Episode 28 of The Data Science Podcast with Fexingo explores the growing energy footprint of data centers and AI training. Lucas and Luna break down a real 2025 case: a midwestern utility that had to fast-track two natural gas plants to power a single hyperscale campus. They discuss the gap between model efficiency gains and total compute growth, the role of on-site renewables, and whether the grid can keep up with demand from training runs. Listeners learn why your next model might be constrained not by memory or data, but by the local power substation. Plus, a brief note on how listener support keeps this show ad-free.

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