AI-Native Architecture
6G design principle embedding AI at every network layer — air interface, resource management, orchestration — rather than adding it as an afterthought.
AI-native is a design stance more than a feature, and the contrast that defines it is "native versus bolted-on." In 5G, AI mostly arrived as an add-on — NWDAF analytics layered onto a core that was designed without it in mind. AI-native flips that: you assume from the first architecture diagram that learning and inference are everywhere, from the air interface and resource scheduling up through orchestration and management.
The consequences run deeper than sprinkling models around. It means designing interfaces that expose the data models need, building in the compute to run inference where decisions happen, and treating model lifecycle management (the MLOps concerns) as a first-class part of the network rather than an afterthought. The motivation is that 6G's complexity and speed will outrun hand-crafted algorithms and human operators, so intelligence has to be structural. It's a guiding 6G principle today rather than a settled architecture, but it shapes a lot of the design thinking going into the next generation.
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