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The AI landscape is undergoing a significant strategic shift from large, centralized, general-purpose models (LLMs) to an ecosystem of smaller, decentralized, specialized models (SLMs) operating at the network edge. This transition is driven by economic viability, performance requirements (low latency), privacy and security concerns, and the need for greater personalization. The future of AI architecture is a hybrid cloud-edge model, where foundational models are trained in the cloud and then distilled into specialized SLMs for edge deployment. This paradigm shift necessitates new architectural approaches like Federated Learning (FL), advanced model compression techniques, and deep hardware-software co-design to optimize for efficiency, particularly TOPS-per-Watt, rather than just raw computational power. This evolution will lead to an "agentic AI" future, characterized by autonomous, collaborating AI systems operating within a Zero Trust security framework, fundamentally altering competitive dynamics and requiring a re-evaluation of data gravity.