
Thermodynamic Space Supercomputers
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The global AI infrastructure build-out is colliding with hard physical limits on Earth. ● Power Constraints: Data center demand is projected to rise 165% by 2030, creating 2+ year waits for grid interconnection and transformers. ● Thermodynamic Inefficiency: Earth-based centers waste ~40-60% of their energy fighting heat (PUE ~1.58) and consume billions of gallons of water. ● Data Friction: High egress fees ($0.08–$0.09/GB) and bandwidth bottlenecks fragment distributed training runs.