• Google introduces a comprehensive framework to measure AI’s environmental impact
• Efficiency gains cut energy use per Gemini Apps text prompt by 33x in just one year
• Data center emissions dropped 12% in 2024 despite 27% growth in electricity demand

Google is sharpening its focus on the environmental cost of artificial intelligence, releasing its first comprehensive methodology for measuring the energy, water, and carbon footprint of its AI models.

“AI represents one of the most significant technological transformations of our time,” the company said, adding that its applications — from diagnosing cancer to predicting wildfires — will require robust energy infrastructure and more efficient systems to be sustainable.

Executives framed the move as both an accountability step and a competitive edge. Until now, detailed data on AI inference impacts has been scarce across the industry. By publishing its framework, Google aims to set a benchmark for transparency as AI’s energy demand grows.

The company reported rapid progress in efficiency: over the past year, the median energy consumption and carbon footprint per Gemini Apps text prompt fell by factors of 33x and 44x, respectively. “The energy consumed per median prompt is equivalent to watching television for less than nine seconds,” Google noted.

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These gains are tied to investments across multiple fronts — from scaling next-generation clean energy and designing smarter grids, to optimizing every layer of its operations. Google pointed to custom-built hardware, energy-aware software, and data center efficiency as core drivers. In 2024, the company cut emissions from its global data centers by 12% despite a 27% rise in electricity consumption as business expanded.

“Transparency is key to progress,” the company said. “We hope this study contributes to ongoing efforts to develop efficient AI at this critical time for energy, sustainability and scientific discovery.”

For Google, the challenge is balancing AI’s transformative potential with its energy footprint — a tension that investors, regulators, and peers will increasingly scrutinize as AI scales globally.

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