AI’s Energy Paradox: How More Compute Could Cut Industrial Emissions

Episode notes

Get in touch - leave me a message What if AI’s biggest climate impact isn’t chatbots, but cutting real energy waste in buildings, grids, and factories? In this episode of Climate Confident, I’m joined by Philippe Rambach , Chief AI Officer at Schneider Electric , to unpack one of the sharpest tensions in climate tech today: AI is increasing electricity demand, but used well, it may also be one of the tools we need for decarbonisation, emissions reduction, and a faster energy transition. You’ll hear why Philippe argues that the real opportunity is not in chasing every shiny new model, but in applying AI to physical systems: reducing peak demand, optimising building energy use, supporting grid operators, and helping companies move from pilots to production. We dig into Schneider Electric’s work on using AI to cut energy waste, including the striking claim that in some energy-saving applications, the carbon emitted to run the model can be dwarfed by the energy saved. We also get into the hard bits people love to ignore because apparently spreadsheets and wishful thinking are still considered strategy in some quarters. Why do so many AI pilots fail to scale?

• Electrification Is Now an Energy Security Strategy: Australia’s Diesel Risk, Solar Surge, and Battery Lesson72 / 100
• Animal Agriculture’s Stranded Asset Risk, and Why ESG Keeps Missing It
• No One Wants to Ship Water: The Energy Security Case for Flow Batteries
• Why Traditional Marketing Creates Greenwashing Risk in Sustainability
• Fake People, Real Projects Killed: AI Disinformation and the New Clean Energy Bottleneck