Freakonomics Radio - 682. Should A.I. Move to Space?

Episode Date: July 24, 2026

At first it sounds ridiculous. But it might be inevitable. Guest host Steve Levitt talks to a team that hopes to push A.I. infrastructure off the planet. Part one of a two-part series.)   SOURCES: Blaise Agüera y Arcas, vice president and fellow at Google, C.T.O. of technology and society. Travis Beals, senior director of product management at Google, Project Suncatcher lead. Will Marshall, co-founder and C.E.O. of Planet Labs.   RESOURCES: What Is Intelligence?, by Blaise Agüera y Arcas (2025). "Towards a future space-based, highly scalable AI infrastructure system design," by Blaise Agüera y Arcas, Travis Beals, Maria Biggs, Jessica V. Bloom, Thomas Fischbacher, Konstantin Gromov, Urs Köster, Rishiraj Pravahan, and James Manyika (Google, 2025). Reason, by Isaac Asimov (1941).   EXTRAS:  "Are Our Tools Becoming Part of Us?," by People I (Mostly) Admire (2024). Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Hey there, it's Stephen Dubner. Let's start with a puzzle. I'm going to name some industries, some varied industries, and you have to figure out what they all have in common. Okay? Here's the list. Agriculture, ride share services, global banking, meteorology, military communication, insurance, shipping logistics, disaster management, broadcasting. So what do they all have in common? All of these industries and many more are heavily reliant on infrastructure,

that is based in space, which means that space-based infrastructure is an industry of its own. Here is Will Marshall. Everything from the rockets, the satellites, communications is a big one. The idea of all that up is around 300 billion a year. Marshall got his PhD in physics at Oxford, where he conducted experiments in quantum superposition

under Dirk Baumester and future Nobel Prize winner, Roger Penrose. But Marshall's true love was space. So he went to work for NASA at its research center in Mountain View, California. Among other things, NASA has been developing and managing satellites for decades. We actually started a project called PhoneSat, because my colleague at NASA kept on holding up his phone because it was pretty new at the time. First iPhone had come out, and they were like, these new smartphones have a lot of what you need in a satellite. They have batteries and they have sensors and they have cameras and they have fast processors. They even have GPS to know where they are.

They're kind of incredible, and it's all stuffed in a little box, and it costs 500 bucks. This led Marshall to ask himself an obvious question. Why are we spending $500 million on satellites? What are the extra six zeros doing for us? Marshall eventually left NASA to help run a startup that made small, inexpensive satellites, which they called doves. They had thin, rectangular wings and a big central camera for an eye. Like any good startup, they made their first prototype in a garage. or if you are Will Marshall in a garage.

Literally in a garage, we leverage smartphones, and we plumped the satellite down on the desk of the venture capitalist that we went to see as opposed to have any PowerPoint deck. That was certainly a shock to people, and enough people were crazy enough to give us a check. Those checks helped fund what became Planet Labs, a publicly traded company that is today worth around $10 billion. Their vast array of tiny satellites constantly scan the Earth's surface and provide data for all sorts of public agencies and private firms.

So that has been a success for Will Marshall, but he wants to go way beyond that. He thinks the next frontier for space technology is to provide computing power for AI. Today on Freakonomics Radio, my Freakonomics friend and co-author Steve Levitt explores how a breakthrough technology is entering its space age. That's in four, three, two, one, lift-off. This is Freakonomics Radio, the podcast that explores the hidden side of everything with your guest host, Steve Levitt.

Hi, I'm Steve Levitt. From 2020 to 2025, I had my own podcast on the Freakonomics Network called People I Mostly Admire. And I had a guest on my show in 2024 who changed the way I thought about artificial intelligence. Since then, AI has only become more relevant, and so has this guest work. I'm Blaze Aguera and Arcas.

I am a vice president and fellow at Google and the CTO of technology and society. So just this morning, I've been using AI to work on some artificial life simulations that I've been playing with. So it's a combination of coding and paper writing. Blaze has had an unusual life. Let me give you one example. One of his first jobs was with a U.S. Navy Research Center where he reprogrammed the software that stabilizes aircraft carriers to reduce the seasickness of the crew. At the time, he was only 14 years old. When I talked to him in 2024, we had a conversation about how you can know if AI is truly intelligent.

The moment you start to have something that behaves intelligent, the question like, well, but is it really intelligent, is an interesting one to ask. a lot of philosophers talk about this as the philosophical zombie problem. Could it be that something could behave like a person but is dead on the inside, has no inner life, there's no there, there's nobody home? Is that meaningful? But the trouble with the whole philosophical zombie question is that it's almost like a frontal attack on the whole idea of doing science. And this is one of the things that led Alan Turing to propose the Turing test. He was basically saying, look, if all of the tests check out, then that is a very good. That is your answer. And I'm kind of with touring on that one. It can be a shock. Maybe intelligence is this

much more general accessible faculty. It doesn't have to depend on any particular biological substrate. You know, it doesn't have to be a person in the sense that we've always understood persons. But I'm sure that we're going to be debating this one for a long time to come because it really cuts to the quick of what we consider to be our special sauce as humans. So when Aguerre Ayakis told me he had founded a new AIR, project at Google called the Paradigms of Intelligence Team. I wanted to know more. At its heart, it's an AI research team, but we have a few philosophers on the team. We have neuroscientists on the team. We have people from other disciplines, sociology. And part of the

reason for that is that I've always thought that understanding intelligence generally is key to making progress with AI, and that AI in turn can help us to understand ourselves. I see the study of intelligence as a connected field, both in its analysis of how we work and in its attempts to synthesize intelligence outside the human brain. So mostly when people say they work on AI at a place like Google, it means you're part of a huge team that's building enormous models and training them on endless amounts of data for the next release. But that's not what your team is doing.

You're working on more philosophical issues, foundational issues. Could you give some examples of the kinds of specific problems that your team is tackling? Some of the problems are philosophical, but they're also very practical. A lot of the breakthroughs in AI have come from people who study neuroscience and study the brain and are working at the intersection of those things. So we're very much in that tradition, too. Examples of some of the more heady problems that we're looking at now are not just how individual intelligent agents work, but how multiple agents in interactions with each other,

lead to larger collective intelligences. And even how, when you look at what we think of as single models, they look internally like societies of sub-agents. We think of this as the social intelligence hypothesis, meaning that intelligence is inherently social, whether you think about it at the scale of societies or even at the scale of brains. So an example of what you might do would be to create a set of five or six different AI agents who were taking on different roles or personalities? One is the skeptic, one is the optimist?

Or what specifically are you talking about? Yes. So we can definitely, and we do do those sorts of experiments, where we take agents and we put them in societies or institutions or conversational structures with each other. And you can ask questions like if they all are set up to agree with each other versus set up to be misaligned with each other, to disagree with each other, which of those kinds of setups leads to better problem-solving of the collective? And it turns out there that it's better

if you have them disagreeing with each other than if they're all preaching to the choir, as it were. But then you can also ask the same question of an individual model, and this is a little bit mind-bending, but if you look at the chain of thought, which is to say the inner monologue of an individual reasoning model, then one of the things you find in there is that there actually voices or characters that have been developed that do exactly the same thing inside those models. It's like the model is talking to itself, arguing with itself, kind of like Gollum versus smuggle at Lord of the Rings or something, but often with more characters. And you get that behavior just from training reasoning models to do a good job of reasoning.

So I think that there is a really profound lesson in this, and it's a practical one in terms of how to build AI models, but it's also a philosophical one in the sense that it suggests that intelligence is fundamentally social, both at the scale of brains and societies. I don't know anything about neuroscience, but I know my own experience is that my brain works exactly like you've just described. I've got four or five different parts of my brain that stake out different territory and some are risk-taking and some are cautious and some are skeptical. So I definitely feel like my own brain is in no sense unified. It's some kind of of a competition for time on the stage. What I've never been able to figure out at all, though,

is who is the it that's behind the scenes? Who's deciding who the winner is among these different voices? Well, that is the place where I think a lot of folk psychology and philosophy also goes wrong in assuming that there is a homunculus, you know, that there's a mini-steve, you know, and a mini-blaze, you know, inside our brains, when, in fact, what you are is that collection of voices. I'm really excited to hear that that's how you think of yourself, how you model yourself internally. It's how I think of myself too. But many people think of themselves as being very unified as having just one self. But we know from neuroscience and we know from psychology that your perspective is closer to what is really going on.

If you, for instance, do radical brain surgery on people and split their brains in half, which used to be done quite a lot as a last ditch surgery for addressing epilepsy, you can see that the two have. the two hemispheres of the brain are working independently of each other, and yet people's sort of impression of themselves remains unified. So it's kind of like every part of the brain is working as a member of a team. They all know that they're on Team You, and they're modeling themselves as a whole, and yet also in a kind of competition, as you're describing, for who says the thing that is going to come out of the mouth.

And that's how neural nets work too. You know, artificial neural nets have these soft max layers, as they're called, which are essentially an internal competition for which part of the network will get to emit the behavior. Now, you published a book last year based in part on the work that you and your team are doing

at the Paradigms of Intelligence team. It's called What is Intelligence? Lessons from AI about evolution, computing, and minds. It's full of a lot of very bold ideas. Let me just hit on a few of those. For instance, you argue that the primary function of intelligence and life itself is prediction.

What do you mean by that? This is in many ways an old idea. One of the longstanding critiques of AI models is that they're just autocomplete on steroids, that they're just predicting the next token. I always kind of thought in my heart, as it were, that that could not be the secret of intelligence. Surely it's not just predicting the next token.

So I was as shocked as anybody when we made really large-scale next token predictors, and they started to get the answers right. to hard mathematical word problems and write poetry and all this kind of stuff. I found it very surprising as far as my intuition went, but if you really start to look at what it means to predict, the surprise dissipates a bit. The reason we've got brains is because we live in a complex world,

and our actions can have effects on that world in our own futures that can either hurt us or help us. And so in order to be able to distinguish between actions that will lead to good places or to bad places, That's exactly what you have to do. You have to predict. Not just predict the world in your absence, but do what statisticians call conditional prediction, meaning predict the world conditional on action A and action B, and then decide which prediction you like better. In some sense, it's almost a tautology. Like, of course, that's what brains are there for. If you couldn't act to change your own future, then there would really be no point in having a brain. And if you couldn't predict the effects of your actions, then there would also be no point in having a brain. What people might find confusing about this, though, is that we've built these neural nets, these AI programs, and it is bizarre that the objective is to predict the next word. And I think what you're arguing is that you can't predict the next word well at all unless you have a pretty complete model of how the entire world works, because you don't know if you don't have the context, you just be terrible at that. And at least my own experience, as I've interacted more and more with AI, is to really reconsider who I am and to think that, yeah, that kind of what AI is doing feels a lot like what I do, that if I'm really tired or maybe had too much to drink, I start making the exact same kind of mistakes that AI makes when it doesn't exactly understand what's going on.

It's been interesting. I wouldn't have thought that I would come to see myself as being like AI, but I really have. Yeah, that's exactly what I meant when I wrote the title of the book, what AI is teaching us about intelligence generally, including about ourselves. So I think that's real. It doesn't mean that you're just babbling, that you're just, you know, saying stuff. In a way, when people say, oh, are people really just next token predictors? The error isn't the word just because a lot is implied by that. And in particular, what I think trips a lot of people up is that they imagine that the mind that is doing that predicting or the process that is doing that predicting is outside the thing that is being predicted. But the key thing to understand there is that the sequence of events that you're predicting include what you are going to do. They include your own actions. They are part of that stream of events that you experience too.

And what that means is that in order to do a good job of predicting the future, any future that involves your own actions, you actually have to be predicting yourself as well, modeling yourself. And I actually think that that's where consciousness comes from, not consciousness in some kind of mystical sense, but in the sense that knowing what it's like to be you and knowing what you're likely to do in the future and what others think about you and what others think you think they think and so on, you know, I think that is the functional essence of what consciousness really is. Now, someone might get the idea from our conversation so far that you're a dreamer or

or a philosopher, but that's so far from the truth. You've got a long track record of building things that really work. And one of the projects that your paradigms of intelligence team is working on now is called Project Suncatcher. And if you pull it off, it will be perhaps the most ambitious engineering undertaking of all time. Would you say that's a fair assessment of the degree of difficulty, or am I exaggerating? Yeah, yeah, I think it is a fair assessment. actually, much as it makes me very afraid to say that, yes, I think that's correct. There's a lot of controversy around AI data centers and their power consumption.

But one thing that's not disputed is that that consumption is growing. The International Energy Agency expects that about half the increased demand for electricity in the U.S. through 2030 will come from data centers. I was really worried about this a few years ago, because even though the number is still small, how much electricity is being used today on AI, that is still a very small number. But it is growing so quickly. And so if you think about that exponential growth over the course of the next 10, 20 years, then you really start to get worried. My trajectory emotionally about all this is that I started off really concerned about it. And then I kind of reassured myself that we were working on a lot of things that I believe are going to be able to gain us at least a factor of 1,000 in efficiency. And that reassured me that it's going to be fine.

We're going to solve this problem through increased efficiency. So we wouldn't need that much electricity in the end, because even though we'll build bigger and bigger models and use AI more and more, we'll get so good at saving the energy that it won't really be a problem. That it'll be a wash. That's right. Then something happened. Right.

So that was the first and second parts of my emotional journey. But then there was a part three, which is that a factor of a thousand in efficiency is in an exponential landscape that only buys you, maybe a decade. And that sounds crazy to say that a factor of a thousand only buys you a decade. But, I mean, if you just look at Moore's law, you can see how many factors of a thousand we've gotten in amount of computing since the 1940s. And it really is only a decade. And I see no reason to believe that the demand for AI is somehow going to saturate in the next 10 years and will be done. So that's what really got me thinking about the supply side of energy, not just

how much energy AI is using, but how we could think about obtaining more energy. for AI. And you're obviously not the only one thinking about this. All the big AI companies have been scrambling to find reliable power sources. Yes. Amazon, meta, Google, Microsoft, they're all making big investments in nuclear power in various forms. Microsoft even has plans to restart three-mile island, which seems like one of the odder choices. But this all has to strike people as being pretty surprising, given that nuclear power has been mostly out of favor for a long time, at least in the U.S. Another option would be to look to solar power, which has been expanding like crazy. What are the pros and cons of solar power when it comes to these massive data centers?

I don't think any of these things should not be pursued. We should be pursuing all of them. But the trouble, of course, with solar power on Earth is that the sun is only up half the day. and also they have to be oriented toward the sun. There is cloud cover. There's the changing angle of the sun, which means that you get less when it's not overhead. So intermittency is really the big issue, and the fact that battery technology is still not anywhere close to where it needs to be

in order to smooth that power curve from solar. So at least on Earth, it won't be able to fulfill the entire demand until or unless we make really radical changes in how we store power. And even if we do that, we also need to think about how much of the Earth's surface we're willing to cover with solar panels. I think we can certainly afford to cover a lot more of it than we have today, but there is a limit, which is not just human habitation and so on, but the rest of nature.

The whole planet is solar powered, and so there is ultimately going to be a zero-sum game there. So you've got a different solution in mind, and it's one that at first sounds completely ridiculous, I have to say. What is Project Suncatcher? Basically, the concept is that you move AI into space. And you use solar power, but rather than solar power on Earth, you have orbiting solar panels that are in sunlight almost all the time in something called a sun-synchronous orbit. These are orbits that go around the Earth in such a way that the solar panel is facing the sun. So those sun-synchronous orbits allow a solar panel to gather about eight times the energy

of a solar panel on the ground because there's no nighttime and there's no atmosphere. So solar of hower works really great in space, but the problem we've always had is that beaming energy in the same way that moving people or materials through long distances

is not very efficient. So what good does it do you to have this energy in space? The good that it does you is if you move the actual computation to space also. So if you want your farm-to-table distance, as it were, to be as short as possible, then the way to solve for that is by putting the data centers in space with the solar panels. I'm just trying to imagine this, because I've seen these data centers,

and they are not small buildings. I mean, these are some of the biggest buildings that we ever built. How are you going to do that in space? So there's a short-term answer and there's a longer-term answer. The short-term answer is that, you know, of course a data center is a giant building, and we can't be talking about literally moving those buildings up into space. This is not the Death Star. It's very energetically costly to lift mass into space.

So none of this makes sense unless you think, A, that it's going to continue to get cheaper to move mass into orbit, as it has been. There's been an incredible decline in cost over the last 10 years, thanks in large part to SpaceX. And we believe that will continue to be the case for some years to come. So you have to believe in declining costs to launch, but you also have to believe that you can do an orbiting data center in a much more physically lightweight way than a terrestrial data center. So what would your data centers look like? They're not buildings. They're something very, very different.

No, they're not buildings. In the beginning, they look like dragonflies. So with giant but very, very thin solar wings and with a body in the center where they're not. the computing happens, and the body should be as lightweight as possible. Basically, you need as much area as you can for solar collection. You also need area in order to radiate heat, but then you want your mass to be as small as possible. And when you put all of those constraints together, it turns out that you can design satellites using today's technologies that put you on the right side of the economics with some pretty

modest assumptions over the next decade. And so this data center that you're imagining, it's not a single satellite. No, it's a swarm. How do you imagine controlling a swarm of satellites that are rocketing through space at many tens of thousands of miles an hour? Can you just give us the broadest view of how you pull this off? You have to obviously work through a lot of the details of the orbital mechanics of this. There are also many other things to think about. out, like safety of the low Earth orbit environment, you want to make sure that you're not

creating space junk, because with massive numbers of satellites, you want to be very careful that collisions are extremely, extremely rare because those are super costly. They create a lot of pollution in low Earth orbit. And you want to be sure that if the worst happens, if there is a collision, it doesn't result in a catastrophe where we end up with all kinds of junk that makes that extremely valuable real estate around the earth that poisons the well for us and for everybody else. So there are many important considerations. But basically, the swarm has to be self-organizing in a lot of ways. So you need to imagine those spacecraft being intelligent in their own right, not just being all controlled from the ground. You have to imagine them being really

robust and communicating with each other via light. So laser, so-called free space optics is a really important part of the puzzle. You can always use radio to communicate with the swarm, but ultimately we're probably going to be using a combination of radio and light also for ground to space communication. How many satellites would be working together in this form, in your vision? Well, it's an exponential. I can't give you a single number because if you really zoom out over the 50, 70 year time scale that I think you need to in order to understand where this will go, I know that it's a little bit tricky for me to say that because it sounds like science fiction if I talk about things that are 50, or 70 years out, but that's what today would have sounded like to somebody in 1945 at the dawn of the computing age. And I think we're going to undergo a similar exponential explosion.

We're talking about near-Earth orbits in the beginning, but by the time we get toward the end of this century, I'm sure that we're going to be talking about very, very thin orbiting structures both around the earth and around the sun that require scientific notation to describe. So really, a really large number. I think most people's reaction, including mine, when you first hear about this, is like, wow, that's kind of crazy. Could that actually work? Right?

Like, it seems like it shouldn't work. After the break, the details and the prototypes. I'm Steve Levitt. This is Freakonomics Radio, and we'll be right back. I'm Travis Beals, senior director, product management. And what I do is I lead Project Suncatcher. Project Suncatcher is Google's effort to put solar-powered data centers in Earth's orbit. Beals runs the project from his home on an island off the coast of British Columbia,

Canada, near where he grew up. How did you come to be part of the paradigms of intelligence team? Did you volunteer or were you drafted? I volunteered for that. Back in 2016, I started working on Google's consumer hardware effort, so like Pixel and later Nast and so on. And Blaze was also part of the leadership team. At some point, Blaze, I think this would have been early 2024. Blaze had the idea for doing AI in space. And he talked to me about that, and I thought it was really interesting. We convened a small group of folks, and this was almost all people's part-time or 20% time, to try to figure out why it wouldn't work.

Let's find the reason this is not going to work. And we couldn't. We kept finding all these things. We're like, well, maybe this, this won't work. And then actually, no, no, there's a way to solve this. There's no physics reason this is not going to work. and the economics of it seemed like maybe that could work. At some point, we got this far enough along that it's like, okay, we need to start doing this for real.

Now I'm wondering, somebody at the top of Google must have said yes to this at some point. How did that conversation go? I don't know if I can get into everything that went down in that room, but I can tell you a little bit about that. So obviously this is a big deal trying to present an seemingly crazy-sounding idea like this to company leadership. Blaze and I are all set to go present this to Sundar Pichai, the CEO of Google and Alphabet. And so I walk into the room. And then Sergei, who's one of the co-founders of Google, walks in as well. And I was not expecting that.

And so you're nervous? This is high stakes now, suddenly. Yeah, but Sergey was actually also supportive of doing this, which was great. He's also realistic about, and I think this is true for all the leadership, that this is hard, too, right? This is not an easy thing to go do. We've gone from this being a thing that's. sounds crazy and impossible to actually this is possible. This could make sense. It's just going to be really hard. I can't help whenever I think about Project Suncatcher. The Manhattan Project

always pops into my mind. And obviously they have very different objectives, but they have this feeling of trying to apply physics in a way that seems unreal, but making it real. Do you feel like Robert Oppenheimer sometimes? I thought you were going to go with the Apollo project there. I mean, that would have been the more straightforward kind of moonshot project here to make an analogy, too. Could you talk about moving data through space? Because the linchpin of this whole thing is the fact that everything is hard to send through space, except data. We're really awesome at sending data through space. Yeah, and we keep getting better at that.

So the way we send lots of data around on Earth, we usually do that through light in the infrared through optical fiber. And if you want to send data around in space between satellites, kind of awkward to imagine stringing fiber between satellites, but it turns out you don't need to. You can just beam the lights directly from one satellite to another. In a sense, that can actually work even better through free space than through glass. There's two reasons behind that. The first reason is that the speed of light through vacuum is faster than through glass.

And the second reason is you can use more optical bandwidth, right? So I mentioned that when you're sending light through an optical fiber, you do it in the infrared, and that's because outside of these narrow ranges, the impurities in glass will absorb the light. So you're limited in how much optical bandwidth you have through an optical fiber. If you want to send light through space, you could, in principle, use the entire infrared and visible spectrum, and that's about 30 times more spectrum. So there's hard engineering problems here, but if you solve those, in principle sending data through space, can be even better than sending it through glass on Earth.

The idea behind Project Suncatcher is that the AI computing happens in space, powered by the sun, and then that data is beamed back down to Earth. But why bother to send all those computers up there? Why not bring the power back here? The idea of doing space-based solar power has been around for a long time. The earliest mention I could find of this was an Isaac Asimov short story, I think was from the 40s. It is an interesting idea, and maybe someday we,

see people doing that. But you have this challenge. You've got to send that power. And it's a lot of power we're talking about for this to be worthwhile down to Earth and somehow safely receive it there. And that power transmission and reception problem is a really hard problem. So in some sense, the insight behind something like Suncatcher is like, hey, space is a great place to do solar power. But with AI, maybe we actually have a really important and useful way to use that power in space. The thing that's different about AI versus some other Apple applications you can imagine, like, I don't know, running a steel mill or something like that, you're not moving a lot of atoms around, right? Like, you just need to send data up and down to these satellites. You don't need to send, I don't know, iron ore if you were going to imagine a steel mill in space. Now, by my rough calculations to generate 5% of U.S. electricity demand via solar energy on Earth would require covering an area of about a thousand square miles, about the size of Rhode Island. You've said that solar panels are eight times as efficient in space,

but that still implies that you need something like 100 square miles of solar panels in orbit to produce 5% of U.S. electricity. I'm not going to try to real-time check your mouth on that one here. But yeah, it's a lot of area. Here's the thing. There's a lot of space and space. Space is just mind-bogglingly big. The first time I really got a sense of that actually was I was in high school and I was taking a computer animation class and I decided I wanted to do a fly through of the solar system but to scale.

And when you try to do it to scale, it's so hard to make anything visible because everything is so small. Like the planets are so small compared to the vastness, even just of our solar system. And so you talk about these enormous areas of solar panels that you would need. And yes, it's true. And it would be smaller than what you would need if you're trying to generate the same amount. of power down on Earth. But then if you compare that area versus the amount of area that you could, in principle, build in lower Earth orbit, and this dawn dusk, sun synchronous, lower Earth orbit, it's nothing. It's like a tiny, tiny drop in the bucket. We tried to build visualization of what

it would look like if you sort of zoomed back from Earth and you're looking at Earth and you're looking at this seemingly enormous amount of satellites there. And it looks like nothing. You can't see the satellites because there's such a tiny percentage of the total area, the total volume there. The problem is getting it there, of course. You need a lot of rockets to get these things into space. Or potentially you need maybe still a decent number of rockets, but you need to reuse them a lot. And reuse of rockets is a great way to make rockets cheaper, right? That's how you can get a lot of launches with a reasonable number of rockets. So in the short run, from what I've heard, the biggest barrier to doing this is not even really the physics. It sounds like you're pretty confident of the

physics. It's just that for orbiting data centers to be economically viable, you need the cost of rocket launches to fall. How much does the cost of launching these rockets have to fall where this now becomes economically sensible? In the draft paper that we put out, we talk about $200 a kilogram as being an important milestone. I mean, that's not the only thing that you have to make progress on by far, But that's a good sort of milestone to think about. What are the launch costs right now? They're quite a bit more than that, but they've been coming down at, you know, a really impressive pace, right? So this is, in some sense, it's not about where things are today.

It's about skating to where the puck is going. Spoken like a true Canadian. Project Suncatcher plans to launch its first satellites in 2027. To build the prototypes, they're partnering with Planet, the company founded by former NASA scientist to Will Marshall, who we heard from earlier. Here's Travis Beals again. We want to launch two satellites, and the reason for that is one of the things we want to test

is an optical link between the two satellites so that we can run a compute workload that involves multiple satellites, because that's one of the important aspects of the system. So we want to test out the simplest possible version of that, and in order to do that, you need two satellites because you need both endpoints. There's a couple other things we want to test as well.

One is just making sure all the thermal solutions behave the same way in space that we expected them to, that we tested in the lab. And then another aspect of this is tolerance to the radiation environment in space. So that's something else we also tested on Earth. But, you know, doing things in space is never exactly the same as trying to test things out in the lab on Earth. Doing things in space is where Will Marshall comes in. We've got the record for the most satellites put into space. We've also got the record for the most blown up going to space. More from Marshall after the break.

I'm Steve Levitt. You're listening to Freakonomics Radio. Were you one of those kids who loved outer space from an early age? Yeah, I saved up my pocket money and brought a pair of binoculars when I was eight or nine and eventually built my own telescope because I couldn't afford buying one. I wouldn't have been so surprised if you'd ask my 16-year-old self that it's building a telescope that I'd be building telescopes when I'm in my mid-40s, just in space looking down, I'd say, okay, well, maybe I'm looking the wrong way, but fine.

You may remember Will Marshall from the beginning of the episode. He's the former NASA scientist who started a company by prototyping satellites in his garage. That company, Planet, is the one now prototyping Project Suncatchez satellites, but that isn't their main business, not at all. I asked Marshall what Planet does. Basically, we monitor the planet, the whole Earth, every day, to help people make more informed decisions across security, civil government, commercial applications, non-profits. And we do this, having launched hundreds of satellites that imaged literally the whole Earth landmass every single day. And with advanced in AI, help people track changes, answer questions they need in more or less real time.

We think of it, a bit like Google index the internet to make it searchable. We're sort of planet indexing the Earth to make it searchable. Do you see yourself more as a data company or are you a satellite company? No, definitely see ourselves first and foremost as a data company. Calling planet a satellite company would be a bit like calling Google a server company. And they've got lots of servers, but that's not what you see and that's not the products you use, right? The products we give to people are data services, earth imagery itself, as well as analytics on top of that. If you're a farmer, we're going to give you intelligence about fields.

If you're an intelligence official, we'll give you information about what's over the corner in some theatre. If you're a civil government actor, we might be helping you to stop deforestation across large areas where you can't monitor. We're giving actual information to help people make smarter decisions day to day. So in that sense, we think of ourselves more like a Bloomberg terminal, but for Earth data. People set up their data feeds. It helps them make smarter decisions day to day. and the satellites are a back-end. Now they're pretty cool and sweet,

and I can geek out on that because that's my area, but in the end, they're back-end to those services. How many satellites do you need to take a picture of every piece of land on Earth every day? Well, yeah, we figured this out when we left NASA. We would need about 100 imaging satellites. We've actually got about twice that number in space. It's the largest Earth imaging constellations.

And basically, they'd act as a line scanner for the planet. So imagine the orb of the Earth, you have a line of satellites in the ring that are going in a polar orbit, so over both poles, and the Earth ends up rotating underneath that line. The line actually stays fixed with respect to the sun, and the Earth rotates underneath, and each one takes a strip of images, but the Earth is rotated slightly by the time the next satellite comes down, and therefore you end up systematically line-scanning the Earth once every 24 hours. When I think about what your company could do and be,

there's this real dichotomy, right? You could imagine all of these wonderful things that would be good for the world and helpful, whether it's fighting forest fires or helping in drought. But then there's also sort of this specter of surveillance, which I know makes a lot of people nervous. Who are your customers? And how do you think about that problem? So let me start with the customers.

We service customers in civil government. So that's agencies like NASA that buy it for science research. it's the Brazil federal police that are trying to stop deforestation. We work with commercial applications. So insurance, for example, after disasters, instead of sending a person out to see if that farm or that house has been affected by the flood or the storm, they can just look at our imagery instead and save themselves a lot of time and effort. And then there's defense and intelligence applications. For example, we work with the U.S. Navy, monitoring the South China Sea, where they're tracking illicit activities, North Korean evasion of oil sanctions. We work with Ukraine on trying to help them

monitor Russian activities. And then we work also with a lot of different other kinds of actors that are less prominent. Like we work with think tanks, NGOs like the Red Cross or Amnesty International, Human Rights Watch, who are tracking events like refugee camps around the world. We also work with news media to shed light on events as they go on around the world. To the surveillance, surveillance question. I think of this as we are helping people have better information to help them make better decisions. That's especially true in sustainability, stopping deforestation, illegal fishing and all that. It's kind of obvious. But even in the security realm, our theory of change is kind of simple. Greater transparency leads to greater accountability, leads to greater security, better decision-making.

If you look back in history, the history of conflict is highly correlated, with people having a lack of information. The Cuba missile crisis was a situation when the US didn't know and got surprised by the Soviet Union putting missiles in Cuba. It was because they didn't know that they were there, the Cold War almost became hot, or vice versa when the US had put missiles in Turkey

without the Soviet Union knowing. When we have better information about what each other are doing around the planet, it tends to diffuse situations. It's less risky, less miscalculations and so on. Your company made the headlines recently when you made the decision to restrict access to some satellite data in areas affected by the war with Iran.

Correct. Was that an easy decision or a hard decision? It's always a very hard decision. We always get in a situation where there's active conflicts, whether Ukraine, Gaza, Iran, where there's a tension between ongoing operations and trying to ensure the legitimate interests of people not to be exposing ongoing operations

that could put military personnel or civilians in harm's way, and at the same time trying to provide transparency and accountability in the public interest. And what we've typically done is put a delay around some of our imagery so that it ensures that we can't have civilians and military operations put in palms away. But at the same time, it ultimately provides transparency and accountability. How do you get your satellites into orbit? You don't actually do launches yourself, right? Well, I've been practicing with my right arm for quite a while now. And no.

Obviously, we've launched on now 41 rockets to space. Just give you a sense that 38 of them got to orbit, so three failures in our time where the rocket blew up somehow. So it's a risky business still. But yes, it's becoming more routine. We've launched over a dozen times on SpaceX rockets. We've launched on an Indian PSLV rocket. We've launched on various rockets from around the world.

So basically, we stick up our thumb at the launch side and hit the ride because normally we're not the main payload. there's a big billion dollar satellite going to space or something, and we're hitching on the side of those with lots of little smaller satellites. Interesting fact, we've got the record for the most satellites put into space. We've also got the record for the most blown up going to space. A common misunderstanding of what happened in the launch sector, a lot of people know that launch costs come down.

But for the first, more than a decade of launches for SpaceX, they really bought US launch costs down to be comparable with international launch. launch costs. Really, the US launch costs have been kept arbitrarily high by a central monopoly because US government satellites had to be launched on US rockets by law from Congress. So, Boeing and Lockheed team together to ensure that they had a really expensive rocket because they had to be used because they were the only rocket provider. But we as a commercial company weren't limited to US rockets and we went around the world and like the Indian PSLV very reliable, at least at the time, and it was quite cheap. SpaceX ultimately got not just to that point,

but then brought it down even further, about 4 or 5x further than that. And that's really really a boom, but that wasn't how we first got to space at least. That's been a further help. But remember, even a 4 or 5x reduction in cost, which really helps the whole space industry and has helped spawn it, is less significant purely from an economic standpoint than the 100 to 1,000x improvement in cost performance of satellites that are going in. So in each kilogram that you put up into space, if you can get 100 times more data because you've made the satellite. Adelaide like way smaller, then you have won big time, even if the launch cost didn't change one iota. So you've been working with Google's Project Suncatcher team to build and test prototypes of orbital data centers.

Do you think over the next 50 years orbital data centers are a long shot, a sure thing somewhere in between? I actually think it's quite clearly going to happen. And here's why we did a calculation with Google back eight or nine years ago. now looking at all the cost of data centers on the ground, all the cost of data centers in space. And we sort of did modeling and figured out that by around cost of launch coming to $200 to $300 to kilogram, it would just be cheaper. Purely on cost grounds, it would be cheaper to put them in orbit than to put them on the ground. And I remember Sergey and Larry saying, well, let's come back in about 2030, when we had

predicted that launch costs would come down to there and start there. I said, no, let's come back five years before that, because this is going to take a little bit of time to actually build these technologies, the thermal technology and other things. And that's exactly what happened. So last year, Google came back to us and said, let's start this project. And so it's an early days. A project is a moonshot, as they call it, a bit like Waymo or quantum computing, which has turned out to be quite successful. It's an ambitious project, but ultimately, I think it's inevitable that within a 10-ish year timeframe, most computers being built on the Earth will be going to space. I actually think not only is it going to be economically

cheaper, but it's going to be more sustainable. Earth's life is so precious. It's incredible, but yet we're whittling away a lot of it. We're doing deforestation for lithium, for cows, for whatever we're doing it. And we need to put our energy-intensive infrastructure into orbit, if we can, to not have a collision course with a biodiversity on the planet. As Jeff Beasers has said, We need to zone the Earth, rural, and light urban and put intensive stuff in orbit where there's lots of energy and we don't conflict with that precious biodiversity. So you're part of this big space economy right now. If these orbital data centers do turn out to be viable, where will they fit into the space economy? Will they be a small part?

Will they dominate the space economy? Roughly the ladder. So if you think about the rough maths, the current space sectors of all the 300 billion annually. But if you just look at compute spend, it adds up to more than that every year that is being spent and it's being projected to be spent. Again, we're talking years out from now, I'm not talking about tomorrow. But yeah, in 10 years' time, I'd imagine it would probably be bigger than all the rest of the space economy combined. That was Steve Levitt in part one of a two-part series. Next week, Steve looks at how data centers might fit into the rest of the space economy and who's going to be regulating them.

Do we need a major disaster happening first until we see the need for something like this? That's next week on Freakonomics Radio. Until then, take care of yourself. And if you can, someone else too. Freakonomics Radio is produced by Renbud Radio. You can find our entire archive on any podcast app. It's also at Freakonomics.com where we publish transcripts and show notes. This episode was produced by Augusta Chapman and edited by Gabriel Roth.

It was mixed by Jake Loomis with help from Jeremy Johnston. The Freakonomics Radio Network staff also includes Dalvin Abouaji, Eleanor Osborne, Ellen Frankman, Elsa Hernandez, Ilaria Montenacourt, Pete Madden and Tago Jacobs. Our theme song is Mr. Fortune by The Hitchhikers, and our composer is Luis Guerra. As always, thanks for listening. Google doesn't take titles all that seriously. in the internal kind of corporate directory, you can make up your own title. If you try to declare that you're the CEO, I think someone will probably stop you. The Freakonomics Radio Network, the hidden side of everything.