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The self reinforcing loop of the decade
Compute shifts from training to inference as agent deployment scales → agents require persistent, low-latency compute → that style of compute requires more baseload power → power demand collides with a grid that can’t be built fast enough → resulting energy bottleneck becomes the rate-limiter on scaling → this drives continued sustainable energy R&D and labor force pressure → we augment labor force with physical AI at roughly the time that robotics is becoming performant enough to help → robots themselves are inference endpoints and present another layer of inference demand → this feeds back into the original compute and energy constraint
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