{"href":"https://api.simplecast.com/oembed?url=https%3A%2F%2Fshared-everything.simplecast.com%2Fepisodes%2Fthe-economics-of-ai-beyond-earth-dImK4iYH","width":444,"version":"1.0","type":"rich","title":"The Economics of AI Beyond Earth","thumbnail_width":300,"thumbnail_url":"https://image.simplecastcdn.com/images/ffdb3e35-ef24-4933-8ca3-b2448234e81f/b597f6ee-b37b-4aa7-a060-ea8f97cc9431/set-dark-stacked.jpg","thumbnail_height":300,"provider_url":"https://simplecast.com","provider_name":"Simplecast","html":"<iframe src=\"https://player.simplecast.com/5397de62-013e-4292-bae2-fb26a5331a47\" height=\"200\" width=\"100%\" title=\"The Economics of AI Beyond Earth\" frameborder=\"0\" scrolling=\"no\"></iframe>","height":200,"description":"On this episode of the Shared Everything podcast Nicole sits down with Dan Nishball, Ellie Holbrook, and Harley Blackard of SemiAnalysis to explore one of the most ambitious ideas in AI infrastructure: space datacenters. Using SemiAnalysis's orbital compute cost model and detailed report as a starting point, the conversation examines the real economics behind deploying AI infrastructure beyond Earth, including launch costs, cooling, hardware reliability, and power. Along the way, the group discusses the constraints already shaping AI growth on the ground, from semiconductor supply and grid access to labor, datacenter construction, and energy infrastructure. Rather than asking whether space datacenters are possible, the discussion focuses on a more practical question: under what conditions would they become economically rational?"}