Source: SiliconANGLE
The shift from cloud-centric to edge-deployed AI workloads is creating hard architectural constraints: robots and autonomous systems require real-time processing that can't tolerate latency from round-trip calls to distant data centers, forcing chipmakers and infrastructure providers to embed processing power directly at the point of action. This is fragmenting the unified cloud computing model that defined the last decade. Companies now maintain parallel stacks for centralized analytics and distributed edge inference, each with different hardware, networking, and operational requirements. Infrastructure providers who can bridge this gap will gain advantage; those whose business models depend on centralizing workloads will not.