you get power costs, you know, that are that could be, you know, four cents, type rate, it gets people's attention and at the scale. And so we do, as a country and as globally, have limitations on the power side, and AI machine learning require much more. So I think that's really gonna be a shift in thinking how far away from centers, and you can definitely use it for their training workloads. It's just a matter of dual purpose, how far they can be away. And how do you see these major cloud companies and AI players hunting power and managing site selection? And how do you think applied is situated to compete for these contracts? I think the hyperscalers we see are are great at this, in terms of But the scale that's needed and the pipeline of new capacity needed, I think caught all of us a little off guard in terms of solving for it at least in the near term. So I think, you know, they've stated, again, some recent industry reports they were hopeful to do two out of every three campuses would be self builds. But frankly, I think what we're seeing is they're only able to do one out of three. So that means partners at at least at this point who can provide power and build the right product and solutions for them are available, you know, two thirds of the market roughly can be, you know, new providers or emerging players who can deliver for that. And what would you say are some of the biggest risk factors facing HPC infrastructure providers? And with that in mind, how would you say that apply to set up to tackle some of these user risks? I think the cooling solutions are probably the technical things that we're seeing. It's undetermined, you know, what what will come out on top. I think using, a combination of air cooled and liquid cooling is the way to go. I think AI, if you're using water cooled, that's a real issue in some parts of the country where you're you're dealing. So water utilization efficiency is really big. And so by the apply team is really thinking through those, how to minimize water, how to take in the advantage of environments, where they can provide a combination of air cooled and liquid cooling and liquid cooling over time, I think closed loop systems are coming. So I think the apply team is looking at all those ideas and and figure out the best solution with their customers. So maybe one last question. I would love know if you have any comments on where we are in this hype cycle and how trends, demands, you know, are expected to evolve in the next call it two to three years. I mean, I think we have line of sight. If you talk to hyperscale clients and and enterprise clients, we have pretty good line of sight at least into twenty six twenty seven, you know, this cycle. It's undetermined, you know, after that, that's you know, the evolution of these type offerings is gonna be super interesting. And we don't know how long the GPU limitations are gonna last as well. So I think in this window though of, you said, two to three years, it's about taking advantage and fully utilizing the GPUs that are available. And that requires different models and and different product solutions kinda test the market and I think applied well positioned there among a few others, especially with the NVIDIA ecosystem. I think they're particularly strong in this phase, and the next phase, that we we wanna focus on you know, with the executive team and and the leadership team here is building that next generation of partners. It's gonna be about partnering in a flexible model. To support their growth and then in turn applied script. Well, thank you so much, Jared. Incredibly insightful. Thank you for taking your time today. We really appreciate it.