AI is becoming the next engine of national productivity, but the way AI compute infrastructure is built cannot scale fast enough. Urban cores have capital and demand, but their grids are full. Rural markets can handle training, but sit too far from where AI is used. The regions in between have power, land and the will to revive, but remain structurally undercapitalized. The takeaway from our 4 part series is that inner-ring suburbs are the answer. These regions are the only places that combine power, labor, and proximity at scale.

The Inference Districts is our framework for directing capital into the overlooked geography best positioned to scale compute capacity. It is not a conventional data center or real estate thesis, but a deployment model for building the next layer of compute where power, labor, and demand can align at scale. As AI shifts from training to inference, this layer becomes the backbone of where productivity gains are realized and where the next phase of economic growth concentrates.