Putting AI compute in orbit is no longer a slide deck. On October 1, 2026, the first prototype satellite of Project Suncatcher lifted off aboard a SpaceX Falcon 9. The fridge-sized spacecraft carries four of Google's own Trillium TPUs and runs an open-weight version of Gemma to answer simple queries, but each session is capped at 15 minutes. Heat dissipation is the bottleneck.

This is not a stunt. Google's research preprint spells out the full system design. The biggest enemy of on-orbit inference is not radiation — it is heat. There is no air or water cooling in vacuum, so chips must shed their heat through radiators, and those radiators are among the heaviest components on the satellite. That is why Suncatcher today can only run short bursts, and why Google's own blog post calls cooling "a crucial research challenge."

Why TPU + a satellite constellation

Google is not trying to build one giant satellite. The plan is a scalable compute layer: 81 satellites flying in formation at 650 km altitude in a dawn–dusk sun-synchronous orbit, neighbors separated by only 100 to 200 meters, linked by free-space optical lasers. The bandwidth target is data-center grade. A bench-top prototype at Google has already demonstrated 800 Gbps each way, 1.6 Tbps total, from a single transceiver pair — promising, though still short of the multi-terabit goal.

Will the TPU survive space radiation? The team blasted Trillium (Google's sixth-generation v6e TPU) with a 67 MeV proton beam to test total ionizing dose and single-event effects. The result was striking: no hard failures up to 15 krad(Si) on a single chip. The most sensitive component, HBM memory, only started showing irregularities past 2 krad(Si). A shielded five-year mission is expected to absorb only 750 rad(Si), so Trillium has roughly an order of magnitude of margin — rare for a commercial AI accelerator.

The economics

Launch cost has always been the biggest barrier to space-based compute. Google's analysis, using historical and projected pricing data, projects launch prices falling below $200 per kilogram by the mid-2030s if the current learning rate holds. At that point, the cost of putting an equivalent kilowatt of compute in orbit becomes roughly comparable to the electricity bill of running that same compute in a terrestrial data center. That, more than any single technical milestone, is the bet Suncatcher is making.

The next step is already locked in. In early 2027, Google will launch two more prototype satellites in partnership with Planet, the Earth-observation company, to validate TPU operation in real orbital conditions for an extended period and to test whether distributed inference over laser inter-satellite links actually works. Only after that does the larger engineering deployment — tens to hundreds of satellites — come into view.

How does this compare to the rest of the field

Space-based AI data centers are not a Google-only race. Last November, Starcloud put an Nvidia H100 on a satellite and demonstrated a slimmed-down version of Gemini. SpaceX has filed plans with the SEC to begin deploying "orbital AI compute satellites" as early as 2028. From paper to orbit, Google is currently furthest along, but "furthest along" and "commercially viable" are very different places. Brandon Lucia, a professor of electrical and computer engineering at Carnegie Mellon, put it bluntly: it sounds like science fiction, but most of the technology has already been proven at lab scale. The things that actually block deployment are heat, reliability, and the inability to walk over and swap a chip when something fails.

So today's Suncatcher is not the answer — it is the question list. The 15-minute duty cycle, the radiator mass, the optical link bandwidth, the radiation margin: each item still needs real on-orbit data. Google calls it a "moonshot," and the label is honest. The payoff, if it ever comes, is at least a decade away, and even in the best case space compute will not replace terrestrial data centers — it will sit alongside them as an additional layer.

For anyone watching AI infrastructure, the real takeaway from Suncatcher is not the "space data center" tag. It is the set of engineering trade-offs the project exposes so starkly: chip power vs radiator mass, link bandwidth vs formation control precision, radiation hardening vs manufacturing cost. As AI compute moves from the ground into orbit, those same trade-offs will resurface, in more extreme form, on every roadmap that touches them.

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