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That's right. You know, Ann, some of the best lessons don't come from a classroom. They come from experience on the Power.

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The Power of Advice published by Capital Client Group Inc. Practically every day on this show, we report on A new partnership between AI companies, chip makers and their investors. Now it's time to go beyond those headlines and talk about the infrastructure of AI, the engine that's truly driving all of this. Now, meeting the accelerating demand for computing power requires physical data centers and energy to go along with them. Construction and operation of those sites brings up issues of location, water usage, sustainability, not to mention how these massive effective factories are funded and integrated into the communities in which they find themselves. So to get some context on the real world footprint of AI, I'm delighted to welcome Wes Cummins, the CEO and co founder of Applied Digital, the publicly traded company that designs business builds and operates data centers for AI, cloud networking and blockchain workloads. Wes, thank you so much for joining us here from Dallas today.

A (6:05)

Yes, thanks Anne. Thanks for having me.

B (6:07)

Well, Wes, to set the stage for this, this is an industry that does run the risk of being highly jargonized. And what we want to do is make sure that our audience has some of the level setting items under their belt. So I'm going to start with a question from one of our listeners, Marissa in South Carolina, who wrote one what is the measure? What is the metric for measuring data centers? I've heard some of the size of football fields. That's the physicality of it. Others advertise the number of megawatts at this point. How do you think about it and what does it mean when it comes to hyperscaling?

A (6:40)

Yeah, absolutely. So I think it's important when we think about AI factories that we're building. So you have the old style of data center that was really serving up mostly video applications like this, and then your Netflix, your YouTube, all of those types of video applications that we've all grown to use significantly over the past 20 years, those were based in metro areas, those were around square footage. They didn't require the amount of power that the AI factories really require. So just to give context, those types of data centers would deliver about 7 kilowatts for an entire rack of compute. We're delivering at our first facility in Ellendale, North Dakota, about 135 for a rack of compute. So significantly higher power density versus what you've seen in the past. So that means that you're going to use a significantly higher number of power over a smaller square footage or smaller footprint. So you really should be looking at the power loads of data centers when you're measuring how large they are going forward. When you're specifically talking about AI factories, this doesn't mean the old style of Data center, the video streaming, that's not going away. So both of them continue to grow, but with the infrastructure we're doing, it's really around the amount of megawatts that we're delivering.

B (7:54)

Let's talk about how Applied Digital started life a little bit differently, using energy for a different focus, which is actually crypto focused infrastructure. Talk to us, Wes, about that shift from your crypto focus to now your AI focused and what actually makes you able to play in both of those arenas.

A (8:15)

Sure. So we started building data centers for bitcoin and it could be any crypto, but it was really bitcoin miners. We were not a bitcoin miner ourselves. We haven't been a bitcoin miner, but we built large scale data center capacity for bitcoin miners here in the US that started in 21, and then in 22 we started adding what we called at the time high performance computing. And high performance computing and AI factories kind of blend together. The reason we call it AI factories, Nvidia calls it AI factories, but also it really captures the scale of what we're doing. HPC historically and even when we started was, you know, you're looking at 5, 10, 15 megawatt builds, and now we're doing hundreds and hundreds of megawatts. And so that really captures the AI factory. But the, you know, we, we went and secured a significant amount of power and then we added a different group of people back in 2022 to start going after high performance computing, which has grown into AI factories. But we'd already started building large scale data centers. And again, that was around Bitco. It was a lot of similarities in supply chain. There's massive differences when you look at the type of build they are, one versus the other. But we were one of the first companies down the path of building very large scale AI infrastructure AI factories. And it's just been a really young market. So we started this in 22 and really started building our first really large scale facility in 23. But our start was data center builds for bitcoin miners, of which our biggest customers, the largest bitcoin miner in the world, which is marathon.

B (9:52)

And so talk to us about how the decision to pivot actually happened. Did you have one of the hyperscalers come to you and say, look, forget the, forget the bitcoin play, work with us instead. Did you actually make the strategic decision to change and then proactively go out to the AI universe which came first?

A (10:09)

So it was, it was proactive on our part. We didn't have Anyone come to us. The, you know, it was really, we have this power asset. We know how to build high power density data centers. We, we spent a lot of time looking at high performance computing facilities and then we spent, you know, a significant amount of time hiring the correct people, getting the right supply chain partners and we've continuously refined that. But we did this pre chatgpt. So it was really what else can we do with this power asset that we have and our ability to go and build at scale and build quickly. We started down this path and then it was just really, you know, I'll call it a little bit of luck for sure. Where ChatGPT hit in December of 22 and it was probably in March of 23, we realized the demand is going to be extremely large. And then for us to break into the hyperscale market, I thought for a newcomer, what we need to do is like eventually there's going to be a significant amount of demand for this type of data center capacity. We started building it ahead of having a customer. We spent close to a billion dollars at our building in Ellendale, North Dakota before having a customer. Now we've signed a customer for 400 megawatts there. We have another campus going in Harwood, North Dakota which is just outside of Fargo and we're already doing construction. We expect to sign our second customer for that campus in the near term. And really it put us on the map to have this capacity available. And the thought process inside the company was the, the GPUs were really hard to come by. So the, the H100, when those hit the market in March of, of 23, it was really almost impossible to get your hands on those GPUs. And it was supply chain issues. But internally inside the company we thought, hey, it's a lot easier to fix wafer throughput for, for semiconductors, advanced packaging. That supply chain is going to get fixed a lot faster than we realized what type of data center infrastructure was going to be necessary for, for these workload. You, if you just thought forward, you know, 18 or 24 months, you go, it's the, the data center capacity is going to end up being a bottleneck because getting power, getting, permitting, getting supply chain and then actually building the facilities we thought would be the issue. So we started early and we started big and it's, it's really worked out with how the market has come together for us.

B (12:32)

So you took the strategy, which is a risky one of build it and they would come right.

A (12:37)

It was, it was appeal to dream strategy for sure.

B (12:39)

And that hasn't worked. I mean that hasn't worked out for intel, for example, if you look at their foundry model. So risky when you're, you've got a capital intensive construction model, which is the one that you have. But.

A (12:48)

Absolutely.

B (12:49)

But a customer did come. So talk to us about that. Talk to us about who signed up with you and when they arrived.

A (12:54)

Yeah, so we signed Coreweave about three months ago. We signed Core Weave initially for 250 megawatts. So we have 100 megawatt building. This was the, the Field of Dreams building, if you, if you will, that we started building back in 2023 and that will turn on actually this month. We're the first of October, so we expect that to turn on first capacity this month. It'll be I think the largest. We've done some work around this, but let's call it one of the largest supercomputer deployments. The largest or one of the largest liquid cooled direct to chip liquid cool deployments in the world. And we designed the infrastructure really to be that, to be that large deployment. And it was really first of its kind in liquid cooling as well. And so it'll be one of the largest deployments. And then we have a second building that's 150megawatts, it's under construction. And a third building that is not far behind that, that's also 150 megawatts. It gives us that 4 megawatts of capacity. And we've now started on with a 280 megawatt campus in Harwood, North Dakota that we think expands beyond that. Ellendale itself, we think expands well beyond a gigawatt. So we, we have a lot of power locked in place, but we have supply chain which is, we should discuss more is going to become much more important I think in, in the year ahead. But, but yeah, that, that's the scale that we've built at these, at these facilities.

B (14:12)

Well, let's talk about supply chain and let's just take Core Weave as an example. Where's. As a framework within which to dig in. Just clarify for us in your relationship with coolweave, for example, who is actually responsible for getting hold of the chips and supplying those versus everything else in the infrastructure side that you just talked about.

A (14:32)

So our customer, and this instance Core Weave is responsible for the chips they choose, which server is going in, who the chip manufacturer is, who's manufacturing the servers, network gear, they decide. So that architecture, excuse me, they decide. What our job is, is to build a highly Technical building that runs at five nines reliability that provides the appropriate cooling and the appropriate power with the, the appropriate redundancy. And then our job is build it and then also make sure that that building operates. You know, their job is they'll, they'll bring their, their equipment and they decide on what that equipment is and they own that equipment.

B (15:09)

So let's talk about the cooling, let's talk about the location that you've chosen. Let's talk about building in the Dakotas. We've got to talk about the proximity to energy. Walk us through how you worked through that.

A (15:21)

So, so a couple of things about North Dakota and why we're building that. We're really the company that's building that into a big data center region. So we have the, the 400 megawatts contracted with Core Weave. We think that Excite expands. We have another one, like I said in Harwood, we think that site expands. The reason we love North Dakota, cold weather. So the, the use a significant amount of power there, create, it creates a lot of heat and you have to cool. So what does the cold weather help us with? It helps efficiency and to cool much more efficiently than you would in say Texas or in Louisiana or somewhere in Florida. And what that does is it drives the PUE, which is the efficiency measure, close to 1 and 1 is perfect. So that means that you're using all of the power just for the it load and not for cooling or mechanical around that. Now it's really impossible to get there. But then there's also water efficiency. And so our facilities are actually, because they're in North Dakota, they're designed to use no water. It's a closed loop system. So we don't use, use water. Now the last piece of North Dakota is, it's a very energy rich state. So they're, they're producing six to eight times more energy inside the state than they're using inside the state. So they're a big net exporter of energy in the state of North Dakota. And so what our, what we have is access to power and in many cases what I call the right kind of power, which is a lot of renewable, it's a big wind energy state as well as being in oil and gas and coal states. So we locate next to substations in the instance of Allendale, 2 gigawatts of power feed into that substation, 2 gigawatts of wind power feed into that substation and oftentimes go unused. And as we attract more customers to North Dakota, we expect it to really grow into a data center region. And we expect to be the leader in that state.

B (17:07)

Talk about access to talent. I just looked up the population of North Dakota in 2024 was under 800,000 people. And what you're building in your vision for being the data center state requires a lot of, I would imagine, technical skills, you know, a specific kind of workforce. Where are you finding the talent in a relatively small state when it comes to a population size?

A (17:28)

Yeah, so, so we're doing two things. We're, we, we hire people with the, the right skill set that have a lot of experience. We put them in North Dakota. But the other piece is a, a significant amount of the jobs that go with these data centers, even the highly technical jobs we can train for. And we're training the local workforce. We're creating jobs, creating really significantly higher paying jobs than what the average is in the state of North Dakota. And we're training them on site. We've actually attracted people back. Great stories. In Ellendale, N.D. we've attracted people back who grew up in Ellendale or a town next to Ellendale, North Dakota. They moved away. We brought people back from Colorado, from Nevada that have moved back to have the skill set that we need. And they've moved back because now there's a job opportunity for them, a highly high paying job opportunity. And so we're attracting those. We're working with universities, we're working with technical schools both in north and South Dakota. And we're training people to actually do these jobs locally in the state and creating as much benefit as we can in the state. But I think you'll see that talent pool in the state of North Dakota grow significantly, largely driven by the programs that we're developing there.

B (18:38)

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B (19:48)

Has this been embraced just from a human perspective? Wes? So our producer, John grew up in Northern Virginia, and his father joked to him when he was talking about preparing for this conversation that he, quote, lives in the cloud because the community has just been clogged with data centers in these. You know, they're not the most esthetically pleasing buildings to look at. Like, have. Has the local community said, okay, the jobs are coming, this is a net positive. We really embrace it. Or has there been some resistance to what's being done to the landscape and to what they've.

A (20:15)

So in Ellendale specifically, we'll do that, and then we'll do Harwood next. So in Ellendale, we've worked really closely with the community, we've integrated into the community, we've created a lot of economic benefit, and we've been welcomed there really largely. It's been a fantastic partnership. So building in these small communities. Ellendale is 1100 people, Harwood is 800 people. You. You really need to work closely with the community because it is a big change, but it's the perfect change for these types of locations. So there, there's a lot of construction going on right now. And so we have, you know, 900 people on site in Ellendale right now every single day. That will subside over time. And then what we end up with there is creating two, three, maybe 400 jobs that are permanent so it doesn't overwhelm the town. And then you also have a culture there where people are used to commuting 45 minutes or an hour to go to work. So you really are encompassing the surrounding towns as well. And so we've had a really great working relationship with the town of Ellendale, and so far, we have a great working relationship with the town of Harwood as well. There was a little controversy around that just big new project. There's always going to be opponents to that when we Harwood, but not from the town residents themselves. There's been overwhelming support. You start getting kind of outside. You know, AI is kind of a hot button these days anyways. And so you're going to start to get some outside influencers to try to come in and stop things. What a project like ours does is it brings a significant amount of tax revenue, it brings jobs, it brings economic growth to a community that is not growing. And what they. What the community receives out of that is infrastructure and then other economic benefits that just build on all of that tax base. Schools benefit in a huge way. All of the roads, the sewer, the water, all of those things that I mentioned, these are things that most people don't think about. I live in Dallas, and you probably live in a metropolis, too. And so you don't think about those specific items. But a small town where you're dealing with a thousand people or 800 people, just keeping up with the. The basic, basic infrastructure can be extremely difficult.

B (22:25)

That being said, you have had opposition. You've had folks who are worried about noise. You've had folks who have worried about the environmental impact. You've had folks who have been worried about the possible effects on water quality. You've said you don't use water for cooling, but there is resistance out there. You've acknowledged that. Wes, where does it land? Are you seeing growing resistance to these kinds of projects, or is it very localized?

A (22:46)

No, I think. I think it. So I don't see it. Again, I don't see it locally with the people that are in, that actually live in the town where we're working. We see very little. There's always going to be someone that doesn't like you. That's, you know, the way of the world. It's a way of life. But what you're seeing is overall, there's, you know, there's much more attention on AI. If you rolled back two years, no one really understood exactly what was going on. But now you're seeing it. So what we. We focus on what we can. What we can control. Here's what we can control. And then we try to educate people around this. We don't use water. Right. That's one of the biggest pushbacks, is they're going to use all of our water.

B (23:24)

There is that preconcept, for sure.

A (23:26)

And if you look at what we did in Ellendale, it's a really great example of doing things the right way. Our company is very focused on doing things the right way. And at Ellendale, we've actually lowered the power cost for MDU users by, I think, 5.3 million in. In 2023 and $5.7 million in 2024. And the reason we do that is because we go and we Locate next to the substation. There's excess power there already. And so we utilize that infrastructure. And when the utility doesn't have to build additional infrastructure or very minimal infrastructure for the amount of electricity that we use, the savings in North Dakota have to be shared amongst their constituents from the psc. So we actually save people power on their power bills.

B (24:08)

We're going to move on, Wes. So let's talk about where all the money is coming from for this building at scale requires enormous capital. You've got a partnership with Macquarie. I read that they committed up to $5 billion in partnership with Applied Digital. You've just raised a convertible bond. So talk to us about your capital needs and where the cash is going to come from to build all of this.

A (24:27)

Absolutely. So we've worked really hard over the past 18 months to move our financing from the public company. So in public company you do the convertible debt. You could do equity issuance down to the asset level because this is an asset heavy business. So you move to the asset level. The Macquarie partnership is a huge step on the asset level financing. So that's the equity portion. And so that 5 billion of equity committed by Macquarie actually translates into about 20 to 25 billion of total capital because you add the Macquarie equity and then you do project finance. So think of, for, for the people who are watching. Think of you. You go to buy a house or you build a house, you need to come up with 10 or 20% down. So in our case it's 20 to 30% down on the equity portion and then you borrow for the remainder. And the borrowing is typically the lowest cost of capital. And we're in process right now. We talked about it on our last earnings call of finalizing our project finance for the first two buildings. Yeah, and so we'll do that. But, but that'll just create a flywheel for us with the Macquarie equity portion and then the project finance piece on the debt where we should be able to raise, you know, a significant amount of capital to efficiently fund all of these facilities that we're developing.

B (25:42)

Well, let's step back though, Wes, because let's just talk about financing in the ecosystem more broadly. Right. There's, you've raised capital that it is sort of more traditional with Macquarie putting in the equity and you'll lever up against that. That's sort of traditional style project financing. When you take a big step back and look at AI more broadly, there's a very non traditional form of financing starting to pop up and that's people like Nvidia making $100 billion commitments to the likes of OpenAI. That kind of ecosystem wide financing is under scrutiny now because folks in the market, and frankly people like me are saying, oh, is that just money now going round in circles. We now at the point where the biggest chip providers are propping up their clients who are burning through cash to make sure that this all sustains itself and you're in the thick of it. That's going to at some point if it works out, it in years to your benefit. If it doesn't work out, it is just cyclical that in years to your dis benefit. So where do you stand on that?

A (26:35)

So here's the way I would think about it. I know there's a lot of talk about this, this circular, you know, money reference and video with OpenAI and Nvidia with others. But, but the reality, let's take a different approach. This is all new, you know, if you think about where this is. So we're 2025. This, this really kicked off two and a half years ago and the, the financial aspect is still catching up and it is catching up in a big way. But people are having trouble wrapping their head around this. The absolute scale and size that is needed for the builds of the data centers for the chip deployment. This is truly an infrastructure business. So, so artificial intelligence gen AI is completely driven by the hardware that you can run, both training and inference. It's completely hardware driven. There is some software aspect to it, I don't want to say that, but web 2.0, what we all got accustomed to, was completely software driven. Web 1.0 was very hardware driven, big build out and that one definitely got ahead of itself. But I would signal here, caution here on this build out for AI. It's really Nvidia is stepping in and they're being a leader in seeing the future, making sure these things are built at the pace that they need to be built at. So the Nvidia investment in OpenAI really creates something inside the entire ecosystem. Right. It validates OpenAI. And so what does that do for someone like me? Okay, well now I can probably go and build a Data center for OpenAI and get the standard traditional banks to finance that data center build because of that backing of the largest company in the world. But the other piece that we're seeing is all of the largest companies in the world are the ones spending the vast majority of the money here. Microsoft, Amazon, Google, meta and then OpenAI.

B (28:25)

That's the problem, that's the assets MAG7 accounting for 35% of the S&P 500 by market cap and 15 to 20% of CapEx. That kind of concentration is exactly why people are saying, are we in a bubble?

A (28:39)

Yeah. Is that a problem? Or would you rather have people just having stocks that go wild off of IPOs and then raise money and then spend that money and then servers sit in boxes? That's what I saw in Web 1.0.

B (28:50)

Right. Let's hold that for it. Fermi America data Center complex building looking to build the biggest complex to date in Amarillo, just filed to go public. It has zero revenue. It's being valued at $14 billion. That is exactly what you're saying. It's an IPO with zero revenue at that kind of valuation. And I guess the question for you, as is your own market cap is $6 billion, your share price is up 178% over the last 12 months, you're burning cash, you're not yet profitable and your trailing revenue is under $150 million. Do you as the CEO look at your share price and worry that the market has gotten ahead of itself and can you sustain this level?

A (29:32)

So I, I, for us specifically, let me not talk about Fermi, but the, for us specifically, we have contracted $11 billion in revenue that we signed. We will generate from that one contract, not including the others that are in the pipeline for us, 500 million of NOI, or what people would call EBITDA on an annualized basis. And we're locked in for 15 years. And so of course we burn money now because we're building massive projects that don't generate revenue, but they'll start this month, that starts and then it'll ramp up significantly. So if you look at the future, which is the stock market is a future discounting mach. So if you look at our business and we've talked about getting to a billion of noi, which I think we have pretty good line of sight on, when you look at our market cap at 5 or 6 billion dollars versus 500 million going to a billion of net operating income, I don't think it's wildly overvalued. You could even argue it's undervalued, but it's finally given us some credit for all the work we were doing. And then these, we're putting these really long term contracts in place, like I said, 15 years with five year extensions. So you get a business model that goes forward that actually has a lot of predictability and a lot of cash flow generation.

B (30:43)

We're going to stop there Wes, come back. These contracts are kicking in and we want to hear how it's going. Wes Cummins, thank you for joining.

A (30:51)

Thanks, anne.

B (30:52)

Well, it's 4:00pm on the east Coast. There's the bell. The market's now closed for the week. John, tell us what caught your eye.

C (31:00)

Right. It was an interesting and very active week for auto companies with the expiration of the 7,500 federal EV tax credit. Tesla reported quarterly vehicle deliveries up 7% over the previous year. Meanwhile, Rivian's deliveries were up nearly 32% from the year prior. But the company narrowed full year guidance sending the stock down. And you're not just talking EVs here. Shares in Stellantis rose after the company reported a 6% rise in third quarter U.S. sales fueled by growth across Jeep, Ram, Chrysler and Fiat. And finally a look ahead to next week. We'll be hearing from some prominent brands reporting quarterly earnings Monday. Constellation, that's Modela, Corona Pacifico and other adult beverages. Then we get spicy on Tuesday with McCormick & Co. And the Week rounds out with Pepsi, Delta, Delta, Levi's and more. Lots to cover and I'm looking forward to it.

B (31:46)

Cannot believe that we are already getting in to the early innings of earnings season. It feels like we just just finished that. Well, we're very excited about this for this weekend. First of all, weekends are always fantastic. Second of all, the weather we're here in New York spent to be pretty good this weekend. Third of all, and just sharing it with you all, our producer John is getting married on Saturday. So big shout out for producer John and his fiance. He is unbelievably going to be back already on Monday so we're going to have to see how it went. We'll be asking you some questions. Maybe we're going to push for some pictures. That's it folks for today's Brew Markets Daily.

C (32:20)

Brew Markets Daily is hosted by Ann Berry and produced by John Couteau, Tarkab Delatif and Emily Milian. Our technical director is Luis Farias and Jesse Derozier runs Audio. The president of Morning Brew Inc. Is Devin Emery.

B (32:32)

Wake up on Monday with the Morning Brew newsletter and tune in to Neil and Toby on Morning Brew Daily. Have a great weekend end.