The idea of moving artificial intelligence data centers into orbit has captured the imagination of investors and technology entrepreneurs seeking to overcome mounting constraints on Earth.

But aerospace engineers are warning that the concept remains far from practical, arguing that enormous technical, financial, and operational obstacles make space-based AI infrastructure one of the industry’s most ambitious and controversial proposals.

Demand for AI computing power has exploded over the past two years as companies race to build sophisticated foundation models. That surge has triggered an unprecedented wave of spending on data centers, with major technology companies committing hundreds of billions of dollars to new facilities worldwide.

The rapid expansion, however, is running into growing bottlenecks. Utilities are struggling to provide enough electricity for new AI campuses, aging power grids require costly upgrades, and local communities have become increasingly resistant to large-scale data center developments because of their heavy energy and water consumption.

Against that backdrop, several companies have begun promoting orbital data centers as a long-term solution. Among the most prominent is Starcloud, a startup backed by Y Combinator, which raised $170 million earlier this year to develop space-based data centers with support from SpaceX. The concept envisions massive satellites powered continuously by solar energy, eliminating dependence on terrestrial electricity grids while providing dedicated computing capacity for AI workloads.

Supporters believe that uninterrupted solar power in orbit could eventually offer a cleaner and potentially more scalable source of energy than increasingly constrained land-based infrastructure.

However, a detailed engineering critique released by Irish aeronautical engineer Brian McManus, creator of the YouTube channel Real Engineering, in collaboration with IEEE Spectrum, argues that the proposal dramatically understates the technological barriers involved.

McManus was particularly critical of Starcloud’s technical white paper, questioning both its engineering assumptions and the optimism surrounding the project.

“It really seems like anyone with some renders and a white paper written by someone being gassed up by an overly agreeable AI can get VC funding these days.”

He added, “Billionaires will attempt to pull the rug over your eyes and convince you that this technology makes total sense, but reality is, this technology is dumb.”

The criticism comes at a time when enthusiasm surrounding SpaceX remains exceptionally high following the company’s public listing, which significantly boosted its valuation. Investors now see AI infrastructure, including Elon Musk’s broader vision of orbital computing, as a potential long-term growth driver.

Yet aerospace specialists argue that building industrial-scale computing facilities in space would require breakthroughs across multiple engineering disciplines simultaneously.

One of the most immediate challenges is heat.

Modern AI processors generate enormous amounts of heat even inside conventional data centers, which rely on sophisticated liquid cooling systems, chillers and air-conditioning infrastructure to maintain stable operating temperatures. Cooling becomes significantly more complicated in space because there is no atmosphere to dissipate heat through convection. Instead, thermal energy must be radiated away, requiring extensive cooling systems and massive radiator surfaces.

According to McManus, the quantities involved would be extraordinary. Using conventional coolants such as glycol, each orbital facility would need to circulate more than 150,000 pounds of coolant every second.

He compared the required flow rates to industrial-scale infrastructure.

“Emptying an Olympic swimming pool in 40 seconds,” he said.

He noted that such volumes are typically associated only with gravity-fed hydroelectric dams.

Scale presents another major obstacle.

Starcloud’s proposed facilities would reportedly deliver five gigawatts of computing capacity, placing them among the largest computing installations ever conceived. To generate sufficient electricity, each spacecraft would require solar arrays covering approximately 1.6 square miles, nearly 5,000 times the surface area of the solar panels attached to the International Space Station.

The sheer size would translate into unprecedented launch requirements. McManus estimated that, even before accounting for coolant, pumps, fuel, shielding, structural components, and attitude-control systems, each station would exceed 113 million kilograms.

He described the scale in stark terms.

“More than an aircraft carrier sitting in orbit.”

He continued: “More than six times the total mass launched into space in history.”

Beyond construction, orbital operations introduce additional risks. The Earth’s orbital environment is becoming increasingly congested with satellites and debris. Millions of fragments, ranging from defunct satellites to tiny metal shards, already pose collision hazards.

Large solar arrays spanning square miles would present enormous targets. Even small debris traveling at orbital speeds could puncture cooling systems or damage power-generating panels, necessitating expensive repair missions.

The risks are already familiar to SpaceX.

The company disclosed that its Starlink satellite constellation performed approximately 300,000 collision-avoidance maneuvers during 2025 alone, illustrating the growing congestion in low-Earth orbit.

Radiation represents another challenge.

Unlike terrestrial data centers, computers operating in space are continuously exposed to high-energy particles capable of damaging semiconductor components or corrupting stored data.

McManus warned that these effects could be especially problematic for AI workloads.

“Ionizing particles passing through satellites will burn out a transistor or flip a bit of information stored inside.”

He added: “This would result in the mother of all AI hallucinations without a software constantly checking results.”

To address that risk, spaceborne computers typically perform redundant calculations and continuously compare outputs to detect corrupted data. Systems aboard the International Space Station already employ such techniques, but extending them to AI data centers operating at multi-gigawatt scale would add further complexity and computational overhead.

Maintenance also poses difficult economic questions.

AI chips generally remain commercially competitive for only two to four years before being replaced by newer generations. On Earth, operators can routinely swap processors during scheduled maintenance. In orbit, replacing millions of aging chips would require repeated launch campaigns and robotic servicing technologies that remain largely experimental.

McManus also questioned Starcloud’s financial assumptions.

He argued that the project’s projected launch costs and payload estimates appear overly optimistic given current launch economics and the unprecedented mass involved.

He concluded that Starcloud appears designed more to capitalize on investor enthusiasm surrounding artificial intelligence than to solve near-term infrastructure challenges.

“This is just one early rushed concept to fundraise and move on,” he said.

He added: “In the ever evolving world of tech, first movers are being heavily rewarded.”

While orbital data centers remain a compelling long-term concept for some technologists, experts say terrestrial power grids, advanced cooling systems, and more efficient semiconductor designs are likely to remain the industry’s primary focus for years before computing in space becomes technically or economically viable.