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Not All AI is Created Equal: The Case For Climate-Positive Machine Learning
AI's energy footprint has become a legitimate concern, from growing data centres, training runs and increased inference at scale, the threats to rising carbon emissions are readily reported across media. But the conversation rarely distinguishes between AI that consumes energy and AI that saves it. This session makes that distinction clearly and argues it matters enormously for how the sector thinks about AI investment and regulation. Open Climate Fix presents forecasting as a case study in climate-positive AI: models that are deliberately lightweight, trained on open data, and deployed to solve problems, curtailment, balancing costs, grid stability, where better predictions directly reduce emissions. The session challenges us to ask not just "how much energy does this AI use?" but "what is it actually for?"
