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Can We Build AI in Space?

2026-02-18 · 11:25 UTC ·Tomas Pueyo from Uncharted Territories ·15 min read

Brief

Tomas Pueyo examines whether orbital AI datacenters could be a practical response to terrestrial power constraints, especially if AI expansion outruns grid build-out. His argument is less about futuristic science fiction than systems-level mass and energy accounting: if launch costs fall sharply with Starship and if power generation, storage, cooling, and shielding can be simplified in orbit, then the economics may approach those of Earth-based compute. The article focuses on first-order constraints—solar generation, batteries, radiators, GPU mass, radiation hardening, and orbital geometry—rather than software or market demand.

The strongest technical claims are that space offers continuous, higher-intensity solar input and potentially simpler thermal rejection than intuition suggests. Pueyo argues that in sunlit orbits, datacenters can avoid the heaviest element of off-grid terrestrial systems—batteries, which he says are more than half the mass—and use lightweight panels that are roughly one-tenth the mass per square meter of Earth installations. He also contends that radiative cooling may be adequate if the solar array itself doubles as a radiator, with panel temperatures around 60°C and GPUs adapted for hotter operation near 97°C. On reliability, he suggests AI inference and training are inherently more tolerant of bit flips than traditional space computing, reducing shielding needs, and points to Google’s Trillium radiation tests as evidence that modern accelerators may survive a five-year orbital mission. Because the article is a preview, it stops before the full cost comparison, but its framing is highly relevant to AI infrastructure because it links compute expansion directly to launch economics, power availability, and datacenter thermal design.

Why it matters

Tomas Pueyo argues that AI datacenters in orbit could become cost-competitive with terrestrial facilities if power bottlenecks on Earth worsen, citing Elon Musk’s claim that within roughly three years electricity scarcity could constrain AI growth.

Key details

The core mass-saving thesis is that orbital solar gets about 5x more usable energy than terrestrial solar—because panels can stay continuously illuminated and avoid roughly 30% atmospheric losses—so a space datacenter could use far fewer panels and eliminate batteries entirely if placed in a continuously sunlit orbit.
The article assumes SpaceX Starship eventually reaches 150-200 ton payloads at about $100/kg to orbit; at that price, launching a 16 kg fully loaded GPU system would cost roughly $1,600-$3,200, versus about $56,000 for the hardware itself, making transport a relatively small share of capex.
Pueyo claims lightweight space solar arrays can weigh about 1 kg/m² versus roughly 10 kg/m² on Earth, because they do not need glass, aluminum frames, or weatherproofing; he also cites sub-1% annual panel failure rates in protected near-Earth environments with minimal radiation coating.
On radiation, the piece argues AI workloads are more fault-tolerant than conventional deterministic computing, so only sensitive components such as memory may need substantial shielding; it cites Google proton-beam testing showing Trillium TPU HBM irregularities beginning around 2,000 rad(Si), versus an expected shielded five-year mission dose of 750 rad(Si), with no hard failures up to 15 krad(Si).
Thermal management is presented as the biggest surprise: using the Stefan-Boltzmann law, Pueyo estimates that solar panels absorbing about 1,225 W/m² could radiate their heat from both front and back surfaces at an equilibrium near 60°C, while GPUs might be designed to operate near 97°C—high but within the broader 90-125°C tolerance range of datacenter, industrial, or military-grade silicon.

Cleaned source text

title: Can We Build AI in Space?

author: Tomas Pueyo from Uncharted Territories

content_type: newsletter

publication: substack.com

published: 2026-02-18T11:25:00+00:00

source_url: gmail://19c70806b85c2a9e

word_count: 3388

It looks like radiation, cooling, shipping costs, and other apparent obstacles are manageable, and will make datacenters in space competitive

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Can We Build AI in Space?

Tomas Pueyo

Feb 18| | | ∙| | Preview

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This is the only article of the week. The 2nd half is premium.

Elon Musk is betting his companies SpaceX and xAI on space datacenters.

He believes that, in three years’ time, AI companies won’t have access to enough electricity to power their data centers, so only those who move the data centers to space will be able to continue growing their AIs.

The question becomes: Can you build working datacenters in space at a reasonable price?

As I ran the numbers, I realized that Musk is on to something: Datacenters in space are already in the ballpark of costs of land-based ones, and might soon be cheaper! This article will explain why.

In the process of writing it, I studied dozens of sources, including many space datacenter reports. I also wrote atweet to gather feedback from the community—which Musk responded to. That said, as it’s my first foray into space datacenters, I’m still guaranteed to have made mistakes; I just don’t know which ones. However, it doesn’t look like they would change the conclusions. Please point out mistakes if you find them.

What Does a Space Datacenter Look Like?

At its core, a datacenter on land is pretty simple:

1. The GPUs¹

2. Some other IT stuff, like memory, radiators to cool the system, connectors, etc.

3. A source of electricity. If you want your data center to be independent from the grid, you’ll want:

1. Solar panels to convert light into electricity

2. Batteries to store the excess electricity from the solar panels during the day and power the datacenters at night

Image generated by nanobanana, so the accuracy won ’t be perfect. It’s just to give you an idea. The datacenters include the IT stuff

If you want to put this in space, you need to fit that stuff into a rocket.

But that’s a lot of stuff, especially the three bulkiest elements: solar panels, batteries, and radiators. SpaceX is building Starship, a rocket that can carry 150-200 tons to space at a cost that should reach ~$100/kg. That’s ~15x cheaper than what they can do today (and 45x cheaper than the competition), but it’s still quite expensive. So you want to strip as much of that weight as you can. How do you do that?

1\. 5x Solar Power

Solar panels produce about 25% of the electricity they could theoretically generate because of the day-night cycle, the seasons, and atmospheric conditions like clouds or storms. But in space, the Sun is always shining. If you have the right orbit, you can keep your solar panels lit all the time, and that provides two benefits.

First, you get ~4x more direct sunlight, because the Sun will be perpendicular to the panels all the time, and there will be no night.

Second, when solar rays cross the atmosphere, they lose about ~30% of their energy.² Eliminate the atmosphere, and you eliminate this loss.

Add these factors together, and you get ~5x (and up to 9x) more energy from your solar panels than you would on Earth—or, in other words, you need 5x fewer solar panels (and their mass) in space than on Earth.³

2\. No Batteries

If your solar panels are perfectly illuminated, you don’t need to buy and transport the very heavy batteries, which represent over half⁴ of the total weight. Massive win.

But how do you get your solar panels to always face the Sun?

3\. The Right Orbit

One way to achieve that is by orbiting the Earth around the poles, in what’s called a sun-synchronous orbit:

The yellow satellite is following a red orbit that passes over both poles, and as a result never falls into the shadow of the Earth. It always follows the dusk-dawn line. In the future, when there are millions of such satellites, every evening might bring a rain of dots across the sky —although since the point is to gather as much sunlight as possible as energy, it’s unlikely that these satellites will be very shiny. Source.

The problem with this is that it’s quite expensive to get satellites into these orbits. Normally, rockets are launched from as close to the equator as possible, in order to use the rotation of the Earth to move faster.

Here we see the Earth from the top, from the North Pole. The rocket is sent from Starbase, on the the US / Mexico border , becauseit’s as close to the equator as the US can safely send rockets into space from there, because if they have a problem they fall into the sea.

But if you send them into a polar orbit, you can’t use that inertia, so it’s much less fuel-efficient. It’s easier if your satellite follows an orbit that isn’t too distant from the equator’s plane.

Here, we have both a polar orbit and an equatorial orbit.Source.

The problem here is that the satellite ends up in the shadow of the Earth… Another solution is to send the satellite far enough from Earth,⁵ and not quite on the same plane as the equator, so that it won’t pass through the Earth’s shadow.

This orbit always sees the Sun. High-Earth Orbit (HEO) reaches 40,000 km, which is a bit over 3 Earth diameters, so this is far enough to have many of these orbits sunlit all the time. I ’m not saying this is what xAI / SpaceX will do, at least upfront. Sun-synchronous is probably better early on, but it’s also quite limited in terms of how much space is available on that one orbit.

The exact orbit will need to optimize for:

100% sunlight

Closest proximity to Earth (to reduce fuel costs of the rocket to reach a distant orbit)

Efficiency for rockets from Starbase to reach

But if the satellites are far from Earth, isn’t this going to create some _latency_? Won’t signal take too long to come back to Earth? Yes, but it doesn’t matter:

Even if the satellites were far away, say at 5,000 km, the time for the signal to go back and forth from Earth would be 30 ms.

For most AI uses, a few milliseconds (or even seconds) of delay doesn’t matter that much. Think about how long some AI tasks take today, from seconds to minutes, and even hours!⁶

OK we got rid of more than half of the weight from batteries and 5xed the efficiency of our solar panels. What else can we eliminate?

4\. Lighter Solar Panels

Once you’ve disposed of the batteries, the solar array is the biggest source of weight of Starlink satellites today,⁷ about a third of the total.

But in space, solar panels are much lighter than on Earth. A solar panel on Earth weighs⁸ about 10 kg/m2 or more⁹, while in space it can weigh as little as 1 kg/m2 or less.

That’s because, in space, there’s no gravity, atmosphere, rain, hail, dust… The panels only need to be lightly structured; they don’t need glass to protect them, aluminum frames, stiff backsheets, sturdy mounting, rails, clamps, grounding…

They do need some coating to protect against the intense solar radiation and flares, but if the satellites are close enough to Earth, they’re protected by its magnetic field, so with minimal coating, they can withstand failure rates of less than 1% per year.

Musk’s SpaceX (and Tesla!) design and participate in the supply chain of solar panels, so I assume they’ll adapt them as much as possible to their needs.

This is already quite optimized, though, so I assume there’s not too much more to do here.

5\. GPUs

Now we need to optimize the weight of GPUs, but these are actually not very heavy relative to their cost. To get a sense of this, an NVIDIA GPU today costs ~$25k and weighs from 1.2 – 2 kg, while a fully-loaded system (if you add all the other costs, like memory, cooling, etc) costs as much as $56k and weighs 16 kg.

Sending 16 kg to space today is expensive ($24k), but it won’t be with Starship ($3.2k when it costs $200/kg, half that when it costs $100/kg). And that assumes all the weight of what we use on Earth would be carried to space, which is unlikely. These systems will be streamlined for weight, so that the cost of shipping the GPUs to orbit will be a tiny fraction of the cost of the GPUs. We’ll see in a moment why.

6\. GPU Shielding

But besides their weight, GPUs face another problem in space: radiation. On Earth, we’re protected from solar electromagnetic rays by the atmosphere and our magnetic field, but these protections are much weaker in space—when they exist. This is a serious problem for computers in space today, because these rays cause mayhem in computers. As a result, they need shielding, which is expensive and heavy.

One of the issues is that electromagnetic rays flip bits and cause havoc in existing systems. For example, imagine that a computer has the following number: 1000001000001, which in decimal is 4161. If that first bit receives a solar ray and flips to 0, that number is now 0000001000001, which in decimal is 65. Imagine that you’re calculating 4161*10 and instead of getting 41,610, you get 650. Every downstream calculation will be monumentally off. Catastrophe! As a result, computers in space today require electromagnetic shields that add to their weight.

But this is not how AI works. AIs are not deterministic, they’re probabilistic. AIs have massive files with billions of parameters that each add a tiny amount to the final value. Querying a GPU is kind of like taking a poll to millions of people. Another way to think about it: In your brain, connections between neurons are constantly dying and forming. Any single one of them is not important at all. They can be severed, and everything will continue as normal.

The result is that GPUs don’t need as much electromagnetic shields as traditional computers, so this added weight can be avoided.

The rest of the software can also be adapted to this situation, tolerating random errors instead of assuming perfect calculations all the time.

Some systems, like the memory, will still need shielding, but if you’re limiting the shielding to only a few small parts of the satellite, the cost in weight will be tiny.

With this type of treatment, Google believes their chips can last 5 years in space—about the lifetime of a datacenter:¹⁰

> _We tested Trillium, Google ’s TPU, in a proton beam to test for impact from total ionizing dose (TID) and single event effects (SEEs).

> _The results were promising. While the High Bandwidth Memory (HBM) subsystems were the most sensitive component, they only began showing irregularities after a cumulative dose of 2,000 rad(Si) — nearly three times the expected (shielded) five year mission dose of 750 rad(Si). No hard failures were attributable to TID up to the maximum tested dose of 15 krad(Si) on a single chip, indicating that Trillium TPUs are surprisingly radiation-hard for space applications.—_Google

7\. GPU Maintenance and Replacement

GPUs have a high failure rate, but you can’t swap them in space. So what are you supposed to do?

One of the measures will be to make them more tolerant to heat (we’ll talk about this next). This should reduce failure rates, especially in space.

But aside from that, most failures happen at the beginning of a GPU’s life, so if you test the GPUs on land first, you should be able to reduce the failure rate dramatically, so that your average GPUs lasts ~5 years.

8\. Radiators