Starcloud's Philip Johnston: Why the Cheapest Compute Will Be in Space

Energy and cooling limit data center growth. The talk reports a data-center-grade GPU running and training a model in orbit, and argues about the economics, which informs thinking about future compute supply.

• Philip Johnston says space solar removes the two largest costs of an Earth solar build, permitted land and battery backup, and needs about 8x fewer cells. He puts the break-even launch cost near $500/kg, roughly 10x below today, versus Starship's $10-20/kg design target.
• Johnston's radiator math: panels yield about 200 W per square meter while a radiator at 50 C sheds about 800, so radiator area is roughly a quarter of panel area. Because dissipation scales with temperature to the fourth power, running at 80 C nearly halves it.
• Starcloud handles radiation bit flips mainly through ground testing, according to Johnston: four rounds of proton testing at a Knoxville cyclotron plus heavy-ion runs at Brookhaven, compressing about 5 years of dose into 24 hours to guide shielding and software choices.
• Johnston says the planned 88,000-satellite constellation (about 200 kW each, roughly 20 GW total) targets inference only. Large-scale training would need a gigawatt-scale structure about 4 km by 4 km that he estimates is at least 15 years away.
• On debris risk, Johnston argues low orbits limit Kessler-style cascades: Starcloud's first satellite flies near 400 km and deorbits naturally within a few months, and he cites SpaceX running about 10,000 satellites with sophisticated collision avoidance and no collisions.

• inference — Running a trained model to get answers — the phase where AI is actually used, as opposed to trained.

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