📉 An AI prompt uses less energy than nine seconds of TV – 33-fold reduction in one year

• A median text prompt in Gemini consumes 0.24 Wh of energy, equivalent to less than nine seconds of TV watching.
• Google's AI infrastructure has reduced energy consumption by 33 times and carbon footprint by 44 times over one year.
• The study shows that previous estimates of AI energy consumption may be inaccurate because they don't measure real production usage.

Energy consumption significantly lower than previous estimates

Google has published the first comprehensive study measuring environmental impact from AI services in a real production environment. Researchers examined energy consumption, carbon emissions, and water usage for Google's Gemini AI assistant through detailed monitoring of the company's AI infrastructure.

The results show that a median text in Gemini consumes 0.24 Wh of energy. This is substantially lower than many public estimates ranging from 0.3 Wh to 6.95 Wh per prompt. To put this in perspective, a modern TV consumes approximately 100 watts, meaning 0.24 Wh equals less than nine seconds of TV watching.

The study compared two measurement methods. The existing narrow method, similar to previous benchmark studies, showed 0.10 Wh per prompt. The comprehensive method that includes the entire production environment showed 0.24 Wh per prompt. The difference highlights the importance of measuring the entire AI infrastructure, not just the active AI accelerators.

Comprehensive measurement of the entire AI stack

Google's method includes four main components of energy consumption. Active AI accelerators account for 58 percent of total energy. Host CPU and DRAM memory use 25 percent. Idle machines needed for high availability and low latency consume 10 percent. Data center overhead from cooling systems and power conversion represents 8 percent.

This holistic view of energy measurement differs from previous studies that often focus only on GPU energy during benchmark tests. The researchers argue that this method provides a more accurate picture of AI services' actual environmental impact.

Dramatic improvements over one year

The most striking finding is the enormous efficiency improvements over time. Between May 2024 and May 2025, energy consumption per prompt decreased by 33 times. The carbon footprint dropped by 44 times for median prompts in Gemini.

The improvements come from several areas. Smarter model architectures like Mixture-of-Experts activate only a small part of large models for each prompt, reducing computations by a factor of 10-100. Efficient algorithms and quantization use narrower data types to maximize efficiency without compromising response quality.

Optimized inference and serving include techniques like Speculative Decoding that serve more responses with fewer AI accelerators. Custom-built hardware like Google's TPUs are designed for higher performance per watt. The latest generation Ironwood is 30 times more energy-efficient than the company's first publicly available TPU.

Low environmental impact compared to other activities

A median prompt in Gemini generates 0.03 grams of CO2 equivalents and consumes 0.26 milliliters of water. Water consumption equals five water drops, which is significantly less than previous estimates of 45-50 milliliters per prompt.

Google's Water Risk Framework from 2023 ensures that all new data centers in water-stressed areas use air cooling during normal operations. This is expected to further reduce water consumption as older facilities reach end of life.

Significance for future AI development

The study underscores the importance of standardized and comprehensive measurement methods for AI's environmental impact. Previous estimates have varied by an order of magnitude for similar tasks, hindering transparency and accountability.

The researchers identify three main factors behind the differences between their results and previous estimates. In-situ measurements in real production environments provide more accurate results than theoretical models. Existing measurements of AI inference often use open-source models that may not represent the latest efficiency technology. AI inference in production environments can be more efficient than benchmark experiments through economies of scale and better prompt batching.

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