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Created: 4 hours and 10 minutes agoSelf destruction in 59 days, 19 hours<br>Theme:Tokyonight (Dark)Nord (Dark)Dracula (Dark)Taurient (Dark)Catppuccin (Dark)Cursor (Light)Flexoki (Light)

Self destruction in 59 days, 19 hours<br>Tokyonight (Dark)Nord (Dark)Dracula (Dark)Taurient (Dark)Catppuccin (Dark)Cursor (Light)Flexoki (Light)

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📄 orbital-compute

Orbital Compute

Last updated: 29 May 2026

Purpose and scope

Purpose : Track the development of orbital AI data centres as a proposed solution to terrestrial compute, power, and cooling constraints — and assess the investment implications for AI infrastructure, chip architecture, and terrestrial capex.

What belongs here :

The physics case for space-based compute (power, cooling, networking)

Engineering constraints and counterarguments to the bull thesis

The keystone economic assumption: launch cost per kg (Starship)

Market participants and announced orbital compute programmes

Investment implications for covered stocks and themes

What does NOT belong here :

Terrestrial AI capex and power demand forecasts → capex-infrastructure-and-power

Compute supply/demand balance → demand-vs-supply

Launch market structure and Starship economics (primary) → Launch market structure

Megaconstellation infrastructure and LEO economics → Megaconstellation economics

Company-specific investment theses → individual companies// notes

Sourcing standard : Every datapoint traces to a primary source (filings, transcripts, investor commentary, engineering publications). Promotional claims from vendors and early-stage companies are flagged. Wikilinks for internal cross-references; inline URLs for external sources.

Maintenance : Update as programme milestones, launch cost data, or new market entrants emerge. Date-stamp additions. Review when a major player announces a concrete programme.

Background: Why the question is being asked now

Terrestrial AI data centre expansion is running into three compounding constraints: power grid capacity and permitting timelines (often 4–7 years to new grid connections), cooling density limits in hot or water-stressed regions, and the cost and land footprint of building at the scale hyperscalers now require. See capex-infrastructure-and-power for the current capex and power demand picture.

Against this backdrop, a small number of investors and technologists — most prominently Gavin Baker (Atreides Management), and subsequently Elon Musk via SpaceX/xAI — have argued that Low Earth Orbit (LEO) is a structurally superior environment for AI compute. The argument is not new (space-based computing has been discussed for decades) but has become more investable as launch costs have fallen dramatically with Falcon 9 reusability, and Starship threatens to fall further still.

As of May 2026, this has crossed from thought experiment into early-stage capital deployment: multiple startups and at least one hyperscaler are publicly pursuing orbital data centre programmes.

The physical case: three structural advantages

1. Power: always-on, more intense solar

Solar irradiance in LEO is approximately 1,361 W/m² — roughly 30% more intense than at Earth's surface, with no atmospheric absorption or scattering. More importantly, in a sun-synchronous or other optimised orbit, a satellite can remain in continuous sunlight for extended periods, eliminating the day/night cycling that forces terrestrial solar installations to carry large battery reserves.

The investment implication: orbital data centres are not grid-constrained. The terrestrial bottleneck of substation queues, transmission build-out, and local permitting simply does not exist. This is the most compelling part of Baker's argument from an infrastructure scarcity perspective.

2. Cooling: passive radiation to deep space

The vacuum of space eliminates convective cooling, but provides a radiative heat sink at approximately 3 Kelvin (−270°C) on the shadow-facing side. A radiator mounted on the dark face of a satellite can passively reject heat without water, refrigerants, or energy-intensive compressors.

Terrestrial data centres spend heavily on cooling — water, mechanical systems, and the energy to run them. In principle, passive radiative cooling eliminates this opex category.

3. Networking: lasers through vacuum beat fibre

Light travels approximately 47% faster through vacuum than through glass (due to the refractive index of silica). Inter-satellite laser links (ISLs) — which SpaceX already uses commercially on Starlink — communicate at the vacuum speed of light. A dense constellation networked by ISLs would have lower latency and higher throughput per link than terrestrial long-haul fibre.

For distributed inference serving global users, this could meaningfully improve response latency, particularly for cross-continental requests.

The engineering constraints that complicate the thesis

Thermal...