Starcloud's Philip Johnston: Why the Cheapest Compute Will Be in Space

Title: Starcloud's Philip Johnston: Why the Cheapest Compute Will Be in Space Platform: youtube Creator: Sequoia Capital Source: be/94b6i5jI1nE Transcript source: deepinfra Summary: Philip Johnston, co-founder and CEO of Starcloud, takes the AI Ascent 2026 stage to make the case that the future of AI compute is in orbit. He walks through the economics of building data centers in space: why one square meter of solar in space generates e... Transcript: - Thanks so much for having me. My name is Philip Johnston, and I'm the co-founder and CEO of StarCloud. And just like the previous company, we have also been abusing GPUs in ways they were not designed for.

So yeah, we're building data centers in space mainly for the energy that we can draw. And I will spend the next five minutes explaining why it will soon make much more sense to build data centers in space than it does to build them on Earth. And then I'll take five minutes for questions. um, start thinking of some questions. Before I do that, though, I want to show a quick video, which is actually the deployment of StarCloud 1. And this had five NVIDIA GPUs on it, but the most significant was the NVIDIA H100 chip.

And I'll just quickly play the video first. Star cloud one, separation confirmed. So, you don't normally get as great a deployment video, by the way. Half the time it like deploys into the shadow. So the reason this was so significant is until this point many people thought you actually couldn't run state-of-the-art terrestrial data center grade GPUs in space for two main reasons. One is the thermal dissipation. So they're very power dense. They produce a lot of heat. And the second is the radiation tolerance. So people thought that you would this chip, we were the first to train a model in space.

We actually trained Nano GPT from Andre Carpathy. And then we also were the first to run a version of Gemini, the first to do high-powered inference on SAR data, so other satellite data. And so it was a very significant step in proving that we can actually run the state-of-the-art terrestrially. But the Yeah, I think maybe to make the case for why it will soon make more sense in terms of energy cost, I'd like to quickly draw a comparison with, with a solar project on Earth, since solar is the cheapest form of energy that we have on Earth.

So if you want to build a solar project to power a new data center, you have three main costs. So the first is the cost of permitted land. And in fact, in North America, that's actually the largest cost or can be for most new solar projects. The second is the cost of battery storage and backup power because we're only, you know, we only have peak power for about four hours of the day. So we need to charge those batteries to use at night. And then the last is the cost of the solar cells So how does that compare to building a similarly sized solar project in space?

Well, in space, number one, we don't need to pay for permitted land. So your biggest cost is gone. You don't need to pay for battery storage and backup power because we're 24 seven in the sun. So your second biggest cost is gone. And then you need eight times less solar cells because one square meter of solar panel in space produces eight times the energy of one square meter of solar panel on Earth. And so you can clearly see there's a break-even point where the launch cost comes below the cost of permitted land, batteries and solar.

And we see that break-even cost to be around $500 a kilo, so about a 10x reduction from where we are today. But that's well within range of the launch vehicles that are coming online. So for comparison, Starship is designed to produce launch costs of around $10 to $20 a kilo. And so I think I'll just finish by playing you one final concept video. A constellation that we're building now, so we've just filed with the FCC for a constellation of 88,000 satellites. Each one's about 200 kilowatts. It will enable us to deploy on the order of 20 gigawatts of new compute capacity, really just scratching the surface with this new constellation.

And it will enable -- it's basically for all inference workloads. And so this could be -- and, yeah, maybe I'll start the video and you can -- get a sense of it. So in this case, it's to generate a 3D video, but it could also be for back office business processing agents, code generation agents. They'll come up via optical link to this constellation in this dawn dusk sun synchronous orbit, means it's always in the sun 24/7 power, sub 50 millisecond latency to anywhere on earth, all optically linked. And this really is the start of the largest infrastructure project ever.

I mean, we're talking about, just for this constellation of 88,000, we're talking about $100 billion of capex spend, which is actually much lower than it would cost to do terrestrially. And not only is it the start of the largest infrastructure project, it's also, in my opinion, the start of a Kardashev Type 2 Dyson Sphere type civilization and potentially Kardashev Type 3. I will finish there, and we have about four minutes for questions. So any questions? Yeah, we'll start at the front. So the intuition on the availability-- The intuition on the availability of solar is obvious.

Can you just give us the napkin math on the radiator equation again? Yes. Yes. For anyone that's thought about it, it always feels hard. And then also please say something about the availability of Dondusk, that orbit is finite, right? Yes. Yeah. Yeah. It's a great question. So, Because space is a vacuum, it's actually much harder. Space is only three degrees Kelvin, so very low ambient temperature. But because it's a vacuum, as you rightly point out, it's actually quite difficult to dissipate that heat. And what it requires is a large surface area so that you can emit that in infrared.

So everything that's warm is glowing in infrared all the time. If you had an infrared camera on my face, you'd see that I'm glowing. and the radiator, if you keep it around 50 degrees C, will dissipate around 800 watts per square meter. So what that means is if you've got a, you need about a quarter again the surface area in radiator than you have on solar panels. So if you had a 400 square meter solar panel, you'd need an additional 100 square meters of radiator to dissipate that heat. There's a very nice equation called the Stefan Boltzmann equation, which basically says that the is proportional to the fourth power of the temperature.

So if you can jack up that temperature instead of being 50 degrees to 80 degrees, which is like a temp-cent increase in Kelvin, then you can actually half the surface area of your radiator or close to half the surface area of your radiator. And so that's what we're working on with NVIDIA now. If anybody was at GTC, you'll have seen Jensen walk out to the deployment video of StarCloud 1, and then he spent five minutes talking about the new Space Rubin 1 chip that we're working on. a hotter temperature without having a higher failure rate.

And the reason you want it to run a hotter temperature is so that you can lower the mass on the radiator. Great question. What about Kessler syndrome, the favorite thing, right? Now they want yet more satellites, they get more in consistent orbits as everyone will want. What happens then? Yeah, it's a great question. It's also related to the question asked about the space in space. I think So it's something we take incredibly seriously. Everybody needs to be a responsible user of space. We do, and everybody else, we're keen to make sure that space is usable forever.

For the first few satellites, so you can solve it in a few ways. If you fly at a relatively low altitude, the chance of a Kessler type effect is extremely low. So, our first satellite we're flying around 400 kilometers altitude. That means that it will naturally deorbit within a few months. And so if you were to have a collision at that altitude, by the time it gets around to the next orbit, you're already a few hundred meters below where you had the collision. And the chance, yeah, chance of Kessler is very, very low.

And Yeah, as you fly higher, it's actually extremely unpopulated, those high orbits, because then you start to edge into the Van Allen radiation belt. But, I mean, we actually have a pretty good case study for this, and that is SpaceX is now operating around 10,000 satellites without ever having a single collision in low Earth orbit. having a pretty sophisticated collision avoidance. The other reason I think people think this is more of an issue than it than it actually is. And the reason the space is so much larger than it looks is when you see a map of all of those satellites, each dot on those maps is about the width of California.

And you're representing something that might be this wide by something the width of California. And so people can often think the space is very congested. It's actually We can easily fit on the order of terawatts of compute in just this Dondas-Synchronous orbit without having you know, huge problems with collision avoidance. Any other questions? Yeah. Is radiation, like bit flipping, is that something you actually have to think about or consider? How does that impact stuff? Yes, it is something we have to think about. So the way that we're solving it is just an enormous amount of ground testing.

So we've done four rounds of testing at the cyclotron down in Knoxville. It's a high velocity proton particle accelerator. And we take all that telemetry and then that informs our choice on shielding. And then for heavy ions, we have to go to the Brookhaven National Lab and we basically run all the chips through the through the space environment. So over a 24 hour period, you can put it through five years worth of radiation dose. And then we take all that data and we then use that to inform shielding, but also software development choices for it.

Yeah. Yep.. This is almost exclusively--I mean, actually, for the foreseeable future will just be for inference. And the reason it's for inference is, number one, inference is going to be like 99% of the compute market very soon anyway. So even if we, you know, we wouldn't want to be running a large training set. Well, running large training sets will be a very small percentage of the total in five to 10 year time of AI workloads. But secondly, it's very hard. We would need a, Yeah, we would need to dock together a large 5 gigawatt kind of structure.

I actually have a video of that here, but I won't waste everyone's time with it. Unless anybody wants to see a video of a 5 gigawatt data center in space. Do you guys want to see that? We made this video because we didn't want people to be like, oh, you could never train a model in space. So this is what a 5 gigawatt, 4 kilometer by 4 kilometer structure in space would look like. So this would be a Starship launch vehicle with a 40 megawatt is what you can fit per Starship launch vehicle, which will connect to a central spine, which is connected to this enormous solar panel.

On the back there, we have a one kilometer by four kilometer radiator. And Yeah, that's how you would train a large model. But as I say, it'll probably be at least 15 years before we get to anything like that. One more. I think we're--OK, 40 seconds. Or did we--OK. Oh, sorry. By when do you think the majority of the data center will be in the space? Oh, that's a great question. And I actually wanted to ask all of you. Let's run a poll. So the question I want to ask is, when do you think it will be cheaper to run compute in space for anybody?

It could be for SpaceX or for us. And the four answers will be the next five years. it will be cheaper. In five to 10 years, it will be cheaper. Sometime after 10 years or never. Okay, so who thinks Within five years, it will be cheaper to run compute in space than terrestrially. Thank you. Hmm, interesting. Who thinks five to 10 years? Thank you. Who thinks beyond 10 years? And who thinks never? Brave. OK, that's interesting for me to see. Yeah, I think we are out of time, so I will leave it there.

Thank you very much for your time.