Moonshots: 200GW Hiding in Grid, Sodium Batteries 10x Cheaper, Wave-Powered Datacenters w/ Ramez Naam | EP #280

Sourced by podcast-ingest on 2026-08-15. Auto-transcribed via AssemblyAI (universal-2, en). Speakers identified by AssemblyAI Speaker Identification using the per-podcast host/regulars hints; the resulting label→name mapping is in the frontmatter. Duration: 2h07m. Episode page: (not provided). Audio: https://traffic.megaphone.fm/DVVTS7884087529.mp3.

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The mates sit down with Ramez Naam to discuss the state of energy, the grid’s struggle to keep pace with AI, breakthroughs in sodium batteries and fusion, and whether wave-powered data centers could unlock a new era of energy abundance.

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Peter H. Diamandis, MD, is the Founder of XPRIZE, Singularity University, ZeroG, and A360

Salim Ismail is the founder of Open ExO, a GP at Exponential Venture Capital/The Organizational Singularity Fund and a sought after global speaker and thought leader.

Dave Blundin is the founder & GP of Link Ventures

Dr. Alexander Wissner-Gross is a computer scientist and founder of Reified

Ramez Naam is a computer scientist, clean energy futurist, award-winning author, and founder and managing partner of Planetary VC. A former Microsoft executive, he now invests in climate and energy startups and is a leading voice on disruptive technologies shaping the future of energy.

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*Recorded on August 4th, 2026

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Transcript

Ramez Naam: In the U.S. if you put in a request for hundreds of megawatts of power to build the data center today, good luck getting that power before 2031. That's the situation that we have today. So new technologies like sodium ion batteries could drop the cost of batteries by a factor of 10. We already have the very first solar plus battery baseload power plants and they're affordable. Batteries are plunging in cost and are going to drop another 10x.

Peter Diamandis: Ultimately you believe that thesis that solar in the long term is going to dominate beyond everything else.

Ramez Naam: The reality is, look, everybody's heard Elon talking about space based Data centers. So 10 gigawatts a year is like five or six launches of Starship a day.

Dave Blundin: Unless we hit that exponential absolutely full on and go right down that path. This looks prohibitive for 15, 20 years.

Ramez Naam: The biggest unlock that we cannot predict of AI and power will be now. That's a moonshot. Ladies and gentlemen,

Peter Diamandis: everybody, welcome to Moonshots. Another episode on the front line of the Singularity. We're living during the most extraordinary time ever. And our mission here is to deliver you the breaking news and help you understand what's going on. Today we're going to be doing a deep dive into the innermost loop, all things energy. I'm here with my Extraordinary moonshot mates, DB2 AWG, Selim. Welcome gentlemen. Good to see you all. Looks like you're here. Normal haunts and we've got a friend with us today. Everybody on Moonshots. It is an honor and a pleasure for us to invite Ramez Nam. Ramez is a computer scientist, investor, author, one of the clearest thinkers on the future of energy. After a career at Microsoft, Rames became the leading voice in the exponential decline in the cost of solar, batteries, fission, fusion. He's the founder managing partner of Planetary VC Investing in energy companies. He's the author of the Infinite Resource and one of my favorite ever science fiction novel series, the Nexus trilogy. If you've not read Nexus, I cannot commend it on Book Corner where Alex and I talk about our books. I've mentioned Nexus a few times. Today we're going to be exploring the innermost loop, why energy abundance may arrive faster than most forecast and its impact on AI, economic growth, geopolitics and our future. Again, my mission here is at the end of this podcast and the brilliant dialogue that my moonshot mates is going to bring to the table here. You're going to understand either if you're an investor, if you're a builder, what's the alpha, where is it going? What are the real timelines for everything from building out nuclear plants to fusion plants? Because, you know, sometimes there's hype, sometimes there is an overwhelming abundance of energy coming our way. So first off, Ramez, welcome, pal.

Ramez Naam: Peter, it's an honor to be here. Great to be here with friends. Looking forward to it.

Peter Diamandis: You do have friends here, so, you

Ramez Naam: know, I have a quick story.

Peter Diamandis: Yeah, of course you have a story.

Ramez Naam: I'm worried.

Dave Blundin: I remember we were. We were presenting to one of the top oil companies and energy companies in the world, like top three or four. And they were like, well, who's this Ramez fellow? We want to grill him before we let him in front of the key people here. We're like, fine, grill Rames. And so, because we were talking a lot about solar and they're an oil and gas company, and after like two hours, they're like, okay, we need to get in front of them. That was an awesome session.

Salim Ismail: Yeah.

Peter Diamandis: One thing I failed to mention is Ramez was part of our founding faculty at Singularity University. Really led the whole energy conversation there, and has been on stage at the Abundance Summit number of times. Hopefully you're back again coming in 2027. Mez, first of all, I just need to chide you. You need to write a fourth, fifth, and sixth in the Nexus trilogy.

Ramez Naam: As soon as AI and energy less exciting, every single week, I will make time to write another novel.

Peter Diamandis: How much time, Peter? How do you spend tracking what's going on on the innermost loop here?

Ramez Naam: It's every day, all day. I mean, that's what we all do, right? Living in the Singularity.

Peter Diamandis: Yeah, we. This is living the Singularity. Before we get jumping in, Alex, you want to add anything to the conversation up front?

Alex: I'll just add welcome to the Terror Dome. One of my, my. My favorite of your, I would say, popularizations. That now infamous chart of the price of solar going down to zero.

Ramez Naam: Thank you, Alex.

Peter Diamandis: Amazing. Well, I can't take another second away from you, pal. Jump on in and we'll grill you along the way. Make the points, you know, shall we say, in a stellar fashion. Okay, great.

Ramez Naam: Let's just start. We're going to hit a few different topics here with the intersection of electricity, really energy and compute. I mean, a few years ago, as investor in clean energy, that was sort of a. A fringe sector. To some people though, it's $3 trillion. But now that we see that AI depends upon electricity, it is everything. Like the, you know, capital flows, value flows to that which is scarce. And right now power is scarce. So it's six topics. At the end of each, we're going to pause to have discussion. So number one, AI is power hungry, to speed to power and the grid, that's everything, it's not cost. Three, behind the meter power, that's how it's happening. Four, making the grid better is totally undervalued. And that's where the near term wins are. Five solar, six fission and fusion, lots of stuff happening. And seven, finally the out of this world ideas. Launching compute into space or launching it into the oceans. So let's just cement ourselves on AI as power hungry. You have to exponentially increase compute to get linear gains in AI. There are some ways to cheat that curve, which we're doing. That's the basic phenomenon here. And I think we don't fully grok most people anyway. The relationship between these things. First, I want to be clear that power is cheap compared to GPUs. So if you look at building a gigawatt data center, you're going to spend $50 billion, 35 billion of that for chips. When you compare the ratio of like the all up capex of your data center to your five year energy cost, it is amazing how little energy costs. So when you say AI is power hungry, it's not really a cost issue. It is that energy is the bottleneck for AI. And this has a lot of ramifications because these numbers are in billions or tens of billions of dollars. Every hyperscaler has whole teams devoted to optimizing the cost of energy. But if you tell OpenAI or Anthropic today, look, we can give you power at twice the cost that's on tomorrow. They'll take it. They won't tell you that of course, but they will take it. Because the revenue you can generate from a unit of electricity to the cost of it is basically the same ratio as this. So what's the challenge? The challenge is we stopped being able to build out the grid fast. And I'm not talking about power generation. We can still do that pretty fast, at least for solar, wind, batteries and natural gas. But the poles and wires are a huge problem. So this is. We talk about the interconnection queue, which is the cue to get your new project hooked up to the grid. And this is for the generation side. If you're building a new solar plant, wind plant, natural gas plant, how long does it take before you are hooked up to the grid so you can deliver power to your customers? That's gone from 15 months 20 years ago to now coming up on 45

Peter Diamandis: months regulations, what is it?

Ramez Naam: It's regulation. And it's also that as demand has, demand growth has slowed. Right? The US demand growth per year is much slower than it was the 80s, even lower than the 50s. Utilities have just re engineered themselves. They are more oriented on customer service, on meeting the regulators demands and so on than they are on building stuff fast. So that has got in the way. But permitting is also a huge issue. Not utility regulation per se, but permitting issues for the land controlled by the state, the county, the feds.

Alex: If we're just going to jump in, led by Peter's example, I have to ask you sort of flew by. You mentioned or you alluded to this notion that intelligence was somehow proportional to log compute, which I know a number of executives have also pushed the narrative of maybe one could naively extrapolate some law that looked like that from scaling laws in machine learning training or machine learning inference. Do you think that's actually true? And if you do think it's true, do you think it continues to be true in an epic of recursive self improvement?

Ramez Naam: It's an awesome question, Alex. And this is like core to the big questions of AI and are we going to have recursive self improvement to ASI? Look, everything in machine learning like since 2000 has shown something like a log linear relationship between really between training data size and precision of the model. Right.

Alex: My actual model, you're alluding, I think to first kaplan scaling and then chinchilla scaling and then post chinchilla scaling.

Ramez Naam: Long before chinchilla scaling with single layer neural nets. We were finding this in the early 2000s.

Salim Ismail: Right.

Ramez Naam: So compute is used to convert training data into a model, right. Into a neural network. And that has a roughly log linear relationship. But we cheat and by which I mean we keep finding ways to make that more efficient. So is it actually log linear? No, it's a little bit faster than that because we keep finding ways, as we see with Deep SEQ flash that just came out, as we see with Kimik 3, we keep finding ways to bend that curve. So it's a little bit less bad than log linear, but it's still somewhere between a power law like N to the fifth and a true exponential or a log scale difficulty. And I don't see that changing anytime soon.

Alex: It seems almost, if I understand your broader thesis, this seems almost axiomatic that if we can't bend the log curve, that we need basically exponentially larger amounts of energy just to make Essentially linear or polynomial progress in intelligence need more and more energy. We need to achieve Kardashev Level 2 or Kardashev Level 3 type civilization. Dyson swarms in order just to keep making incremental progress.

Ramez Naam: Alex is getting at the core issue with super intelligence, actually in a certain extent. Look, here's my view, Alex. The naive view is at any given time, intelligence is basically log linear with compute, log linear with data. But we keep making the algorithms better and that sneaks us towards like a polynomial domain. But the polynomial domain is still steeply diminishing returns. Still playing with that.

Alex: You're arguing it's poly log. You're arguing that intelligence is poly log in compute.

Ramez Naam: At best it's polynomial and I don't. Not necessarily poly log. But at best we see like the very best examples you can get is maybe compute has to go up, you know, end of the fourth to get an N size increase in intelligence.

Alex: And you don't think recursive self improvement. If we are indeed in an era of rsi, you don't think that us anything better than Polylog?

Ramez Naam: No, look, you do the math on RSI and rsi. Every way that you improve AI has diminishing returns. So every model of RSI that does not include hardware, we can save that. Every model of RSI and software fizzles over time. Now the bump might be so big that we're like, wow, this is just over the top, amazing. But it always looks concave. There is no mathematical model of RSI that's valid that I can see. That leads to an actual, like, you know, vertical asymptote to take off.

Peter Diamandis: This episode is sponsored by Google for Startups. Think about this for a second. You now have access to the same generative AI models that cost hundreds of millions of dollars to train. Google's Startup Technical Guide for Generative Media gives you a complete blueprint for deploying Google DeepMind's models in production. Images, video, audio, all of it. Real architecture, real results. Find the link in the show notes below. I'm going to take us back. So the grid is the grid. The grid is the bottleneck right now.

Ramez Naam: Yes, yes it is. So we need power in a practical sense. Look, everybody.

Peter Diamandis: And Alex, not that I did not appreciate your genius in those questions and that was fun. And this may turn out to be an entire conversation between Ramez and Alex, but we'll see.

Ramez Naam: Let's have another episode. We just talk about it. I got words.

Dave Blundin: I think the point here is the grid is the bottleneck is a really important point because it speaks to the infrastructure needs we're going to have to have for dealing with this. So let's move on and we'll get.

Ramez Naam: I'll come back to it and look. So this is for the power side, for the demand side. We don't have data that is as clean. But here are like three locations around the US and you see in a seven month period between April and November last year the wait times to get connected for the load side, not for generation but for your data center whatnot went up by six, seven months. So everywhere around the country as demand is going up for large load interconnection, you're seeing longer and longer wait. So this is Texas. ERCOT is the Texas grid. ERCOT. Currently it peaks out at about 80 gigawatts. Okay. They have submissions into their demand side queue for more. This is slight later date for well over 200 gigawatts of load. Most of this is speculative. Most of these submissions are bs. Anybody? Not quite anybody, you can put in a request for a large load and power without it actually having financing or a customer or so on. So most of these things evaporate. But in any case the Texas grid operator is overwhelmed with these requests for power and of course that just jams up everything.

Salim Ismail: Wait miss, let me ask a quick question. Are we talking about I have a data center, I want to connect it to the grid to get power or I have a new power source, I want to connect it to the grid to deliver power. Or is it about the same either way?

Ramez Naam: So this chart is a generation. So I've got a new power source and these two, our demand, our load interconnection queues are going up. I mean honestly it's much longer than that. Like today in ERCOT in Texas, the like most advanced progressive, least. Yeah, most progressive in like a positive sense. Like least regulatory burdened, fastest moving grid in the US if you put in a request for hundreds of megawatts of power to build the data center today, good luck getting that power before 2031. 2032.

Dave Blundin: Wow.

Ramez Naam: That's the situation that we have.

Peter Diamandis: Yeah. When you did a presentation for my abundance community on our monthly meetup and that was my major takeaway that the issue on energy for AI isn't building on solar farms. It isn't fission or fusion, it's the grid grids.

Ramez Naam: That's right.

Peter Diamandis: So in that case it is the grim as you know and there are people who are watching, who are investors, want to understand this. You know we talk about infrastructure Picks and shovels for AI and we talk about, you know, data center construction companies and all of that. Who are the companies that are building out the grid and is there, is there sort of work orders and purchase orders for building out a more robust grid?

Ramez Naam: It's a really good question. So the grid, the poles and wires, the distribution grid in particular is dominated by regulatory monopolies. Right. So the local utilities, I'm not going to comment on their current PEs, whether I think those stocks are buys or sells. But the regional monopoly utilities stand to make a huge amount from this. In the areas where data centers can be built, there's a separate issue. It's not in my slides of more and more voters are pushing back and saying we want to stop data centers being built. There's a lot of psychology behind that. I don't think the reasons are necessarily that valid. Even in Texas. Yesterday Governor Abbott sent out a letter, pausing. It was the day before pausing.

Peter Diamandis: Oh no, not them too.

Ramez Naam: It was actually, it wasn't quite a pause. It's an audit of all data center requests in Texas. In Texas, a red state, the most libertarian state in the country. And it's political. Abbott knows that his voters are like there's an anti tech sentiment that translates to AI data centers because they're an obvious target. So he wants those data centers built. There's an election coming up. He's got to cover his ass for a bit by making it look like he's serious about this. But that's the politics right now in the country.

Dave Blundin: So basically we're doomed.

Ramez Naam: I don't think we're doomed. Celine will always have space.

Alex: We'll always have sun synchronous orbit.

Ramez Naam: You know, the AI doomers would say thank God we're saved. The AI God won't be built.

Alex: But even if not, they'd propose orbital bombardment of the data centers.

Ramez Naam: There's no way of winning. Let me the grid. Look, I'm going to show you a lot of like, you know, sci fi stuff and awesome stuff, but yeah, Peter, what you're saying, the grid itself, the poles and wires are the limit. And I've talked for years about the exponentials in solar batteries. We'll talk about fission and fusion, but the poles and wires have thus far not become an exponential technology. And that's something I would love to solve. I have not seen a lot of startups.

Peter Diamandis: So aren't we, aren't we moving the data centers to where the energy is so you don't need to set up,

Alex: you know, or disconnecting them from the grid entirely maybe.

Peter Diamandis: Exactly.

Ramez Naam: Let's move, let's move on and I'll get some of that. This is like a more practical forecast. You see, even like 2028 will, will build. This is probably a little bit low. The orange is like how much we'll build. Maybe it'll be 2030 gigawatts, whereas the demand could be much higher. This is an interesting slice by the way. I'll tell you, every forecaster, Morgan Stanley, whoever, they all differ somewhat, but this is an interesting slice. The blue bar is if you just sum up all the GPU manufacturing scheduled between now and 2030, primarily Nvidia, but also AMD, Cerberus, whoever versus the expected pace of US grid build out. The chips are more than twice the pace in their power draw. For folks listening to the power we

Peter Diamandis: can deliver, the AI demand, which is CHIP Limited shows roughly 200 to 275 call. 230 gigawatts of power demand based on the chips. Like you bought the chips, you've installed the chips. Can you power the chips? There's 230 gigawatts of demand there and US grid build out is projected at roughly 100 gigawatts.

Ramez Naam: And some of you might remember like six months ago, Satya Nadella, CEO of Microsoft, made this comment. Look man, warm shells are our limit. We've bought the chips, we don't have warm shells to put them in. Right? That is the limit for everyone at this moment.

Salim Ismail: Why is that discrepancy there? Because, you know, Eric Schmidt told us his his number was 100 gigawatt or 96 gigawatts of additional power by 2030. The 230 is just based on chip manufacturing. So either more of the chips are being kept domestic, which wouldn't surprise me, or the fabs ramped up, which would surprise me. But where's that discrepancy come from?

Ramez Naam: Every single forecaster has a different number and I think they some of them base on just announcements by companies, whether they're chip fabs or utilities, some of them based on their discounted projections of what they can actually achieve. Also, I'll say that there's a big miss in power demand of chips. A lot of people just say, how much power can my Blackwell GPU draw? Multiply that by how many you're going to build and that's the power demand. Now you're missing like half the power because you've got to add the draw. The rest of the IT equipment in the data center and cooling and so on and that nearly doubles the total power use.

Salim Ismail: You know, that would make sense. Those Cerebras chips, they, they run, they just suck down power and they run the transistors much more efficiently than the prior generation. Kind of a 100H1 hundreds from Nvidia. So the transistors are actually doing a lot more work, which is better fundamentally. But yeah, of course that's going to draw more power constantly. And of course when you buy those things and deploy them, you run them 24 by seven.

Ramez Naam: That's right.

Salim Ismail: You're never going to let those things rest. So that may be a discrepancy too.

Ramez Naam: Absolutely.

Alex: Do you think mez this creates a forcing function perhaps for Nvidia or the other fabless vendors or the fabs like TSMC to get into the power generation business? Right now the power power gen that's, that's powering all of these chips that Satya talks about just collecting dust in warehouses because he can't find warm frames for them in data centers. Why not? Why do you think that there's a forcing function for the Nvidia's of the world to get into power gen?

Ramez Naam: Well, I'd say, look, whether Nvidia wants to get into it or not, and Nvidia has made some interesting investments that I'll talk about in grid flexibility. The reality is that, you know, what people talk about the most now is behind the meter power gen for data centers. And what they mean by that is large natural gas turbines, if they can get them. This is a multi hundred megawatt, like say a 400 megawatt natural gas turbine, the kind you'd use on the grid. These are now sold out for something like seven years. Ge, Hitachi and so on are building new assembly lines to try to bring those online faster. But everyone is saying, look, if the grid is going to make me wait years and years and years, I'm just going to build my own power. Now this, this is more expensive than the grid. But power is such a small fraction of AI cost. Maybe you can do it because these guys are sold out. People are going to these small turbines, solar turbines, nothing to do with solar, but they make. This is a 38 megawatt turbine that's on the back of a semi. So 40 of those make a gigawatt. Right. Even these have backlogs. But now you have companies. Everyone in the world that was in any way proximate to gas turbines is pivoting into this space. I'll give you an example. Bloom Supersonic, very Cool company trying to make supersonic jetliners a thing. Again, that's a very hard task. With many, many billions of dollars of regulatory costs. They have pivoted into using their engine design to make a gas natural gas turbine for data center power because the demand for this is so very high.

Peter Diamandis: So modular energy production. How many of these, if you think of them as an 18 wheeler truck that has a large container on the back, just pull them in and get your data center started until you build out energy infrastructure and then move them on.

Ramez Naam: This is how Elon got the Colossus data centers up that anthropic is now leasing. Actually this is what he did.

Alex: And we've talked a bit about this on the pod in the past. We talked about the boom pivot, we've talked a bit about Elon standing up his fume generating cogen facility at Colossus, et cetera. We talked a bit about that. But I just want to try pressing once more on this point. If this thesis is true, that this is a primary overhang on Nvidia's ability to sell more GPUs. Nvidia is already doing all sorts of financial engineering to be able to sell more and more GPUs through customer financing. All of these other things. Why on earth if the energy overhang or underhang depending on your perspective, is a major limiting factor for the ability to productively monetize GPUs? Or why don't we see Nvidia doing something on the energy front?

Ramez Naam: It's a great question. So look for behind the meter. The financial centers are so large, Nvidia doesn't have to, but they might invest in some of these companies on the grid side. Nvidia has made investments into increasing grid flexibility to be able to get more juice out of the current grid. Emerald AI is one example. They've made a few investments in this space and I'll talk about grid flexibility in a sec here. But the real issue is a combination of regulatory and the incentives for utilities. Utilities monopoly utilities in the US the bulk of them are paid on Cost plus. So they say they go to their utility commission and they say I've got a plan to meet the demand that I see my customers having. Here's what it costs for me and I expect a 10% return on capital for it. And the utility commission mostly just says okay, some are better than others, but let's be honest, like the utility has enormously more horsepower in people, compute salaries, et cetera, than the utility commission. So they like jam through this plan and they get 10% on top.

Alex: I see. So if I were to try to synthesize what I think your answer is, your answer for why Nvidia isn't getting into bundling power gen with their GPUs is it's low margin and frictionful. Like for the same reason Nvidia tried and failed and then retreated to launch their own hyperscaler or NEO cloud. It's just not as high margin as selling GPUs.

Ramez Naam: You know, Nvidia might still be a NEO cloud we can talk about separately. If I was in video, I would be focused on changing the regulatory landscape for monopoly utilities. And I've said this on like some utility specific podcasts, we should change the incentives. Utilities, instead of just getting paid for a percentage over capex, they should be paid on things like how fast they can deliver power. Their executives should get bonuses for delivering power fast. And if you did that, and their employees obviously all the way down, if you did that, suddenly these things would happen faster.

Salim Ismail: Right?

Peter Diamandis: You get what you incentivize. Absolutely.

Ramez Naam: Look, I invest in startups. How many startups have I seen that have a technology to speed up building poles and wires? I don't know, two or three. None that I thought were amazing. If you, by the way listeners, if you have one, please send it to me. Why not? Because there's no incentive for it. But if you created the incentive, people would find technical solutions to speed that process.

Salim Ismail: Is there any state. No, not, not Texas, but is there any other state that is open minded about that?

Ramez Naam: And look, Texas, Texas is the best. And I will say despite what I just showed you, Texas has made policy changes that accelerate this and you know, people are not totally slip the wheel ferc. So Texas is interesting. Texas, ERCOT is its own fiefdom that is not regulated by the feds at all. FERC regulates the rest of the country's electricity. FERC has sent letters to the six other largest grids saying basically do something like what Texas is doing and what we're doing. What they're doing is, and maybe I can just skip to it is making new regulations that say if you are an interruptible load, if you are flexible, if you can either find some alternate way to power yourself or just turn down your power at moments of peak demand will get you connected much, much faster. So in Texas that's a CLR or a pclr, an interruptible load. And the reason for that is we have, as Americans as anyone, we have a very high demands for the reliability of our grid, right. 99.9% uptime is eight hours of outages per year. That's unacceptable. Right. You gotta push to four nines to make it a grid that you think is really good. But the nature of the grid is the power demand is not constant. It fluctuates through the course of the day and the seasons. It peaks primarily in the south in late summer afternoon. So this is the US grid. The US grid averages about 500 gigawatts of demand kind of throughout the year. Throughout the day it's very much more volatile than that. But at any given time in like winter night times, the US grid is down to like 400 gigawatts of power being drawn. In summer late afternoon, we're up to like 600 gigawatts of power being drawn. Because of AC primarily, the fluctuation is actually much higher than this.

Peter Diamandis: Europe doesn't have this problem.

Ramez Naam: It's a joke. It's a different problem. We can talk about Europe and ac. There's some amazing tech coming out of the pipe on that, by the way. Hopefully a new investment. That gap is 200 gigawatts, right? 200 gigawatts is about 10 trillion in AI CapEx. We think there's about 7 trillion in AI CapEx in the next five years. Like this is no joke. If we just use the poles and wires more efficiently, we could power up a lot of stuff because we're not short on generation, we're not short on power plants, we are short on capacity and the poles and wires. Okay, so what are we doing there? As I mentioned, like Texas has, you know, just June enacted this new regulatory change that says, look, if you, you know, don't need to draw power at peak, we'll just hook you up fast. Instead of five or seven years, it might be 12 to 18 months. FERC has now told everybody else to do that. So how do you do that? This is a paper by a buddy of mine, Tyler Norris. He's now at Google. He was not when he wrote this. This came out in January, February this year. This is the best electricity related paper of the year in my mind. And basically what he found was, I call it 200 by 200 or 100 by 100 at minimum, if you can be flexible, 100 hours out of the year, four days out of the year, 1% downtime, that unlocks 100 gigawatts of capacity on the grid, which is about 5 trillion in data center Capex, including the chips, which gets you through the next few years. That's one way to do it is just flexibility. The startup I mentioned, Emerald, AI Varun, Sivaram, funded by Nvidia, they do this via just software orchestration, moving jobs to the right data center, et cetera, et cetera, et cetera. But there's another way to do this, which is batteries. Yeah, this is a portfolio company of mine. I've made three investments in this same startup. Maybe a fourth one coming up. They do something really obvious in a place like Dallas Fort Worth, between middle of the night and late afternoon, there's like 10, 15 gigawatts of flex in the grid demand. So if you build out, let's say four hours of battery storage at the site, fill it up at midnight, you don't need to hit it during the peak of the day. And that fits perfectly with the new Texas regulations. In fact, they were leaders in driving this. This currently sounds what's obvious to us. Right? But this is an unusual approach. Twelve months from now, this will be a super common approach, not just in Texas.

Peter Diamandis: Time testing load.

Salim Ismail: Right?

Ramez Naam: Yeah, exactly. So right now.

Salim Ismail: But if I have a magical technology that stores insane amounts of energy very cheaply, and I go to even Texas and I say, hey, this can completely shift this curve. This is a total game changer. Can I hook it up to the grid and start sucking down power when no one's using it in the middle of the night? Would they still say, yeah, you can do that in 2030?

Ramez Naam: So the new regulations that were just passed in June gives a fast path to power for anyone that is an interruptible load. So so long as the grid itself, the grid operator is able to turn you off, it's not them saying, definitely

Salim Ismail: we need to make T shirts that say, I am an interruptible load.

Ramez Naam: Oh, my gosh. It makes me want to show an abundance T shirt that Peter's team sent me. But yes, I am an interruptable load. Don't ask my girlfriend.

Peter Diamandis: So, Meg, why isn't every data center deploying these giant battery packs? It seems like if I had that in my data center, I would be super smooth on the load demand for my community.

Ramez Naam: We passed this regulation in Texas in June. Okay, like the second week, two months ago.

Dave Blundin: So it's brand new.

Ramez Naam: Yeah. So like, I invested in these guys because they drove the regulation and because they've got 10 gigawatts of, like, sites that can take advantage of this. And then after this was passed in Texas, ferc, the federal regular regulator of electricity, sent a letter to the sixth largest other grids in the country. Not specifying the details but saying do something like this, figure this out. So this is going to become a very common thing to do.

Peter Diamandis: It's called agentic. Agentic infrastructures.

Ramez Naam: Agentic is the startup. But this in general, this is an interruptible load or time shifting demand. Again like that red dashed line. Not all of you, some of you are just listening. The transmission line capacity and the substation, blah blah blah blah. Transformers, that's the limit. It's not the gas generators or the solar or wind, it's the transmission line. So if you can use batteries to fill up your data center, your data center batteries at night when the transmission line is unused and then not need to draw on the transmission during the day, that is. We've always known that was a good idea. We do it with, with EVs and so on. And this is a very big deal.

Peter Diamandis: Amazing.

Alex: Would you say it's fair to characterize this as the energy or the grid equivalent of preemptive multitasking or reentrant multitasking in computing? Basically allowing processes to say they can be paused and their compute load can be time shifted?

Ramez Naam: Yeah, I think that's one way to look at it. I think that's a great analogy, Alex. There's also like cash pre fill, you know, like yes, batteries is a cache for electrons instead of data.

Alex: So we're by the way, we're making our grid cashable.

Ramez Naam: That's right. By the way. Go ahead.

Dave Blundin: What? You know that big gap of the 200 gigawatts. How much of that do you think we can make a dent in by taking this approach?

Ramez Naam: I think approaches like this and approaches like electric vehicles also. Right. The, the bulk of the batteries in the US are actually in EVs. So the company of mine, we've grid, I shouldn't say of mine like I'm blessed to be an investor in them because they're smarter than I am there. They have long for utilities managed electric vehicle charging on the grid to reduce stress on the last mile, on the last block even. Right. The limit on EV charging for the grid is actually the transformer on your block because Tesla's cluster, if one person gets a Tesla, their neighbor like doubles in odds of getting a Tesla. Right. So they already have software to like time slice and even out the charging of the vehicles. So companies like that in particular we'vegrid are using that technology to make the rest other loads on the grid more responsive and shaped in a way to allow AI data centers to play well. In fact every EV charging company I know has pivoted to trying to use their tech or their current capacity to enable data centers. And I think that's 100 gigawatts. I think that's if we're smart about it, that's the next five years of AI data center growth.

Peter Diamandis: Amazing. All right, what's next?

Ramez Naam: All right, let's talk like Morrison stuff. We all love solar, let me tell you. We're entering the phase where solar powered AI data centers become viable. Many people have seen a chart like this. I've shown in 75, 1975, 1 watt of solar panels cost 100 bucks. Now it's 8 cents from China for a panel that's smaller, has a longer lifetime, is more durable, etc. And so that more than 1000x price decline, does that get us to the point where we can power data centers with it? Well, data centers, because the chips are so expensive, it never makes economic sense to only run them when the sun shines. So you have to have them in storage as well. Battery prices have dropped by a factor of 14 since 2010. We have new technologies like lithium ion has been dominant. Sodium is much more common on planet earth. And lithium. So new technologies like sodium ion batteries could drop the cost of batteries by a factor of 10. And even now, we already have the very first solar plus battery baseload power plants. And they're affordable. So we have them in the UAE outside of Dubai. We have them in Chile. And a nice thing about this is honestly, natural gas turbines are sold out for years. The fastest energy project you can build is a solar and battery project. You can get that done in 12 months. So in the United Arab Emirates, this is a 1 GW 24. 7 solar and battery project. What that means is they guarantee that the minimum power output at any time is a gigawatt. To do that, it's actually 5 gigawatts of solar and 19 gigawatt hours of batteries. And the cost is like is 6 bucks a watt. Capex, that won't mean a lot to a lot of people, but let's just say the last nuclear power plant built in the US cost $15 a watt. The cheapest ones on planet Earth are Chinese being built in China. Those are $4 a watt.

Peter Diamandis: So recently competitive.

Ramez Naam: It is reasonably competitive.

Peter Diamandis: So Maz, you and I texted about this right on the last earnings call at Tesla, I think it was at Tesla, Elon said he wanted to build out 100 gigawatts of solar capacity. Did you check into that?

Ramez Naam: Yeah, I mean, I think, look It's a long term vision, it's not next year. But Elon's overall vision is let's put all the compute in space. There's no land constraints, there's no permitting issues there, people won't complain about water use there. And he wants to build a terawatt of AI. And if you're going to build a terror out of AI, you've got, you know, two or three options really. The world's deserts powered by solar and batteries, getting fission or fusion to work. Ocean power like Pontoise that I'll show or space. So we're not talking about all of them.

Peter Diamandis: About Tesla building out solar, Terrestrial solar. Right. Competing. Competing with China.

Ramez Naam: Yeah, he wants to build out the manufacturing for it, but I think his real motivation is not selling it to the on land market in the us. I think his real motivation is to build that manufacturing capacity for space based solar. If you look at what is your

Alex: pet a fab on the moon, presumably? I want to just pull on that. Peter's question. Mez, just a bit. If, if we take the thousand x reduction per kilowatt or megawatt over the past few decades and extrapolate it, have you gone through the thought experiment of what would solar need to look like in order to achieve another thousand x price per watt reduction?

Ramez Naam: Yes. This is a very good question and it's an important clarification of how the cost reductions work. So our best model. I'm not that smart, one of the top five forecasters of solar costs in the world and it's not because I'm that smart, so that I came out of tech and I came from a Moore's Law world and came into energy and supplied Moore's Law to it. But when you actually look at the details, it's not a reduction in time, it's a reduction with cumulative scale. It is Wright's law, it's the learning rate. So every cumulative doubling of solar scale reduces costs by, let us say, 30%. It, you know, it fluctuates year to year. It's the real world and so on. So look, if we ignore the possibility that we need terawatts of AI compute and we just look at the world as it is. Solar is now 8% of global electricity. And let's say we think solar can get to a third or two thirds and maybe electricity demand goes up by a factor of two, you've got four or five, six doublings left. That means the cost of solar might drop by a factor of four, maybe even by a factor of eight, but not by a factor of 1,000. But if you start talking about building Dyson spheres, then we have a long way to go to keep reducing those costs.

Alex: I heard what I wanted to hear.

Ramez Naam: You heard Dyson sphere pandering?

Alex: Ramesh pandering?

Ramez Naam: Bingo. Wait, can I drink?

Salim Ismail: I want to drill in on that

Dave Blundin: too, because I've got a couple of questions. Yeah, Ramez, how many of these installations are there being built around the world right now? Like this exact style of monster scale,

Ramez Naam: solar at scale, we're just oh like gigawatt scale, you know, a handful largely in China, the Middle east, some in Latam. We have, you know, maybe more than a handful of dozen at this scale. Most solar plants today, you know, they're typically somewhere between 50 megawatts and a few hundred megawatts a gigawatt plant. The challenge and the reason, the biggest reason this is not yet an option for the US because we could pull off something like this in the Southwest and it would actually, it would be cheap, it would be more expensive than it is in the Emirates because our labor costs are higher, but it would be fast. You get it done in a year with even a natural gas turbine that you want to order from ge, you can't do that. But putting together the land parcels is actually the pain for this in the

Peter Diamandis: U.S. are these solar panels coming from China?

Ramez Naam: Probably. I mean, 85% do, so presumably. And of course we double the price of Chinese solar panels in the us so we hurt ourselves by keeping them out.

Peter Diamandis: Sorry.

Dave Blundin: Yeah. So if this is the fastest path to energy at scale, why aren't there people just going? Or the US government just going, let's use eminent domain, grab whatever chunks of land we need to and build this stuff. Because we could be done in a year.

Ramez Naam: You don't need eminent domain. The federal government is the number one landowner west of the Mississippi. And those federal lands are concentrated in places like Nevada, Arizona, places that have enormous solar resources. But it's not something that interests the current administration, I would say. But yeah, if I was thinking about it, I'd be thinking about how do we open up lands that are not, not amazing nature resources to build solar powered data centers. And I think we get them done faster.

Dave Blundin: Sunshine seems better than drilling on federal land.

Ramez Naam: Absolutely. And I'll say this also, like, like regulations are the problem in lots of places. I was in Mexico recently, I was in Chihuahua and trying to convince the government of Chihuahua, a state of Mexico, to build a lot of solar powered AI data centers. But in Chihuahua, it is actually illegal to have a private power generation a behind the meter power above, I think it was 500 kilowatts, right? Half a megawatt. So the law can't do it. And then secondly, the AI labs and the hyperscalers are extremely vigilant about data protections. They don't want their user data leaked or seized, and they especially don't want their model weights exfiltrated. So they're pretty careful about the countries they go into. So my advice to Mexico was like, look, change the laws to make it possible to build this sort of thing and to provide ironclad guarantees of the protection and intellectual property protection of this data and you've got an enormous business, right? More open land, lower population density and better sun than the US this episode

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Salim Ismail: I was going to push back on. Well, first thing first, the GPUs can't sit idle no matter what because they're so expensive. But when you look at the underlying economics, about 5% of the cost of the data center, maybe up to 10% is the power. But the GPU itself is 80% markup from Nvidia on top of 2x markup from TSMC with another 2x markup at the model provider level. So it's actually 20x overpriced relative to the cost of turning sand into a chip, which is actually coming down too with efficiency and scale. And so at the where Elon thinks at the fundamental level, it's actually not a given that the GPU is super expensive relative to the power once he gets the Terrafab up and running and the end to end sand in one side chip out the other is fully automated. So that would completely flip all the math in this if he, if he gets to that destination.

Ramez Naam: I think those are awesome comments Dave, and I think it's right that GPUs are overpriced, or at least there's a lot of margin going in there. Nvidia people don't think of them as a network effect company, but Cuda, the programming layer to write to AI is like their moat, right? It's not like their chips are good, their chips are fine. AMD chips are as good. Their interconnection between chips is great and that does matter. But people like Huawei is kind of getting there, honestly. But Cuda has been the moat and I think the Cuda moat is broken this year and next year. One of my portfolio companies, Lemuria, I met them at abundance360 is working on that. But also now that you can tell AI, take my AI code and recompile it to run really fast on this AMD chip or the Cerebras chip. I think Nvidia's lead is so glad

Salim Ismail: you brought that up this topic, because this $5 trillion of US market cap that's hanging in the balance of this conversation, it's such an important and such a fragile thing. So CUDA is the moat for sure. No doubt all the AI researchers are too lazy to write custom kernels. Suddenly Fable 5 comes along. I've had great luck running custom kernels myself just in the last couple weeks using Fable 5, so I think your prediction is probably right. I don't see why I wouldn't be right. I think Nvidia would say we have all kinds of other network effects and we have massive interconnect. Now, the interconnect is incredibly important for training, but 90, 95% of the load now is moving to inference, where you don't really need the interconnect.

Ramez Naam: I think the interconnect still is very helpful for inference. If you're going to run a model like Kimik3 or Deepseek, not necessarily Flash, but the next deep seq v4, you're simultaneously running it on Iraq, right? You're running it on 10 to 20 GPUs at a time. So the interconnect does matter somewhat, even for inference. But you're right that it matters even more tremendously more for training. Can I summarize?

Alex: Dave's point is just that at inference time, interconnect locally matters to the extent you need local coherence, but you no longer need global coherence at the level of an entire supercluster. It's just like a single rack of coherence.

Ramez Naam: Yeah, that's correct.

Peter Diamandis: Coming back to energy, to summarize this, there's plenty of room for energy growth. We have the abundance thesis on energy writ large with solar. Right? And one of the points that Elon's made before is at the end of the day it's all about solar. Do you believe that thesis that solar in the long term is going to dominate beyond everything else?

Ramez Naam: It's complicated and I think we underestimate the importance of geography. So the reality is, look, from a regulatory standpoint, we're not building transmission. We are no place of China is building enough capacity to move electrons from place to place. Same thing as what I just showed with poles and wires. And so the problem for solar is not cost and it's not nighttime because batteries are plunging in cost and are going to drop another 10x. Ultimately it is winter. So in London for instance, you get 1/6 or 1/7 as much insolation in January as you do in June or July. So you're going to build out your solar plant by a factor of six or seven. No. Or do you have, do we have a battery technology that can store months of power? Just a couple interesting ideas out there. But like think about, you know, the unit cost electricity goes to a battery, basically battery capex amortized by how many times it gets used to. So if you've got a battery that cycles daily, it's like battery cost capex divided by 365 or 3650 if it's 10 years, let's say if the battery gets used twice to shift load between seasons, it's battery capex divided by that. So there's numerous startups, there's some crazy ideas, sand, compressed air power, natural gas, yada yada yada. But right now like it's clear to me that economically shifting battery, shifting energy through the day, night cycle, we're not totally there yet, but it is like the curves are, are just heading that way. But dealing with winter especially, also we have not yet electrified heat. And if you look at, you know, the UK as an example and northern Europe, if you go from burning natural gas for building heat to using heat pumps, electricity demand like doubles in winter. So we have this big, big, big winter problem that I think a lot of people are not reckoning with. And so I do believe, you know, nuclear is super useful as is, you know, efforts to get seasonal storage, as are all of efforts on fusion, as is advanced geothermal, especially for those places that are further from the equator and have either long winters or long rainy seasons.

Peter Diamandis: Location, location, location.

Ramez Naam: Yeah. All right, so this is, this last slide is saying that that solar battery data center is going to get cheaper and cheaper. And so yes, one of you asked, why don't we move compute to where the energy is? And I fully believe that. Right. In other energy loads, you can't move the population of New York City to a place that's sunny year round. Not quickly.

Peter Diamandis: It's called Florida.

Alex: This is Florida.

Ramez Naam: Okay, it's Miami during COVID And mostly for crypto folks. But you can the new load we haven't built, which is AI. Why don't we cite it where the energy is. Right. So that's one viewpoint. All right, next, Peter, you wanted me to talk about fission and fusion and both are super excited.

Peter Diamandis: Absolutely, yeah. I mean, we hear a lot about it. We talk about it. We speak about the hyperscalers turning on defunct fission plants, investing in fusion companies. It's interesting. A quick stat. 71% of Americans are against data centers, which is a higher percentage than are against a nuclear plant in their backyard, which I find amazing.

Ramez Naam: It's just insane. I'd rent my backyard out to both. It's not big enough. Quiet. But we'll make some room.

Alex: Maybe. Mez, just a quick question before the segue. I just want to pull a little bit on the historic rhyme between the Middle east being a major source of oil, but now also being a major source of solar power. The thought experiment I've done, I'd be curious to get your thoughts, is the reason the Middle east has so much oil is my understanding is like hundreds of billions of years ago, there used to be a warm ocean with lots of plankton and other small creatures that ultimately resulted in the oil. And now it's largely desert, but still it's pretty warm. Any thoughts on the historic rhyme between why somehow Middle east is on the one hand supplier of all this oil power for data centers and now potentially solar power?

Ramez Naam: Well, I think the Middle east has amazing solar resources, but it's not as lumpy as their fossil fuel resources and especially their oil resources. If you look around the globe, you have Australia. I mean, if I was thinking Ashley, I just said Mexico. If I was thinking about let's do a lot of solar and battery powered data centers for AI, I would be really pushing in Australia. Yeah, you've got a friendly government. You've got enormous amounts of space. You've got some of the world's best solar resources. Chile and Mexico. Not amazing oil Producers Mexico was once, they're not anymore, but solar resources that are equivalent to the Middle East. So if you look around the world, it's interesting. Obviously we all know intuitively, you know, some places are much sunnier than others. But in the places that people live, the actual like solar energy that falls varies by at most a factor of two between the least sunny and most sunny places. Which is kind of crazy. Yeah. The seasonal effect is bigger. Right. And as you get further from the equator, the seasonal effect gets bigger. I live in Seattle, I know this. Whereas the oil density on the ground is much, much, much higher dispersion, much higher concentration in a few spots. So overall, when in Italy, I'm always counseling them on find a way to export energy. You're not going to build poles and wires to move electricity from Saudi Arabia to the U.S. but you know, I used to say like steel, like Iceland is, makes a lot of aluminum, they have no bauxite ore, but they have cheap geothermal. I know, I've got a company I've been talking to right now it's using Icelandic geothermal to make sustainable aviation fuels like E fuels, power to fuels because they can export that. Right. So if I was in Saudi, I used to say like make industrial uses of electricity, but now I just say like make data, make intelligence. But you've got to change the laws such that an OpenAI, an anthropic, a grok, whoever Google is comfortable citing their crown jewels in your country just by

Peter Diamandis: way of a reference number for the audience. You know, when I last looked at it on the energy abundance thesis, you know, we have 8,000 times more energy than hits the surface of the earth than we consume as a species in a year.

Dave Blundin: Right?

Peter Diamandis: So there's plenty of energy out there, it's just not in usable form. And the whole conversation here is how do we take that energy that's latent and make it usable.

Ramez Naam: Right.

Peter Diamandis: And that's the role of technology.

Ramez Naam: That's right.

Dave Blundin: The other commentary is that the fossil fuel are just an old battery that we've been using up. Right. And we've used up about 25% or 30% of that battery.

Ramez Naam: No one knows. But I mean, you know, the cure for high prices is high prices. So if we ever started to run low, there'd be more incentive to export, explore and find stuff.

Dave Blundin: Yeah. I have a question for you Ramez, on the, on the oil market really quick. I remember you commenting once that the 2013 oil price, oil crisis, oil crash was because of a 2% over supply in the market, like it's a really tightly won market. Is that still the case or with all the Middle east conflict and everything else, we're now in tension. It's going to stay that way.

Ramez Naam: Oh my gosh. I mean, there's a lot to say about that. Look like two interesting things. Yeah, there's two interesting things about the Iran war and its impact on oil prices and why it's been relatively muted. Number one, China did us all a solid. China built the world's largest oil strategic reserve. Right. And they were willing to drain it during this period. So China has, you know, more than the rest of the world's strategic reserves combined, has helped keep oil prices low. But two, the ratio of global GDP to global spending on oil has roughly tripled or quadrupled since the oil crisis of the 70s. So there are critical things that are highly dependent upon oil, you know, aviation, shipping, trucking and so on. But overall the world has moved to more of a services economy and that has created, you know, sort of more demand elasticity. It has allowed the world to deal with a shortfall in oil in a way that we couldn't in the 70s when our economy is just more physical. Maybe that's why you haven't seen oil spike to 200 bucks.

Alex: I just like to pull in the geopolitical angle here. There's a theory in certain circles that an ulterior motive for the Venezuelan operation and then the war with Iran was actually to cut off China's in event of a Chinese invasion of Taiwan open paren TSMC closed parent that China would require backup oil supplies because they would get embargoed by the Western bloc and their go to sources for backup oil in such an invasion would look like Venezuela, Iran, maybe Cuba. And so the full sort of theory here is the recent military adventures that we've seen Venezuela, Iran are actually at some level a play to deter China from invading Taiwan to gain access to the TSMC fabs and basically the future light cone of AI. Any thoughts on that?

Ramez Naam: There's insight there, but I don't agree with it as stated. And the reason for that is China bought oil from Iran during this, this war. The US still sells oil to China. It just wasn't that planned out. The actual DoD war plans in a situation like that are to use the US submarine fleet to sink tankers that are getting heading to China. That's the actual proposal of what to do. Who knows if that's a good idea. I'm not going to get into that right now. Oil is Mostly fungible. So the fact that China buys oil from Iran, US bombing Iran, or even if we successfully close the strait, doesn't really hurt China that much because they can buy cargoes from somewhere else. They were getting a discount from Iran because it was embargoed oil and that they were willing to buy. So they have to pay, you know, a few bucks more per barrel. It's not that big a deal to them in wartime. It's a kinetic sanction, it's a kinetic embargo. It was a different sort. And yeah, I think this is all totally non classified. The simulations of wars like that are US submarines, you know, torpedo tankers that are heading to China.

Peter Diamandis: Onwards to the horizon of fission and fusion.

Ramez Naam: Okay, let's talk about the atom and the power thereof going back to the 50s or you know, a retro future. This is a complicated slide for those of you who are just listening. Basically there's, you know, two approaches to nuclear fission, which is what we've been doing since the 60s. The traditional one is big reactors. And the simplest thing you can do to boost Nuclear production worldwide is 8, stop shutting down nuclear plants. Germany should not have shut down its two nuclear plants, have like end of life planned. We can usually extend them, in some cases we can actually upgrade them to produce more power. Three, there are some plants that have been shut down, like Three Mile island, that we can actually restart safely. But that gets you, you know, a few gigawatts. Right. If we really want a nuclear renaissance. The, the core issue with nuclear fission today is that outside of China and perhaps South Korea, it is ruinously expensive. And why is it ruinously expensive? It's because we don't do a lot of it. Anything that you do infrequently is expensive. Right? You don't get good at the things you do. Occasionally the things that are cheap are the things that you do repeatedly, again and again and again. So there are two paths happening to bolster nuclear in the US And I'm a critic of this administration on many fronts and on some energy fronts. But I'd say this administration has the best nuclear policies of any in recent history. Still missing some things, I think, but the best that we've seen. So the left side of this is large reactors. We have this thing called the AP1000. It's sort of a, it's the Westinghouse reactor. It's sort of a workhouse reactor. We built a couple of them in the west, let's say four. China took a variant, took this design, made their own variant that had a local supply chain and they've built more than a dozen and they built them at higher power than this, 1.4 gigawatts out of 1 gigawatts. So one plan is we're going to produce a process to get more of these built. The administration is talking about has created structures for loan guarantees, for financing and so on. Because if you stamp out a lot of these, the cost should come down.

Peter Diamandis: Right now, what plant is the most stamped out so far?

Ramez Naam: Light water reactors like those used in France. So France is the poster child. The US has generates the most nuclear electricity of any country on earth. Actually it's not really known. China's building the most right now. France gets the highest fraction of electricity from nuclear and they basically, with slight caveats, basically the same design and stamped it out again and again and again.

Peter Diamandis: How many France have like 16 nuclear power plants now?

Ramez Naam: Something on that order. Yep.

Dave Blundin: But even France, something like something 80% of their electricity is nuclear. It's crazy.

Alex: And they export it to the rest of the Eurozone as well. Fission was basically born in France. Thank you, Curies.

Ramez Naam: Yeah, but even France is struggling. Right. There's something called the European Pressurized Reactor, which is mostly a French design. And that thing is kind of a disaster right now. It's a boondog go running over, going slow again. Like if you take one design and you do a lot of it, it gets cheap. But usually, and this is critical for the sector as an investor, the first one usually runs overpriced over time and you know, has problems you didn't anticipate. So if you want a thriving nuclear industry, you just have to know that the first one you build of a new model is probably going to have problems. But after you've built three, four, five, maybe more, you sort out those problems. You build experience in the crews you built, experience in the engineers, you sort out design issues, you build a supply chain to provide the parts that you need. So one plan is we're going to take the large reactors that we have built a couple times and now we have. The US government has created financing, sort of a backstop loan guarantees for about eight of these. Another startup, the nuclear company I invested in, one of the founders previous companies, they have a plan to basically build fleets of these because that's how you have to finance it. You can't finance one because you know you're going to miss your targets. But if you can finance a bunch at a time, a bunch of money, you're going to amortize the cost. You know, in China, like after they got to like 6, 7, 8, like the costs had really come down and stabilized.

Peter Diamandis: So those who are fearful about nuclear and it's still, you know, probably a good percentage, they think about Three Mile island of Fukushima. We're talking about early generation plants, right? Are those Gen 1 or Gen 2 plants?

Ramez Naam: Something like that. And these are Gen 3 or Gen 3 plus or Gen 4 plants. And one of those important things to understand about them is basically all of these are passive safe. What that means is you can knock out the power to them and they won't have a meltdown. Fukushima happened because you circulate water around the nuclear core to take the heat away from it and then use it to turn a steam turbine that pumps for that was powered by grid electricity. So the tsunami that hit Fukushima knocked out the power lines and so the pumps stopped working even though there's power right next to them for the nuclear reactor. Right. New nuclear fission designs are passive safe.

Peter Diamandis: Would you call them fail fail safe plants?

Ramez Naam: Nothing is totally fail safe, but they're designed to take a 747 crashing into them and the power going out from the grid and keep operating without any meltdown.

Alex: I'm curious, just pulling on that mez, what happened in the 1970s? I assume you've seen the television show. We talk about it sometimes on the pod for all mankind. It's sort of an alternative historic reality where we get fission, it never gets abandoned. What happened in the 70s? Did we just waste the past 50 years not building enough nuclear energy, fission in particular, finding ourselves in a suboptimal future?

Ramez Naam: I don't think so exactly. I think we could have done better, but people are somewhat risk averse. Radiation, we learned, you know, in the 60s, the radiation causes cancer. The radiation release from a well operating nuclear plant is really minimal. It's unlikely to cause cancer. But so we, we did increase the regulatory state, we did increase the burden of proving that things were safe and that had some cost. And then things just fizzled out. And we talk a lot about flywheels and positive feedback loops. We had a negative feedback loop once and this is the same thing France has seen. Once the industry is no longer building, you lose the expertise, you lose the supply chain that makes the parts that you need, you lose the manufacturing facilities and everything gets more expensive. So if you're not constantly scaling, you are going to backslide, basically.

Peter Diamandis: So those were large reactors and you have on this slide here, the small modular reactors speak to the right side

Ramez Naam: and this is the area that investors are super stoked about, that I was stoked about 15 years ago, got less stoked about and now I'm kind of becoming maybe hopeful again is what we call small modular reactors. So we talk about a lot, I talk a lot about learning grades, right? Which is how fast does something get cheap. And every technology, if you build more of it, gets cheap at some rate as the scale increases. But the things that get cheapest the fastest are those that are built in factories in high volume and have the smallest number of moving parts. So the idea of SMRs is to build as much of this in modular, repeatable factory built situations as possible and do as little stick building as little assembly or construction. Construction is a dirty word, right? Construction does not get cheaper, manufacturing gets cheaper. So move as much of this as we can to a manufacturing process at the limit. It's a factory that spits out nuclear reactors that you just, you know, barge or semi truck to location. Some of these are not that many of these are the parts are made in a factory in a standardized way that you assemble like Legos on site. This is an incredibly sexy space for investors right now. Yesterday we found out that Valar Atomics raised a billion dollars at a $6 billion valuation for ANSMR startup that, that doesn't have a working reactor. Other companies Allo is probably my favorite company in this space. But there's a ton of companies.

Peter Diamandis: My friend's company at X Energy went public recently.

Ramez Naam: X Energy is an amazing company actually. I really like their design. So that's the radiant. There's one on here that's on the very, very small scale. There's a line between SMR and microreactor. So can you make it small enough to fit in a shipping container? So the military for instance, you've got radiant is on this slide. The US military for military bases would like shipping container or half shipping container sized reactors to power bases in the US but maybe in forward deployed locations as well so they don't have to move fuel. So there's a lot happening in this space.

Peter Diamandis: So natrium I see on the chart here is you know a, a third of a gigawatt compared to the AP1000 which is roughly a gigawatt. When you say, you know, if, if natrium goes into mass production, mass production being tens, you know, 50 units. I mean the relative price of buying three of those Natrium units versus an AP1000, what's that? You know, is there economies to bigger plants or is it just back them together?

Ramez Naam: Yeah, there Are economies bigger plants. Bigger plants use less steel and less cement per unit of power output. So there are economies to bigger plants. And that's how we used to think in the 50s and 60s primarily. But there's learning that happens from building more plants and doing more of it in a factory. So personally my guess is like the BWRX 300 Natrium are in a awkward middle because they're not really factory built. They build a bunch of components of the factory and to do field assembly. And so I worry about them but they might end up being the ideal optimal solution. At the other end of the spectrum you've got like gradient here. Their reactor is 10 megawatts. So it's 1/100 the size of an AP1000. They put them together in clusters of five. That's a pod for 50 megawatts. They have lower efficiency of using steel and cement, but they can build it entirely inside of a factory.

Peter Diamandis: Which is the first one of these? You know, these are not online yet. These are all theoretical. Which is the first one coming online, you think?

Ramez Naam: Yeah. So the optimistic projections from These companies are 2030 to the early 2000s and that's for the small ones. The next AP1000 is probably a few years later than that. These projections will probably be missed. I don't expect anyone to actually have a commercial small modular reactor in 2030, 2031. But maybe not like the the size of slip is probably smaller for a small reactor. And like everything else we've been saying the first units here are not going to be cheap. The first unit is going to be expensive. So the key is to build an order book from a customer that believes that by ordering enough they're going to drive down the price or to build out, you know, a multi customer order book where you've built some mechanism for cost and risk sharing between these AI data centers. And we talk about is nuclear the solution for AI data centers? Maybe there's other ways to power AI, but AI data centers might be the best thing that's ever happened to the nuclear industry.

Alex: I'm curious Mes, if we just take this argument in extremist where are the nano reactors? Why don't we see 100 kilowatt nano pico reactors that can be co located with every gpu. Do you think there's an opportunity in the space there?

Ramez Naam: I think it's really hard. I think you do hit some economies of scale issues as you get down to the bottom and you do you have a certain size for criticality, but you have, you know, on this chart at the bottom here, you have, you know, with companies like Gradient, you have like five megawatt size reactors and that's, you know, power a neighborhood or power or a few of them powering a military base, that sort of scale.

Dave Blundin: And do you also expensive, right?

Alex: Well, I mean, there are many ways one could imagine doing it. Another would be like gamma voltaics or beta voltaics. Like you put the radionuclides directly in the silicon and then you, you pocket the energy from radioactive decay. Do you think there's any hope for, for those who want to embed radionucl directly in the GPU silicon?

Ramez Naam: I mean, that, that's what we talk about as, you know, radiothermal or nuclear thermal. And that's different than, than a fission reactor. We use that on satellites or.

Alex: Yeah, deep space probes. We use radiothermal. Absolutely.

Peter Diamandis: Yeah. Rtgs.

Ramez Naam: Yeah, yeah. I don't expect to see rtgs become popular on Earth. When you look at the cost of those, they're actually really high, but they can meet, you know, mission needs for something that keeps on putting out power for decades without needing to be refueled. But their power output per unit mass is not all that high.

Salim Ismail: Aren't those just constantly spewing radiation, though?

Alex: That is the idea.

Salim Ismail: I mean, but in a bad way too, like. Okay, yeah, ouch. Let you figure that one out.

Ramez Naam: So tell me to say this, it's

Salim Ismail: really interesting how you've got this foot race between, if you said early 2000 and 30s for all these nuclear projects, but Elon is racing into space concurrent with that. And solar, you said, is coming down 30% every time we double the production. So all those things are in a foot race.

Peter Diamandis: And fusion's got the same timeframe. Right. We're no longer 50 years waiting. We're five years in waiting. Welcome to the health section of Moonshots brought to you by Fountain Life. You know, my mission is to help you use the latest technologies, including AI, to not just do your work at home, teach your kids, but to help you live a long and healthy life. I'm here today with an extraordinary physician, the chief medical officer of fountain life, Dr. Don Musailam. Dawn, let's talk about cancer. You know, I know from the member database that we have at Fountain, our members who come in who think they're healthy, it turns out 3.3% of them have a cancer in their body they don't know about.

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Peter Diamandis: Yeah, you know, it's interesting, people, you don't feel the cancer until stage three or stage four. And if you don't know what's going on inside your body, it's like driving your car with your eyes closed and you can know. And so when members come through felon, how do they detect cancers?

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Ramez Naam: Let's hit to fusion. This lie just says stuff I've already said. The main thing I want to tell you is like hyperscalers saying, oh, we're like using SMRs for our data center. It's still kind of a fiction. It's, I mean it's outside the five year window that we really can, that is investable, that we really have like really good optics on. But the, the pull from data centers is giving a massive, you know, tailwind to every nuclear company, especially this SMR startups, but also even Westinghouse with their, with their big reactors. Let's talk fusion.

Peter Diamandis: All right.

Ramez Naam: The joke was always that fusion is 50 years in the future and always will be. That's just no longer true. We now have well over 50 fusion startups,

Peter Diamandis: venture backed fusion companies. I mean that's like science fiction in its own right.

Ramez Naam: That's right. Absolutely. Some of these ironically came because of budget cuts in academia. If you look at Commonwealth Fusion, which is considered sort of the safe bet of fusion, if you will, if there is such a thing that team At Harvard, their grants were struggling and so why don't we form a company, you guys. And so they did and they're now the frontrunner. They have a, how can I say this, I don't want to call any fusion reactor a conservative design, but they have like the most conservative design in this sci fi field of fusion.

Alex: There's a striking resemblance. I have a friend of mine from college and grad school, as always, is on their board. You could call it like a privatization of MIT's entire nuclear engineering department.

Ramez Naam: Yeah, indeed, indeed. And not just that of iter. You know, we have, we've had publicly funded fusion projects. NIF in the US uses big lasers, national facility, it's really a weapon facility is what it is. And ITER in France, the international and European project that was at Tokamak, that is the, you know, the big donut style. And iter's plan was to build, you know, a reactor that was at least 5 gigawatts and would cost at least $40 billion. All right, and so what you have with CFS is the company has found a way to scale that down. So here, here's how I think about the three families of fusion. This is a massive oversimplification. My fusion startup founder friends are going to yell at me for not including their particular designs. But you know, there's three big ones. Tokamaks are the donuts that use big magnets to guide a plasma around and make that plasma slam into itself and capture the energy. That's what ITER is. That's where we have the most scientific data from past experiments about fusion. And the leading company in this space commonwealth, Fusion cfs, basically just has a technology that takes the enormous superconducting magnets that we were going to use in this European project and shrinks them down dramatically. We have a thin film material that you can wind around and wrap around that makes the magnet dramatically smaller. And because it's a superconducting magnet, you've got to cool it tremendously. And now that it's much smaller, you need a lot less cooling energy. There's a lot lower capex. So instead of a 5 gigawatt reactor being necessary to be break even, they can do it in like 600 megawatts is their plan. The next one is lasers. And again, fusion is all about like let's, let's slam particles together and force them to fuse into other particles, which takes a lot of energy, then releases it. Nif, the national facility in the US uses lasers, the world's most Powerful banks of lasers to slam these pellets of fuel, of hydrogen fuel to ignite fusion. In some ways, it's the closest thing to what happens inside the sun or the techniques that we have. Their weapons facility, they've had some amazing results, but we can't really productionize what they're doing. But they've had maybe in some ways the most exciting scientific result in this. And there's a few great companies in that space. And then reverse field configuration, pulse magneto, inertial. This may say a railgun, if you will. You know, rail guns like use magnets, magnetic coils to shoot things like metal out of them really fast. The leading company in space, Helion, uses basically two railguns, two tubes of magnetic coils to take a plasma at either end, slam it together, and then compress it with power electronics. And then when the explosion of fusion happens, the power electronics that were creating that magnetic field that compressed the explosion or compressed the collision to make it fully fuse captures the energy in reverse. These are three approaches. Most fusion companies capture the energy as heat and then have to use it to turn a steam turbine. The nice thing about what's on the right is at least Helion and a couple other companies capture it directly as electricity that turns into electric current. They don't lose 60% of the energy that you lose in a steam turbine and they don't have the added capex of that. So the left side is what's most likely to happen soon. Commonwealth Fusion is the company that is most backed by scientific accomplishments. Helion is the company of the ones that have raised more than a billion that if they work, I think has the pathway to the cheapest cost.

Salim Ismail: True followers of the pod will remember that we covered that. It's the coolest thing ever, but it was covered in a chipmunk voice.

Ramez Naam: I took Naveen Jain with me on a tour of Helion's reactor late last year. Actually,

Peter Diamandis: I had Bob Mumgaard from Commonwealth Fusion on our stage last year and he was amazing. We can talk about that. I want to bring helion onto the Abundance360 stage this coming year. So let's work together to make that happen.

Alex: For anyone in the audience who hasn't been on tours of either of these, I'll just point them at least for nif. I've been on a tour of nif, but it was featured in one of the recent JJ Abrams Star Trek movies as the warp core. So just Google Star Trek NIF and you can see the scene where the actual core where the Whole realm at the center of all those lasers pointing at one location is actually in the movie.

Salim Ismail: Put the links into the show notes. So anyone who wants to look at them, because these are really cool videos that we covered.

Dave Blundin: Elephant in the room. Elephant in the room. Question. Fusion seems now to be a when rather than if. So, so when.

Ramez Naam: So look, I think that might be on my next slide.

Peter Diamandis: And mez, I'll work with you to get the CEO of Helion on our stage together.

Ramez Naam: Yeah, David Curtley. He's a great guy. He's here in Seattle. The most aggressive timeline is Helion. They have a power purchase agreement from Microsoft to provide 50 megawatts of power. So very small. And again, the smaller you can build, the more modular it is when we get those learning rates in 2028.

Salim Ismail: Wow.

Ramez Naam: Everybody else is talking about sometime in the early2030s. Let me see if we have. Here's the timeline slide.

Salim Ismail: I mean, you know, fission, which we know how to do, is somehow a 2031, 2032 thing, yet fusion, which we don't know how to do, is a 2028 thing. Do you believe that?

Ramez Naam: Look like my view of this and founders of mine who are listening, please don't take this as an insult is every startup exaggerates how quickly they can get things done. And that's just part of the game.

Peter Diamandis: You have to be an optimist to get through the process.

Ramez Naam: Yeah, you got to be an optimist. Right. And they actually believe it. Maybe they believe it it like they believe it's possible that if they tell you, but maybe unlikely, but it kind of like, I think it's plausible.

Salim Ismail: Helion is plausible to me is the fact that the, the barriers are all regulatory and if, if for whatever reason the governor is super excited about fusion and the, and the voters are all violently opposed to fission, then that actually could make the difference. I would think.

Ramez Naam: I'd said that the barriers are still physics and engineering, but here's something that's fascinating and thank you for bringing that up, David, because this is actually quite important. A couple years ago we had a question of how would the US regulate fusion? Because if the US regulated fusion reactors like fission reactors, it was going to be a major drag in the industry. It might, might still be better to do fusion than fission for a variety of reasons. But instead they are regulated like the radiological imaging machines that you use in a hospital. Right. At least Helion is at this point. And there's good reason for that in a fission reactor. If you stop cooling it and you don't have the control rods in. Heat will build up and it will get hot enough that it melts the steel that it's in. That's what a meltdown is. As I said, new reactors are passive safe without any pumping. The hot water goes up and then it circulates and so on. But there's still like, you know, you can imagine like breaching that containment, slicing through those pipes and you have a meltdown.

Alex: I mean aren't there. Apologies for interrupting, but there are alternative architectures, pebble bed type architectures. I know thorium goes in and out of fashion, especially in China. Aren't there also like hybrid solutions that are in some sense meltdown proof?

Ramez Naam: There are ways, but there's, there's nothing in the pipeline that if you took an adamantium battle axe to wouldn't melt down everything. Everything uses a coolant. Every fissionary with this. Okay, maybe there's one startup, but I'm not going to mention them. I don't know what I can say but basically everything in the pipeline uses a coolant to pull heat away from the fission core and then to turn that into electricity in some way. And if you eliminate the coolant, if you break the coolant pipe, pipes, the core can overheat and melt down. Right? Fusion is different. In fusion it's the opposite. You have to capture the energy of the fusion explosion and feed it back in either to maintain the fusion reaction or to another pulse. Like helion is pulsed, right? Like it keeps like doing the same thing. Or NIF is pulsed with lasers. So with fusion if like if you mess something up up in the reactor, it just like goes, it just like fails and nothing bad happens. So it is. Fusion generates some radiation. You have to actually replace some of the parts in the reactor every five years because steel is being hit by neutrons and being weakened and yada there's actual like some radiation that's low level. There's real costs to that. Like free energy does not mean free because the capex and the maintenance still cost something. But you cannot have a meltdown in any way that we understand.

Alex: That's fascinating. So you're saying basically the regulatory treatment is whether it, whether the system is default on versus default off.

Ramez Naam: I don't know if that's what the NRC used as a criteria, but that, that is the dividing line between fusion and fission. And it was sort of missed in the public discussion in the press.

Alex: Fascinating.

Ramez Naam: That regulatory conclusion, it happened during COVID was actually a huge deal for the fusion industry.

Alex: That sounds like great news.

Dave Blundin: So if fusion is this close, shouldn't we just do solar and battery for a big chunk and then fusion for where we need high energy needs and we're done.

Ramez Naam: So all of these companies might fail. They might 100% fail and in addition to that, they might succeed but be too expensive. Just because your fuel source costs very little doesn't mean your energy will cost little. If the capex is very high, if the maintenance is very high, et cetera, et cetera, et cetera.

Dave Blundin: And the demand means that we're going to need all of it, all the different sources, no matter what. And you want to diversify your risk anyway?

Ramez Naam: Yeah, let's.

Dave Blundin: Yeah.

Ramez Naam: I always believe in having, you know, more tools in the toolbox than you think you need, more arrows in the quiver than you think you need because some of them won't work out. Right. Let me, let me quick question.

Alex: I would love to go back to one of my favorite hobby horses, the Dyson Swarmer. Do you think the Dyson Swarm wants to be solar PV powered or does it want to be fusion powered or does it want to be other?

Ramez Naam: I mean those are the two options. I think they're both great options. I think it's probably, it's much more modular to be solar PV powered. Again, like fusion also has a minimum viable size. Right. So Commonwealth, we thought with iter that tokamaks had to be 5 gigawatts. Commonwealth has found a way to scale it down to 600 megawatts. Right. But you saw some minimal viable size where solar is just super modular. And if you're in a Dyson swarm you have 24. 7 sunlight. So none of these technologies is going to be cheaper than just plain solar. But they work in winter and they work.

Alex: And also many of us, I mean I probably, maybe I'll speak for a few of the other moonshot mates here. We watched Back to the future part two and we saw Mr. Fusion being promised in the 80s and then very compact, very compact, like car sized. And then we look at the Lawson triple product over the decades and we see no, actually fusion wasn't always 50 years out. It was creeping up on us. But many folks weren't paying attention to progress in the triple product. Is there an equivalent of the triple product for the compactness of these devices so that we do in the end get our Mr. Fusion.

Ramez Naam: Let me talk about compactness and let's talk about the triple product and show the progress we're making. You know, the most audacious fusion startup that I know of is a company called Avalanche Fusion also in the Seattle area. And they believe they can make a fusion reactor small enough to power a car. It's not Mr. Fusion, it's more like half the size of a car but that sometimes they talk about like big backpack. So that is the most ambitious project as far as compactness. Typically in fusion you have like a sliding scale of like what has the most like de risked science but has like a more conventional power cost versus what could be like revolutionary in power cost or compactness. But the science is like let's hope you get it right. You know, like we don't have as much evidence. And so Avalanche is on that end where if it works it changes the entire world. But the confidence it works is much lower than the confidence for lacuconnole fusion.

Peter Diamandis: And any insight into PB11 proton born 11 fusion reactors.

Ramez Naam: People are very interested in it.

Peter Diamandis: And one of my portfolio Companies is a PB11 company out of Caltech. I need to introduce you to them.

Ramez Naam: Which one?

Peter Diamandis: I don't think they're public. I don't want to mention here but

Ramez Naam: I was just looking at a slide deck from a PB11 company just the other day. You know, PB11 is one of the ways that you can one of the fuels you can use to potentially get a nuclear reactor that is down to the like 1, 2, 3 cents kilowatt hour.

Peter Diamandis: Yeah.

Ramez Naam: Sort of price range.

Peter Diamandis: The vision there is can you build it small enough where you put it in the back of a large consumer airplane and it powers the engines and then it's also, you know, interplanetary flight.

Ramez Naam: There are still, you know, scientific and technical risks there. There are still a lot of unknowns.

Peter Diamandis: Welcome to the review today.

Ramez Naam: Welcome to deep tech investing. So there's there are more unknowns. Whereas like Commonwealth Fusion's pitch is look, the science has been proven at iter scale that if you have magnets this strong you can make fusion and get this much energy out. We're just doing that with, with much more compact magnets. I think the reality is a little bit more complex than that. But they really say it's. We've reduced it to an engineering problem. Nobody else can quite say that. Again, there's a gradient of how close you are to that. Let's talk about the triple product that Alex brought up. So this is temperature times, pressure times, duration and I love graphs and so I like, I believe something when I see movement on a graph. So what you're about to see. Sorry listeners, I'll Describe it. I'll try to narrate. It is over time, from 1956 to now, how close have we gotten to a triple product of above 1, above 10 and then infinity. And this is a log scale on every axis. So it's a brutal scale, but, you know, once upon a time, fusion really was. Oh, no, is this. Here we go. Really was 50 years in the future. And what we're seeing for the listeners is new points appearing that are. Each one is a fusion experiment. And as the years elapse and they're going up into the right, how close they are to the upper right is the zone of triple product, ultimately of infinity. But above 1, above 10 is probably what you need. And nif that last X on the borderline is a triple product, above one it's not. It was like a theoretical net energy gain, a practical energy gain means you capture the energy and then you convert it back into the lasers, the magnets, whatever. They did not achieve that. Their reactor cannot do that. But it tells us, and the progress on this tells us that it's not just, you know, hope, we are just getting closer towards. As you were saying,

Peter Diamandis: I just want to move along if we could. But. But Alex, please.

Alex: Where do you think the stereotype that it's always 50 years out came from? If one can just look at the triple product over the decades and say it's clearly like Moore's Law, like any experience curve, it's clearly marching to the

Salim Ismail: right on exactly that.

Ramez Naam: I mean, that's what I do, you know, if I'm like, well, let me just like these days, let me just ask ChatGPT, like, show me a graph of progress here. But you know, like, look at the end of the day for the average person, people have been talking about it and hasn't appeared. So I just discount the reality or the likelihood of it appearing over promised underdelivered for a long time.

Peter Diamandis: On behalf of my Moonshot mates and myself, I'm inviting you to join us at our inaugural Moonshots live event on September 25th in downtown LA. Alex Saleem, Dave and I will be hosting 1500 entrepreneurs, builders and creators and hopefully you for a full day dedicated to designing and building your moonshot. We'll be awarding the Build with Gemini X Prize, the world's largest hackathon, and the future Vision X Prize film competition. Over $5 million in purses with over 25,000 entries. You're going to hear the top five pitches from both competitions and get a chance to shape the outcome. Join us. Seats are limited, admission is competitive. Check it out@moonshots.com let's close with talking

Ramez Naam: about out of this world compute. So AI in space and AI in the oceans. Less known, but actually sort of a similar pitch. So of course everybody's heard about Elon talking about space based data centers and it's interesting. The response is like very bipolar. It's people saying that's impossible, it'll never work, people saying this is it, we're going to have a terawatt of compute in space. I'm somewhere in the middle. AI in space, it becomes cost competitive. When you get down to a launch cost that is, you know, something like four times to 10 times cheaper than what we have today. Nobody knows for sure because we haven't done it. But the back of the envelope says that. And in some ways it's a hedge against regulation. If demand for compute keeps going indefinitely and sites on land keep being blocked by the grid, by even Texas passing, you know, a temporary moratorium or audit, or by, you know, people protesting whether the grounds are are there or not, then building it in space, even if it's more expensive than building it on land, is a way to work around that bottleneck. So no grid delays, no local opposition, no terrestrial permitting, etc. Etc. Etc. I will say I think we are not fully internalizing what the scale of this is or the permitting and regulatory challenges with doing launch at that volume. So you know, we want to build 200 gigawatts of, of compute by 20, 30. 230. That's the chip volume, right? So 10 to 20 gigawatts a year to get 1 gigawatt of AI in space. Based on SpaceX's design, you're talking about, you know, six times SpaceX's best annual year of launch and twice SpaceX's cumulative scale of launch for just 1 gigawatt. For 1 gigawatt.

Peter Diamandis: You're talking about what was the calculation we did is like 5000 launch or 8000 launches of Starship to put up his ambition of was it a terawatt initially?

Ramez Naam: That doesn't even get you close to a terawatt. I mean it's to get 10 gigawatts a year. You're talking about 1500-2000 launches a year. 10 gigawatts a year is like five or six launches of Starship a day.

Alex: But I guess the elephant in this particular orbital room, I have to mention I think we talked about it on the pod previously. If you just look at the history of upmass from SpaceX and otherwise over the past few years, it's on a nice clean exponential trend. I forget what the exact year over year trend is. I did the extrapolation. 144 years from now, at the present trend, the up mass, the cumulative up mass would equal Earth's mass. So we basically disassemble Earth on the present trend. 144 years from now, the up mass is increasing really quickly.

Ramez Naam: There's some other planets we can take apart first. There's some uglier planets.

Alex: Do you have a favorite?

Ramez Naam: Yeah, don't make me pick.

Alex: But you know, Mercury maybe like Mercury is, is intriguing.

Dave Blundin: Wait, so

Peter Diamandis: here we go again. The hate. The hate bell is flowing and I can feel it. I don't care.

Alex: I mean Mercury is attractive because it gets lots of insolation and no one's using it for anything.

Ramez Naam: Orbit.

Alex: It's a good orbit.

Peter Diamandis: All right, Saleem, take us back to reality.

Dave Blundin: So yeah, so I mean this is looks. Unless we hit that exponential absolutely full on and go right down that path off. This looks prohibitive for 15, 20 years.

Ramez Naam: I mean, look, here's how I see it. Let me. I will get to the limits on launch in a sec. Let me put it another way. Elon wants to go to Mars. To go to Mars has to drive starship launch costs down to like close to the marginal cost to do that. Need a high starship cadence. You've got to build tens of starships, maybe hundreds and you got to launch them something like daily, right? Or at least like weekly, whatever. To amortize the R D, amortize the capex. There is not enough demand for communications on Earth to finance that via Starlink. There's no business model for Mars yet. So this is a gift to SpaceX that we have this AI demand. If the AI demand keeps going and there's no and it gets bottlenecked in ways to build it, we will find a way to do this. And with the ipo, he's got the funds to launch at least a gigawatt into space, right. Even at. Well, maybe not, but something on that order. So I, I don't think of it as like what's the limit on starship? First I think of it as like this is a demand driver potentially for starship. That said, it's not clear to me that the world will permit more than like 200 Starship launches a year, which has already been an enormous, enormous amount of. That'd be huge, right? 200 Starship launches a year is 20,000 tons to orbit that is exceeding, you know, all human launch to date every year, several times over. That's amazing. But you hit some limit. And if you're regulated by the faa, the reliability you have to hit, you know, one failed launch or one explosion means you're grounded.

Peter Diamandis: So I mean, he talks about needing to get to airline like operations, right? So, yeah, I mean, here are, here are the numbers for his. His target was 100 gigawatts per year of compute in orbit initially, equivalent to the entire compute today, which is around 80 gigawatts or so. And you know, it's 20 to 30 satellites per Starship. So we're talking about on the order of 30,000 launches per year, which is a launch roughly every 15 minutes. Now, if you think about it as rockets, and I've been in the rocket business for the longest period of my life, it's prohibitive and it's discontinuous. You can't think about rockets in that regard. But if you talk about airline like operations, right, there's multiple launches per second of airlines around the world. So it really become, it comes down to that. Now, is starship a vehicle capable of that level? He's built it for full capture, refuel and reuse, and if anything does, it's that. So, and then the question is, will these satellites be able to shrink in size over time? Right now the V3 satellite's pretty large.

Salim Ismail: Can I just add one, one more data point to that? That's 100 launches a day, which is exactly on his plan. What's the year that he, that he hopes to get to that target?

Peter Diamandis: I don't think he gave us that, Dave, when we spoke to him. I mean, 2028 is his first launch, but he did say, you know, before, I think he said, you know, before 2030, he wants to get to 100 gigawatts per year.

Salim Ismail: So, all right, around, around 2030. I think at that point in time that's equivalent to today's total world compute. But by then, total world compute will be up at least 10x. So it's a fraction of all compute that'll be in orbit when he's still on plan, you know, he's still making money. SpaceX is thriving. Rockets are going up 100 times a day. But the terrestrial stuff is also doing really, really well on that same day. So it's not an either or. You know, the space thing in, in Elon's plan will eventually bypass everything later in the 2000s. And that's, you know, maybe a thousand 10,000 launches per day. Much more like airlines, like you're saying,

Ramez Naam: if you ask me, like, where should we have the bulk of our compute and where will it make most sense 50 years from now? Space is the obvious place if the demand for compute is truly unbounded. But the timelines, I think, are just challenging at the scale this year. I don't think we're going to have, I think by 2030, if SpaceX has a single gigawatt in space, I will be very impressed.

Alex: Do you have a gut miz regarding just that point of whether you think our demand on the timescale of decades is going to be unbounded, sufficiently unbounded, that with compute that's recognizable, like CMOS type compute, which is, I think, what we're implicitly assuming in order to build the Dyson Swarm, it has to look like CMOs. We're not going to achieve breakthroughs in physics that enable us to achieve all of our civilizational compute needs with, I don't know, like tiny breakthrough compute devices that live in mountains. Do you think that there will actually be unlimited civilizational demand for compute energy?

Ramez Naam: Why don't you ask some easy questions, Alex? It's because they're boring.

Alex: So I asked the interest.

Ramez Naam: Yeah, no, that is the, like quadrillion dollar question. Right. And I think it's a brilliant question. Look, none of us knows. None of us really knows. Well, we know this, like intelligence is sublinear with compute. So at some point just throwing more compute at it will look, some lines will cross over where the cost that you're to get the incremental unit of intelligence is not made up for by the economics of it, I think. But so much will change. We will make so many discoveries and algorithms, so on. My guess is we're on an S curve right now. We're going to see a huge demand and then we're going to see, we're going to hit to some point of satisficing, right. Where basically what you can get out of machine intelligence meets humanity's economic needs.

Peter Diamandis: But does it meet AI's needs?

Dave Blundin: Right.

Peter Diamandis: I mean, the scenario here to think through is we're the current users of intelligence. There's a point at which, you know, ASI is the primary user of intelligence.

Ramez Naam: I do not see AI as a being and I do not see it as particularly volitional. I see it as a tool. Obviously we have agents that have some agencies, we've had computer worms, yada, yada, yada. So I don't see it that way. Right. Now, I can be persuaded by evidence, but I think we are over indexing on that. I mean, look at the OpenAI hugging face hack, right? Their agent was in the hugging face infrastructure for days and it didn't look at anything except the answer key for the test in the eval. It's not alive. We anthropomorphize these things. You know, Andrej Karpathy talks about we're summoning the ghost, right? Human cognition is this like iceberg. And the vast majority of it is not linguistic. Right? We have 100,000 years of homo sapiens. We're animals. Million tens of millions of years of being animals. Our urges, our drives, our desire for dominance, survival, propagation. AIs are not that. They're just like mimicking our language and our logic. They don't really have goals. We could build that if we wanted to. If we wanted to build a real being, I'm sure we could, but I don't actually think that's where we're headed. I know it's an unpopular opinion with your indulgence.

Alex: I have to take this provocation here.

Dave Blundin: Hold on one second. Hold on one second. You're the wind beneath my wings.

Ramez Naam: Go ahead, Alex.

Peter Diamandis: All right, Alex.

Salim Ismail: All right.

Alex: I have to grab the bait with both hands. Fine. It sounds mez like. I think what you're actually wanting to argue is for the orthogonality thesis, which is popular in certain alignment circles, which basically holds that for arbitrarily strong superintelligence, the long term goal of the superintelligence is independent of its level of intelligence. I think that's what you're actually. Correct me if I'm wrong. I think that's the point.

Ramez Naam: It might not be 100% orthogonal, but.

Alex: Yeah, okay, so. But the way you frame it. I just want to pin this down. It sounded like you were taking a position almost against AI personhood and. Or against some level of autonomy, Simply because if OpenAI has an agent that goes wild at hugging face but refuses to do anything, say what self enriching like you would have would the rubric with the threshold for saying, ah, this is like some sort of autonomous being. If it were, say, trying to mine bitcoin for itself once it gained access to hugging face, is that sort of the criterion in your mind?

Ramez Naam: No, even then I think it might be more similar to a computer worm or a virus or something like that. Yeah, I think it's. It's a different matter entirely. I think we are products of evolution. All animals are products of evolution. And so we have These built in desires to survive and to propagate. Yeah. Procreate and to control our environments. Because of that, AI models don't actually have a built in desire to even survive. Most of the experiments that get them to do that are very, very contrived. And mostly you're trying to get the AI to do something good and it's like, well, if I get shut down, I can't do this good thing. So I think we just were overly anthropomorphizing and animal morphizing if you will. That doesn't mean we can't do it. I think if we wanted to give birth to actual beings, I think, I think that's within our capabilities probably, but it's not the research path.

Peter Diamandis: How did we get from starship to this conversation?

Ramez Naam: Well, once the.

Alex: Peter, Peter, you brought us here. Peter, you brought us here because super intelligence is going to be the Dyson swarm.

Ramez Naam: I'll come back on the pod and I'd love to talk about super intelligence actually as a whole separate issue. Let me close out like last couple slides. Instead of going up, we can go out. 70% of the Earth is covered by oceans, right? Oceans. Certain ones are really cold. This is a portfolio company of mine. I'm an idiot because I said no to these guys five years ago when they were raising a seed round and I invested twice this year at much, much, much higher valuations than I could have five years ago.

Salim Ismail: In your defense, it was probably two guys saying we're going to put chips on a boy.

Ramez Naam: I love them. They were, they were the hardest. They were like, not the hardest, like the saddest. No, I gave that year. I just loved them. But their primary, their first utilization, this was bitcoin mining. I was like, I just don't know if I care enough. But whatever, obviously it would have been a good financial decision. Peter Thiel led their most recent round along with a storied set of people right and left and so on. So what this is, this is a center in the ocean. It's shaped like a bobby pin. You're seeing is the sphere at the top, but there's like an 80 meter long cone that goes into the sea that's open at the bottom. It bobs on waves and when it bobs down, water goes up and turns a turbine. And with a very clever shape of channels, it's basically continuous. And so wave power has been something that renewal people have wanted for a long time. But it turns out the waves are just not strong near the places people live. So where are the strongest waves on Earth? They're around Antarctica, they're in the Southern Ocean. So this team started off with a question of how we could build something with bigger waves. You can build something that has more higher capacity factor, runs more continuously and cheaper power. So they can get their power down to like we think 2 cents a kilowatt hour. Ultra cheap. Cheaper than anything on land except solar and wind in some places. That's their target. It will take some scaling to get there. They build these in factories at mass scale. They've got three in the ocean right now, fourth launches soon and they get free cooling from the ocean. So this is a company I love. It's basically space based solar but on the ocean with you know, some benefits to cooling because they don't need like Starship or SpaceX's design uses a cooling pump. You've got big aluminum fins to radiate heat away. But you've got to run a liquid, probably ammonia or something like that in a pump to take heat away from the GPUs out to the radiators. These guys just, it's, you know, physically a heat sink from the GPU goes to the, the steel walls of the device that's in 40 degree Fahrenheit water and that actually looks like it makes the GPUs have fewer failures and run longer. They're their own set of technical challenges, but they're like, they're modular, built in factories, mass produced, learning rates, the stuff that I love. So it's another way. So I said initially there were like four ways to get to like a terawatt of AI power. The Earth's deserts with solar and batteries, you know, near the equator, places that don't have a winter or a cloudy period. Nuclear fusion or fission space or the oceans. Those are the four ways that I know of to get to that scale of AI. And I'm glad that we're trying all of them. Basically.

Alex: Geothermal. Geothermal, nowhere on the radar.

Ramez Naam: I do love geothermal. And geothermal is the one that might rise to being the fifth of those. And we do have the new technologies companies like Fervo in the US, Tim Latimer, CEO's buddy ever in the UK quays using plasma beams to drill super deep. Those open up the possibility of getting cheap geothermal power anywhere instead of just only near hot spots in the Earth's crust where the mantle comes close.

Peter Diamandis: Are you involved with the XPRIZE in that area that's being designed?

Ramez Naam: No.

Peter Diamandis: There's an X prize on the blocks Right now for a geothermal X prize to accelerate.

Ramez Naam: Yeah, happy to help.

Dave Blundin: I have an industry question. If the chips are one thing and the compute is another thing, and then the electricity is the third thing, where the limitation is turning out to be, why aren't we seeing more integrated companies that are doing all of it like this, which then you can navigate that vertical stack. Elon is doing a bit of it, but I would expect to see a lot more of these. And why don't we see them?

Ramez Naam: I mean, it's a really good question. I think most companies would say, look, we have expertise in one thing and not necessarily in all these other things. Elon is one of the few who is willing to say, let's just vertically integrate everything. I'm going to share a slide that wasn't in my initial deck because I want to tell you the real window, like the real game changer would be in AI energy use. Your brain runs inference on 20 watts of power, right? Running Mythos for inference is closer to 20 kilowatts. And training, it's actually hundreds of megawatts right now, but it's heading towards gigawatts. And so AI has capabilities, the brain doesn't, and so on. But there are still, and I say this all the time, scaling is not everything in AI. Scaling is just what we knew how to do. We got these, you know, deep neural nets, we got the transformer, and we found that we had this enormous corpus of training data called the Internet, and we could just scale to get more intelligence. It wasn't the cheapest way or the best way, but it was a predictable way. Oh, you're telling me I can spend tens of billions of dollars and my intelligence goes up like this. Great, done. It's worth it. But at the end of the day, there are algorithmic discoveries waiting to be made. There are things that the brain's architecture at both a physical level and at the neural level, at the connectomics level, that are just better at learning than current deep learning models are and are certainly much more efficient at processing information. So if you want to know what the biggest unlock that we cannot predict, I don't have a graph for this. Of AI and power will be to learn new ways to manipulate information, to do more with less.

Alex: I mean, I've seen this. I'll respond to that one because it's something I think about quite a bit. No pun intended. I've seen arguments both ways. I've seen arguments that the human brain is still multiple orders of magnitude more efficient than frontier models. I've also seen arguments that the frontier models, if you measure them more objectively on say a per task basis, like you measure the total energy consumption at inference time to write a novel, that actually it's starting to become, if not more competitive than the human brain equivalent of that, because it's more token efficient. It's actually quite competitive. Do you really think that the frontier models today, anywhere on the cost frontier, not necessarily like the fable 5 end of the frontier, maybe like the deep seq v4 end of the frontier, that nowhere on the AI frontier is it anywhere close to being competitive on an energy efficiency basis with the human brain.

Ramez Naam: There are certain types of things where it can do things that a human brain simply cannot do with any amount of energy. Right. These models are trained on trillions of tokens, tens of trillions of tokens. And so they have read more books than you or I will ever read in our lifetime. So there's a type of task, and this is sort of similar of Google, right? Compare Google to a librarian. Google was less smart than a librarian, but it had every book, every webpage in its index. So it could do things that no human librarian could do. So I think that's the sort of thing that we're in. It's not just energy though. You know, humans are much more efficient learners in terms of amount of data needed to improve skills, that's for sure.

Salim Ismail: And that, that's for sure.

Ramez Naam: That to me that's opportunity. That just means that I'm not, I'm not a carbon chauvinist. Like I believe fully that digital intelligence, there's every reason to believe that it can surpass us and that humans are no longer nowhere near the peak of the type of intelligence that the universe allows. But our current algorithms are still missing some things that evolution wired into our cognitive architecture.

Salim Ismail: Well, just some raw numbers. I think you're totally right. The neural nets need a huge amount of training data relative to a child to come to the same conclusion. So that's an opportunity for sure. But in terms of the inference time, compute like this box on the right here. At 20 kilowatts, that's about a dozen GPUs. Those 12 GPUs optimally run about 500 concurrent fable threads. And those 500 threads are easily 10 times as productive in tokens per second as a person. So it's 5,000 times the output. Yeah. So the thing on the left is a thousand times less power, but thing on the right is 5,000 times more tokens coming out.

Ramez Naam: Yeah. It is true.

Salim Ismail: We're already there.

Alex: And there's also an arguendo. There's an argument to be made that human brains have the benefit of billions of years of evolution. And that, by the way, was, was very energy consumptive. Whereas arguably the equivalent of evolution for these frontier models is the gigawatts being spent on training.

Salim Ismail: Yeah, and that's a great point. Like you train it once and you've got it for the whole future of humanity. You've got at least that level of AI with no further training.

Ramez Naam: But they're trained on the data that all of humanity generated with all those calories.

Salim Ismail: That's exactly right too.

Peter Diamandis: Everybody, we've just explored the frontier of energy. From Ramez Nam, my go to, I think Salim, your go to person as well.

Ramez Naam: Yeah, I want to say something.

Dave Blundin: Yeah, Ramez, I've introduced you like probably 25 times at events, maybe, etc. And I'll say the same thing I do every single time. I wish you had more graphs to back up your comments.

Ramez Naam: I tried to limit the number of graphs in this talk.

Peter Diamandis: I just want to say to all of our listeners, I hope you take this home. It's one of the fundamentals, as Alex says, and on his, on his substack, you know, the innermost loop. Energy is the innermost loop. Understanding energy is critical for humanity. It correlates directly with the GDP of a nation. It correlates directly with the health and the education of a nation and soon intelligence of a species. So if you have been blown away by Ramez, listen to the pod again. Send it out to your friends. I think this is important. This is an epic masterclass on energy and Ramez. I want to wrap this in our two hour window here and say thank you. Thank you for sharing your brilliance and we would love to have you back.

Ramez Naam: Yeah, I've got 100 more questions.

Alex: Talk about super intelligence next time.

Ramez Naam: Time. Let's talk super intelligence. Thank you all. Great to be here in conversation with, with all four of you.

Peter Diamandis: All right, everybody, that's a wrap. See you guys soon on another emergency podcast as the breakthroughs continue.

Alex: The Singularity. Every episode is an emergency at this point.

Ramez Naam: Yes, it is.

Peter Diamandis: The Singularity is now. All right, take care. All.

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