Here is the pattern playing out across embedded teams right now: developer hears "edge AI," installs TensorFlow Lite Micro, gets inference working on a dev board, declares it a success, then hits a wall three months later when memory pressure, scheduling conflicts, and firmware drift compound into something much harder to unwind. The problem was not the framework. It was skipping the architecture layer that has to sit underneath it. Before any AI framework discussion is worth having, there are three foundational decisions that determine whether an embedded edge AI deployment will actually scale or quietly fail. Decision 1 — ISA selection: why RISC-V is winning the argument Proprietary ISAs work — until you need to customize the hardware pipeline for a specific AI workload, at which point licensing constraints and vendor roadmap dependency become real friction. RISC-V eliminates both.…