The Hidden Energy Cost of Artificial Intelligence
By Aashiva Kaul | Sanjay Kaul | 5 min read
AI workloads—from training large models to real-time inference—drive substantial electricity demand. Data centers, cooling, and specialized accelerators contribute to a growing energy footprint that often relies on carbon-intensive grids. The piece calls for quantifying energy costs in deployment decisions and comparing green alternatives. It highlights strategies to reduce consumption, including more efficient models, energy-aware scheduling, and renewable-energy sourcing. Policy and industry reporting frameworks could push AI providers toward transparent energy metrics.
