Well, divide et impera. Fairly straightforward for AI inference (not training): The existing Starlink constellation:

3491 V1 sats × 22.68 m² = 79176 m²

5856 V2-mini sats × 104.96 m² = 614 646 m²

Total: 0.7 km² of PERC Mono cells with 23% efficiency.

At around 313W/m² we get 217MW. But half the orbit it's in shade, so only ~100MW.

The planned Starship-launched V2 constellation (40k V3 sats, 256.94 m²) comes out at 10 km², ~1.5GW.

So it's not like these ideas are "out there".

Take those 40,000 satellites, and combine their solar panels, and combine the cooling panels, and centralize all the compute.

Distances are not our friend in orbit. Efficiency hyperscales down for many things, as distances and area scale up.

Things that need to hyperscale when you scale distance and area:

• Structural strength.

• Power and means to maneuver, especially for any rotation.

• Risk variance, with components housed together, instead of independently.

• Active heat distribution. Distance is COMPOUNDING insulation. Long shallow heat gradients move heat very slowly. What good does scaling up radiative surface do, if you don't hyperscale heat redistribution?

And you can't hyperscale heat distribution in 2D. It requires 3D mass and volume.

You can't just concatenate satellites and get bigger satellites with comparable characteristics.

Alternatives, such as distributing compute across the radiative surface, suffer relative to regular data centers, from intra-compute latency and bandwidth.

We have a huge near infinite capacity cold sink in orbit. With structural support and position and orientation stabilization for free. Let's use that.