AI Is Helping Build the Power Source It Desperately Needs (Brandon Sorbom w/ Commonwealth Fusion Systems)

27:13Brandon Sorbom:And so you have these control rods and you're basically balancing out a process where you get it just at the point because you don't want it to peter out as well. So you're sort of like, you're always making it so that it is just at the edge of being critical and it keeps going. Fusion is a much different process because in fusion, instead of having a chain reaction where you're actually, your reaction is causing itself to go, the process that powers it is really the temperature of the plasma. And if you don't confine that plasma, the temperature goes down very, very fast to the point where you can't even have air in the chamber.

27:50Brandon Sorbom:So in one of these systems, whether it's a tokamak actually or any other fusion system, you have it in a vacuum chamber because you don't want any other stuff that's going to be able to cool down this very hot plasma. And it's actually, it's kind of funny because the temperatures of these plasmas are in the 100 million degree range, right? Which you would think like, you think 100 million degrees, that's like lava, right? It's gonna like touch the wall and burn through the wall and burn through the next thing and burn through the next thing. But the plasma is so diffuse compared to even the air around it.

28:24Brandon Sorbom:Fusion grade plasma is about a factor of a million times less dense than air. And so what actually happens if the plasma were to touch the wall is even though it's so much hotter than the wall, it's so much less dense than the wall that the wall will cool the plasma down basically instantly and kill it. And so the magnetic fields are actually not to protect the wall from the plasma, even though the plasma will cause the wall to ablate at like the nanometers. And you have to worry about that because there's stuff that you don't want to continually ablate your wall if you're smooshing the plasma into it.

28:57Brandon Sorbom:But really the magnetic fields are there to protect the plasma from the wall, not the other way around because you don't want that plasma getting anywhere close to the wall. And actually, if you were to, you know, if you were to accidentally open a valve and let some air into the plasma chamber while it's going, that would also extinguish and kill the plasma. And so it's sort of fundamentally different from fission, fusion is, because it's so easy to turn off. You can sort of think of like fission is kind of like default on. If you have enough of the stuff together, it's like default on and you have to like do work to get it to stop.

29:30Brandon Sorbom:Whereas fusion is the opposite. And so fusion is kind of default off and you have to do all these special things to make it work. And that's why, I mean, that's fundamentally why we've had, why we have fission power plants far before fusion power plants, because it's, you know, it's a double edged sword. It's, it's very, very inherently passively safe, but that's also why it's been finicky enough that it's taken all this work over the last 50 years to get this like fragile little star that we're trying to create on earth and keep it around long enough to make energy. because it keeps wanting to turn itself off.

30:04Brandon Sorbom:Right. Wow. Yeah, it's like, you can't access this. This is a cheat code. You can't do this. Exactly. Yeah. And so going back to MRIs, it's funny. So we actually don't, you know, we don't refer to fusion as nuclear fusion because we don't want to confuse it with fission. And the word nuclear has been so tied to fission. And the tie-in to MRIs is that MRIs were first, and actually still in a research setting, you'll see these things called NMRs. which is exactly the same thing as an MRI, but it stands for nuclear magnetic resonance. And it's because the process of an MRI is that you are also affecting the nucleus with magnetic fields with a resonance, but it has also nothing to do with fission.

30:44Brandon Sorbom:So people invented NMRs and we're like, wow, this is a great way to image things and we can get really high quality scans. And they put it out in the market and like nobody wants to get in a nuclear tank. So they quickly rebranded it to MRI, magnetic resonance imaging, so that people would feel safe in this thing and not feel like they were like launching themselves and, you know, inside of a reactor pool. That makes a lot of sense. I can see why people would by just the knee jerk reaction, because this isn't a subject most people have a full grasp of. Apart from it, I would say. Yeah. So I have a data center question.

31:20We keep hearing about that there's this big projected need that's going to continue to increase by 2030 and so on. Is there any chance Fusion arrives in time to be of assistance there, like we've heard about from people in the AI space? Or is this really more of a 2035 and forward play?

31:44Brandon Sorbom:I mean, we are pushing as fast as possible to get the first power plants on the grid, like early 2030s. Wow. That's an aggressive goal. Yeah. But I think, you know, every day, it's one of these things where like every day counts. You have dual pressures of, you know, you have climate change on one side and you have demand on the other. And both of those are pressures to go fast, right? Both of those things, you know, the solution is not to go slower. It's to go faster. but you gotta get it right at the same time right you gotta get it right yeah yeah i saw a talk that you did that where you started really like i've got a need a need for speed i like this guy already favorite yeah one of my favorite quotes oh my gosh well can ai help you with that process as is all of this like these collaborations that you have you know does this make you more bullish that you're able to hit those timelines like how how how does ai impact that in your opinion yeah Yeah, definitely.

32:41Brandon Sorbom:I mean, I think the AI will allow us to basically, I mean, at the end of the day, even on Spark, so Spark is the first device that we're building right now that we just shipped a magnet into the tokamak hall for, and then Arc would be the power plant after that. there's going to be a lot of learning that we're able to do even on Spark that will be able to affect the final design of Arc. And so the faster we can utilize Spark as a learning, you know, Spark, we're very confident that Spark will get Q greater than one, but there's still a bunch of learning that we'll be able to do to optimize how Arc runs past that point, right?

33:17Brandon Sorbom:And so the AI techniques will hopefully allow us to learn much faster on Spark. So you still have to run the experiments on the actual device and get like, you know, truth of like, what actually what is what is nature, you know, what is nature going to tell you, but we want to use these techniques to basically speed our, you know, there's this whole canvas in front of us, this whole space to explore, and we want to be able to explore that space as efficiently as possible as we map it out. And that's what we really think. That's what we're really optimistic that these AI techniques will allow us to do.

33:51You're basically putting your, well, I mean, I was going to say something else, but I mean, you are literally putting your money where your mouth is because you have this deal with Google where you just signed a direct power purchase agreement with them, which means that you actually have to deliver, right? Yep, definitely. So you got to make it happen.

34:10Brandon Sorbom:It's sort of circular, right? We're using AI tools to build something that may eventually provide power to a data center. Yes. Yes. I like it. It's, it's very interesting in that regard. Well, because the safety profile is so different, I understand it actually is as a good thing in terms of the regulations, right? Because you're regulated more like a particle accelerator than a nuclear reactor. Exactly. That's awesome. Yeah. And that's, that's huge. Yeah. Because a lot of the cost of fission is driven by the fact that you need to be very tightly regulated for safety, right? Like you need to make sure that like all of your materials are like way higher grade than standard materials that you would get, which leads to a lot of cost burden on building these things.

34:51Brandon Sorbom:And we're actually, like you mentioned, we're regulated as particle accelerators effectively in the United States. And actually a couple other countries are following suit because that's just a much more relevant and appropriate regulatory regime for something like this, which default wants to shut itself off. So how, and to bring this around to AI a little bit here, how does AI help you build a fusion machine? Like, are there specific problems that Torax, for example, is helping you solve that humans alone weren't on their own? Yeah. So we had these two collaborations with DeepMind, which is with Torax and Jax, and then with NVIDIA.

35:35Brandon Sorbom:And they're both, they're kind of different flavors. So I'll start with the DeepMind one. The DeepMind one is really controlling plasmas. And so these plasmas, as you can imagine, they're very hot things, right? And if you think of, if you zoom in to like the atomic level of like what's happening at the atomic level, you have these particles that are bouncing around. Heat is effectively just velocity. Like from a physics standpoint, like there's an equivalence between like how fast the particle is bouncing around and temperature. And as you can imagine, like higher temperature, the faster particles are moving around.

36:07Brandon Sorbom:And so the control of a plasma is on pretty fast timescales because you have this very wiggly thing, right? And so one of the ways that we've sort of clamped down on the plasma is you can really increase the magnetic field. And that's just sort of like a big hammer to sort of just squeeze everything in. And you can get a lot of confinement that way. But another way that you can get good confinement is by having fast control systems that can look at the plasma. And if you see the plasma starting to look like it's going to go out of confinement and hit the wall and turn itself off, you can say, be like, okay, adjust this magnetic field here.

36:44Brandon Sorbom:So you push it in from this direction instead. Uh, I'm sort of oversimplifying, but like, you know, you can, it's basically like an active, an active control problem where if you have a plasma that, that wants to, the term of art that we use is a disruption where there's a plasma does something that, you know, moves it in an area that you don't want it to be, or makes it, you know, turn into shapes that you don't want it to, which will eventually extinguish itself. you can actively control the magnetic fields that are shaping it to basically, you know, stuff it back into the box that it's supposed to be in.

37:15Brandon Sorbom:And so what Torax will hopefully allow us to do is have better control systems on plasmas to really do that fast control. And I want to emphasize that's not something that we're banking on in order. One of the sort of, you know, high level design philosophy of Spark is that we said we are not allowed to like take credit for fast control in order to make spark work. So spark is going to get q greater than one with or without fast control. However, with fast control on top of the high magnetic fields, that's all additive. And so it would be really, really great to have a fast control system like this and to be able to sort of detect, okay, like it looks like the plasma is going to disrupt.

37:57Brandon Sorbom:Now change around the shape of the plasma so that it won't disrupt in the moment. and to do that on fast enough timescale, you can't have a human, it's not on like second timescales or minute timescales or you can have a human adjusting a knob. You really need a fast control system that knows what's happening and is kind of learning from the plasma because a lot of these things are very complex phenomenon that plasma is sort of a collection of, I don't even know the name for the number, but it's a collection of 10 to the 20 particles. I don't know how many billion, billion that is. It's like a hundred billion billion.

38:35Brandon Sorbom:So there's many, many particles that are all interacting with each other electromagnetically. It's a very complex system. And so having like an AI, like reinforcement learning system that can go in and say, we don't know why exactly we're tweaking the knob like we are, but we notice that there's this behavior that happens when we measure certain other effects. That's something that would be really beneficial to control plasma yeah would it be able to work real time as well so like once you have a simulation like you can just have it going so you almost would be would you need a supercomputer with every fusion machine that you create then in an instance to run this quickly well the goal would be actually that you don't need a full supercomputer is that you actually if you have a model that's good enough you could have it react fast enough that you don't need You know, this giant supercomputer helping you run the machine.

39:25Brandon Sorbom:And like the AI is really taking on the heavy lifting here. Yeah, you would train it essentially. And then, you know, you can distill it down so that it can run on, you know, whatever is convenient. Okay. But that actually brings us to the other collaboration, which is with NVIDIA and doing surrogate models. And so going back to supercomputers, there are simulations that people run on. And OK, like if you run a certain set of conditions, what is what is the prediction for the output of the plasma on a global sense? And in doing that, you're going to like all of the details of like what's going on, you know, inside of the plasma.

40:00Brandon Sorbom:And that requires a superconductor. But people have started. And these are some of our colleagues back at MIT are leading the charge here. People have built these things called surrogate models where you still have to run these supercomputer simulations. but the traversing of the space of options, you can greatly optimize by like order of magnitude or two orders of magnitude by using AI techniques to say, okay, what is like the best trajectory through this space to go through so that we can minimize the number of supercomputer simulations. And one of the things that we're working with NVIDIA on is making these surrogate models to make sort of, this is, you know, buzzword, but like a digital twin of the plasma.

40:38Brandon Sorbom:Yeah. So that we can say, we now, you know, we've built this thing with Spark. You can sort of think of, you know, the DeepMind collaboration piece is sort of around the real-time control system of the Plasma. But then the NVIDIA piece, and we're also working with Siemens here. So Siemens, we use a lot of Siemens products that, you know, we're cataloging everything of like how the machine is built, how the data is collected. and with the collaboration with NVIDIA, we're basically wanting to create this digital twin so that we can make predictions for the global performance of the machine. If we want to run experiments or tweak things, we'll be able to learn a lot faster because we sort of have like a digital lab, if you will.

41:17Brandon Sorbom:We can start to make more predictions of, okay, if we run this experiment, what do we think we're going to get and have a much better prediction of what we're going to get? What would you say? because okay so you're taking the data from the the spark once it's once it's up and running you'll will you be feeding that back into a model um and if you are i guess what would be the thing that that like what breakthrough in ai specifically would be most helpful to you like because we're hearing a lot about science being applied to or ai being applied to science right now um you know they're talking about novel physics problems but what kind of ai capability isn't available right now that would actually help you achieve this faster?

41:57That's a good question.

41:58Brandon Sorbom:That isn't available right now? Yeah. Or perhaps you have everything you need and it's just you need to just install more magnets. I don't know. But you tell me. I think it's really just using the tools that people have developed. Nobody has really applied these tools because there is a lack of hardware to apply the tools to. We're sort of in this really exciting space where we can use these tools that have been developed for other things, but we can now say, okay, now take, take these, you know, take these techniques and models that, you know, that NVIDIA has and that Google DeepMind has developed and actually apply them towards the specific problem of, you know, add the physics of a fusion plasma and then add the experiments that we're going to have available to us to actually run those things in real life and train the models with.

42:47Brandon Sorbom:So I, I mean, I, I don't know if I can really speculate. I mean, people are, as you guys well know, you know, people are developing new and better ways to run AI models every day. But I think for us, it's really right now just a question of taking what, you know, what exists and then putting that onto the fusion problem. Yeah. And then training it on a real machine. That makes sense. Yeah. I suspect that would be a really great opportunity to create a new, you know, learning model once you have that data and you can get that flywheel where you're just speeding the data back into it. Yeah. Totally.

43:24Brandon Sorbom:Yeah. That's so exciting. Do we have a timeline of when Spark is going to be up and running? No worries if you don't. As soon as possible. So we're, you know, the goal is to get the machine together by the end of this year and then be turning it on, like getting first plasmas in 2027 and then getting to QG1 as fast as we can after that. I do have to say like every, It's one of these things where, you know, there's about, you know, some, there's somewhere on the order of like 10 ,000 unique parts that go into Spark. Much, much higher actual part count because there's a lot of things, you know, we have 18 magnets and those all have subcomponents that are different.

44:04Brandon Sorbom:But there's a lot of things that have to be built. And so right now, I would say, you know, when people ask, you know, a question that I get asked often is like, what's the thing that keeps you up at night? Like, what's the big challenge? And people are always surprised when I say at this point, it's not really the science and the technology that keeps me up at night. It is like, it is those 10 ,000 parts. It is just the execution challenge of having an organization that can bring all these things together and put them together in the right order and the right sequence, you know, and make sure that everything, you know, everything comes together properly.

44:38Brandon Sorbom:it's just a huge execution problem like we have a thing called an integrated master schedule which is like an enormous gantt chart right of like all the tasks that have to be accomplished these thousands and thousands of tasks and we have like a team of people that maintain this schedule because the schedule is changing all the time because like if a supplier slips a delivery on something you have to like refactor the schedule all of a sudden and imagine you know you like multiply that by out to like thousands and thousands of these things that you're doing and so it really is just like a project management challenge of putting all the pieces together making sure the things that we're going out and having fabricated and shipped and stuff like that you're still basically building your own supply chain right effectively yeah oh yeah we're definitely our supply chain team is uh probably like at least 100 people and we have about 1200 people right now in the company and there's about like roughly a hundred of them are in supply chain and every you know and it's a very technical supply chain too these are like there's actually several people on the supply chain who have like technical phds who are it's not just like you know like make po's for things which is that that's really important too to get that right but there's also like people who go out to the sites of vendors and are like okay like this thing is you know You're making this thing slower than we would like.

45:59Brandon Sorbom:How can we help you make this faster? Yeah. Type of thing. Yeah. So I think it's important to call out that Fusion isn't so much a single breakthrough, but a stack, essentially. Magnets, machine, factory, as your team has mentioned before, then grid economics. And I was wondering, what does factory mean here? Could you walk us through that a little bit? Yeah. Yeah, so the factory that we have making magnets is, so there's many pieces that were, we make the designs for everything, but then like the vacuum vessel, for example, that was a big thing that we recently got one of the halves of the vacuum vessel shipped over.

46:40Brandon Sorbom:So that was fabricated out by a great partner that we have in Europe. And then we flew that half over on an Antonov. The other one is actually on a boat right now. That one, the second one we didn't need as fast, so we were able to ship that one much cheaper going on a boat, but much slower for that one. But for things like the magnets, that's really like there's nowhere that we could go to say, OK, here's a design for a novel superconducting magnet. Can you please build this for us? And so we actually built a factory to build the. So we design the magnets and the technology to be modular. So even though there's 18 of these big these big toroidal field magnets, These are the big D-shaped magnets that kind of make the ring in the tokamak.

47:25Brandon Sorbom:Even though there's only 18 of those, each of those 18 coils is made up of 16 nearly identical sub-modules that we call pancakes. And so there's 288 pancakes that you have to make for all of the 18 magnets, plus a few extra first pairs. Let's round up to like 300. So you have to build 300 of these pancakes. And that is a big enough number that you can say, okay, let's make an assembly line. to make these now. This isn't just like a craft bespoke, you know, you're like hand winding these things. Now you can start to say, okay, if you have to make 300 of these and you start doing the math and you're like, okay, if it takes, it takes me a year to make every pancake I'm hosed because you're never going to make them in time.

48:06Brandon Sorbom:So yeah. Right. Uh, that would not be good. Um, so we, we basically came up, we're now like in the factory, we're sort of on like gen five or gen six of the factory tooling where we've, you know, sort of continuous improvement where the The first pancake that we made, sure, that took like several months to make the first pancake. And we've actually made all the pancakes now for the TF coils. But at our peak, at our peak rate, we were, we went from, you know, the first pancake taking a couple of months to two pancakes per day coming out of that factory. Yeah. Because we were just able to figure out how to like, how to get things automated, parallelized.

48:45Brandon Sorbom:you have like really smart people you know the engineers and technicians who work on that line are constantly like okay how can i do how can i make this process faster how can i eliminate waste from this system and just make it like you know using like literally using the same techniques that people use in automobile and plane assembly lines to say okay how do we get these fast that's awesome and is ai able it sounds like ai would be able to help you in that process obviously if you're automating like building a factor basically yeah we we were early days enough that there's only a couple areas so far but that's not because there's a lack of areas it's just because there's there's so much learning that we did but there's definitely like you know um i don't know if i'm i'm trying to think if i'm allowed to say exactly which pieces we applied this to uh or if that's if that's ip protected but we use like image recognition for some pieces of the of the process where it was like you know you could either have a person like sit and look at micrographs of a, you know, of a scan of a non-destructive scan of something, or you could say, okay, have a, you know, have an AI like image recognition tool that's been trained on what a particular type of defect looks like.

49:52Brandon Sorbom:And you, you train it. So you have, you have this thing that's going over it. And then there's a robot that goes over and actually is able to fix the defect, like without, you know, with very limited human intervention. And I would say right now, there's still a lot of things. We actually have a lot of robots in our factory. Now there's probably like six or seven stations in the factory that have robots that are still assisted by people. But that we're figuring out how to actually like automate that so that those people can go on to the next, you know, they can go on and automate like the next piece of it.

50:25Yeah. I mean, if you got PhDs working on like POs or whatever, you need to, you need to apply their brain skill. You need to have them learn something, you know, figure it out and then you can automate it and then have them work on the next hard problem.

50:37Brandon Sorbom:It's really cool that the team that we have, this is, you know, it's a bug and a feature of the fusion industry that there is no existing fusion industry. So we have to build it ourselves. Like, it's not like we could just go and like hire people who have a background in this. And so it's actually really cool. One of the most, I say, rewarding pieces of my job, like the physics and the technology is really cool. But just being able to meet and work with so many talented people from all different walks of life who've like people who've built like high performance race cars, like a lot of people, as you can imagine from, you know, from like the, from the space industry who've like built rockets, um, and things and things like that.

51:17Brandon Sorbom:Um, and you know, people who've built oil rigs and people who have built, which have crazy technology. I didn't realize like how crazy the technology is on like oil rigs and deep city drilling. Like that is like ridiculous technology. Um, you know, people who, you know who've worked on other renewable projects there's like people from all these different all these different areas of technology and backgrounds that are all sort of coming together to apply their expertise and skill sets so like how do we make you know fusion is a very early industry and it's like they're these everybody in the company is shaping that industry right now which is super exciting so for people watching who want to like follow this place closely what's what's the milestone they should be watching out for that says we're there yeah so uh our ceo and my co-founder bob put out um there's a really great i don't know if you guys put like hyperlinks on your uh on your notes for the show i can send you uh he put out a really great uh open letter basically to the um it was called uh building building trust let me see if i can yeah building trust infusion energy.

52:26Brandon Sorbom:And this was the, just to exactly answer your question, like what are the milestones that we want to see if you're following along this field that most people don't like have a formal training in. And so it was things like, you know, the first step is like, you need a stable plasma and then you need to get the plasma hot enough. And then you need to get a high enough value of this, this thing that we talked about earlier called the triple product. And then the next milestone after that is getting q greater than one so that is enabled by having a high triple product but that's like more power out more energy out than than in and then the next step after that is you have to um uh you have to make enough actual electricity um so it's one thing to make when we say q greater than one that's like that's just like pure energy but obviously you you know when you convert energy like in as like heat to electricity it's not a one-to-one you're just always, you know, entropy always gets you.

53:25Brandon Sorbom:So you always lose a little bit. So then the next step is, can you make net electricity? And then the last step is, can you make that electricity at a cost competitive rate? You know, at the end of the day, it does nobody any good if, you know, we get to the end of this whole exercise and we say, great, you know, it's a million dollars per kilowatt hour for this great source of power. Nobody's ever going to buy it, right? And so from the beginning, we've had to, you know, you know, even Spark, even though Spark is not a commercial product and will never be a commercial product. Spark was designed, we made design choices in Spark that was like, okay, if you project this forward to the power plant, has to be economic.

54:02Brandon Sorbom:And that was a constraint, even on Spark of like, you can't, you can't like cheat yourself forward on a system that is going to cost a hundred times more than you could. As a first of a kind, sure, you can get away with like, okay, it's the first time you build something. There's always, you know, first of a kind things always cost a little bit more, but they can't be like a hundred times more than your final product is going to be a billion dollars is not a feasible product for real humans yeah yeah that makes sense so i had one small follow-up to what you just said like do and maybe you don't want to say this for competitive reasons which i totally understand and i'll ask a different question but do you have a target like when you're just designing like arc right the power plant version do you have a target cost for the electricity that you have on your roadmap where you're like, this is the price we're going for?

54:52Or are you just like, we're going to try and make it as cheap as possible?

54:55Brandon Sorbom:Well, I mean, it's trying to make it as cheap as possible, but I think that we think that we can, you know, sort of be competitive in like target costs of, of like, of like natural gas to renewables. Like that's, we actually think there is a, like, if you like, you know, first principles, you know, look at should cost models and things like that, we actually think that you could, and like on a non-subsidy basis, right? Like you could actually be cost competitive with those things. And that's for, you know, that's for an anth of a kind, you have to climb down a cost curve. That's not the very first plant, right?

55:30Brandon Sorbom:Yeah. But I mean, at the end of the day, I guess the first principles behind that is at the end of the day, fusion is really like the physics of fusion lends itself to that because once you've built the plant, the fuel is effectively free. It's not actually free. Like you do actually, the fuel does cost a little bit, but the fuel, because you burn so little of it, because fusion is about a million times more efficient. The, because you are breaking nuclear bonds as opposed to electronic bonds, like most, you know, like fossil fuels and, you know, even conventional, you know, like solar and things like that.

56:07Brandon Sorbom:You're about, you're about a million times more efficient per volume of fuel that you use. and so and the fuel itself is not i mean deuterium is something that is you can you can basically filter out of every uh i think it's every 6 000 um water molecules is actually d2o molecule so you can you can filter it out and the mass difference is two you know a deuterium weighs twice as much as a hydrogen so that's pretty easy to build systems to you know to separate that out um and then tritium even though it doesn't occur naturally on earth you can breed it from lithium which is extremely abundant and because you know and people then then people are all lithium are you going to be competing with batteries but because you're using so little of it to breed your fuel it actually you know it's you you use the equivalent i think we did like the napkin math once and we said for you know the entire lifetime of our power plant it was something like you would use the equivalent of like the lithium and 100 tesla batteries over the lifetime of the plant wow you actually need more batteries you know maybe yeah right yeah that'd be a good case so so that's actually you know sort of from like a fundamental physics first principles like you start going through and it's like okay to like build these things it's a lot of like steel it's like steel concrete the superconductor is a weird thing um but the actual superconductor itself even like you you go and actually run the numbers on okay you take one of these tapes that's a superconductor.

57:34Brandon Sorbom:The superconductor is called rare earth barium copper oxide. So of course, everybody freaks out when they hear rare earth because they're like, oh my God, are you going to have enough rare earth? But then when you do the math, you look at it, one of these superconducting tapes, and most of the tape is substrate. It's like steel. And then about 1 % of the actual superconducting tape, which itself is like less than a millimeter thick, um one percent of the thickness of that is the rebco material and the rebco like is a lot of other stuff besides rare earth so when you you run it back and you say for an entire arc power plant you need roughly 100 kilograms of rare earth material in all the superconductor you just have spread it out over a very large a large surface area effectively of all this tape that you're wrapping the magnets with and so you're like sprinkling this like dust um in in very you're you're doing it very strategically.

58:26Brandon Sorbom:Um, but yeah. And so they actually like raw materials of stuff that you need is, is not like, is not a lot of crazy stuff, which is why we think that it's not, you know, fantasy to say that you could actually have, and you look at the size of these plants and they're roughly the size of like a comparable, like natural gas plant. It's like, okay, if you're building something that's the shape and the size of a natural gas plant and uses steel and mostly mostly steel and concrete and not too many weird materials sure you're arranging those materials in a very different configuration than you would a natural gas plant but at the end of the day like the materials are the materials it's not anything that's unattainable or is going to be a giant supply chain nightmare or yeah exactly yeah wow so in a sci-fi world would would this potentially lead to a point where you have these automated factories that are making these uh you you know, fusion machines and power plants that then feed power to the power of the automated factories and you basically have a fully cyclical, fully cyclical like world of abundance potentially?

59:31Brandon Sorbom:Well, we'll see. Yeah. I mean, this is, you know, it's a reinforcing, you know, reinforcing loop. I think there's always going to be clever humans in there. Yeah. Hopefully in some way, shape or form. Yeah. Fingers crossed. Fingers crossed. But yeah, I do think that this is one of the things that got me really excited about fusion is that it's not, you know, I, I got excited about it at first because I said, okay, this is a clean technology, like, you know, for climate change, obviously, you know, this would be a great thing, but it's so much more than just that, like climate change. It's like really great for climate change.

1:00:05Brandon Sorbom:And then that's just like scratching the surface of not only can you replace everything with something that doesn't emit carbon, but you could also start producing more energy. And you like history has shown that when humans have more energy available to them, they do really clever stuff with that energy. I can think of a few things off the top of my head, but I'm sure there's things we haven't even thought of yet. If you were to say boundary condition now goes from current energy usage of the world to 100 times that, there's very clever people out there that are going to figure out how to solve problems using that extra energy.

1:00:42Brandon Sorbom:That's really the thing that gets me excited about fusion. yeah how much scale would be required to to get to that point where you can start doing this novel stuff like how many power plants would you need so in order to replace sort of like all of the electricity generation um you'd need probably about uh let's see to get to like 20 to 30 percent um we did we ran these numbers a little while ago um but you'd be roughly like five to ten thousand power plants, which isn't, I mean, that's like, that's just like, you know, these, these power plants are the, are 400 megawatts electric, which is kind of like the average size of, of a power plant.

1:01:22Brandon Sorbom:And that like, that is the number of, you know, there's somewhere around, I think like 20 or 30 ,000 of these power plants on this curve in the world. And so if you replace like half of that, that's, you know, you're talking like five, 10 ,000 of these, of these power plants. So it's, a big number, but then at the same time, there's airplanes, which are very complicated things. We've built tens of thousands of airplanes in the world. That's very true. People have built all those power plants to begin with, right? All of those assets that exist today, we have built those. That's right. Hard problems were built to be solved.

1:01:59Brandon Sorbom:Exactly. Well, Brandon, thank you so much for taking the time out to join us today. I really appreciate it, man. Where can people go to learn more about what you and Commonwealth are doing? Yeah. So we have on, on our website, we've been, um, we have a bunch of blog posts that we, we have and we update there. Um, I know our, uh, our, our Marcom's team has, you know, updates, they share updates on, you know, all the various, uh, social channels. You know, if you search Commonwealth fusion systems, social media platform of choice, um, we, we should hopefully have an account there. I know they're on LinkedIn as well.

1:02:31Brandon Sorbom:They announced things. So yeah, keep, you know, we try to be pretty transparent to also if you if you really want to geek out about stuff we actually do like to publish in the scientific literature as well we feel very strongly about that but as a topic like that we we sort of owe it to the world for this like topic that not a lot of people know a lot about to actually publish in the scientific literature and say like all this stuff that we're doing isn't just like full of hot air there's actually peer-reviewed and there's other scientists who are not affiliated with us who look at it and say like yes that looks like it's feasible.

1:03:03Brandon Sorbom:So you can also search in the scientific literature. We do everything. We've also made it a point to publish all of our stuff open access. So, you know, so you can look. I think actually the very first ARC paper, that was before the company started and didn't have the money to publish it open access. But there's a copy of it out on archive. Excellent. That's awesome. Well, to everyone watching, thank you so much for joining us today. It's been a lot of fun, and I hope you learned a lot about fusion. If you haven't yet, please take just a minute to like and subscribe to the channel and pop on over to the Neuron.ai and sign up for the newsletter today.

1:03:38And on that note, that's it for today. Farewell for now, humans.

1:03:58Thank you.