AI’s real bottleneck isn’t compute. It’s distance.

Enterprise AI's critical bottleneck is not computational power but data proximity and latency—organizations must move compute closer to sensitive data rather than continuing to centralize AI workloads in distant cloud centers to comply with governance requirements, accelerate iteration cycles, and reduce costs. The emergence of deskside AI supercomputers enables teams to run sophisticated models locally while maintaining data sovereignty and governance control, fundamentally shifting IT architecture from cloud-centric to a hybrid model where edge compute handles iterative work and cloud resources scale for frontier model training. This represents a strategic pivot for IT leaders: the competitive advantage now goes to organizations that can balance immediate, local AI experimentation with the cloud's unlimited scale, requiring new infrastructure, governance, and resource allocation strategies.