Bitcoin Miners Own the Power Gold Rush
Transcript — TFTC
Article: https://www.tftc.io/bitcoin-miners-ai-data-center-transition
Transcript page: https://www.tftc.io/bitcoin-miners-ai-data-center-transition-transcript
Published: 2026-08-05
Machine transcription, lightly cleaned; may contain errors.
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[0:07] Brandon Bailey: You've had a dynamic where money's become freer than free.

[0:09] Marty Bent: Let me talk about a Fed just gone nuts, all, all the central banks going nuts.

[0:15] Brandon Bailey: So it's all acting like safe haven. I believe that in a world where central bankers are tripping over themselves to devalue their currency, Bitcoin wins. In the world of fiat currencies, Bitcoin is the victor. I mean, that's part of the bull case for Bitcoin. If you're not paying attention, you probably should be. Probably should be.

[0:34] Marty Bent: Probably should be. Brandon Bailey, welcome to the show, sir.

[0:39] Brandon Bailey: Thanks for having me. Excited to be here.

[0:42] Marty Bent: Excited to have you, dude. I mean, we've been— I mean, I've known you for what, probably like 7 or 8 years now at this point in the Bitcoin mining space. And I mean, you've done an incredible job of knowing what's happening inside and out, starting at Galaxy, moving on to Nakamoto. You just launched dimetrics.ai to help people grasp what's going on with the Bitcoin mining and AI compute themes sort of colliding with each other. And I'm incredibly excited for this conversation because I think despite what many Bitcoiners will get butthurt about is that AI's taking the wind out of the sails of Bitcoin. I'm incredibly excited about what's happening in AI, and I actually think it's going to be massively beneficial for Bitcoin overall. And so having you here to help get a lay of the land of what's happening on the compute build-out side, I think is going to be incredibly valuable.

[1:42] Brandon Bailey: Absolutely. I'm really excited to dig into the conversation. There's a lot to talk about.

[1:49] Marty Bent: Well, a lot to talk about. Where do you think the best place to start is? Is the history of this emergence of AI compute? I think maybe starting there, the intersection of Bitcoin and energy infrastructure that really exploded in 2021 with the Bitcoin miners were able to do successfully in terms of locking down power and really ingraining themselves in energy systems. And then the appearance of AI compute really— I don't want to say throwing a wrench, but really accelerating things on that front as well.

[2:25] Brandon Bailey: Yeah. Let's jump into it because that history there, especially in the 2021, 2022-ish era, is ultimately what led a lot of these Bitcoin mining companies to stumble onto this gold mine of the power portfolios that they ultimately amassed. It was really around 2021, shortly after we had the China mining ban, which resulted in as much as 30% of network hash rate coming offline and this redomiciliation— hopefully, that's a word— of Bitcoin mining hash rate relocating to the US. Bitcoin mining was largely dominated by China. That's where a lot of the network activity occurred after the China mining ban, we saw this gold rush of reestablishing Bitcoin mining hash rate and compute in North America, which led to some of the first publicly traded Bitcoin mining companies. That's where you had companies like Marathon and Riot and CleanSpark really start to emerge in that timeframe. We were seeing the institutionalization of Bitcoin mining during that era, which led to a lot of these companies acquiring massive amounts of power so that they could ultimately have a pipeline to build out these 100, 200-megawatt data centers that were all going to be allocated towards Bitcoin mining, which made a lot of sense at the time. Bitcoin ran from, I want to say, the lows, coming off the lows of last cycle, I think Bitcoin touched maybe around $4K in the bear market post-2017, and then from that point ran up to the highs of $60K. So the margins on Bitcoin mining were just incredible during that 2021 bull cycle. So all of the investment that was pouring in from these public companies acquiring that land and power made a lot of sense given the economics at the time. And so that's what ultimately allowed these companies to be sitting on effectively gigawatts of power. And then as we've now seen AI really blow up, all starting with the advances that were made with ChatGPT and et cetera, you've seen obviously an explosion in just demand for AI. And we're going through this massive CapEx bull cycle. all of these Bitcoin miners were super well positioned because they already had all of this energized power. That is one of the biggest bottlenecks right now when it comes to just trying to stand up more compute.

[5:21] Marty Bent: Yeah. I think it's been funny watching it all play out because you're seeing the— I mean, going back to the 2021 era, having been involved in Bitcoin mining back then and still to this day, the mad dash, the lessons that you had to learn to acquire power were pretty massive. It was a steep learning curve. And it's interesting watching the AI world get into this, obviously out of necessity, and the amount of power that's needed is almost incomprehensible. And it's bringing a different caliber of capital to it, right? Because you have these hyperscalers, you have Fortune 100 companies really leaning into this, putting tens of billions of dollars of CapEx, trillions of dollars estimated in the next couple of years. And it's interesting to see how they're entering this market and really trying to, I don't want to say bully, but just brute force their way in and really coming to understand the limits of the energy infrastructure that exists in America that Bitcoin miners have become very intimate with over the last 10 years, or more specifically, the last 7 years. And I actually think it's good overall because it's making the public aware that, like, hey, energy systems are important.

[6:49] Brandon Bailey: Absolutely.

[6:50] Marty Bent: And if you want to barrel into this AI future, we really need to figure this out first. This is like layer zero of what we're building here. Totally.

[6:58] Brandon Bailey: I also would add to that as well. Like, I think it also kind of is touching on the fact that I think that there's a lot more education that's required right now when it comes to understanding, like, energy policy, how the grids work, right? You know, the need for more generation, which has been a talking point for a long, long time. And I think that there's a lot of FUD right now, which is really just result of, I think, miseducation. When it comes to the amount of power demand that is out there to ultimately serve this compute and what it ultimately means for local communities, I mean, we saw that with Bitcoin mining. It's kind of interesting because it's like history repeats or history rhymes to a certain degree. So a lot of the criticisms we saw of Bitcoin miners entering certain communities with respect to the power demands, you're seeing a lot of those same kind of arguments being made with respect to these AI data centers. And so, you know, I just think like the, the tensions and the challenges you're talking about with respect to just getting like, you know, your, your actual site energized or getting an interconnection agreement there, those are really, really difficult challenges that you can't just always brute force your way through or just throw more money at the problem to solve it.

[8:19] Marty Bent: Yeah. And so with that in mind, let's walk through this. I think we can do a sort of order of operations of how this actually scales, starting with Bitcoin miners that locked down land and power over the last 5 years. What are the decision frameworks that they're operating now as the AI companies come to them and say, hey, you have these power deals, we have a need for power, what are the sort of economic decisions that are being made by the miners right now? What's the opportunity cost and how do you think they should be approaching the question of, should I mine Bitcoin or should I put GPUs in a data center with the power that I own?

[9:06] Brandon Bailey: Yeah, that's a great question. So I think it really starts first with sort of The easiest way when you're thinking about this decision, you're walking through this calculus, is you can just look at market comps first and foremost. One of the things that I've been looking at just as an investor in this space is that most Bitcoin mining companies maybe trade within a range between 4 to 6 times EBITDA effectively. You have more traditional data center players like a Digital Realty Trust or Equinix that trade closer to 20, to call it 24 times EV to EBITDA. And so there's a massive discount that is being applied to the valuation multiple for using Bitcoin mining as an offtaker of that power capacity relative to more traditional data centers as an offtaker of that power capacity. And so when you look at that relative arb there, there's a huge potential for these miners to just be rerated by changing effectively the off-taker of that power or moving to AI versus Bitcoin mining, just purely from a multiple expansion standpoint. One of the reasons why there's such a large variance between the multiples for those businesses is because with the data center space, and when you look at the economic terms of these agreements, you're getting a 10 to 15-year lease effectively that's guaranteed cash flow. If you look at historically the income stream of Bitcoin mining, it's very volatile. You have things like the halving, which are going to cut your economics in half, and then you also have the fluctuations of Bitcoin price dynamics. You have periods of really, really impressive gross margins in mining, but you also have these longer lulls of more challenging economics. And then you also have more challenges with respect to the economic useful life of the ASICs. And so it really just boils down to the fact that you've got 10 to 15 years of locked-in, pretty much guaranteed cash flow. Your tenant effectively in these leases are the most profitable, largest companies in the world. You've got a Google or you've got an Amazon, you've got a Microsoft effectively as your tenant. These companies generate billions of dollars of cash flow a year. So that lease is essentially viewed as being as good as gold, or if you have a parent guarantee from one of those companies, that lease is pretty much as good as gold. And then the other big thing there as well is you actually can get financing to actually do the build-out. One of the challenges we had in the mining industry was How do you actually establish a credit market to ultimately support that industry? So you had a lot of these companies had to rely solely on equity dilution to ultimately be able to fund the build-out. And then you get into these massive questions of returns on capital. Are we actually creating value here given what's happening with the economics and the dilution that investors are taking on? With these data centers, it's the opposite. you're seeing companies able to finance the bulk of the CapEx cost and to be able to do that at an incredibly favorable interest rate. So that's a lot of what these companies are looking at. And when you compare those two, it's almost like a no-brainer, especially if you can land one of those hyperscaler tenants, you would do that deal all day.

[12:51] Marty Bent: your mind, who's done this the best so far, the transition from Bitcoin mining to HPC?

[12:59] Brandon Bailey: I think for all of the ones that have signed leases, I think that they've done a really, really excellent job. Those companies are Core Scientific, Hut 8, Cipher, Galaxy, TeraWolf, and Applied Digital. I think you really have to commend Core Scientific because they're honestly, they're the pioneer in this. They were the first ones to really make this pivot. They started the wave for these Bitcoin mining companies and helped to enlighten the market on the potential of this transition. I think they have 590 megawatts of CRIT IT capacity, all leased with CoreWeave. And they've already begun delivering that capacity. I think they've done a great job to date. I think Cipher has done very well, TeraWolf, Hut 8 as well, Asher over there. I think all of these companies are doing an excellent job carrying the flag and helping to pave the way for other Bitcoin mining companies to demonstrate this ability. Right now, because the way that I would also characterize this is there's a couple different phases as we go through this transition, right? The first phase is, can you actually sign a lease, which is very challenging. The sort of process of ultimately courting one of these hyperscalers or large neo-clouds is very— it's a very tedious, arduous process. It's very difficult just because of the level of demands and expectations that they have, right? And if you haven't already delivered one of these data centers before, right, they're going to spend an incredible amount of time diligencing you, right? They've got to feel comfortable that you can actually deliver. So phase 1 is really, can you get the lease signed? Can you get to a definitive agreement? Then phase 2 is, can you actually execute on delivering this project on time? And that's kind of where we're at with a lot of these companies. Core Scientific is the first to actually deliver a couple energized buildings, and a lot of the other companies are coming up on that. That's really the other big risk vector, which is can you actually deliver the construction? And then after that, it's really just maintaining and operating the data center. But the upside an investor standpoint is really on phase 1, can this company actually sign a lease? And then 2, can they actually deliver it on time? To me, that's where I think that that's the biggest window of an opportunity for investors that are looking at these companies for their potential to ultimately transition that compute or that power capacity.

[15:57] Marty Bent: For phase 2, correct me if I'm wrong, but it's a bit different than Bitcoin mining, where a lot of the Bitcoin miners, when they would lock down power, and say, okay, I'm going to build a 200-megawatt facility, a lot of them would do a lot of home-baked solutions to actually manifest that Bitcoin mining operation. But from what I understand with the AI compute is that a lot of these providers already sort of have out-of-the-box solutions or maybe design specs that they'll just hand over and say, hey, here's how we operate, build this. And it's not really, you're not really dependent on sort of getting creative and coming up with your own solution. I feel like everybody in Bitcoin mining in 2021 to today was sort of learning on the fly of like how to actually build a data center. But I think to your point about the demands and the diligence that is necessary to get these hyperscalers comfortable with partnering with you, It comes down to, can you actually build to spec what we need? And here's the design.

[17:02] Brandon Bailey: That's absolutely right. It's really a whole different philosophy and approach. Bitcoin mining is like, how can we stand up this infrastructure the fastest and in the most cost-effective way possible? Which, in Bitcoin mining, you could actually be rewarded in a sense for cutting corners or finding ways to, how do I just get this infrastructure stood up in a cheap way so that you can maximize that payback period. You wanted to do that in Bitcoin mining because you have difficulty. It's a race against the clock. You know more power capacity is going to be coming online, or more hash rate is going to be coming online, and that's going to impact your margins. The faster you could get your machines online and energized, that was only going to help you. With high-performance computing and these data centers, it's completely opposite, right? It is much more about, can you build to the spec? Can we make sure that we have all the redundant systems because uptime is vital here, right? And so it's just a very different approach and sort of design philosophy. And these data centers are much more complex than Bitcoin mining data centers and have significantly more redundant systems, the cooling systems, the intricacies of all of those components. It's just a whole different sort of ballgame when you're comparing those 2 worlds.

[18:35] Marty Bent: Yeah. What are your thoughts on the scale of the demand for compute, the individual sites, and the spectrum of scale that may emerge? Because I think obviously, You hear the headlines, Colossus 2 going to be a gigawatt. Colossus 1 was what, 300 megawatts? You have many—

[18:55] Brandon Bailey: Yep.

[18:55] Marty Bent: Hyperscalers going out there and saying, we're going to go build a gigawatt, multi-gigawatt facility. And I think we're beginning to see as the sort of supply gets tapped on these large-scale operations, people beginning to look at smaller scale, like 20 to 50 megawatt, and trying to figure out like, are these viable for for AI compute data centers. And that's, I think, a part of the market that many people are trying to explore. And what are your thoughts on that spectrum of scale?

[19:30] Brandon Bailey: I think the demands for power capacity and compute are enormous. And I think that you're going to see, you know, sort of all, all parts of the spectrum be in demand. To your point, the early innings of this, it was all about the mega sites, the 1 gigawatt site, you know, hundreds of megawatts of scale. And, you know, what's, you know, what's really fascinating about this is like you kind of have to really think about the undercurrent and what's driving it. And it's really, to me, the way that I would describe it, it's the game theory that's at play here. Right. And you kind of have this game theory playing out on 2 different levels. There's the game theory of nation states understand that AI is this massive revolutionary technology, and that we need to be a front leader, or we need to be the leader in this new technology. And so the US is largely in an arms race with China on trying to be the world leader in AI technology, right? Which means that the US needs to foster inside of its borders an environment that can allow this industry to flourish and ultimately make sure that it has the resources that the— make sure that the entrepreneurs inside of our borders have the resources and the access to the raw inputs such that they can go build and create this to try to win that, right? You have that game theory playing out, the US versus China in this race. Then you also have the hyperscalers themselves competing with one another, because these businesses had been more sleepy, less growth-focused, just printing cash. The cloud business is just crushing it, the margins of a Google Cloud, Azure, AWS, just crushing it. And those are massive cash cow businesses for those companies. But with AI, there's a massive risk now to some of the moat that they've established. And so you're even seeing these companies competing with one another. So you have Nvidia competing with Google, Google, Amazon, Meta. They all recognize that this is a massive opportunity for them to sort of grab market share and also protect the moats that they've already established. So each of them are incentivized to spend a tremendous amount of money on CapEx to make sure that they can protect their moat and also try to gain market share. And so those 2 dynamics to me are what are really driving the enormous demand for compute. And of course, you have SpaceX now as well, but all of those companies are competing. And what they can't afford to have is to say, we didn't invest enough money, And we allowed Google to have more access to data centers, which gives them just an unfair advantage, right? Because they didn't invest enough CapEx. So to me, that's what's driving a lot of the undercurrent of the demand for power. So that's one component. And that's where we saw like the demand for these, you know, 1 gigawatt sites, etc. But now that you're seeing a lot more pushback, right? Like the new trend has been the social climate and the political climate around data center development. You're seeing a lot more pushback in communities around wanting these massive-scale projects. And we've even seen some projects more recently be canceled or paused because of just the pushback in local communities. So that's where I think that there's more of an opportunity for these smaller-scale sites. where it's like maybe we don't go 1 gigawatt, let's go 50 megawatts, let's go 20 megawatts, right? It's a little bit easier to get through and get approved. So that's where I think you're starting to see more demand or increasing demand for those sites. I think inference training is also driving demand for those smaller sites. So I think that there's a massive opportunity on that front right now for some of the smaller players And I think that you're just going to see more overall demand across the spectrum. But I think it's going to be easier for people to get things stood up in the smaller sites. And then the last thing I'll say is that I think that because of the level of difficulty with respect to just getting power energized, I think that you're also going to see the data center world take some lessons learned from the Bitcoin mining space with respect to these modular data centers, chicken coop style data centers, and using those as a way to just to try to get compute stood up more quickly. So, you know, there's a lot of interesting early trends that we're seeing across this space. But, you know, I ultimately think that you're going to see the demand across the board. What's up, freaks?

[24:45] Marty Bent: This work was brought to you by our good friends at BitKey. Guys, things happen, things get lost. You need to replace your phone. Life happens. And BitKey has been designed with this in mind. So if you're looking for the easiest way to secure your Bitcoin off the exchange, go get a BitKey. It doesn't come with any seed phrases. There's no single point of failure. It's a 2-of-3 multisig. You have your device, you have the mobile app, and Block stores a key in the server. So you have collaborative multisig there. On top of that, the new BitKey has a screen so you can actually see and verify what you're approving. You can look at transactions, addresses, account changes. The difference between trusting and knowing, uh, has been built into the new BitKey with the screen. They've changed code delegation, uh, and other advanced features like inheritance recovery. If you're looking to get your Bitcoin off exchange, you haven't done so, and you're looking for the easiest way to do that, go get a BitKey. Uh, you can use our code today, TFTC, to get 10% off the new BitKey. So go download the BitKey app. Use the code TFTC to get 10% off the new BitKey and start your self-custody journey today. This episode has been sponsored by our good friends at BitKey. Sup, freaks? This rep was brought to you by our good friends at Lygos Finance. Celsius, BlockFi, FTX—

[25:52] Brandon Bailey: they took your Bitcoin and gambled it away.

[25:54] Marty Bent: Lygos can't because they never hold it. Non-custodial lending on Bitcoin's base layer. Your keys, your collateral. It's verifiable on-chain. Go to lygos.finance. Tell them that TFTC sent you. Now, to your point about the hyperscalers, the Googles of the world, it was funny how quickly not funny, but just interesting to observe how quickly they went from using their cash flows to buy back stock to the CapEx expenditure, to the point where Google, what did they do? $84.5 billion in an equity raise that Berkshire Hathaway and others participated in. So literally issuing stock to invest in this CapEx buildout.

[26:31] Brandon Bailey: They're in full growth mode.

[26:34] Marty Bent: And I think that's a big question on everybody's mind. Is this a bubble? And I know I have my thoughts, which I don't think it is. Like, I think there could be bubbly aspects of some parts of the market, but when you look at the demand for tokens today as it stands with the agentic economy being what, 5, 6 months old in earnest, and then you think of robotics coming down the line and you think of the demand for tokens that exists today and you just project forward as adoption continues to increase at the individual and enterprise level. And then you get into robotics, and I think— I don't even think we've seen the tip of the iceberg in terms of how many tokens we're going to need to effectuate this agentic and AI-driven economy.

[27:22] Brandon Bailey: I totally agree with you. My line of thinking is pretty much aligned with you, which is like, it's not exactly the same as 1999. I mean, you're already seeing the demand Right. From like a usage perspective, you're already seeing the revenue. You're already seeing like the demand side of the equation from a profitability standpoint. Just looking at, you know, Anthropic's revenues, if you look at like Google's revenues, even for like Gemini, like you're seeing the signs when these companies announce their earnings that the profitability element of this is real. And then I also agree with you that like there are several different waves of this. Like right now, you know, there are more people that are not using AI, right, than are using AI, right? Like we're still very, very early in getting people to just be using LLMs in some form or fashion on a daily basis, right? So I still think the adoption curve of using this technology is still very early. It's early from an enterprise level. It's early from just an everyday like consumer perspective. So I still think we have a lot more runway from that perspective. And then exactly like you said, you also have sort of the next phase, which is like, once we've built the intelligence layer, right now, how do we start putting that intelligence into things like robots? And the demand for that is also going to be pretty insane. And when you think about that too, it's like, we're gonna need a lot more memory, we're gonna need a lot more CPUs, GPUs, like, There are so many elements of this overall AI trade that are going to have tailwinds behind it from my perspective for many, many years to come.

[29:14] Marty Bent: Yeah. And I guess that's the— I mean, maybe I think obviously one of the big memes the last couple of months is your token usage has been subsidized by Anthropic and OpenAI, and they're going to need to really push that, the cost that they're bearing onto the end user, which is beginning to happen. You have companies like Uber saying we blew through our budget in the first quarter, first quarter and a half of this year, first 4 months of this year. And I think that's one thing I'm still trying to wrap my mind around. It's like, okay, what is the actual cost of a token, particularly from a frontier model versus an open source, open weights model? And how does that affect the demand for power in the long run? Like, the point I'm trying to— like, does token usage get so efficient and so cheap that the return on the capital invested in the infrastructure compresses over time? Or is the demand for tokens, even if they are becoming more efficient and cheaper, just going to be so massive that it doesn't really matter? Like, how are you Thinking of that calculus.

[30:29] Brandon Bailey: Yeah, I mean, I'm also trying to wrap my head around that efficiency dynamic with respect to tokens per watt or however you want to characterize that efficiency metric. I think it circles back to, I believe it's Jevons paradox that people talk about, which is like, the cheaper something gets, the more you'll want to consume it. I feel with my own personal just usage of LLMs, like, that's kind of where I'm at. I'm like, the more tokens you're willing to give me, the more tokens that I'm going to consume. Or, you know, I'm going to look at it as like, okay, well, I might have been restricted from doing these other things I want to do because I've hit my usage limit. If you give me more tokens now, there's more things that I can do. So I think that You know, as things get more efficient, you will also have probably step changes in the overall consumption and demand that probably offset a lot of the efficiency gain and continue to keep the overall demand level or the need from a power sort of perspective, you know, elevated or there. You still have, like I said, you still have that undercurrent of the game theory of Meta is competing with Microsoft, is competing with Google, is competing with Amazon, and they're all going to want to make sure that they have enough runway such that they can protect their moat, protect their margins. Then there's going to be all kinds of other things that I think that we haven't imagined as well that are just going to allow for more consumption, more demand for using the tokens for other use cases, other applications. So that's where my current thinking is on it as we think about that, but it's going to be very fascinating to track that trend over time.

[32:29] Marty Bent: Yeah. And bringing this back to energy infrastructure, I feel like in the next year, all the nooks and crannies of available power on the grid that's willing to either— that's either available to be acquired by companies looking to train or run inference on these models, or that is willing to transition from a use case that is not AI compute. We're going to hit that limit of where like, all right, we've sucked all the energy, all the available capacity out of the grid, and now we need to expand generation? How do you, how do you view the generation expansion playing out?

[33:12] Brandon Bailey: I think that that's going to be another big wave, which is like the, you know, bring your own power, behind-the-meter power at a lot of these campuses. So, you know, I think to your point, the generation piece is, is going to be interesting to see how we ultimately solve that. You know, obviously there are plans for like nuclear projects and things like that to try to bring more baseload. But the one of the big challenges we're already observing with some of that is the, the timing mismatch, right? Like how long does it take to actually build out or stand up? You know, at least for a nuclear plant, for a nuclear plant, it could take 5 years or longer. The demand is here. We need— we can't wait for that. We need this demand now. But I think like the next phase of it is like, how do you bring more like gas peaker plants or other things online, or whether it's solar or what have you, all different forms of generation to these data centers. And you're seeing some early elements of that. But I think that that's going to be the next sort of wave, just because I think the grid interconnection piece is going to become more challenged. And then I also think if you are able to tell the narrative of having brought your own power to a community, then you're not necessarily taking anything, you can make this point that you're not taking anything away from the local community. So I just think that that's also just gonna be viewed more favorably from a social and political perspective. But that's also a way that you can increase the amount of capacity at where you already have some of these existing campuses. So I fully, fully expect that to be another big wave. And we're already seeing companies kind of hint at this piece.

[35:00] Marty Bent: Yeah.

[35:01] Brandon Bailey: I've seen Tyler Page on various talks really talk about the behind-the-meter power opportunity at many of their existing campuses. Core Scientific and Adam Sullivan have also talked about that. From an investor standpoint, I actually find that that's an element that I don't really think is getting enough attention, which is what is the incremental power capacity potential from, you know, bringing additional or, you know, behind-the-meter power solutions to an existing site to expand the potential leasable capacity at, you know, an existing company's, you know, sites or campuses.

[35:43] Marty Bent: Yeah, it's so fascinating. How big is this opportunity? Like, what is happening right now, like, in terms of just like broad Implications on humanity and markets specifically. Again, going back to everybody drawing parallels to the dot-com bubble, I don't think the parallels are as clear as many people think they are. I think they're actually completely different. And I think there's a lot of doomers out there who are like, it's a bubble, it's going to pop, it's going to pop. Look at SpaceX. Yeah, it may be overvalued right now, but again, you look at like what Midjourney launched last night with that MRI competitor where you can do scanning, image scanning of bodies in a minute. And it's like, this technology is valuable.

[36:26] Brandon Bailey: Yep.

[36:26] Marty Bent: If it works as advertised, you could replace every MRI in the country, in the world in the next few years and have a better product that saves millions of lives. And so there's a there there. And I use the tech, and that's another thing we can get to eventually is like we're talking about the infrastructure build-out, but on the application usage? Like, I know you have built a product using this and we'll talk about DiMetrics, but I think just first trying to paint the picture of what's happening here from an economic disruption standpoint and the opportunity of like wealth creation that exists.

[37:10] Brandon Bailey: It's remarkable. I think it's a generational wealth creation opportunity for people. I think it's already largely been that if you just sort of look at how some of the various names across the broader AI sort of bottleneck thesis or bottleneck trade have performed. So I'm talking about, look at Nvidia over the past few years, Micron, SanDisk, AMD, Intel, right? the returns on some of these names, I mean, you're up 3 to 10x across a lot of these. You could have almost just picked any semiconductor stock and had generational return type of performance on some of these names. And so from an individual investor standpoint, or even if you're an institutional investor, It's been incredible market performance just on the back of what's happening here with respect to the amount of CapEx that's being poured into the space. But then when you talk about, so what does this mean for people's everyday lives? I mean, it's incredible and it's still hard to almost fathom or imagine some of the possibilities. I mean, we'll talk about it, but DiMetrics is for me personally, one of those things. I've always wanted to build an application of some sort. I have no coding experience, knowledge whatsoever, but leveraging these tools, I was able to actually take something, an idea of mine, and actually turn it into some kind of tangible product that other people could use or some other sort of application. So when I think of the power of this technology, it's a, it's a massive force multiplier for people, right? Just the extension of the, the, the, like the, the amount of additional knowledge that it can help provide an individual, right? You can basically learn anything now, you know, and get that information instantly. Like prior to the LLMs, right, you could always go to Google and do a search, but that wasn't always effective. You can basically learn about any industry, any incredibly complex topic or subject and get an incredibly accurate or reasonable response. You could have it provide you direct sources to like, you know, peer-reviewed papers, what have you, and even have the system explain it to you, like give you an ELL 5, right? Which is remarkable. So like, I think the tool really empowers people to learn about whatever they want. I mean, which is massive. I mean, I just think that that's massive for people. And it's really hard to kind of quantify, like, if you wanna put this into like a GDP perspective, like, what does that actually mean for productivity? It's sort of hard to actually quantify that at this moment in time. But you know that it actually has massive implications for society just in terms of leveling the playing field, right? Intelligence is now a commodity. That's, you know, you can't say enough about the importance of that. Ultimately for the world and for civilization.

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[44:35] Brandon Bailey: I totally agree. I think that what you just laid out is sort of maybe the counterpoint to the— I kind of view it as a little bit of like the counterpoint to the narrative that AI is going to take all the jobs, which is like using LLMs is a skill. Like you have to, you have to learn how to maximize the output of the system. It's not something that you can just like go to and say like, I'm just going to give it a prompt and it's going to just do all the things. You actually have to work on your prompt engineering. It's one of those things where you just have to spend time understanding the system and how it works in order to get better at it. I think that the people that invest that time or token maxing, so to speak, you almost have to go through a phase of token maxing so that you can learn how to use this system. What are the LLMs good at? What are they not good at? What additional context do I need to include in my prompt to make sure that I can one-shot this output versus going through many iterations and back and forth with the LLM? So it's a learned skill. And I think that that's where we're at because we're so early in this inning. People are still trying to figure out how to use the system, how to get the most value out of the system. But I think the people that are investing the time to learn that right now are going to set themselves up in such a way that they're going to be some of the most valuable people for enterprises, right? Because companies are going to implement strict budgets, right? Everyone gets a certain amount of tokens. And then it becomes that efficiency game. How do I get the most output for the least amount of tokens possible that I can spend? And that's going to be one of the— I think that's going to be the primary way that enterprises start evaluating individuals or employees, which is like, how much output can you derive from leveraging this tool? And to your point about being forced to actually use it, if you're one of those people that are anti the technology or a laggard, you're just really setting yourself up to be at a tremendous disadvantage relative to potential, some of your other employees internally. And so it's kind of an interesting incentive mechanism where you're doing yourself a massive disservice if you choose to not use the tool. I mean, same with the internet age, right? Same with the internet age. I think it's really no different In this instance here.

[47:20] Marty Bent: What's your personal journey been like mapping the territory and getting used to these tools?

[47:23] Brandon Bailey: Man, it's been— it's like a roller coaster. It started out as like, wow, this is amazing, it can do all these things, this changes everything, to wow, there's a— you know, like that's how it started. And then the more I used it, I was like, wow, it's missing all of these— it's missing things. Like I have to provide additional context. You can't— like, I guess the way that I would describe it is like the LLMs aren't great at assuming— like, they're not great at seeing around corners. I'll put it that way. Right? Like, unless you give an LLM like the context of I am trying to build this system, we need to build a really strong foundation. these are all of the potential pitfalls I'm trying to avoid. Here's why I'm trying to avoid it. Can you help me think of a way to craft the best possible system that can avoid these potential edge cases?

[48:23] Marty Bent: Right.

[48:24] Brandon Bailey: If you don't give it that context, it'll just give you an answer without thinking ahead to where that thing could break down, which leads to a significant amount of iteration and back and forth and having to rebuild. And so that's where I think, to me, that's where it kind of comes back to that token maxing thing. It's like I had to go through a lot of that to get to a point where I understood like what, what things do I need to provide this LLM to make sure that I don't have to spend a lot of time and a lot of tokens doing this back and forth. So for me, it's really been about like, how do I create the right processes effectively when I'm trying to build something to make sure that I'm setting myself up for success without burning a ton of tokens. And now that I've kind of learned how to do that a little bit better, I'm kind of like back on the upswing of like, this is amazing. And you also kind of work through the evolution of the different models. So you kind of see the different step changes as the models improve. And that just kind of reinforces everything.

[49:31] Marty Bent: as well.

[49:33] Brandon Bailey: And the last point I'll leave you with, which is like, you know, when you think about where we're at with the improvements to the models, think about how much compute like Opus 4.6 was trained on or like, you know, 5.5 was trained on. And you think about how much more compute is now being energized, right? The bulk of what we've been talking about from a power capacity and utilization standpoint Like models aren't even training on the power capacity that we're talking about that's coming online. So when you really think about the future step changes of these models and what they're going to be capable of with all the new compute and power that's being thrown at them, you know, you got to be really excited for what could become possible in the near future here.

[50:23] Marty Bent: Yeah. Yeah, it's insane. And it's like, to your last point there, it's like, because the mod— I mean, I played with Fable 5 before it got banged out behind the bushes by the US government. Banned after 2 days. And I was like, holy crap, this thing is really good. Step up. Opus 4.6 for the written word, for the— Opus 4.6 is the brain of our OpenCLAW agentic system that we've built out here at TFTC. it does very well. I can't imagine what happens when you have 2 more step function improvements at a similar cost to what Opus 4.6 is today. It's mind-boggling to think of, okay, how much better is this going to get over the next year or 2?

[51:13] Brandon Bailey: Totally.

[51:13] Marty Bent: And then to your point about that learning curve too, I think that's one thing that over the last 2 months has really, going back to the context and Making sure that you're prompting it the right way. And I think having sort of persistent memory for context for your agent is imperative. And we've been in the process of building out our company brain for the last 2 months, and it is insane how just building knowledge graphs and semantic search functions within the server that our agent runs so that it can ping first before it burns tokens, so it gets all the context from the knowledge graph and the—

[51:52] Brandon Bailey: Yeah.

[51:53] Marty Bent: semantic search system that we set up, and it's just able to one-shot things in our voice, and it knows our business inside and out. And I think once you understand the tooling that you plug into these LLMs, that's when you really unlock the superpowers.

[52:09] Brandon Bailey: Totally. Totally. It's like the LLM wiki or like Coparty's concept. Massive. That's a huge one. The memory files, I agree with you. It's kind of like once you figure out that setup, right, it's like all the connectors, the skills, all of that stuff really unlock significantly more value for you and also allow you to reduce the amount of the token burn. But like, that's what you kind of have to go through the trial and error of like figuring out, like, how do you map out the system, right? It's like you kind of got to build your own system map for how you're going to operate this system. And then once you've kind of perfected that, you really start to see the fruits of that labor.

[52:53] Marty Bent: Yeah. Wild times. So let's talk about DiMetrics. I mean, you mentioned it. This was an idea in your head for many years, and now with the tools, you were able to build it. And I've been playing around with it this week. And if you're a nerd on the forefront of following what's happening on the infrastructure buildout, this tool that you've built is incredibly powerful.

[53:18] Brandon Bailey: Yeah, so the way that I would— the simple one-liner I would give for it is it's really a tool for the individual investor that is interested in the digital infrastructure space. So market intelligence platform for digital infrastructure specifically. And the whole concept and project, it started as a spreadsheet. from my days at Galaxy and working in Bitcoin mining, I would track a lot of these companies' sites and all of this, all the financial information about how much hash rate they had, how much Bitcoin they mined, and all of this stuff, literally just via spreadsheet. As these companies were making the transition, I was trying to do a similar thing where I'm just trying to track, map out, Where do they own sites? How much power capacity they signed a lease? You know, kind of doing all the typical work that you know an analyst would do if you're looking to be an investor in this space or in these companies. And it was with the LLMs where I was like, maybe I can automate this workflow, right? It takes an enormous amount of time to ultimately comb through SEC filings and to go through investor presentations and and keep up with. all of the information that is required for you to map this out. And with every new company going into this vertical, right, it becomes even harder for you to ultimately be able to track and follow everything that's happening in the landscape. So for me, I was like, I wanna take what I'm doing in spreadsheets and see if I can automate that workflow into basically a dashboard effectively. That I could just use to monitor and track what's happening in this space without having to commit so much time to doing the manual labor. And, you know, what I ultimately started building out was like the backend database to support this, started trying to leverage the LLM to ultimately learn how to read through the investor presentations, like basically map out this whole thing. And where I kind of see the real value proposition for this dashboard is when you think about other market intelligence tools like a FactSet, like a Bloomberg, there's Koyfin, there are other newer ones as well that do an excellent job of what they provide you. But what it is is more high-level information, right? They're giving you the— here's the line items directly from the balance sheet, from the financial statement. Here's what the total debt number is. Here's what the cash balance is. is, here's what the revenue is, here's what the CapEx is. What I wanted to build was something that went super granular and super deep into the individual components of specifically the data center space. I wanted to build a market intelligence tool that was going to tell you what is every site that a company like Cipher Mining owns. What is every site they own? What's the gross power capacity of that site? What's the leasable power capacity of the site? When is that power going to be available? What's the energization timeline? Where is it located? Who's the utility provider? I wanted to go super, super deep into creating a tool that would give you the industry-level granularity as it relates to these companies. So if you're an investor in this, you don't have to still comb through all of those filings to pull out this information. And so that's what the DiMetrics market intelligence platform ultimately tries to provide. And then it ultimately is trying to give you that level of information through the MPC connection. And so it's like, how do you take that level of information and incorporate it into whatever agentic workflow or process that you already are familiar with working with. And like, that's what I think the real value add or like the biggest sort of feature is of the platform is being able to connect DiMetrics database to your Claude or Codex or what have you, and then tell it or ask it like, give me every lease that Cipher has signed, right? What's the lease rate? And then ask it like, how does that compare to TeraWulf? Or rank all of these companies, show me the top 10 companies that have signed like the most profitable lease from a gross margin perspective, right? So that type of analysis would have taken many, many hours for you to map out as an individual. Now it's just a simple prompt. And like, that's the value proposition. That was something that I was ultimately just trying to work through. on my own as an investor, as somebody that actively trades a lot of these names, and ultimately wanted to provide that type of level of market intelligence to a broader group of people. So I wanted to do that. And then the other big thing that I wanted to do is when, again, when you think about the broader AI bottleneck trade, I think of groups like Centrini Research, FundAI, semi-analysts that all do amazing, incredible, excellent level of work and research as it's related to this trade. But a lot of what they're mostly focused on is going super, super deep into the chip component, the semiconductors, the hardware, the compute, and they do a phenomenal job with the research that they do. But I feel like a lot of— I feel like the data center space, and more specifically, these miners converting their their capacity are somewhat undercovered. They're not as strongly represented relative to the semiconductors, the Microns of the world. And so that's something that I wanted to try to bring a little bit more light or shed a little bit more light on is highlighting the opportunity that these companies present as a bucket within the broader AI bottleneck trade. With the terminal element of DI Metrics, I try to do exactly that, which is show you the performance of these Bitcoin miners relative to the semiconductors, relative to the AMDs, the hyperscalers, etc. These companies have actually provided almost not quite as good of a return as the memory companies, but actually better than a lot of the other components. storage, et cetera. I feel like that is not talked about enough, and I still think that there is an incredible rerating opportunity for many of these names. I really wanted to just try to bring more attention to that, as well as highlight exactly what is that opportunity and how do you think about it? How should you think about this?

[1:00:30] Marty Bent: Yeah, I love it. It highlights on the application side, somebody like you, an incredible analyst, who's been analyzing the infrastructure build-out of Bitcoin mining and now HPC compute or AI compute as well. I keep making that mistake because Bitcoin mining is technically HPC as well. But the AI compute, like, and you have this experience and this depth of knowledge that very few people have, and you're able to leverage the tools to build a tool that other analysts can tap into and you can get paid for it. And it's like, That's like you have your knowledge graph of DI metrics that only somebody with your experience knows how to map the territory of this particular layer of the infrastructure build-out, the sub-theme within the AI build-out. And you can leverage the tools to build a tool that others can leverage to incorporate. And there's definitely, like you mentioned, SemiAnalysis focused on chips, like they can then expand. Yeah. their depth of knowledge and the AI build out into the infrastructure layer as well. And on your point of rerating, again, bringing back the fears of the bubbles, where would you say we are in the process of rerating these companies that are earnestly making this transition, are going to execute, and those who are Excuse me, those who are in the process, and if they do execute, like, what do the reratings look like from here?

[1:02:05] Brandon Bailey: Great question. So I kind of break it down into 2 separate buckets in terms of how I'm looking at these companies. There's the companies, the Bitcoin miners, if you will, that have signed leases, and those that have not signed leases. And so the way that the rerating process works, there's kind of 2 big phases. The first phase is, can you sign an initial lease? We touched on this. That's the biggest rerating upside you see. Once a company has signed its first lease, that's where you see the largest pop in a lot of these companies. That's been true for Cipher, Hut, TerraWolf. That's why some of these companies are up as much as 5 to 7x. from the point that they had no leases signed to signing that first lease and where they are today. A lot of it has to do with the fact that in the early days, because it's changed a little bit, but just to give the context, the big opportunity was the fact that these companies were highly correlated with Bitcoin. If you look at Bitcoin's price performance, more recently, let's say over the last 12 months, 12 to 18 months, Bitcoin hit a high and then it started to sell off. Many of these miners sold off with Bitcoin despite the fact that they had announced that they were looking to pivot to HPC AI. The value of their power portfolio, the value of their megawatts was declining because Bitcoin was declining, and you basically had the market not believe that they had any real potential of converting to AI compute, which created a massive opportunity from a value perspective. So then once you had Core Scientific sign its first lease, you saw this massive rerating because the market was basically assigning like a 1%, very low probability to that ever happening. And so once you saw that first lease get signed, you saw a huge step up in the probability, which caused an enormous sort of rerating or revaluation of the megawatts of those companies. So that was sort of the earlier opportunity that has resulted in pretty tremendous upside. But then even after a company has signed a lease, there's still a pretty large discount that gets applied to even names like a Cypher, a Hudday, a Core Scientific. And that second component is the market assigning a probability of them being able to finance the build-out of the compute, and then them also being able to actually deliver on delivering the building, so the construction risk. Once they raise the capital, which many of the companies have been able to do more recently, that de-risks the project. And so there's a little bit of a rerating on signing a very attractive debt financing. And then there's a rerating, again, once the company delivers a successful shell on time, that construction risk effectively goes to zero. And so the easiest way to look at it from my perspective is if you look at Digital Realty Trust or these traditional data center players, that's your new benchmark. And these are effectively real estate plays, and the way that you evaluate real estate is you can look at things through the lens of a cap rate. You could say a stabilized cap rate is roughly 6%. A DLR in Equinix, they're being valued at a 5.5% to a 6% cap rate. These miners at any given time could be valued at a 9 cap. Cipher could be valued at an 8.5 cap or a 9 cap. The delta between where a Cipher is trading and a digital realty trust, that's all the market's discount on the probability of that. That's the execution discount, effectively. That should ultimately trend closer to parity once a company has delivered a powered shell, once they've delivered on a lease. And so what's really fascinating is that you can track that relative spread of the cap rate daily, just looking at the market performance and how these things are trading. And so that is a potential market signal. So that's one of the things that I look at And you could say, hey, these miners have sold off exponentially. We saw a lot of this with the volatility in the market just with the Iran war and the back and forth with Trump's tweets. And every single time you see a blowout in that spread, incredible buying opportunity. That's been one of the ways that you could trade it. Now, you're also seeing an emerging opportunity with the smaller names. We talked about this. That's the smaller Bitcoin miners that maybe have 50 megawatts of power capacity, they've been an afterthought, there's still a massive rerating opportunity for them through the same type of cycle and phase. That's really what I'm trying to triangulate on and highlight as the opportunity, and talk through that overall revaluation process or timeline. But I'll pause there. I know it's kind of a lot. If there's anything you want to dig into, I'm happy to more, but that's kind of how I see the opportunity.

[1:07:33] Marty Bent: Yeah. Well, I mean, it's a big land grab right now. I guess that's the next question I have is how much leverage do these miners who have power and land have in these negotiations? And how much of this is a lotto ticket versus, yes, you may have the land and power, but you've got to execute on the back end and bringing back the fact that A lot of these AI compute, the people that are looking for infrastructure for their compute come with a spec and a design. Like, what it, like, is the execution risk significantly high or is it like, hey, you have land and power, you have the leverage. It's like, how hard is it to figure out how to get this thing, get a lease, get the financing, get it stood up and energized?

[1:08:22] Brandon Bailey: The execution risk is significant, and that's why you have to bisect the field a little bit. If you're talking about a Hut 8 or Core Scientific, at this point, because they've already done it, they've proven it out, any incremental lease is going to be less risky than the prior one. There's a compounding effect to this, of course. For the smaller miners, Yeah, the execution risk is even higher just because they have less financial resources. Maybe they have smaller teams, right? But that's sort of your risk-reward upside. But where it becomes really interesting is that the value of power that is already energized effectively, or the power can already be drawn, is so valuable right now because of the demand and the constraints and all of the other growing challenges that those companies could pursue a JV-style structure or other structures where they could potentially contribute that land and power at a significant markup to where it's currently being valued. You have some of these smaller miners where their power portfolios are only valued at $200,000 a megawatt to $400,000 a megawatt, they might be able to contribute that land and power into a JV at a value that's $1 million or more a megawatt, right? And then lean on the balance sheet and financial strength of a JV partner to help de-risk the execution component, right? And then earn cash flow at whatever their pro rata share is through the JV. But even a structure like that is incredibly accretive for these smaller miners, just given the literal markup that they can ascribe to the immediately available power that they have. And that's what people aren't seeing. And it's a little bit of a snowball effect. Like I'm saying, once you get that first deal done and you've proven that you can execute, it becomes a lot easier for you to get that second deal done, and you might not have to give up as much of the economics in order to get it done. That just gives you a significant tailwind and runway for what these companies could ultimately become. It's really a lot of these smaller names that have not been covered. It's a Sphere 3D, it's a Greenwich, it's what was formerly Maasson, but a big digital energy, That's a DigiPowerX, that's a DMG Solutions, that's a Soluna, and there's many, many others. The subset of these companies is much larger than just TerraWolf, Core Scientific, Cipher, Hut 8, Wolf. That's what I want to shed light on. There's a large opportunity here, and you can pick and choose your spots across the risk spectrum, But the overall rerating opportunity, just in terms of how the power capacity is being valued to where you see a stabilized project being valued, is tremendous, tremendous upside.

[1:11:52] Marty Bent: And I guess to wrap it up, just thinking about not only the opportunities that exist for these individual companies, but I think for the localities too, like wrapping up with, I think obviously ERCOT leaned into this heavily with Bitcoin mining and now AI compute, and there are laggards across the country, some NIMBYism going on. And I think to your point earlier about the narrative and the misconceptions around what's actually happening, like what's going to happen to counties or states that say, we don't want this here? And what opportunity are they foregoing if they neglect to embrace this buildout?

[1:12:37] Brandon Bailey: I think similar to Bitcoin mining, it's a bit of a missed opportunity for just job creation, but more importantly, to bring additional generation to your local county. To me, the simplest way I can explain it, it's supply and demand. Everybody wants more generation, but generation is not going to come if there's no off-taker. People have to be able to make money. And so if you're a county that's pro-data centers, there's a way to kind of take advantage of this CapEx boom cycle and also find a way to bring incremental generation capacity, right, I think, to your county, which could be net great, right? It's also a way for you, depending on, you know, your locality or what grid, what have you, it's also a way for you to maybe bring more renewable energy or more baseload, whatever it is, just more generation in general is great. But it's also an opportunity for you to kind of think about that mix, the grid mix or the energy mix, and also find ways to help to incentivize or shift that in ways that maybe align with whatever your view is in that county. So like, to me, that's what I kind of think the missed opportunity is. But ultimately, counties, states, they all have to compete. And so, you know, I think it's, it's fine if a county doesn't want a data center. You're just choosing to not play in the game. But that may come at future consequences of just you may struggle to get new generation and you may actually end up seeing power rates increase in those counties that were anti-data center. I actually think that would be beautiful if you actually saw power rates drop in the counties that were pro-data center versus the ones that were anti-data center. But, you know, time will only tell.

[1:14:31] Marty Bent: Yeah, you can see electricity dropping, property taxes maybe dropping, because that's another thing I don't think people recognize, like the sales tax on the electricity, depending on where you are, it's just generating incredible revenue for these counties.

[1:14:45] Brandon Bailey: Proper conditions, that's a massive opportunity. I mean, you could mandate however you want to regulate it, but there's opportunities to say, hey, if you build a data center here, you need to contribute X amount of capital into education fund or whatever it is. But there are ways to take advantage of this growth and to capture some of the capital to invest back into the community in various ways. I think that's an absolutely excellent point.

[1:15:12] Marty Bent: Yeah. All right, I lied. That wasn't the last question. The last question is, where does Where do people mess up here? Because you're looking at like queues for interconnection reaching tens to hundreds of gigawatts. It reminds me a lot of ERCOT in '21 and '22 where Bitcoin miners are coming and they're like, or people saw the sort of gold rush of the exodus out of China of hash rate to the US and they were like, yeah, I've got land, I've got power, but they're really just in the queue of ERCOT. having to wait. And then they were going on the back end and selling the dream of, I'm going to get this much power in a year or 2 and we're going to be able to build this massive mining operation. Looking at what's happening with the AI buildout, particularly as it pertains to locking down power, it seems like that's happening again at a scale that's an order of magnitude higher. How do you see that playing out?

[1:16:07] Brandon Bailey: I think you're absolutely right. I think there's a lot of sites that ultimately won't end up ever being built. And I think we saw this exactly like you said in Bitcoin mining, but that's part of the challenge as an analyst if you're trying to invest in this space, which is figuring out what's real and what's not when we talk about pipeline. And a lot of companies talk about pipeline as being real when exactly like you said, you just bought some land, there's no load study that's been conducted, you don't have an interconnection agreement or any real visibility or line of sight into if you're ever going to actually be able to draw power at that site, and if so, to what degree or to what capacity. And you have a lot of people that are marketing these pipelines without having any of those things in place. And one of the challenges as an investor is understanding what risk weight or what probability do I need to assign to a company's pipeline and why? What's the right probability given these various milestones that you could ultimately have in place? But I think that that's another element that makes me so bullish on the Bitcoin miners, which is the grid interconnection queue, or just the request for connectivity, are enormous. A large majority of those are never actually going to be permitted, or they're certainly not going to be permitted on a timeline that is anytime soon. And so to me, I think it just reinforces the incredible value of having already available power, which all of these miners do. And I think that as that plays out, and as more people realize that only 10% to 30% or some small percentage of the overall queue or request will actually be built, I think it's just going to reinforce the value per megawatt of the power portfolios of the companies that already have it. And then you throw in there the political risk as well, and that's just another force multiplier on the value of the companies that already have the energized or approved zoned power capacity.

[1:18:31] Marty Bent: All right, I lied twice, but I promise you, it's the last time I lie. Last question, what does this mean for Bitcoin mining moving forward?

[1:18:39] Brandon Bailey: This is a great, great question. What does it mean for Bitcoin mining? It actually makes me pretty bullish on Bitcoin mining in the more medium to long term. I actually think what we're seeing is just an acceleration to what many, many miners talked about for many years now, which is like Bitcoin mining is going to gravitate towards the stranded forms of energy. And I think that The massive race for AI compute is just accelerating that. So a lot of the grid-connected power capacity is likely going to be allocated towards AI. The mega sites, like, we're moving away from these mega sites. And I think that Bitcoin mining is going to gravitate towards those stranded forms of power. And it's also going to be more distributed. It's going to be about your 5-megawatt site, your 1-megawatt site here and there, that is more of a stranded form of energy, which you could argue is actually great for the health of the network because it makes Bitcoin mining a little bit more distributed and potentially decentralized, which I think is great. I also think that you could end up in a scenario where you just have less overall power capacity allocated to Bitcoin mining. If you have Bitcoin price recover rapidly, the power element, you're not going to have as many people able to quickly plug in ASICs. You could end up in this era where hash price is actually appreciating, because people just don't have power availability to actually go chase those economics, which could lead to a very compelling golden era of Bitcoin mining economics, which I certainly hope for. I think that we should also end up in a scenario where we hopefully move away from the oversupply of ASICs. That's been one of the issues that has really plagued Bitcoin mining, which is just Bitmain, MicroBT, Bitdeer, they're all trying to preserve their capacity at the foundries. They have to continue to mass produce these ASICs despite there not being demand, so you have this massive supply glut of machines. Effectively, and I'm hoping that we end up in a world where the supply-demand between ASICs and the amount available power ends up in a more balanced and normalized regime, which I think would make mining economics more durable over the long run. That's some of my medium-term to longer-term thesis for Bitcoin mining, but I'm pretty excited about it in the medium to long term.

[1:21:18] Marty Bent: I agree there. I think it's overall bullish for the network. Many people, again, like I said in the beginning of the conversation, are like, oh no, AI's taking the wind out of the sails of Bitcoin mining and that's bad. It's like, well, no, I mean, everything's bullish for Bitcoin. Everything's bullish for Bitcoin. We're going to distribute hash rate. It's going to be geographically distributed. This will force ownership distribution to be more distributed. as well. I think this is overall good. And then I think also for countries outside the United States that aren't taking advantage of this AI compute buildout, like there's going to be opportunities for Bitcoin mining to land there as well. And while I do like my hash rate to be American-made, it is good to make sure that it is geographically distributed around the globe in different jurisdictions. I'm bullish as well. Brandon, this was an incredible conversation. Thank you for taking some time out of your day to do it. I think you're doing incredible work. And for anybody listening, make sure you go to dimetrics.ai to check out what he's built. And if you're curious or if you're on the beat of covering this infrastructure build-out, Brandon's built an incredible tool.

[1:22:35] Brandon Bailey: Thank you. It was a pleasure, you know, to, to be able to come on the show. I really enjoyed the conversation, um, you know, as always.

[1:22:44] Marty Bent: Awesome. We'll do it again at some point because I'm sure this, uh, this theme's not gonna slow down anytime soon.

[1:22:49] Brandon Bailey: Definitely not. Definitely not.

[1:22:52] Marty Bent: All right, peace and love, freaks. Thank you for listening to this episode of TFTC. If you've made it this far, I imagine you got some value out of the episode. If so, please share it far and wide with your friends and family. We're looking to get the word out there. Also, wherever you're listening, whether that's YouTube, Apple, Spotify, make sure you like and subscribe to the show. And if you can leave a rating on the podcasting platforms, that goes a long way. Last but not least, if you want to get these episodes a day early and ad-free, make sure you download the Fountain podcasting app. You can go to fountain.fm to find that. $5 a month gets you every episode a day early, ad-free. Helps the show, gives you incredible value. So please consider subscribing via Fountain as well. Thank you for your time, and until next time.