Over the last few years, enterprises bought GPUs as if capacity alone would solve AI. Now they’re running into a different constraint. The next generation of workloads—agent-driven, multistep and constantly in motion—isn’t limited by the amount of compute available. It’s limited by how effectively that compute, data and connectivity are coordinated. We’ve already seen this shift play out. AI workloads are becoming more distributed across clouds, providers and locations. That distribution is forcing enterprises to rethink infrastructure architecture. AI execution increasingly depends on being able to place compute, data and connectivity together across a globally interconnected environment—not inside a single…

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