The Frontier

Anthropic has been supply-constrained on compute, restricting user tokens and cutting off research compute, forcing them to lease Colossus 1 - a 300MW, 220,000 GPU data center - from SpaceX to lift capacity limits.

Anthropic previously enforced a policy to cut off competitors like XAI from using its models, but the compute deal has reopened communication between them.

SpaceX’s vertical integration from chip fabrication to model inference reduces its risk when leasing out compute, as it can more rapidly rebuild capacity.

Building a gigawatt-scale AI data center costs roughly $60B: $19B for the facility, $30B for GPUs, and $11B for other IT equipment like CPUs and networking.

A gigawatt of leased compute infrastructure generates about $15B annually; a model provider operating on that scale can generate $30B in revenue.

OpenAI’s revenue scaled with compute; they generated $20B on roughly 2 gigawatts early this year, implying about $20B per effective gigawatt for inference.

Revenue per watt is increasing as model utility and enterprise willingness to pay rise, creating pricing power for both infrastructure providers and model companies.

Space-based AI compute economics hinge on Starship launch costs; at $300/kg, launching a gigawatt costs $7.5B, beating terrestrial facility costs.

Manufacturing satellites on a production line offers cost efficiencies over building unique terrestrial data centers, where $5B of the $19B facility cost is labor.

Compute scarcity on Earth means AI companies like Anthropic would pay a premium for space-based watts even before launch costs break parity, valuing velocity over price.

ARK analysts project SpaceX could begin scaling space-based AI compute in 2028-2029, reaching tens of gigawatts per year in the early 2030s.

Small modular nuclear reactors are more likely to scale in the US than gigawatt plants, fitting incremental demand like offsetting retired coal plants.