Abstract

Several major technology companies have announced plans to operate AI data centers in orbit. Elon Musk recently claimed: “the lowest-cost place to put AI will be space […] within two years, maybe three.” If a meaningful fraction of new AI compute really is placed in space within a few years, that would be a fairly big deal for AI governance and strategy. Here we try to disentangle the hype from reality and provide a sober assessment of the technical and economic feasibility of orbital data centers (ODCs).

The main case for ODCs is the cost of energy: space solar panels in the right orbits receive more constant and intense sunlight compared to Earth. Moreover, ODCs don’t currently face the same permitting and regulatory delays as on Earth, cause fewer ongoing environmental harms compared to grid or onsite natural gas-powered data centers, and may be more secure against data exfiltration. We find that the cost-competitiveness case for ODCs depends almost entirely on Starship achieving reusability comparable with what SpaceX achieved with Falcon: space-based solar reaches cost parity with present-day off-grid terrestrial power continuously at roughly $250/kg to orbit, and becomes cheaper than any current terrestrial energy source at around $50/kg, from the present-day launch cost of roughly $1,500/kg. Radiative cooling, often cited as a fatal obstacle, appears surprisingly manageable — potentially even cheaper than on Earth. However, ODCs may require substantial (perhaps ~38%) extra non-compute hardware (like solar, racks, and cooling) over 5 years to compensate for their inability to swap out failed chips, and inter-satellite bandwidth limitations likely confine ODCs to inference workloads, at least early on.

Assuming no transformative AI,^[1] but continued demand for data center buildout, we estimate that ODCs are unlikely to represent a meaningful share of compute before 2030, but become cost-competitive with present-day terrestrial data centers within 3–5 years if Starship development stays on track.

Read on the Forethought website here

Introduction & Takeaways

Some of the world’s largest technology companies continue racing for compute. If progress continues, demand for data centers may more than double by 2030.^[2] Increasingly, though, new data center capacity is bottlenecked by multi-year queues to connect to the power grid.^[3]

The result has been a scramble for workarounds. Leading AI labs have increasingly adopted a “Bring Your Own Generation” model to source power, deploying onsite gas turbines and engines to bypass grid bottlenecks. xAI, for example, reportedly installed hundreds of megawatts of onsite gas generation in Memphis to accelerate deployment, and OpenAI and Oracle have placed large turbine orders for new Texas campuses.

Some argue that energy will become the binding constraint on AI progress, given grid interconnection delays as gas turbines are themselves facing multi-year manufacturing backlogs. But the constraint does not appear fundamentally binding (as Epoch notes): turbine manufacture may expand to meet more demand and companies could go off-grid using combinations of gas, solar, and batteries, scaling power in parallel with compute, albeit at a cost premium. This raises a natural question: if you’re going off-grid anyway, then what’s the best way to get power and where is the best place to put your data center?

Some think the answer will be in orbit. In November 2025, Google announced Project Suncatcher, a plan to put TPU-equipped satellites in dawn-dusk sun-synchronous orbit. In early 2026, SpaceX filed with the FCC for authorization to launch and operate a constellation of up to one million data center satellites.^[4] Other entrants include Blue Origin, Ramon.Space and startups like Starcloud, and Aetherflux while China’s Three-Body Computing Constellation has launched 12 operational satellites and run Alibaba’s Qwen3 model in orbit. Recently, at GTC in March 2026, NVIDIA announced the Space-1 Vera Rubin Module, meant to be a dedicated space-rated GPU platform.

At first glance, it seems very unlikely that any meaningful fraction (say, >10%) of additional data center capacity will be placed in space in the next few years. But if the companies betting on space are right, that would be a fairly big deal, and it could change the landscape of AI governance. For example, terrestrial data centers are subject to national and regional regulations, whereas AI developers could potentially exploit jurisdictional ambiguities around compute in space. Also, the path to low-cost orbital compute likely routes through a single launch company, SpaceX, which also now operates a frontier AI lab since its acquisition of xAI. And that might raise concerns around concentration of power.

We’ve been looking into the technical and economic viability of orbital data centers (ODCs). Our core model gives estimates for the total cost of Earth and space-based data centers across several scenarios.

Cost breakdown for three Earth-based and three space-based scenarios building out 1 GW of compute. As best we can determine, orbital data centers could become cost competitive with a bullish terrestrial buildout if launch cost reaches around $100/kg given modest reductions to server and cooling system mass, while a bullish case for orbital data centers with substantial mass reductions and launch at $50/kg may offer cost savings.

The report focuses on three questions. First, what is the basic economic case for a meaningful fraction of AI compute being placed in space? Second, the most obvious physical blocker: can you cheaply cool a data center in orbit? Third: how fast could the shift to space data centers happen, how soon, and what would have to go right?

Here is our provisional assessment:

• SpaceX’s Starship is the only vehicle currently on track to deliver the launch costs and cadence that meaningfully scaling orbital data centers would require. Competitors are years behind, making SpaceX’s Starship the only near-term path to large-scale orbital compute. SpaceX aims to complete Starship development by late 2026, with several necessary milestones still ahead. If development stays roughly on track, Starship could plausibly hit the cost and cadence required to scale meaningful orbital compute within 3–5 years. However, chip production may become the limiting factor by this point, rather than launch capacity.
• The cooling problem is more tractable than commonly assumed. Passive radiators using selective coatings and lightweight carbon fibre panels could achieve ~163–346 W/kg at system level, a 13-28× improvement over ISS-era radiators (~13 W/kg).^[5] No radiator at these performance levels has been deployed at the scale an orbital data center would require, but prototype high-conductivity carbon composite panels have demonstrated the material properties required. At these performance levels, thermal hardware is 2-5% of total data center cost, and actually less than what terrestrial data centers spend on cooling over a comparable lifecycle.
• If launch costs fall enough, the unit economics could favor space. Solar panels in dawn-dusk sun-synchronous orbit produce roughly 3–5× the energy of the same panel at a good terrestrial site.^[6] Space-based solar becomes cheaper than the best off-grid terrestrial installations once launch costs drop below roughly $250/kg using Starlink-like solar arrays. At a launch cost of $50/kg (corresponding perhaps, to a Starship with full reuse as reliable as Falcon), space solar could fall to between $25–45/MWh, making it cheaper than any current terrestrial option available today.^[7] Beyond the symmetric cost of chips, launch cost is the dominant line item for ODCs while power and op-ex dominate terrestrial costs but would be near zero in space.
• The inability to do maintenance would be a large cost. Chips often fail and are swapped out in today’s data centers but a dead chip in an ODC would remain dead, wasting the parts of the supporting infrastructure (power, cooling) and diminishing overall compute. We model this below as a 9% annual bleed causing about 40% overbuy of launch and non-chip hardware over the data center’s lifetime.^[8] Below $100/kg launch cost this might net out against other savings from ODCs but this is a significant uncertainty since the actual rates of chip failure for ODCs could be higher or lower.
• All-things-considered we think that, absent transformative AI, orbital data centers probably won’t make up a meaningful fraction of compute before 2030, but it’s credible that space could house much or even the majority of compute buildout throughout the 2030s.

Read the full report on the Forethought website: Will We Really Put Data Centers in Space?

1. ^{^}

   We hope to do more analysis on how transformative AI might change this picture in the future. Speculatively, our initial thinking is TAI could accelerate the timeline over which compute transitions to space but this is not necessarily the case. In particular, during an industrial explosion pressure to grow rapidly might be so strong as to incentivize aggressive usage of non-renewables on Earth like oil and gas. If so, transition to space might be delayed for a one time boost on Earth, in which case the picture may look similar to the one we outline here, but with the added prologue of a large-scale terrestrial buildout.

2. ^{^}

   McKinsey projects demand growing to 171–219 GW by 2030, roughly doubling from today, in a buildout they estimate will require up to $7 trillion in investment.

3. ^{^}

   See, directing FERC to address large-load interconnection (2025), Reuters, Google says US transmission system is biggest challenge for connecting data centers (2026), Bain & Co., Next phase of data center growth (2025).

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Starcloud subsequently filed for authorization to operate 88,000 satellites and Blue Origin has filed for 51,600.

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The ISS External Active Thermal Control System achieves roughly 13 W/kg. Our improvement comes from three sources: selective coatings (high emissivity, low solar absorptivity, off-the-shelf AZ-93 paint), carbon fibre composite construction (2.4 kg/m² vs ISS’s ~14-17 kg/m²), and optimised operating temperature (40°C vs ISS’s -40°C, exploiting the T⁴ dependence). Each factor is independently demonstrated; their combination at scale is not.

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The solar constant at Earth’s orbit is approximately 1361 Wm^-2. A solar panel in a dawn–dusk sun-synchronous orbit receives nearly continuous illumination (capacity factor ≈ 90–95%), yielding an average power of roughly 1220–1290 Wm^-2 before panel efficiency losses. By contrast, even excellent terrestrial solar sites typically achieve ~20–30% capacity factors due to night, weather, and atmospheric attenuation, corresponding to an average incident power of roughly 270–410 Wm^-2. Thus, a panel in a dawn–dusk orbit produces roughly 3–5× more energy annually than the same panel on Earth.

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This wouldn’t be true if you were then beaming the energy back to Earth, but would apply to orbital compute, where only data needs to be sent to Earth.

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Both terrestrial data centers and ODCs will pay symmetric costs to replace dead chips but ODCs would have to pay the additional cost from lost overhead, i.e. in the earthbound case a technician swaps the dead chips, in the space case you launch entire additional satellites to compensate for chip bleed. We assume you would not send a mission to do maintenance and instead simply let the excess power and cooling go to waste doing no useful compute. Extra power and cooling over fewer chips may increase operating efficiency somewhat but this seems fairly negligible. The figure for chip bleed of ~9% per year is derived from Meta’s The Llama 3 Herd of Models (2024). We cover radiation and other forms of damage in more detail subsequently.

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