
AI is rapidly emerging as the next dominant network workload, necessitating a fundamental architectural shift from static, siloed systems to dynamic, AI-native networks. Unlike previous transitions from voice to data and video, which focused on increasing capacity, AI workloads introduce extreme traffic variability and require deterministic connectivity for machine-to-machine engagements. This evolution demands a "glass box" approach, integrating compute, control, and connectivity into a unified, programmable fabric capable of distributed inferencing. Key innovations, such as AI-RAN and the deployment of AI-ready edge nodes, are already underway to support this transition. By moving away from vendor-locked, hardware-centric models toward software-defined, open architectures, operators can transform networks into execution platforms that process both bits and tokens, ultimately enabling the low-latency, secure, and sovereign infrastructure required for the future of physical AI and autonomous systems.
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