Core Insights - The emergence of edge AI in AIoT applications compresses the traditional "perception-communication-decision-execution" loop into a single terminal device, granting "on-site decision-making power" for the first time in the industry [1][2] - Edge AI represents not just a technological upgrade but a complete restructuring of industrial logic and value chain distribution, transforming AIoT from a linear process to a closed-loop system [1][2][15] Restructuring of Device Forms - The role of devices is shifting from "sensing + communication" to "autonomous decision-making," allowing for a new category of intelligent devices [2][3] - The processing capabilities of edge AI enable devices to evolve from "passive observers" to "active decision-makers," fundamentally changing their function and significance [4][5] Restructuring of Business Models - The business model is transitioning from "selling hardware + cloud platform" to a "scene-defined hardware under smart service subscription" model, emphasizing ongoing revenue streams rather than one-time sales [2][6][8] - Edge AI allows for the monetization of algorithms at the terminal level, shifting the profit focus from hardware premiums to the value of intelligent services [6][11] Restructuring of Industrial Division of Labor - The AIoT industry is moving from a linear value chain to a "cloud-edge collaboration, soft-hard integration" networked ecosystem, necessitating a shift in how companies operate and collaborate [9][10] - The new ecosystem requires companies to possess "soft-hard collaborative" capabilities, as data, computing power, and algorithms must evolve in tandem [10][11] Restructuring of Value Distribution - The value center in the AIoT industry is migrating from the cloud to the edge and terminal sides, with hardware becoming the physical entry point for intelligent ecosystems [11][12] - Companies that define hardware will gain access to high-value data entry points, positioning themselves as primary beneficiaries in the new ecosystem [11][13]