Our approach to energy innovation and AI’s environmental footprint
AI represents one of the most significant technological transformations of our time, which will become more obvious in the coming decade. Applied to fields like medicine, energy, autonomous systems and quantum computing, AI is poised to help people address major societal challenges, whether that’s helping students learn, diagnosing cancer earlier, making complex transportation and cybersecurity systems safer, or even predicting the path of wildfires for first responders.
Realizing the potential of AI will require robust energy infrastructure, more efficient energy use, and even new innovative technology solutions. We’re approaching this from many angles — investing in new infrastructure, engineering smarter and more resilient grids, and scaling both mature and next-generation sources of clean energy. At the same time we are also focused on maximizing efficiency at every layer of our operations — from the design of our custom-built hardware to the software and models that run in our data centers.
In order to improve the energy efficiency of AI, a clear and comprehensive understanding of AI’s environmental footprint is important. To date, comprehensive data on the energy and environmental impact of AI inference has been limited.
Today, we are helping to close this gap by releasing a comprehensive methodology for measuring the energy, water, and carbon emissions of Google’s AI models. Critically, our work on model efficiency is delivering rapid progress. Over a 12-month period, while delivering higher-quality responses, the median energy consumption and carbon footprint per Gemini Apps text prompt decreased by factors of 33x and 44x, respectively. Based on our recent analysis, we found that our work on efficiency is proving effective and the energy consumed per median prompt is equivalent to watching television for less than nine seconds. These advancements build upon our long-standing commitment to data center efficiency. In 2024, for example, we reduced our data center energy emissions by 12% even as electricity consumption grew by 27% year-over-year, driven by the expansion of our business and services.
As we continue to invest in the technology and innovation needed to meet significant new energy demands, transparency is key to progress. We hope this study contributes to ongoing efforts to develop efficient AI at this critical time for energy, sustainability and scientific discovery — to benefit everyone. Read more about how we got to the math and our full stack approach to energy innovation and dive into our technical report.
