There’s a power shortage across the US and globally. By 2028, the power shortfall will hit 10GW that’s equal to the energy used by 7.5 million households. This demand is expected to grow exponentially by 2030. Back in July, London Economics International dropped a report saying that meeting U.S. data center projections for 2030 would require 90% of the global chip supplya scenario they called “unrealistic.”
How do we meet this demand? If you break it down to first principles from easiest to hardest: the easiest scenario is looking at companies like $IREN that have contracted power and a massive pipeline. There are a bunch of companies that fit this thesis—$IREN, $CIFR, $RIOT, Galaxy Digital, etc. But even after you’ve tapped all the available capacity from these operators, there’s still a shortfall. What do you do next? Plus, I wonder how inference demand changes over time once we move past the current RL (reinforcement learning) phase.
Again, using first principles, I believe demand will get more localized in regional towns rather than giant sites like West Texas. When you think about regional towns and the spike in inference demand, it feels like the legacy server rooms in most businesses—like a hospital that stores records on-site or in the cloud—will be replaced by localized data centers on-prem housing high-density racks. This is where the thesis pivots to Edge computing.