Measuring the Environmental Impact of Delivering AI at Google Scale

What was actually measured — A comprehensive “serving AI computer” boundary: active AI accelerators, host CPU/DRAM, idle machines kept for reliability/latency, and data‑center overhead via PUE. Networking, end‑user devices, and training are excluded. Figure 1 illustrates the boundary choices.

Key numbers for a median text prompt (May 2025) — 0.24 Wh energy, 0.03 gCO2e market‑based emissions, 0.26 mL water. This is roughly less energy than watching TV for 9 seconds and about five drops of water. Table 1 breaks down contributions: accelerators 0.14 Wh, host 0.06 Wh, idle 0.02 Wh, overhead 0.02 Wh.

Why do many estimates differ? Narrow accelerator‑only approaches undercount. The paper shows a 1.72× uplift from accelerator energy to total serving energy when you include host, idle, and overhead. In a benchmark‑like “existing approach,” the same prompt would appear as 0.10 Wh.

Year‑over‑year efficiency gains — From May 2024 to May 2025, median per‑prompt emissions fell 44× driven by software/model improvements (33× energy reduction, including 23× from model changes and 1.4× from utilization), cleaner electricity (1.4×), and lower amortized embodied emissions (36×).

How to use these metrics — The authors argue for median per‑prompt reporting to avoid skew from long or low‑utilization prompts, and for standardized, full‑stack boundaries so providers can compare models and target the biggest levers across software, hardware, fleet utilization, siting, and clean‑energy procurement.