Microsoft AI GPUs face power shortage; OpenAI pushes for 100 gigawatts of new US power generation per year
Satya Nadella previously said Microsoft does not have enough power to supply all its AI GPUs, and chips may sit idle because they cannot be connected to power. Citing content from the Bg2 Pod, Rohan Paul pointed out that the bottleneck is not chip supply, but the lack of "warm shells"—completed data center buildings—near large power sources, and that OpenAI is pushing the US government to add 100 gigawatts of power generation per year as an AI strategic asset. Power delivery and construction require multi-year approval and construction cycles, while GPU supply expands on a quarterly basis, so GPUs that are waiting depreciate and become stranded capital.
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Explains, from the perspective that power delivery and construction cycles are far slower than GPU supply, why compute expansion may create stranded capital.
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View original postElon Musk: compute will sit stranded in half-built data centers. > Nscale’s IPO filing (Sept 2026): As of Aug 31, about 25,000 active GPUs against 461,000 active and contracted. Roughly 95% of the GPUs this neocloud has committed to are not yet running. > Wood Mackenzie, July 2026 report: Generator step-up transformer lead times exceeded 160 weeks in Q1 2026, up from 143 weeks in 2024, and high-voltage circuit breakers rose to 125 weeks versus 77 weeks in 2023. GPUs ship in months. The gear that energizes them takes two to three years, so any project that ordered chips before locking in electrical equipment is exposed. > Building your own gas power no longer lets AI builders skip the power queue, because turbine makers are booked years ahead. GE Vernova (Q2 2026): Its gas equipment backlog and slot reservations grew from 100 GW to 116 GW in one quarter. A turbine ordered today won’t arrive until around 2031. On-site power, the usual way around grid queues, has its own multi-year queue. arkolith
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Elon Musk: compute will sit stranded in half-built data centers. > Nscale’s IPO filing (Sept 2026): As of Aug 31, about 25,000 active GPUs against 461,000 active and contracted. Roughly 95% of the GPUs this neocloud has committed to are not yet running. > Wood Mackenzie, July 2026 report: Generator step-up transformer lead times exceeded 160 weeks in Q1 2026, up from 143 weeks in 2024, and high-voltage circuit breakers rose to 125 weeks versus 77 weeks in 2023. GPUs ship in months. The gear that energizes them takes two to three years, so any project that ordered chips before locking in electrical equipment is exposed. > Building your own gas power no longer lets AI builders skip the power queue, because turbine makers are booked years ahead. GE Vernova (Q2 2026): Its gas equipment backlog and slot reservations grew from 100 GW to 116 GW in one quarter. A turbine ordered today won’t arrive until around 2031. On-site power, the usual way around grid queues, has its own multi-year queue. arkolith
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Satya Nadella already said last year that the company doesn’t have enough electricity to power all its AI GPUs. He said they might end up with a pile of chips just sitting around because there’s not enough power to plug them in.
The issue is not supply of chips but missing “warm shells” (empty but utility-ready building ), meaning finished data center buildings near large power feeds.
OpenAI is pushing the U.S. government to add 100 gigawatts per year of generation as a strategic asset for AI.
This power gap creates stranded capital since GPUs depreciate while waiting for buildings, substations, and transmission to be ready.
Even if training stays centralized, inference demand is the big driver for electricity, so any shift of queries to efficient edge devices would change grid and data center sizing assumptions.
The main problem is, power delivery and construction schedules run on multi-year permitting and build cycles, while GPU supply ramps in quarters.
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From 'Bg2 Pod' YT channel