Akamai has spent nearly three decades distributing infrastructure closer to where users and data live. Now AI inference is giving that architecture a new job. Akamai’s John Alexander and Ari Weil join to discuss the shift from centralized AI factories to distributed inference, the emergence of the AI grid, and the infrastructure required to make real-time AI work at scale. They dig into orchestration across CPUs and GPUs, latency and data locality, networking and power constraints, and why data engineering becomes increasingly difficult as inference spreads across locations. The conversation also explores how Akamai, VAST Data and NVIDIA are bringing distributed compute, high-speed data pipelines and a global namespace together to create a more unified architecture for real-time and agentic AI.