arXiv:2511.19468cs.DC
Towards a future space-based, highly scalable AI infrastructure system design
Abstract
If AI is a foundational general-purpose technology, we should anticipate that demand for AI compute -- and energy -- will continue to grow. The Sun is by far the largest energy source in our solar system, and thus it warrants consideration how future AI infrastructure could most efficiently tap into that power. This work explores a scalable compute system for machine learning in space, using fleets of satellites equipped with solar arrays, inter-satellite links using free-space optics, and Google tensor processing unit (TPU) accelerator chips. To facilitate high-bandwidth, low-latency inter-satellite communication, the satellites would be flown in close proximity. We illustrate the basic approach to formation flight via an 81-satellite cluster of 1 km radius, and describe an approach for using high-precision ML-based models to control large-scale constellations. Trillium TPUs are radiation tested. They survive a total ionizing dose equivalent to a 5 year mission life without permanent failures, and are characterized for bit-flip errors. Launch costs are a critical part of overall system cost; a learning curve analysis suggests launch to low-Earth orbit (LEO) may reach $\lesssim$\$200/kg by the mid-2030s.
Yes for now; Alphabet is optimistic costs will come down. They're projecting the learning curve, that the more you do it the cheaper it gets, continues arbitrarily far. However, they recon it will take SpaceX launching 370,000 tons to LEO to make the costs come down enough to be…
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As per another of my comments, Alphabet, applying learning curves arbitrarily far into the future, recon it will take SpaceX launching 370,000 tons to LEO to make the costs come down enough to be worth it: https://arxiv.org/pdf/2511.19468 That's not even $100/kg, that's $200/kg.…
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