As AI models balloon in size and complexity, their voracious appetite for computational power clashes with real-world energy and hardware limitations. A groundbreaking arXiv paper introduces Frugal Machine Learning—a paradigm shift prioritizing efficiency in training and inference through input optimization, process streamlining, and model compression. This approach isn't just technical refinement; it's essential for deploying AI at the edge, where IoT devices demand radical resource awareness.