Data Centers Won’t Be In Space Anytime Soon

We Don't Need to Leave the Earth Behind

Elon Musk, the richest man on Earth, recently exclaimed on a podcast with Dwarkesh Patel and John Collison: “Mark my words…In 36 months, probably closer to 30 months, the most economically compelling place to put AI will be space.”

This may seem like another bold—almost wild—claim in his long-running history of making bold promises, but the comments were followed a week later by the merger of two of Musk’s ventures, xAI and SpaceX. It seems like Musk is at least partially serious about circumventing the resource constraints of Earth and channeling the AI boom into the atmosphere.

But how seriously should the rest of us take this idea?

Proponents of space-based AI data centers eagerly point to three factors in their favor.

First, SpaceX’s reusable rockets have steadily lowered launch costs. Its Falcon 9 rocket decreased costs from around $11,500/kg to $1,500/kg of payload. The next launch system, Starship, is projected to drop that cost even lower, optimistically between $100 to $200/kg . Second, solar-powered orbital data centers can harness 25 percent more useful solar radiation and produce continuous electricity—if positioned to stay in constant view of the sun—thus improving generation efficiency and eliminating the variability that faces terrestrial solar. And, third, the cold vacuum of space provides a great heat sink for radiative cooling systems.

But despite these perceived benefits, large-scale orbital data centers remain science fiction—unless some moonshot-level hurdles are overcome. While the known cost of scaling terrestrial data centers remains high, the unknown costs of sending data centers to space en masse is even higher. We simply don’t have methods of protecting chips from radiation exposure, maintaining acceptable computing uptimes, and resupplying a facility with new components that are remotely realistic for a large-scale, commercial computing enterprise.

Orbital data centers give an exciting vision of a future frontier for both compute and electricity production. But given the enormous technical hurdles, they are not a real solution for the investment, innovation, interconnection, permitting, and other needs of the artificial intelligence industry today.

Radiation-Induced Software Errors

One very significant difference exists between terrestrial and orbital data centers: the former is protected by the earth’s atmosphere, the latter is not, exposing the many chips powering the operation to significantly higher quantities of radiation. Radiation exposure can induce “bit flips”—where a logical zero turns into a one, or vice-versa—or cause permanent physical damage to a circuit. Over time, continuous exposure to radiation will disfigure the semiconductor’s structure and gradually degrade performance until the chip no longer functions.

Training an AI model employs nearly every chip in a data center at the same time in tight coordination. Such deep system coupling makes it possible for a single failure to cascade into a system-level disruption.

Today’s most advanced chips have features made of just a few dozen silicon atoms, an extraordinary manufacturing achievement, but one that is pushing silicon’s physical tolerance to its very limits. Combining stress-induced chip failures with a fragile computing network makes a system ripe for failure. Indeed, Meta’s training of their Llama 3 model on NVIDIA H100s saw 419 unexpected interruptions in just 54 days, forcing operators to handle interruptions happening every few minutes.

An orbital data center doing training would grapple with radiation-induced failures on top of component failures that already trouble facilities on Earth. Radiation-hardened chips do exist for mission-critical spaceflight systems that need high reliability, but these lag multiple generations behind leading chips in computing power, making them poor options to run large workloads on.

The sobering reality is that we have exceedingly little knowledge of how complex computing systems would work in space. A November 2025 Google publication claims that their Trillium chips could perform for 5 years in orbit, but they extrapolated this conclusion from an accelerated terrestrial experiment that exposed chips to protons at a single energy level. In space, satellites are exposed to a constant hail of various kinds of radiation at a much wider range of energies than what Google used. In fact, the first ever experiment to test an AI-grade chip in space began in November 2025 when Starcloud, an NVIDIA-backed startup, launched a single H100 into space. It will take multiple years to even see the results.

Instead of trying to eliminate failures altogether, data center operators have developed means to minimize the induced disruptions using clever software tricks and redundant copies of data and components. In space, however, all of these become exponentially more complicated and expensive.

Data center fault-tolerance software runs in the background like an antivirus program, monitoring the system for errors that look suspicious–like an abrupt loss of a chip’s signal that never restarts. This works fine on Earth where the computing circuitry is the primary concern. But charged particles will collide and interfere with all electronic systems, not just chips. Until engineers create entirely new server configurations, write completely new memory and firmware protocols, and run extensive testing of them all in a high-fidelity environment (i.e., in space instead of in a lab trying to simulate space), radiation-induced failures would create nigh-constant systemwide disruptions, many of which would likely go unnoticed until the very end of a multi-month training cycle.

Terrestrial data centers often supplement these software “watchdogs” by spreading copies of data throughout the physical network of servers and racks. If a chip or a memory drive fails, the information lost can be rebuilt by piecing together its distributed copy. But mitigating radiation’s impact on spaceborne computers without using radiation-hardened chips calls for substantially more redundancy than that employed by any of today’s facilities. Specifically, an orbital data center would need triple modular redundancy—running three identical systems in parallel. This means launching three copies of every chip and memory drive, three times the cooling and electricity demands, and (at least) tripled launch costs and capital expenditures.

High Turnover and Space Debris

The intense burden placed on chips during training creates rapidly accumulating wear-and-tear that limits their useful lifetime to just 2-3 years. Combined with the one-year release cycle for the new generation of chips (recently shortened from NVIDIA’s historical two-year cycles), companies often swap out an entire data center’s worth of chips every few years.

This can’t happen if the data center is in space. A company would have to launch a completely new constellation of satellites; in doing so, they make the old system obsolete for training. They could still be used for inference but would occupy highly desirable sun-synchronous orbits needed to continuously point solar panels at the Sun.

One solution could be deorbiting—aiming a satellite at Point Nemo, the point in the ocean farthest from any landmass, and letting it fall to Earth, burning up in the process. Controlled reentries have a very low (but nonzero) probability of hitting solid ground. But the more objects and debris there are in low earth orbit, the higher the probability that an accidental collision leads to Kessler syndrome, a chain reaction where debris from one collision hits another satellite, creating more debris that hits another, and so on. Since there’s no known way to remove space debris, a single incident could threaten humanity’s space missions until humanity discovers a means of removing space junk.

Orbital Data Centers Are Crazy…But AI In Space Isn’t

These critiques just scratch the surface of the practical elements necessary for a space based data center as a substitute for terrestrial data centers. However, there is one avenue for spaceborne computing that could feasibly manifest in the coming decade: satellite edge computing.

When satellites take pictures of the Earth or scan deep space, they capture terabytes of raw, unfiltered data that need to be sent back down to Earth for analysis. As these satellites precess around Earth, they are only in view of a ground station for a few minutes per orbital pass. Constraints on data transmission speeds limit ground stations to download a few gigabytes per pass, dramatically limiting the utility of satellite data.

A potential use-case for in-space computing is to sort and filter this data in space before beaming it down to Earth, allowing for faster and more targeted insights and lower signal interference between satellites. With a space “data center,” satellite operators monitoring wildfires, weather, or defense targets could perform calculations in space and only download the final results.

The positioning of compute as close as possible to the data source is known as edge computing and mainly aims to minimize latency and lower data transfer burdens. There are multiple companies and public-sector agencies working on various approaches to satellite edge computing, including Starcloud and NASA.

The economics, technological maturity, and tangible market demand for satellite edge computing actually make sense. But edge computing satellites differ greatly from the scale of spaceborne AI-training data centers that Musk anticipates.

Staying Grounded In Reality

In a refreshing moment of wisdom on the podcast, Musk warns that “those who have lived in software land don’t realize that they’re about to have a hard lesson in hardware.” It is tremendously difficult, as he explains, to build power plants and procure necessary grid electronics equipment in the physical world. Developers face bottlenecks for gas turbines and large power transformers, tariffs on solar panels, and—in some regions—long waits to get through an interconnection queue even if they can procure hardware. The idea that anyone, even Musk, can somehow leapfrog these tacit realities by building in space is magical thinking at best.

During the podcast segment on orbital data centers, Patel and Collison ask Musk why he doesn’t just make his own gas turbines, a solid question that he brushes aside without giving a clear answer. Indeed, why doesn’t he invest in manufacturers of gas turbines, large power transformers, or substation equipment that he needs so desperately? And why do Musk and his interviewers contemplate covering Nevada in solar panels but omit even a single mention of new nuclear power?

One could rationalize Musk’s extreme bullishness as a virtuous desire to accelerate human progress. Or, just as likely, it’s nothing more than a public relations campaign to aid his now merged SpaceX and xAI’s upcoming initial public offering. Either way, the near-term future of data centers will assuredly be on this planet, and any who assert that the technology will emerge in the long-term forget that the current discourse is mostly fueled by short-term supply constraints.

The space economy is undoubtedly exciting, but thought experiments about bringing industrial development into the final frontier don’t obviate the need for investment, innovation, and, critically, regulatory reform for industries here on Earth.