Meta announced two energy partnerships on April 28 to power its next-generation AI data centers. The first reserves up to 1 GW of space-based solar capacity from startup Overview Energy, whose geosynchronous satellites would beam near-infrared light down to existing terrestrial solar farms so they keep producing electricity at night. The second reserves up to 1 GW / 100 GWh of long-duration storage from Noon Energy, which uses reversible solid-oxide fuel cells and carbon-based storage to discharge for more than 100 hours.
Both technologies are pre-commercial. Overview's first orbital demonstration is scheduled for 2028, with U.S. grid delivery 'as early as 2030.' Noon's pilot is a 25 MW / 2.5 GWh facility, also targeted for 2028. Meta is therefore not buying power today — it is locking in option value on two unproven supply paths. That option pricing is itself the news: hyperscalers are now hedging across nuclear, geothermal, gas, terrestrial solar, and now orbital and ultra-long-duration storage simultaneously.
The pressure is straightforward. Meta has guided to $115–135B of capex in 2026, much of it AI infrastructure, and is co-located with the rest of the industry in a queue for grid interconnection that already runs years. Inference and training workloads are 24/7 and largely flat in shape, which is exactly the load profile intermittent renewables struggle to serve without storage. Reserving a hundred hours of duration, even speculatively, is a way to make a future where AI runs on clean firm power technically possible.
Takeaway for learners: AI's bottleneck is shifting from chips to electrons. If you are choosing where to build skills, energy systems engineering, grid interconnection, and long-duration storage are quietly becoming part of the AI stack. The deal a hyperscaler signs in 2026 with a startup that has not yet shipped a demo is the kind of bet that will define which models can be trained in 2030.