↩ (@princeofprawns0) I made a first principles, physics based, end-to-end model and interactive simulation room for orbital compute. I broke down the Stefan-Boltzmann radiator temps, radiator mass budgets, failure curves, orbital repair capacity, and the real power-to-compute frontier. I forced the idea to stand on real engineering: heat rejection, shielding, launch economics, silicon learning rates, carbon intensity, and survival probability derived from actual hazard functions. I also tried to enumerate the innumerable “Elon Effect” and other exponential scaling laws.

 This model represents an interactive world where you can visualize the Earth and space, click on satellites, and watch a living breathing snapshot of orbital compute. I visualized AI-driven routing logic that decides where workloads run based on physics: thermal limits, bandwidth, uptime, and marginal cost. When you look at the numbers, it becomes clear that the hardest parts are autonomous maintenance at scale and deploying massive thin-film radiators. Under the right engineering, you do get scenarios where space compute actually beats Crusoe in Texas. If you still don’t buy it after looking at the simulator and charts, you can stress test the physics yourself in the sandbox. Tweak the parameters and assumptions and watch how well your proposition holds up. My personal opinion? Over 90% of AI compute will be in space within 10 years. If anyone’s serious about wanting to learn more, my DMs are open.