00:29:12 · Molly O'Shea, Ezra Feilden, Philip Johnston
[00:29:12] S0: We were just at Rocket Lab and we did a tour. We did a series there And the one topic that keeps on getting delayed is Neutron. How do you think that will or will not affect the market if and when it starts to be serviceable? [00:29:32] S2: Yeah, think just yesterday they publicly announced the first flight will be 2027. Composite manufacturing is really hard. I know from personal experience, very large composite structures often take a bit longer than you expect, especially when you're producing them for the first time and you're running the tooling for the first time. I have a lot of faith that Rocket Lab can get Neutron flying regularly. They've proven that they... You know, a 100 launches of Electron so they can do scale. So, yeah, we're definitely rooting for them as well. [00:30:11] S0: Alright. So now we're walking around here. You have a motorcycle. We're gonna go look at that. Hey. [00:30:15] S1: We can look at it if you want. What... Who's is that? It was a joke, by way. I Bailey's. Think [00:30:21] S0: Does that go into space? [00:30:22] S1: Maybe. Depends how fast they go. Everyone's laughing. I'm permanently terrified that somebody's gonna kill themselves mobile guy, half a teenage. [00:30:30] S0: Yeah, that is a liability. [00:30:31] S1: Yeah, yeah. So this is kind of interesting. It's a half built, zero gravity sort of offloading rig, and so we have these runners that run-in two axises, and so the deployable Starcloud two, runs down this. This is one half of it. Yeah, the other half goes that side, so it's like 120 foot long deployable.
00:30:31 · Philip Johnston, Molly O'Shea
[00:30:31] S1: Yeah, yeah. So this is kind of interesting. It's a half built, zero gravity sort of offloading rig, and so we have these runners that run-in two axises, and so the deployable Starcloud two, runs down this. This is one half of it. Yeah, the other half goes that side, so it's like 120 foot long deployable. [00:30:56] S0: Oh my god. [00:30:57] S1: Yeah. So that small package you saw there extends down the length of this, and then on one side and then down the length of that on the other side. [00:31:06] S0: Oh my god. [00:31:07] S1: Yeah, yeah. And the first few segments of that is a radiator segment. That's the radiator we've designed and fabricated in house. As Adi was mentioning, our design is way more efficient than any other, so when we launch in January, it'll be by far the largest commercial deployable radiator in space, and the the nearest comparison is the International Space Station. Our radiator is at least 10 times less mass per watt of dissipation than the ISS radiator and about 500 times less cost per watt of dissipation than the ISS radiator. Now the ISS is an easy benchmark to meet on cost, but on mass, it's quite an achievement. One [00:31:45] S0: of the biggest topics, and I know we talked about this with Adi, is the economics of this. Are making more economical satellites going out in space and the vehicle itself. So that's one thing. And so I'd love to break that down in the economics there and then also further drill down the difference between on land, like terrestrial data centers versus orbital.
00:32:11 · Philip Johnston
[00:32:11] S1: Yeah. So the whole thing is actually contingent on the launch cost. So if the launch cost gets below around $500 a kilo, then it will be much cheaper to do this in space than terrestrially. And I think a sort of good comparison for this is if you build a new solar project on earth to power a data center, because solar is kind of the cheapest form of energy we have terrestrially, and so the way you can think about it is solar terrestrially has these three big costs. The biggest cost is the cost of permit to land, depending where you are. Second biggest cost is the cost of battery storage and backup power, and then the last cost is the cost of the solar cells themselves. And so in space number one, don't need permitted land, so your biggest cost is gone. We don't need batteries and backup power because they're 20 fourseven in the sun, and then we need eight times less solar cells because one square meter of solar panel in space produces about eight times the energy of one in space. So we have all these savings in space. The main additional cost we have is the launch cost, and so that's where this trade off comes, and that's where we see when you run those numbers, you come out to about $500 a kilo, where it makes more sense to do it space than terrestrially. And, of course, right now with Starship, we're at about $5,000 a kilo. Sorry, with Falcon nine, The design of Starship is designed to produce launch costs of, internal launch costs for SpaceX of sort of 20 to 20 to $40 a kilo. So so long as they pass that launch cost through to us at anything less than, say, 10 times their marginal launch cost or even more. That makes sense for us. Right now, by the way, on Falcon nine, they charge two or three times their marginal launch cost, so it seems reasonable.
00:33:48 · Molly O'Shea, Philip Johnston
[00:33:48] S0: Yeah. Thing that I listened to... So I listened to a handful of your interviews. One of the other very concerning points is chips and chip failures. So how does that work into the model? [00:34:00] S1: Yeah. For sure. It's actually a super, like, relevant point. I think it's actually more relevant and pertinent than even cooling, which is people think is the biggest challenge. We've basically solved the cooling challenge. The other part of it that we're working on is the radiation shielding and testing all the chips in different particle accelerators to make sure they can survive the radiation environment in space. And the reason we put so much time into that is if the chips have, you know, like a 10 or 20% higher failure rate in space than they do on the ground, then the whole economics of the model fall apart, and the reason is the chips are by far the most expensive part of what we're doing actually, And so there's a few ways that you can go around that. I mean, we're not going to have access to the satellites once they launch, so they operate a bit like the way the Starlink satellites work, which is you need to have redundancy on critical systems, you know, things like pumps, and then you over provision things which fail over time like the solar panels, you're going to lose a few percent per year, so you slightly over provision that. And then on the chips, we have a longer burn in period on the ground. Most chips actually fail in the first period of their life and so if you can, put them through a more rigorous burn [00:35:09] S0: Why is that?
00:35:09 · Molly O'Shea, Philip Johnston, Ezra Feilden
[00:35:09] S0: Why is that? [00:35:10] S1: I don't know. It's basically the inform... They call it infant mortality of the chips. Oh, no. Yeah. Oh my god. I know. It sounds kinda terrible. [00:35:18] S0: That's horrible. [00:35:20] S1: So what you want is to get through the infancy period and find out all those failures. So for Starcloud two, we had a three month burn in period. It basically means you run the chips hot for three months so you can find all those failures early. It's basically like, yeah, if you can survive... If the chips can survive three months at full power, the chance they have a failure from that point onwards diminishes very dramatically versus the chance they have a failure in the first three months. [00:35:46] S0: So you have five chips up in space right now, including one, H100. [00:35:51] S2: Yes. [00:35:51] S0: What are they doing?
00:35:53 · Philip Johnston
[00:35:53] S1: So, actually, we have... Yes. We have three chips from NVIDIA, two from ARM, and we also have some CPUs from AMD on Starcloud one. And what they're doing is, at the moment, we used them to do some interesting workloads, like train the first model in space, nanoGPT from Andrej Karpathy. We did the first... Ran the first version of Gemini, but now they're actually doing useful workloads for government customers, so they are, mainly in sort of as a test phase, but we're doing inference on satellite imagery, so on synthetic aperture radar data, on hyperspectral data. So we actually just, will do an announcement about some of the results of this, but we've got some very interesting results. And the use case for that is, let's say the government has a bunch of military satellites. They collect enormous amounts of, mostly imagery, but also other types of data. They don't want to have to wait for a ground station to downlink all of that data, so if they can run inference on that at the edge, we can then send down the insight much quicker. And so, for example, if they're monitoring a patch of water, they might want to know is there a vessel crossing some, some boundary. At the moment, what they do is they take an enormous amount of imagery, and then they have to wait at least an hour and a half to get to a ground station, and then they try and downlink as much as they can, but that's usually not very much. Once we're in space, we have a bunch of optical terminals on our satellite. In real time, they can send data to us. We can then run the inference workloads, on our satellite, and then we can downlink the coordinates of a vessel, for example, in minutes rather than hours and days, which is the current way that this works.