Quantifying Human Impact on AI Energy Efficiency

This research fundamentally shifts how we measure AI efficiency by quantifying the human element as a core variable in the inference equation. For developers running models locally, it proves that guided human intervention significantly reduces energy waste, making consumer-grade hardware viable for practical AI tasks. The framework introduced opens new possibilities for optimizing real-world AI deployment beyond what pure autonomous inference can achieve.