AI scaling pathways: on grid, on edge, off grid, off planet

[swooshing] Latitude media covering the new frontiers of the energy transition. - I'm Shail Khan, and this is Catlist. [upbeat music] - But if you just do this simple math on a single gigawatt scale space-based data center, you end up with a radiator the size of a small town, right? Like between the radiator and the solar panels required, I think you end up with a four square kilometer orbiting asset. And that's obviously complex to manage, but it's also a target. - Coming up, a unified framework for all the crazy data center stories you've already heard and will inevitably keep hearing. [upbeat music] - When utilities need flexible capacity, they can count on, they turn to energy hub. Energy hub works with more than 170 utilities, coordinating over 2.5 million devices to manage 3.4 gigawatts of flexibility. Built for the moments when utilities can't afford uncertainty. 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Fish Tank is known for generating prominent and effective media coverage for the brands they work with. If you want a PR partner that's thoughtful, shoots straight and gets results, you'll like Fish Tank PR. To learn more about Fish Tank's approach, visit fishtankpr.com. That's FISCH, fishtankpr.com. - I'm Shail Kong. I lead the early stage venture strategy at Energy Impact Partners. Welcome. So, massive data centers on the grid, massive data centers off grid, small data centers on the edge, huge data center clusters in space. Each of these might get built. Actually, each of them probably will get built, but how much and when? When you boil it down, I think there are actually two basic questions that play here. The first is the amount of demand for compute in the future. And the second is how to deliver the energy required to meet that demand. I don't really personally have anything insightful to say about the first one, but boy do I spend a lot of time thinking about the second one. And it has occurred to me that I've never really seen anyone attempt a cohesive framework to think through all of these different pathways. You see proponents of one or another adopt kind of a maximalist approach to any given one, but I haven't seen anybody try to think through how they weigh against each other. So I've been trying to organize this in my head. And in doing so, I've realized that each of these different configurations, sightings, types of data centers has a core constraint or two, but each also has its strengths. So to talk through all of them with me, I've brought on my following Jake Elder. Jake works with me at EIP and he leads our research practice that is focused on the built environment, which these days increasingly means a lot of data centers. One more thing, coming up on April 13th in San Francisco, we're going to do a live episode of this podcast at the Transition AI Conference. It should be actually a really interesting conversation. My guest is going to be a mean Vajot, who's Google's chief technologist for AI infrastructure. So obviously relevant to this conversation as well. I rarely do these in person. So if you're in San Francisco or you want to be on April 13th, go sign over Transition AI. Register at latitudemedia.com/events. Here's Jake. Jake, welcome. Thanks. Excited to be here. All right. The premise here is, let's just, for the interest of not adjudicating this question, let's just assume compute demand continues to scale. Let's assume superintelligence, AI, maybe neither of those things, but that the demand for compute and actually the demand for watts to deliver that compute. Let's also assume that there is no massive energy efficiency gain that comes and totally changes paradigm. So if it is true-- and let's assume that's true for the next dot in five years, 10 years, whatever we want to talk about. Let's assume that's true. I think the thing that we want to talk about here is what are the various options to deliver as much of that demand as possible? What are the options on the supply side? And so we're going to talk about the incumbent solution, which is large hyperscale grid connected data centers. And then we're going to talk about each of the alternatives that I think are currently being proposed. Some of them are already being developed. Some of them being talked about on X a lot. And I think we'll compare and contrast. I'm going to talk about the constraints on each of them. But why don't we start with the incumbent thing? The thing we are doing right now, where all the data centers are, which is large hyperscale data centers connected to the grid. What do you think of as being the core constraint to just delivering 10X, the compute in that way? Yeah, no, great question. And I think it's going to make for a great conversation as we look across the different options here. I think the constraints on the grid side are fairly well known at this point. It's a speed issue in particular on the transmission side. How much time will it take to build out the transmission capacity necessary to interconnect these mega-sites, gigawatt scale sites to new power supply, ideally carbon free power supply. And in many markets, right, that's running five to seven years now, which is a pretty massive timeline for data centers, given the power and speed of deployment on the AI buildout that we're trying to drive. Maybe the other couple issues that we should at least be mindful of here are power quality, right? These large data centers, especially as they cluster in certain locations, can have bigger impacts on the grid writ large and the extent to which society regulators and utilities are willing to serve those customers if they have bigger grid impacts, I think, is still a bit to be determined and a space I'm watching pretty closely. And then, of course, maybe the third vector from my side would be, let's call it social license to operate. And we're seeing in many states, right, just blanket bands on new data center developments. We're seeing some developments get pulled years after announcements because of community pushback. And if you listen to Elon, for example, in his best cases for going off planet with compute infrastructure, that's really his argument that at the end of the day, society is not going to move at the pace that the AI buildout requires. And therefore, at some point, we're going to have to abandon the planet and go to the stars. Yeah, we're going to get to the orbital compute thing a little bit later. I think that is a good point, right? People, so you mentioned three things. There's the capacity, actual physical capacity on the grid and deliverability. There's the power quality thing, which I know in of the three, I think is probably the most manageable, honestly. It's like a engineering problem. And then there's the social license to operate, which we're already seeing kind of burst at the seams in some locations despite being kind of in the early days of this trend. And the third one is underappreciated in the question of like, are we going to be able to deliver all the compute capacity that we need via the current paradigm? On the first one, I would say, I do think people can flate the transmission problem and the generation capacity problem. And the thing is, they are both problems. I mean, you said five to seven years. The five to seven years is the timeline to get new gas turbines if you're ordering them. And it's close to the timeline to get new transformers and other switch gear and stuff like that. Like we're all in the like three to five or maybe seven year timeline for that kind of thing at this point. And maybe the timeline also to get like a substation upgraded, which is part of the deliverability thing. But I want to say the timeline to get a new transmission line built, especially if it's like interregional or across daylines or whatever is not five to seven years. It is essentially infinite years in the United States, at least in recent history. I feel like we just aren't doing it. So there's a limitation there that might be even more intractable than just the generation thing. Totally. And I think it's the unique constraint to the grid connected pathway. If you wanted to go towards the other options, we'll explore down the road. And you're going to go off grid, for example, you're still stuck with the timeframes for transformers, the timeframes for generation assets, et cetera. And I know we'll talk about some ways to shortcut that. But the transmission side is really unique to this first scenario. And certainly makes the case that if you want to run around that, you need to think about some amount of on-site power as the only way to avoid having to build more polls and wires to run power from elsewhere to a new site. And so that on the capacity side is one thing that I think gets lost a little bit in this grid connected conversation as it tends to be an all or nothing conversation around how we power these data centers. And I do think there's a hybrid option here where you're still grid connected. But the data center brings some of its own power for a few hours in the day, specifically to overcome that transmission bottleneck. And to be clear, that's what's happening now, a lot of that. This concept, in fact, I've seen people get confused about this because there was some-- I don't remember who put it up, but there's some report that came out that saw like there's like 50 gigawatts of behind the meter generation in development at data centers, right? And some people have interpreted that to be, oh, 50 gigawatts of off-grid data centers getting built. It's actually close to zero of those that are true off-grid data centers. They're all in there. either grid connected but have some binary meter generation or the binary meter generation is a bridge and they ultimately intend to be grid connected. So that is true, there's a hybrid there. But okay, so this is the least interesting one 'cause it's the way we do things now and it's gonna be the way that we do things as much as we possibly can. Like I think you and I agree that like the first thing that's gonna happen as is already happening is that developers are gonna find as many sites as possible that can handle hundreds of megawatts or gigawatts of load. They're gonna develop those into data centers. So like we just assume that happens and we should just assume it's not enough. Or maybe it doesn't happen because of community pushback but either way it will assume it's not enough. Now let's talk with the other, I think, three categories of ways of configurations to get a lot of new compute online. The first one is maybe the least distant which is you still grid connect data centers but they're smaller and you put them at the edge. So I've talked a little bit about edge compute on the podcast before you and I have spent a lot of time thinking about it separately. First of all, define what you think of as edge compute 'cause it is sort of malleable and then like what is your latest thinking on what role that plays in the market? - Yeah, so this is a really tricky question, right? Edge computing has been around for a while. Historically it evolved to serve certain use cases like telecommunications and more recently video streaming for example is something that happens much closer to the edge than other Piper scale data center activities. But moving forward, I think there's a school of thought that says that AI inference in particular might move to the edge and I think the first principles argument that folks tend to make is that latency's gonna matter more. And so citing compute infrastructure closer to demand just has a performance benefit that can't be met via large central sites in West Texas for example. As we've dug in a little more, I think that's a little bit of a red herring and so let's come back to that in a second and talk about why you would actually pursue edge data centers and edge computing. But latency has certainly been one of the reasons historically. That's that edge computing can mean a few different things to your point, right? So in the extreme scenario, I think as you move out 10 plus years more and more is gonna happen on device. We already know that like WaymoCars for example, have a lot of their day to day or all of their day to day navigational tools and driving decisions get made in the car directly. And increasingly as we have models that can operate on a phone for example, you might have a version of chat GPT or Gemini that just operates natively on your phone and doesn't need to go out in the world at all to get access to basic inference results. On the other end of the spectrum, we've seen a few folks announce larger scale projects really think about 20 megawatt style data centers, maybe 15 to 30. And those folks are basically building many hyper scale sites, but they're trying to build them in locations where they think they can get power sooner. And perhaps in a regional node where they could serve, some more latency sensitive applications. But from a design perspective and a deployment perspective, they kind of look like much of what we're building today, just small scale relative to the gigawatt scale assets. My suspicion is that's probably the most economic piece here. And so if this becomes a cost play, that that's the space that becomes most interesting. But again, let's come back to that. And then I think there's this third category, which is really more kind of true. What we might have thought about is edge computing where you've got a 100 kilowatts at a given site or a couple of megawatts at a given site. You could think about these being located at utility substations or in a commercial real estate, office basement. And the reason to pursue that is probably cost at the end of the day. There we know across the folks that we know well, right? That there are a number of individual parcels of land that were provisioned for five megawatts of power and are only using two. And so I think the theory to pursue that is probably more around speed, where you can probably suck up a bunch of assets relatively quickly and start to build out a network. But if you end up in a cost game and you're trying to be the cheapest form of inference, strikes me that that probably struggles because you're subscale relative to bigger sites. - Yeah. You said a couple of things that resonate with me based on what I've learned. The first is that latency is a bit of a red herring. The latency benefit of being edge. Not for zero applications, but for very few, does it seem that you need such low latency that edge has a big benefit over the sort of like regional hyper scale model that we have today. And people use the example of things like autonomous vehicles. That was like a classic case people would talk about is what you need edge computing for autonomous vehicles. But as you said, most of what awaymo needs is inside the car. And so as I understand it, they can operate with compute inside the car. And when they need to go pull something from the cloud, it's generally not so late. It's sensitive that they can't handle the hyper scale. So this concept of edge being necessary for latency purposes, and yet to have that proven to me, I'm waiting for it, but does seem unlikely. Secondly, it's hard to imagine it's cheaper. Now people do make the argument that you might get free land, right? And that could be true. Like if you're taking land that's already getting paid for because it's at a commercial property or whatever it's in a parking lot, it could be any of those places that the utility substation that's already substation, the land could be pretty cheap. But if you look at the fully loaded cost of a data center, land is not a big portion of it. It's a very, very small portion of it. The cost is actually in the GPUs, obviously, in the building, in the labor, all those kinds of things. And as you said, being sub-scale is tough. Maybe you can make some modularization in argument. You have the standardized shipping container, and the shipping container is super cheap and easy to deploy, you just plug it in. But as this are in many other sectors, my guess is you're 300 megawatt data center on a fully loaded, levelized cost of flop is just gonna be cheaper. So it's probably not a costing either, which means it's a speed thing, right? And speed is the name of the game right now. But I think what remains to be proven in Edgeworld is that it can actually be faster at the same scale. This is what we need to find out. - Yeah, I think that's right. And I do think at some point, the speed game is gonna slow down and cost is gonna matter, especially in the inference world. I don't know exactly when that happens. And our future scenario where we're in some kind of relatively quick takeoff around AI capabilities, maybe speed matters for longer because models continue to improve kind of indefinitely. But at some point when we have agentic employees and most Fortune 500 companies in this kind of future, like the cost of those workers matters. And so I do think at some point, if there's an edge build out and we're looking at two or three different edge deployment models, while speed matters, the cheapest of those models might be the one that ends up winning at scale. - Yeah, but I think speed remains a question mark. Like in principle, if you have an existing interconnect, as you said, there's some commercial site that has like a five megawatt interconnect and is using two megawatts. You put three megawatts on there. That should be much faster than waiting for an upgrade in the system. But of course to match the speed with which you need to go, you're gonna go deliver your 300 megawatt data center, you then need to go find 100 of those sites and develop 100 of them. And like in principle, I can understand how that could be faster, but I'm waiting for somebody to show me that that is true. - Yeah, and certainly requires a lot more conversations and turning over rocks and dead leads as you try to build it out. You've got to have 100 success outcomes in terms of site evaluation, not just one. - Right. Okay, so that's edge. So both of those are grid connected. Let's assume the grid becomes the constraint. It just is the constraint, right? Okay, so now we're either gonna get into like, increasingly distant in the literal and metaphorical sets. Well, let's tell me the one that I think is, maybe you'll least talked about relative to how interesting I find it as an answer to this question, which is just off grid. Like, and again, we're not talking about a hybrid version where you have, behind the meter generation, and you're still grid connected. Let's just say, put a data center anywhere. It has an amazing relaxation of a constraint. If you remove the grid as a constraint, we have plenty of land available, right? That is not the constraint here. And you can go where there's the cheapest labor, you can go where there's the easiest permitting and sighting. Like, it does change the game in that matter, but it does have its own set of challenges and constraints, which is why it hasn't happened a lot historically. So what's your perspective on just straight off grid? - Yeah, I mean, you make a pretty good case. It should be pretty attractive, right? There was this foundational study that came out about two years ago that was co-authored by Stripe and Paces and Scale Microgrids, and they found over a terro-ot of opportunity in the American Southwest alone, with high levels of renewable development being able to support those assets, like 50% solar plus batteries at cost parity to using all gas and the ability to get up to, I think, 80% or 90% solar without a meaningful cost increase. So like, from a land perspective and a resource perspective, it makes a lot of sense. And to your point, it can also move really quickly. You can avoid the places where the public really doesn't want data centers, right? You've got such geographic flexibility. It should be the opportunity if you just take a first principles approach. And we certainly don't need to be thinking about going to space until we think about going to remote parts of the Earth, right? But to your point, it's not happening at scale yet. And I think there's a couple of reasons for it. There are some projects that are happening that we can learn from, right? And we've got some some anecdata to support that. I think at the end of the day, the grid's a marvel of humanity And it does have a really good thing. and particularly being a giant shock absorber for any one individual asset. And so if you go off grid and you have to operate on an island, you have to build the whole shock absorber yourself, all of the inertia, the fault response, the ability to black start the asset. And that's just not just expensive, it's really complicated. And there's not a lot of folks out there that know how to run a gigawatt scale grid, right? At all. And so when you think about the risks that these new data center developers are needing to take in the values of these assets, betting on a model where you can't be comfortable or can't guarantee that you're going to have 99% uptime is possibly an on-starter in some cases. And when we've heard some of the early data from some of the off-grid projects that have been built so far, the anecdata suggests they're not able to stay above even 90% uptime. Yeah. Well, they get there over time, probably, right? Like this is a learning curve, and we know that there are power quality solutions that can manage a lot of these issues. But it's a big risk if you're going to be a first mover for a $10 billion asset to design it in a way that you don't know how to manage and operate it and keep it running. Right. It strikes me as one of these things that, like, clearly that is, it should be solvable. It is a real engineering challenge. It appears. And I've, you know, you and I have looked at some of that same data. Like, it does appear that there are actually projects that are mostly these ones that are bridge power projects. So they're currently off-grid intending to be on-grid eventually. But as they are operating off-grid, they are not operating at the normal five-nines of reliability or whatever. Now, interestingly, you may or may not need that. That's it. In some ways, it's sort of a legacy of the cloud business, where AWS and Azure and Google basically promise in their SLA to their customers that they would be able to offer really high uptime. And so they have this, you know, huge redundancy requirement and so on. And the new world of AI, sometimes you do need that. Sometimes you don't need that. And so there may be a class of data centers that can accept sub 99.99% reliability. There's an economic impact, of course, to lower uptime. But again, in a world where we're so constrained on the grid side, it seems inevitable to be that that is going to happen to some degree and that the engineering challenge is going to get at least partially solved. Yeah, I think that's right. And I think over time, we'll figure out better ways to, you know, have more and more checkpoints as you're doing model training runs and whatnot, such that you could tolerate a major outage. I think the key is you could make it work at 90% uptime if you know when that 90% is. I don't know whether you could handle total randomness with that 10% downtime. And if all the downtime happens to come in the middle of big, long, expensive model runs that it takes down, right? I don't know what that does to the economics of those projects. And, you know, I do think we'll learn a lot here. I think it's critical also to acknowledge that, you know, those that operate our larger grid don't yet know how to manage these sorts of voltage swings and harmonic distortions that are coming from these data centers. And so if we can't solve the problem when the data centers are a small part of the overall load on the system, then it tells me that's probably going to take us some time to figure out how to solve it when they're the only load. And it's, you've got a much more constrained set of tools to manage the impact. Virtual power plants are becoming a reliable way for utilities to manage capacity. But enrolling devices is just the start. What really matters is confidence. Knowing those resources will perform when dispatched and being able to prove it from the control room to the living room. EnergyHub's platform handles the full picture from near real-time forecasting, locational dispatch, and the kind of rigorous verification that holds up when regulators, grid operators, or leadership ask, did it deliver? Easy enrollment creates momentum. Proven performance builds trust. That's why more than 170 utilities rely on energy hub to manage over 2.5 million devices delivering 3.4 gigawatts of flexible capacity. See what that looks like at energy hub.com. We're living through a profound economic shift. An energy sits at the center of all of it. Trillions of dollars are flowing into power plants, transmission lines, battery factories, data centers, but the future of energy isn't shaped by technology alone. It's shaped by markets, by policy, by capital, and by the institutions that connect them. I'm Alfred Johnson, CEO of Crocs, the capital platform for the Clean Economy. Join me for my brand new show Critical Capital. As I talk with people deploying capital, shaping policy and building projects. Together, we impact how risk is priced, how incentives are structured, and how progress is actually made. Are you tired of overpaying for big name PR firms, but not really knowing what they're delivering? Is your comms team wasting time reviewing lengthy messaging briefs and decks, instead of engaging journalists or producing content? Are you wondering why your competitors are getting press and you aren't? Fishting PR is an award-winning climate and energy tech, renewables, and sustainability-focused PR firm dedicated to elevating the work of both early stage and established companies. Whether you need to position yourself as a thought leader in between project announcements, or translate complex ideas and technologies into tangible, compelling stories that resonate with the media, Fishtank can help. Check out fishtankpr.com, that's FISCH fishtankpr.com. Yeah, and though cost is not the determined factor in this stuff right now, that's not nothing. The way to engineer yourself into five-ninze reliability off-grid right now is to over-invest in both capacity and storage. You can do that, but it does come at a significant cost. It starts to actually matter for your economics and back to your point. Who finances your $10 billion asset if it is at the top end of the cost curve, essentially? Yeah, and then you start getting into, you need two different fuels. If you're going to use some kind of base load resource, or if it's just gas, you need two separate gas pipelines, and that constraints, ads costs. By the way, we've just jumped in assuming that location doesn't matter in this world, and that you can do everything in remote parts of the country, for example. I'm curious for your take there. My suspicion is that at least to date folks are still generally sensitive to where they're being cited for not all projects, but for most projects. If there were lots of off-grid opportunities in Virginia, for example, I think we'd see them being pursued more quickly than we're seeing some of the stuff move forward in West Texas, in New Mexico, etc. I think that's changing in real time. Historically, there were these Tier 1 markets, like from the Virginia, or Chicago, or Phoenix, or whatever, Atlanta, and they were where 90% of the demand for new data centers was going to be. And there's still that, but it is broadening out quickly. You see all this development in West Texas, for example, so many data centers going into Texas. I think that's just because of speed to power and availability and scale. I think that the constraint of you need to be in certain locations, it still matters from a, is there a workforce? Can you get enough labor, electricians, and construction workers, and water, and all that kind of stuff? But I think apart from that, my sense is that it is not the most important thing. The one thing I do want to say though about the off-grid thing, and you mentioned this before, but let's reiterate it, you're fundamental, assuming you can solve for sufficiently advanced engineering to get to whatever reliability you need, your constraints then on scaling predominantly become power generation and delivery. So you're still, you still need to, because you're probably going to need some gas, you still need turbines. Or if you're doing a lot of solar and storage, you need solar and you need batteries, you need transformers, you need switch gear, you need whatever, all that kind of stuff. And that, that you're now still in that supply chain problem. And I want to mention that because if that is the constraint on really massive scale off-grid, in a minute we're going to talk about orbital, and so we can compare and contrast like, which is the more challenging constraint between those two. Yeah, yeah, no, that's a great, great reminder, you're still stuck with all the generation and supply chain issues. Maybe with one possible exception, which is that your gas infrastructure is going to likely be smaller and more modular, like you're not going to have a 500 megawatt combined cycle turbine. That's your sole generation asset for a massive data center just because of the redundancy issues. And so you can get a lot of one megawatt reciprocating engines today. I know you can find some smaller or derivative turbines or all the repurposed jet engines if you want to get a little bit crazy. But I do think the off-grid option in some ways maybe shortcuts the actual supply chain bottlenecks on the generation equipment side, at least a stomach-stent relative to the other options, but agree with you, there's still a bunch of other pieces of equipment, transformers, etc. That you're stuck waiting for. Okay, so let's shift to the most fun one. We talked about off-grid, let's go off-world and talk about orbital data centers. There's such a long conversation to be had about orbital data centers here. But I want to frame it in the context of these other things. Again, I think the premise here, and certainly the way that Elon talks about it, is the most prominent proponent of orbital data centers, is this is going to be the only way. It's a scalability thing. I mean, he says to, "Okay, let's dispense with the premise." He says he thinks orbital data centers are going to be the cheapest way to get computing three to four years. Correct. I do not believe that. Do you believe that? I think we need to start this conversation with a bit of the acknowledgement. Moving off planet for lots of reasons is a crazy proposition. If you listen to Elon talk through it, it starts to sound like a logical endgame in a world where we're building hundreds of gigawatts of compute infrastructure a year. And Elon asserts that that's going to start happening in three or four years. I don't know. I don't think it is going to be the cheapest source of new compute capacity in three or four years. Nor do I think that we're going to be building hundreds of gigawatts of compute infrastructure per year in the US alone in three or four years. But in a world where we're assuming that we're somewhere between, you know, AGI and some, you know, more super intelligent, you know, computing infrastructure, it's kind of the end game, right? It's kind of the only place you could go to build, you know, infinite amounts of compute capacity. Whether that's in five years or 500 years, you know, I'm not quite sure, but I agree it's not before 2030. As this has become a bigger conversation, people have talked about lots of things that they think are going to be the like the killer of the idea of orbital data centers. I think we should just spend with them because despite what you and I just said, which is both like fairly skeptical on the cause side, I think we both think it's not like totally insane and it doesn't seem like the technical challenges are insurmountable. So people talk about like heat transfer as one of the big problems. I think it doesn't seem like actually that is likely to be, it's not nothing, but it doesn't seem likely to be the thing that kills orbital data centers. Agreed. I think the heat transfer conundrum, right, is that space is a vacuum and it's very, very hard to dissipate heat in a vacuum. I think the whole international space station, for example, rejects less than 100 kilowatts of heat in total and they have a radiator the size of a soccer field, right? And when you think about the compute infrastructure where we're building out like a single and video high density rack could soon be more than 100 kilowatts and they already be in some cases more than 100 kilowatts. On the flip side, of course, heat dissipates to the fourth power of temperature. And so it turns out that the hotter and hotter you run chips and the denser and denser your run chips, the better your heat rejection gets on its own. And so it does seem like as we move to a world of denser and denser computing infrastructure, it gets easier and easier to reject chips. But if you just do this simple math on a single gigawatt scale space-based data center, you end up with a radiator the size of a small town, right? I saw this really great piece of analysis this morning from an analyst called Thunderset Energy. And I think the stats on like the odds that a starlink system gets hit today by a piece of space debris is like a couple percent maybe per year. If you scale that up to a single floating thing that's four kilometers, four square kilometers large, you can basically expect to have a piece of space debris hitting that data center every hour. And I don't know how you operate something that's going to get knocked off or bird and or destroyed just every hour. Like the piece of space debris every single hour, that sounds really complicated. Yeah, I mean to me the thing that seems, and this is sort of related to it, the thing that seems like the hardest to solve, it's all hard. The thing is the hardest to solve with orbital data centers is O and M. Because actually data centers on land require a lot of maintenance and you can't really do a lot of complicated maintenance to a satellite. And so either we solve that with some robotics that's going to be very clever. That seems difficult for me to imagine. Or it's an economic thing. You lose a bunch of you just have some loss rate and you have to account for that. Yeah, I mean, you know, in a hyperscale data center today, right? Like there's a better engineer or a Google engineer that is going to replace every CPU or GPU as it breaks more or less in real time. And in space, if it breaks at least today, you're kind of stuck with it broken. And to your point, maybe in 20 or 30 years, if we're really in some super intelligent future, there's, you know, robotic replacement and waste update chips in real time and whatnot. But but until then, it just adds economic drag on the the overall project. And, you know, we kind of skipped over costs, but it's not clear that there's a real economic advantage here. I mean, the economic reason to do this right is free, free power. You could effectively get 95% capacity factor on the solar panels at a space-based data center because you put it in kind of permanent sun, right? From an orbital perspective. And then there's much better solar radiance. So you get somewhere between five or 10X, the energy output per panel over the life of the panel. Then you would on earth bound panel. And so, you know, power is really cheap. But as you mentioned earlier, you know, total cost-wise energy is only, you know, five to 15% of a AI-focused data center. And chips and maintenance are the rest. And you're stuck with the same chip cost, whether you put the thing in space or on earth. And the maintenance piece gets gets much more expensive. And so I kind of have a hard time seeing it being a cost play, even in a world where launch costs go way down. And if you buy a launch view of the world, that the starships can get super reusable and be able to launch at a hundred bucks a kilogram. And so I kind of come back to like it just has to be the sort of thing that we pursue from a physics perspective because we can't build at the pace needed for for AGI on earth. I think that's right. Okay. So, but that gets us then maybe to close it out into what I think is the interesting comparison that I don't hear people making very much, which is orbital data centers versus off grid data centers. Let's just compare those two. As we said, the rate limiter, we have plenty of land. And you know, in the long arc of history to build many tarot watts, sure, we're going to run out of land. But like to a first order for the next decade, I don't think we're running out of land. So we've got land. And then the rate limiter is all of the other stuff we talked about, you know, turbines or whatever power grid infrastructure and so on. And we certainly don't have enough of that today to go build hundreds of gigawatts a year of off-grid data centers. The rate limiter on orbital data centers is sure there's going to be some like solar for space, right? The Ilana saying that XAI or I guess now now SpaceX is going to develop 100 gigawatts, solar manufacturing at presumably for space. They're also Tesla is going to do it for land. But let's let's say that that's the lesser constraint. The bigger constraint is Starship. Starship has to launch a lot, like a lot, a lot to get that kind of capacity into space and they've got a ways to go. So as I think about it, I'm like, okay, if you're if you're binding constraints is like capacity of Starship launch on one side versus ability to scale up the supply chain for power generation and delivery on land. It's not clear to me that like spaces eminently more skilled on that on that measure of the problem. Like, can we not as a planet go develop 200 gigawatts a year of new turbine manufacturing capacity? It seems possible. Yeah, I think that piece we could. Maybe the question back to you is do you think that society over time is supportive of us building 200 plus gigawatts of incremental gas infrastructure year over year for the next 20 years? And I know that's one of the other concerns that Elon race is right is at some point, you know, the conversation around carbon free energy will shift back in a different direction and do we get stuck in order we can't build that. But then, right, so but then be maximalist on solar and storage, be maximalist on geothermal, be a maximalist on new nuclear. Like are those things all so much crazier than like five Starship launches a day? When you hear him talk you through it and it's basically the ship lands and then takes off again within, you know, a few minutes that that certainly does sound pretty crazy and, you know, solving fusion might even be easier than cracking that code. Yeah. Again, I think for me, it's not that like it's totally insane to do orbital theta centers. That's not my takeaway here. It's just I think if we're going straight to space, I'm surprised that we're not making stopping at a waypoint along the way of doing a lot of off grit. I'm surprised that hasn't happened. Agreed. And I think the other the other constraint that obviously exists across both scenarios and we've kind of washed over in the, you know, decision to talk about a world where we continue to see massive AI progress is just the chip supply chain, right? And in a world where we're building a couple hundred gigawatts a year, I don't know how many chips that actually turns into, but I know that we don't have the semiconductor fabrication space today to build at that level. And so we probably end up bottlenecked by chips before we're really in a world where we can't build everything on, you know, on the ground, for example, and probably before we're in a world where starship, launch costs are so cheap that space becomes the cheapest option. So, you know, if you take that as a fundamental constraint, then I think you probably do bet on the off-grid stuff moving materially faster. But yet, same, same as you, I think, you know, we shouldn't dismiss the orbital option and I think in a world where, you know, Compute Buildout does rapidly accelerate in 20 years or 30 years, there's going to be a lot of AI models being trained in particular in space. And that's maybe just the one last topic we didn't quite hit on is latency in space, right? If you've got latency concerns building in West Texas, then you're certainly going to have latency concerns building, you know, few miles north of the above the South Pole. And so I do still think in that world, we're still going to have to build a lot of our infrastructure here, even if we're training the, you know, brain that is a thousand times as smart as a human in the atmosphere. All right, so I'm going to put you on the spot to wrap up here, 10 years from now, you've got a fixed pie of all the global compute that exists. We have four categories here, hyper scale grid connected, edge, it's to find it as like some 50 megawatts or something like that. So a broad definition of edge off-grid off-world. 10 years, all-compute infrastructure that's operating. What is your best guess? You know, if I were to look forward about about 10 years and as soon we're talking about all-compute infrastructure that's operating, I still think the majority of it's going to be in hyper scale data centers. And that's probably, you know, 50 to 60 percent of the total. Let's assume that on top of that, there's another 10 to 15 percent that gets built off grid in a similar hyper-hyper scale like format, but never can And so that puts us at 65 or 70% that's built in more of a traditional way, whether grid tighter or not. I suspect that the bulk of the rest comes in the edge markets for certain use cases or applications, call it 15% or so there. And I do think we'll see a couple efforts to really build out some infrastructure in space. I mean, no SpaceX and Google in particular are going to take their shot there. And so I wouldn't be surprised if we're training some models. We've got 5% to 10% of our overall compute capacity out there over time. I'm curious. Are you buying or selling? That's interesting. It's so hard. Okay. So again, it comes down to this like how bullish are you on compute demand? Yeah. Like if you told me that the total number, the size of the pie in 10 years is 10 terawatts. I have a very different answer. Yes. So if the size of the pie is 300 gigawatts, right? The great. And that like dictates the shares to me. So it's really hard to know. I would say, I generally agree with you. And to be clear, that's actually like a fairly bullish statement on, it's where I'm going to say what you're saying is bullish on off grid and bullish on orbital, just because you're starting from zero in both of those. And so getting to 5% even 5% of hundreds of gigawatts is going to be a big number to do in 10 years for orbital. So it's actually like a fairly bullish statement on all of them. Again, depending on how big this size of the pie is, I'm, I'm filibustering because I'm trying to figure out which one of these I disagree with the most. I guess maybe where I currently said I'm a little bit even more bullish on off grid. It just, it has the scalability. I think it can have the cost. There are challenges, engineering challenges. But if we're really going to be in this world where we're that heavily constrained, like it's just seems inevitable to me. Do you think that comes from the grid tied large sites or where do you think that that comes from? Where the like what's going from from my view of the world, which of those categories you see losing market share, let's call it if more is going to go off grid. Oh, I see. I'm still having, I mean, you didn't put a lot into the edge category in the first place, but where I currently said I don't, I don't know why we're going to have a lot of edge in the grants. You'll have some, but like in as a portion of overall compute, I don't know why that's going to be a lot. Which is frustrating because it's the least caught. In many ways, it's the most obvious and theoretically fastest way to, you know, deploy compute, right? That's why you and I have spent a lot of time thinking about this over the last three or four months. Totally. And look, I should be the right answer, but I agree with you. Yeah. And I reserve the rights to change my mind, right? Like I think you and I have spent a few months trying to like convince ourselves of edge, and I think we haven't done so yet, but that does matter a time. In fact, if a listener wants to convince us of edge, I would welcome it, Jake and I both, but yeah, we're struggling to find the like it's going to happen. And here's why for all these reasons. Anyway, I would maybe take a little bit away from edge and I guess I'd take a little bit away from grid connected, hyper scale, but I agree with you that that's like most of what we're going to do is just build more green connected, hyper scale. All right, Jake, all the time we've got. Thank you so much. This was fun as always. This was a pleasure. Thanks for having me. Jake Elder is a senior vice president of research and innovation at Energy Impact Partners. This show is a production of latitude media. You can head over to latitudemedia.com for links to today's topics. latitude is supported by Prairie Ventures. This episode is produced by Max Savage Levinson, Anne Bailey, and Sean Marquand. Mixing in theme song by Sean Marquand. Steven Lacey is our executive editor. I'm Shail Khan, and this is Catalyst.