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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Yes—every AI search uses electricity, but a typical text prompt is a small amount. Google reported a median of 0.24 watt-hours (Wh) for a Gemini Apps text-generation prompt using May 2025 data. A long reasoning or agentic task can use substantially more: a 2026 Microsoft Research study estimates more than an order-of-magnitude increase for those workloads. There is no standard energy figure for a consumer feature called “deep research,” so the amount depends on what the system actually does.
How much electricity does a quick AI question use?
Recent published estimates put a simple text query below one watt-hour. Google reported a median of 0.24 Wh per Gemini Apps text-generation prompt, based on a point-in-time analysis using May 2025 data. Separately, Microsoft Research estimated a 0.31 Wh median for optimized frontier-scale inference, with an interquartile range of 0.16–0.60 Wh. These are estimates for different systems and methods, not two measurements of the same prompt. Google’s methodology and results and Microsoft Research’s 2026 study describe their respective figures.
A watt-hour is the energy used by a one-watt device running for one hour. Fractions of a watt-hour are therefore small for an individual task, but they are not zero—and the estimate changes with the model, prompt, amount of generated text, and infrastructure counted.
Why can a deep dive use more power?
A quick question may be answered with one short generation. A task that reasons for longer, generates more tokens, or acts in multiple steps requires additional computation. Microsoft Research’s 2026 study estimates that long reasoning and agentic queries can use more than an order of magnitude more energy than typical queries. The International Energy Agency (IEA) also says some newer reasoning and agentic workloads can consume hundreds or thousands of times the energy of simple text generation. Those are broad workload comparisons, not a multiplier that applies to every long prompt or product feature. The IEA’s 2026 executive summary discusses the range of use cases.
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One illustration—not a measurement of a commercial deep-research feature—comes from a September 2025 Microsoft Research preprint. In its modeled test-time-compute scenario, using 15 times more tokens than a typical query raises estimated median energy to 4.32 Wh, or 13 times the typical-query estimate. It helps show how additional reasoning can change the energy budget, but does not establish what any named AI service uses. The preprint’s scenario and assumptions are available online.
Quick prompt and deep-dive estimates at a glance
| Task or estimate | Energy figure | What it represents |
|---|---|---|
| Gemini Apps text-generation prompt | 0.24 Wh median | Google-reported estimate using May 2025 data; full-system accounting. Google Cloud, 2025 |
| Optimized frontier-scale inference query | 0.31 Wh median; 0.16–0.60 Wh interquartile range | Microsoft Research estimate for optimized production-scale inference, published in 2026. Microsoft Research, 2026 |
| Long reasoning or agentic task | More than an order-of-magnitude increase is estimated; some broader workload comparisons reach hundreds or thousands of times simple text generation | Workload-level findings from Microsoft Research and the IEA; not a universal figure for “deep research.” Microsoft Research, 2026; IEA, 2026 |
| Modeled high-token test-time-compute scenario | 4.32 Wh median | Microsoft Research preprint scenario using 15 times as many tokens as a typical query; estimated at 13 times typical-query energy, not a commercial feature measurement. Microsoft Research authors, September 2025 |
Why published estimates differ
What parts of the system are counted
Google’s 0.24 Wh estimate includes accelerator use, host CPU and memory, idle provisioned capacity, and data-center overhead. If it counts only energy used by active TPUs or GPUs, Google gives a narrower figure of 0.10 Wh for the median prompt and describes it as an underestimate of the full operational footprint. The gap illustrates why a number without a measurement boundary can mislead. Google’s technical paper describes its accounting approach.
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Which workload and deployment are measured
Prompt length, model routing, reasoning effort, and the amount of generated output all affect the work performed. Serving context matters too: Microsoft’s 2026 estimate concerns optimized frontier-scale inference under realistic large-scale deployment assumptions. It should not be read as a measurement of every model, provider, or experimental setup.
When the estimate was made
These values are dated snapshots, not permanent characteristics of AI. Google reported that median Gemini prompt energy fell 33-fold between May 2024 and May 2025, reflecting changes in the service and its infrastructure. Google also says its figures are not independently verified and do not represent every Gemini text-generation prompt or predict future performance. Google’s published estimate and qualifications explain the scope.
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What do those figures mean for carbon and water?
For the same median Gemini prompt, Google estimated 0.03 grams of carbon dioxide equivalent using its 2024 fleet-average grid carbon intensity, and 0.26 milliliters of water using its 2024 fleet-average water usage effectiveness. These are company estimates tied to fleet averages, not location-specific impacts for every prompt. Electricity, carbon emissions, and water are related but distinct measures; a watt-hour figure alone does not determine the local carbon or water impact. Google’s report gives the methodology and caveats.
Does a small per-query figure mean AI has little energy impact?
No single prompt figure answers the question of total electricity demand. The IEA estimates that data centers used 485 terawatt-hours (TWh) of electricity in 2025 and projects roughly 950 TWh in 2030. It also estimates that converting all conventional internet searches to simple AI text queries would use less than 4 TWh annually—less than 1% of current data-center consumption. That hypothetical concerns simple text queries; it does not cover every energy-intensive AI use, such as video generation or extended reasoning. The IEA’s figures and scenario distinguish per-task efficiency from fleet-wide demand.
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The IEA says simple text queries now typically consume less electricity than running a television over the same period. At the same time, AI use is expanding into workloads that can require much more computation. Efficient individual queries and growing overall demand can both be true.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Can you measure the electricity used by your own AI prompt?
Not with a household plug-in meter or circuit monitor. Those devices measure electricity used by equipment in your home, not the remote servers handling a prompt. The figures available here are provider or research estimates based on system-level data and assumptions; they are not a direct meter reading for a particular person’s query.
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