The Exchanges

Q Good to see you. Excited to have you here. Okay, we're gonna dive right in. Um, tell me how a Panthalossa generator works. What is it, and how does it work?

A Cool. Yeah, so a Panthalossa generator, we call it a node. It's, it's a new energy technology. Um, we, we created it from scratch. We created it to do a very particular thing, which is go far from shore, And capture energy where the resource is really good. The resource being the waves. And we wanted it to be able to do it hundreds of miles from shore, thousands of miles from shore. So, um, so I can explain how the power generation piece works, and I will. But it's also two other things at the same time. So a node is also a vehicle. It drives itself. It, it, it, it can be towed. But we designed it so that once you deploy it, it can walk out to the resource on its own. It can walk back under command. It can stay in a region. And that's essential because it's untethered. It doesn't have electrical cables coming home. Um, and then because it doesn't have electrical cables coming home, it also has the payload on board. So each one has a computing cluster or each one has an electrolyzer and it's using the power on board to do things. So So that's, that's part one, is it's, it's three things all at the same time. As far as the power generation piece goes, this is the piece that we developed first back in, let's see, it would have been 2016 to 2019. And the idea is to convert wave energy into hydroelectric power for the first time. Nobody had really figured out how to do this. And we …

AI assessment note: “the idea is to convert wave energy into hydroelectric power for the first time.”

Q Right, right, because you pay for that towing. Um, and because they're big. I mean, I guess we should maybe, for folks who haven't seen it, as I have, like, how big is a node?

A Yeah, uh, so a node is anywhere from 10 meters across at the top, like our Ocean Two that we did two years ago, and Ocean Three is about that as well, um, but up to 30 meters across at the top. Um, And you sort of get diminishing returns after about 25 or 30 meters, and then it goes down in the water column, uh, anywhere from 70 meters to a hundred meters. And so, big system in the scale of human objects, but quite small, actually, in the scale, obviously, of the ocean. When you get out there and, you know, you're at sea and you see one, it actually feels very small, and it's also very small compared to ships. Um, so it's, You know, it's, it's the right size for, for what we're trying to do.

AI assessment note: “a node is anywhere from 10 meters across at the top”

Q You mentioned that if you go out, uh, into these areas in the middle of the ocean, you get a much more reliable resource. Let's, let's talk about the resource. Um, what is it like? How much, how consistent is it in the areas that you're targeting? How much variability by season or weather conditions or time of day? I don't know. How should I think about the resource profile?

A Yeah. So, um, let's start with literally what is the resource. And so, You know, as, as you know, um, the wind, first of all, is created by a combination of thermal gradients created by sunlight, and also a little bit of Coriolis, you know, earth rotation. So, so you get wind, and the wind is sort of a concentrated form of sunlight is one way you can think of that. And then as the wind blows over long distances of water, it first creates ripples, and then those ripples present more of a normal Area to the wind, and then that can push more energy in, and so you get this compounding injection of energy into the water from the wind, that creates the waves, and the waves propagate over long distances without significant loss of energy. Um, so, you know, the waves that you might have on the beach in Hawaii are often being generated by storms in Alaska or storms in the southern hemisphere, very long distances with very little loss, which means that when the wind stops The waves keep going. Even if the wind stops momentarily, you, you've got this accumulation in this big battery, really. And so, a thing that we often say is that the, um, this, this energy resource, particularly in the southern hemisphere, is the world's biggest solar battery by far, and will always be. It's just an enormous storehouse for solar energy. Um, and if you can create the system that just goes and sits in it…

AI assessment note: “which means that when the wind stops The waves keep going.”

Q I would think the generator and the power electronics, too. I mean, that stuff fails on land. It's not necessarily because it's at sea that it would fail, but, like, We've seen that, right? Inverter failures are not uncommon, and solar power electronics are, you know, they're pretty reliable, but they're not perfect. Generators, same thing.

A Yeah, that, that's true, and so this goes to the design philosophy that we have on those things. Um, for our power supplies, for example, the team that we have working on them, um, is a team that came out of Raytheon and Collins Aerospace, uh, Places where they have a need for extremely high reliability, power supplies for avionics, and, and basically, you know, what is the power supply that powers your triple seven? And, ah, there's a whole bunch of design principles in that related to, for, you know, not using software. It's all, ah, analog logic that runs our power supplies. There's no firmware, ah, no capacitors with liquids inside that can evaporate. Um, there's a whole bunch of other design principles that if you follow those, your power electronics really ought to last for the design life without failure. In the event that one does, then that node, which would be, you know, it would be one in a thousand, would potentially be dead in the water, or at least you'd have a fraction of your powertrain go down. It can hopefully be a graceful degradation. Um, and in the worst case, we have to go recover it. We bring it back. We fix it, and we don't make the same design mistake again on the next one. Um, but on the whole, on the average, the fleet should have extremely high reliability for the, for, you know, for these reasons that we've been talking about.

AI assessment note: “There's no firmware, ah, no capacitors with liquids inside that can evaporate.”

Q degree where that would matter, as evidenced by the excitement around Orbital, which also is not You know, uh, not the lowest latency kind of thing. You're offering maybe, but, you know, good uptime, um, probably not best in class uptime, but you also probably don't need that in every use case. So how do you think about, like, what are the, You know, who's your customer for the compute?

A Yeah, so it's anyone you can think of who wants either a lot of intelligence applied to problems, or to make the models better, so that when the intelligence is used, it's more powerful. And so there are these two buckets. The first one is just long-running inference. And long-running inference means, you know, whether it's for Coding. You've got a code base. You want to send it somewhere, have the agents churning on it. Our platform is the perfect place for that. It's very low cost. You can send it. All of the inference chips are running, um, around the clock, and you can swarm agents onto problems. They can be working together, um, on problems. They can be communicating with each other, and it, it actually can be very interactive. You know, the, the additional latency that we have is only like a hundred milliseconds. That, Vanishes into the, even the latency of time to first token on most pre-fill, um, certainly on the interactive latency of a human waiting for an output, you know, which can often take minutes, or if, if you send something away for a long time, it can take even hours. So the satellite latency isn't really a problem. Um, but we're not going to be the thing for like that result at the top of google.com or something that's like driving a self-driving car. So there's a whole class of like super latency sensitive applications where you wouldn't want to use us. Uh,…

AI assessment note: “there are these two buckets. The first one is just long-running inference.”

Q And is it, uh, is it self-contained? Is it, is it filtering in new seawater, running it through the cycle, or is it just, like, ever cycling the same seawater?

A It's, it's mostly cycling the same seawater. Um, the, the seawater, as it goes up into the reservoir, down through the turbine, then returns back to the main tube that Sends it forth into the reservoir, but it is open at the bottom, and, uh, the water in that tube that is sort of, it's sort of like a liquid piston that's doing the compression of the water up into the reservoir, um, under the inertia of that water and the system. That water does have an opportunity to mix with the seawater, so there is some mixing. We're not totally sealed, but by and large, we're not pulling water through. We're not sucking in new nutrients for, You know, things to grow. It's, it's mostly, mostly a closed cycle.

AI assessment note: “It's, it's mostly cycling the same seawater.”

Q means, but I'm still interested in the question of how do you get the thing out there in the first place, and you're, you're certainly starting where the resource is not very good, and so I would presume to the extent that it is propelling itself forward, you know, if you drop it Offshore, just offshore, uh, it's certainly going very slowly initially, I would guess, right? If at all.

A So, so this is a question of where we put the factories, um, and how close are they to places where you can deploy it from its towable horizontal configuration into its operational configuration, and then you're right, how good are the waves there to start producing power and start doing the propulsion? Um, and so, for example, we wouldn't tend to put factories, you know, On the coast of North, you know, North America or something like, like that. We want to put them, uh, in the regions, near the regions where the energy is the best, and in the locations where we want to put them, you can absolutely just tow them 50 miles offshore, flip them, and then they can work their way out into the resource, uh, under their own propulsive power.

AI assessment note: “tow them 50 miles offshore, flip them, and then they can work their way out”

Q Um, you mentioned that this is new and different. I think we should put a finer point on it. What, what is it that is distinct about this approach versus historical wave energy approaches? Like, what makes this unique?

A Yeah, great question. So we, the, the most distinctive thing first, I think, is that we are not doing it In coastal areas. And, and this was marine energy For the most part, for all of history before we decided to pursue this approach. Um, there were patents, scattered patents in the distant past of people thinking about doing something like this, but most coastal, most marine energy has been coastal because of course the idea that most people have had is you want to get the energy back on a cable. And if you're going to run a cable, you better be close to shore. Um, and also many of the, at least wave energy technologies, But also wind energy technologies have all relied on a seafloor connection of some kind, either just to moor the system and keep it in place, keep it from drifting off, or in many cases to actually push or pull against to create the reaction forces that you need to drive your generator or power takeoff or something like that. And so there's been this historical center of gravity to do it close to shore. We decided, um, we decided to cut the cable. And go to the middle of the ocean, and that's for many reasons. It's number one, because that's where all the energy is, frankly. Um, if, if you look at the entire globe, and you say, I'm gonna look at it for how much energy is there in the wind, in the waves, um, the coastal regions are very small in terms of both …

AI assessment note: “the most distinctive thing first, I think, is that we are not doing it In coastal areas.”

Q Um, okay. Okay, so onto then, I think, like, maybe the obvious really big question, which is O&M. Um, you know, most, both energy generation equipment and, uh, data centers require a fair bit of maintenance. How does one do maintenance in the middle of the ocean?

A Yeah. So we, let's talk about the nodes and then the servers as well. So the nodes, this has been our philosophy from the beginning. We wanted to design something that really doesn't require maintenance during operation. Um, and how do we get that? We get that by having the hull be just solid state, completely just steel, marine coatings, and that's it. And then you have your one water turbine. Which is fed by the, uh, fed by the reservoir, and that is also just a very simple rotary moving part, spins on bearings. You can design that for whatever lifetime you choose, you know, five years, 10 years, 15 years, and those are the only moving parts on the whole system. The hull is solid state, it moves up and down in the waves, and the turbine spins inside. None of that should require maintenance, at least not on the time frames that are relevant. So, Um, it remains to be seen whether we achieve that design goal, but we don't know what about that breaks, because we've run our systems at sea. They, of course, survive. We've run our turbines and endurance testing, and they survive. And those are the elements of the system that you need to survive in order for the systems to last a long time.

AI assessment note: “We wanted to design something that really doesn't require maintenance during operation.”

Q But it sort of seems like a chicken or egg, especially the initial propelling, right? Like you're not, you don't have enough resource on shore, so you need to have an auxiliary generator, I assume.

A Exactly. And it's not even a generator. So we said, what if we could, we're all about shapes at Pandalossa. We like shapes that do things. And, you know, that's the, like an airplane wing is a shape that does something. A boat is a shape that does something. Um, we wanted to find a shape that As well as, you know, I was describing the shape earlier that pumps the water. We wanted to find a shape of the hull that, because of the up and down motion, pushes water backwards, and that causes the system to move forward. So in the same hull shape, we have both of those behaviors happening. We have the pumping action into the reservoir. We also have a shape towards the bottom of the system that pushes water backwards, so the system moves forward. The system is always moving forward. It's, it's always moving forward as it moves up and down. And so All we have to do is steer it, and so it's like a Roomba on, you know, you can't stop it. It's always moving forward, but then if you can steer it, you can drive it around. Um, there are videos I can show you of us doing figure eights out there at sea with these systems. You actually have quite a lot of authority to steer them and drive them in circles and drive them any way you want to.

AI assessment note: “it's not even a generator... a shape of the hull that, because of the up and down motion”

Q all the time, because that, that ends up being pretty expensive. So you really want to design for, like, pretty robust operations for whatever designed lifetime. Which gets the other thing you were, I think you were gonna describe, which is in the case of compute, what does O&M look like on the compute side? Because again, we, you know, there's a lot of maintenance on servers on LAN, too.

A Yep, exactly. And so, uh, so you're right. You, you would not want to be going and Um, recovering or deploying each individual node over distances of hundreds or thousands of miles, uh, that would start to break your cost structure. But if you're doing it on the occasional node that fails, and we build all of this into our models, then that's fine. As long as you have sufficiently high reliability on all these components, which, which I think we will. Um, and, and by the way, which we show in our labs, you know, we, we run a lot of long endurance testing On all of these components and including in seawater and so forth. So, um, that's all just an applied engineering problem, honestly, and it's, it's, it's a, in the scheme of applied engineering problems, it's not the worst by any stretch. It's much easier than landing a rocket, for example. Um, so, so we think we're going to be able to, to achieve that, um, on compute. Yeah, this is a whole interesting conversation. And so the problem that you're identifying is that if your servers Have a sufficiently high failure rate, then you're deploying them, you might have, you know, really low cost of energy, all of these things, really high scalability, but if half of them are dead within three months, um, then that's not a good way to deploy compute. And so we spend a lot of time on this. We spend a lot of time on this in, in two diffe…

AI assessment note: “we actually have models where you have the servers degrading according to data”