A ChatGPT query uses about ten times as much electricity as a Google search.

Credit to Alex Erben and Ege Erdil for substantial help with research and calculations. In this issue, “we” refers to our collective judgment. A commonly-cited claim is that powering an individual ChatGPT query requires around 3 watt-hours of electricity, or 10 times as much as a Google search.1 This is often brought up to express concern over AI’s impact on the environment, climate change, or the electric grid. However, we believe that this figure of 3 watt-hours per query is likely an overestimate. In this issue, we revisit this question using a similar methodology, but using up-to-date facts and clearer assumptions. We find that typical ChatGPT queries using GPT-4o likely consume roughly 0.3 watt-hours, which is ten times less than the older estimate. This difference comes from more e

InfrastructureHow much energy does Google’s AI use? We did the mathAugust 21, 2025Amin VahdatSVP and Chief Technologist, AI and InfrastructureJeff DeanChief Scientist, Google DeepMind and Google ResearchTry Gemini Enterprise todayThe front door to AI in the workplaceTry now AI is unlocking scientific breakthroughs, improving healthcare and education, and could add trillions to the global economy. Understanding AI’s footprint is crucial, yet thorough data on the energy and environmental impact of AI inference — the use of a trained AI model to make predictions or generate text or images — has been limited. As more users use AI systems, the importance of inference efficiency rises. That’s why we’re releasing a technical paper detailing our comprehensive methodology for measuring the energy,

Of course this isn’t the same thing as an AI system completely autonomously updating its own code, but nevertheless this is a larval version of recursive self-improvement.There are other self-reinforcing loops at play. The economic value creation has started a flywheel of compounding infrastructure buildout to run these increasingly-powerful AI systems. And robots that can build other robots (and in some sense, datacenters that can build other datacenters) aren’t that far off. If we have to make the first million humanoid robots the old-fashioned way, but then they can operate the entire supply chain—digging and refining minerals, driving trucks, running factories, etc.—to build more robots, which can build more chip fabrication facilities, data centers, etc, then the rate of progress will

DiscussionWith reasonable and somewhat pessimistic assumptions, a GPT-4o query consumes around 0.3 watt-hours for a typical text-based question, though this increases substantially to 2.5 to 40 watt-hours for queries with very long inputs. As shown in the charts below, this is somewhere between a negligible to a small portion of everyday electricity usage.There is a lot of uncertainty here around both parameter count, utilization, and other factors—you can find more estimates with a wider range of assumptions in this spreadsheet, as well as sources for the other everyday uses of electricity. I’ve tried to err on the side of pessimism (higher energy costs) with every assumption, but different assumptions about parameter counts, utilization, and token output can bring the cost into the 1 to

1. A point-in-time analysis quantified the energy consumed per median Gemini App text-generation prompt, considering data from May 2025. Emissions per prompt was estimated based on energy per prompt, and applying Google’s 2024 average fleetwide grid carbon intensity. Water consumption per prompt was estimated based on energy per prompt, and applying Google’s 2024 average fleetwide water usage effectiveness. These findings do not represent the specific environmental impact for all Gemini App text-generation prompts nor are they indicative of future performance.2. The results of the above analysis from May 2025 were compared to baseline data from the median Gemini App text-generation prompt in May 2024. Energy per median prompt is subject to change as new models are added, AI model architect