The $200/kg launch price target is based on 150 ton capacity. That’s a $30m launch costs target. Volume/foldability matters the most because that is the actual constraint that limits datacenter launch to a single NVL72 size.
It will never be an economic thing. Only unpluggable skynet military thing. The weight is not an issue. though. It’s volume.
Incredibly bad, too lenghty, mostly irrelevant criticisms of the fraud of space datacenters, followed up by the Skynet military justification of being unable to unplug skynet.
Space datacenters from SpaceX are a fraud because they have a 5 year lifecycle with deorbiting of entire unit. The costs compared to 30 year lifecycle of terrestrial solar/battery powered datacenter energy is thus 6x higher (costs of shell/shield, solar, radiators is about the same but 6 replacements). Terrestrial building costs are $20/watt. SpaceX ambitions are to get $30m/launch costs. To be only 2x the terrestrial costs, launch costs need to be $1m (just the fuel costs) with deorbit being to fly off into space instead of a salvage trip.
At 12x the costs, the competitive GPU rental hurdle has to be 12x more expensive than earth. Only military skynet applications would pay for this, and specifically, only permit mechahitler to decide if skynet is doing a good job.
The big AI lie is that compute capacity is scarce. The fraud was propped up by SpaceX announcing big rental deals with “friends” (google a big owner of spacex, with deal 1 week before IPO) that have massive exit clauses for the buyers.
AI hype started with Meta/Xai needing all of their compute for their own AI ambitions, and that was the source for compute scarcity. That they have no special competency means that they resell their compute, is flooding an already well supplied compute market.
Headline is innacuralte. Customers who purchase a $200/month subscription, can save up to $13800 on token costs compared to “a la carte” token pricing. Open AI does lose over $1 per $ in revenue, but this is more a function of them having too much compute, and very high training costs, rather than a loss per gpu hour on tokens served, it should stilll be a loss for OpenAI:
Open source model pricing per gpu hour, ranges from 80% margins at high batch saturation per user (low tps per user) to 50% at concurency of 8, to loss at fewer user requests per gpu. https://inferencex.semianalysis.com/compare/deepseek-r1-b300-vs-h200
OpenAI has very high prices, and too much compute. They would likely lose $6000 per user who maxes out their $200 plan, and certainly over $1000 per user.
what do 4 small stars on flag represent?
most of the fuel weight required is to lift the rest of the fuel. Fuel costs is about $1m for full load. Rest of cost is huge staff, maintenance, and capital cost of rocket.