This article explains how approximately 70% of AI workloads are moving to local embedded device processing by 2025. TinyML enables microcontrollers with less than 1MB of RAM to run sophisticated machine-learning models locally, eliminating latency, improving security, and reducing bandwidth requirements. The article features popular microcontroller platforms including ESP32-S3, Raspberry Pi Pico W, STM32 series, and Nordic Semiconductor nRF5340. It highlights how RISC-V open-source architecture is gaining momentum, with AI accelerator revenue projected to exceed $1.1 billion by 2025, and demonstrates real-world applications ranging from smart home AI cameras to industrial predictive maintenance systems.​