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Story U.S. Marine Corps photo by Cpl. Joaquin Dela Torre. The architecture surrounding edge artificial intelligence (AI) – including sensors, networks, hardware, software stacks and orchestration layers – is still being built in silos.
Artificial intelligence (AI) is a pillar of innovation, but its rapid growth comes with an emerging energy crisis. Data centers, the backbone of cloud-based AI, already consume 1 percent of global electricity. Goldman Sachs research projects this demand will surge by 165 percent by 2030 compared to 2023 levels, largely due to AI workloads. As organizations race to scale AI deployments, these energy and environmental issues become increasingly critical.
This isn’t about choosing between cloud or edge as distinct domains. Instead, it’s recognizing a spectrum of resources, from sprawling cloud data centers to far-edge devices like sensors in a factory or cameras on a battlefield. This continuum redefines AI deployment, replacing rigid, one-size-fits-all systems, with a dynamic strategy that adapts to specific workloads. From a self-driving car that needs to react in a split-second to a smart city optimizing traffic flow in real-time.
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