How much energy and water does one AI query actually use — and how does that compare to things you already do every day? This is a genuinely contested question: credible public estimates disagree by 10–30× depending on methodology, so this tool shows ranges with sources, not single truths — here’s what’s actually known, and what isn’t. Hover (or keyboard-focus) any bar or dot for the exact figures and the source behind them.
Estimate basis (applies to all range-based charts below):
Put an AI task on a familiar scale
Pick an AI task. Its estimated energy and water use (colored bar, with the whisker showing the low–high range across published estimates) is placed on the same axis as everyday activities (gray bars). Note the log scale — each gridline is 10× the previous one.
Show this task as:
Low-confidence figure: this video estimate comes from one open academic model (CogVideoX) on research hardware. Commercial models like Sora or Veo have never disclosed per-video figures — treat this as a rough order of magnitude only.
Selected AI task — energySelected AI task — waterSelected AI task — carbonEveryday comparison (context)Low–high range of estimates
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Energy per task
Water per task
Water figures for AI mix on-site cooling and (for some sources) lifecycle/indirect water — see each bar’s source note.
Carbon per task
Why this says 0.09 g when Google says 0.03 g — market-based vs location-based carbon
AI task vs. doing it yourself
Each row compares the device-side energy of an AI query against roughly 20 minutes of active laptop use — a stand-in for doing a simple task manually. Shared log axis.
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AI query (energy)Manual: 20 min of laptop use
Read this chart with four caveats
The laptop runs regardless. You don’t power your laptop down the moment you stop typing, so the manual-task figure is marginal, not truly avoided, energy. The comparison overstates the manual cost.
Substitution cuts both ways. One AI answer might replace several manual searches (favoring AI) — or AI’s near-zero cost per query might create queries that would never otherwise happen at all (the rebound effect, disfavoring AI). Both are real, and this comparison cannot tell you which dominates for any given use.
This only counts device-side electricity. It says nothing about water use, or about where the electricity and water are drawn — a data center’s footprint is concentrated in one community; your laptop’s is not.
It says nothing about community-level impacts — local grid strain, water stress in specific regions like Phoenix — which is the actual subject of the WSJ piece this course read. Per-query math cannot answer a siting question.
When the agent costs more than the person
In July 2026 METR proposed the expenditure horizon — the dollar amount at which an AI agent’s progress on a task equals what a human researcher achieves for the same spend. Above that point the human is more cost-effective. METR measured it on a hard ML-engineering task. Putting the same comparison on an energy axis gives a different answer than the dollar one.
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AI agentHuman researcherLow–high range of estimates
Both bars are the electricity used at the same $3,300 of spend — the point where METR measured Claude Opus-4.8’s progress matching a human researcher’s. Same money, same result, very different electricity.
Expenditure horizon, Claude Opus-4.8
$3,300
The dollar crossover. Below it the agent wins on cost; above it the human does. GPT-5.5 crossed at $2,300.
The agent’s electricity at that same spend
≈15×
Central estimate. The plausible range spans roughly 3× to 75×, driven mostly by one unpublished number: how hard the 32 GPUs were actually working.
Models that made no validated progress at all
2 of 4
GPT-5 and Opus-4.1 achieved a ~$0 expenditure horizon after re-validation. Their energy per unit of progress is not favorable — it is undefined.
Read this chart with five caveats
A dollar of compute is not a dollar of labor. The two axes diverge because a dollar spent on GPU time buys on the order of 15× more electricity than a dollar spent on a person’s time. Cost parity and energy parity are different thresholds, and a metric built for one does not answer the other.
This is one task, and an unusually GPU-hungry one. Progress on the NanoGPT speedrun requires running training experiments, so both sides are dominated by H100 time rather than by thinking. The ratio measures experimental efficiency per unit of insight, not “AI versus humans” in general.
The direction flips for ordinary work. The chart directly above shows a single AI query using far less energy than twenty minutes of doing the task by hand. Task type decides the answer. Nothing here says AI is generally the higher-energy option, and nothing in the chart above says it is generally the lower one.
METR calls its own harness “likely inefficient.” The agents had continuous reserved access to the GPUs, which invites wasteful experimentation. A better-run agent would land lower. This is a first-generation setup and should be expected to improve.
The largest uncertainty was never published. METR reported the hardware (4 nodes, 32 H100s) and the time limit (5 days) but not how hard those GPUs were actually working. That single assumption moves the agent estimate by about 3×, which is more than every other assumption combined.
Right-sizing: your choices matter
For a simple query, a frontier model can use anywhere from about 3× to 100× the energy of a small model that would answer it just as well — roughly 3–5× between adjacent model classes under production serving, rising toward 100× when a reasoning model works a long prompt. Model choice is still the largest per-query lever a user controls.
Small modelFrontier model
Efficiency is improving fast
33×
more energy-efficient per median prompt (and 44× lower carbon), May 2024 to May 2025.
Source: Google, 2025
…but don’t read that stat alone (Jevons paradox)
But over the same stretch Google’s own totals kept climbing: data-center electricity rose 27% in 2024 and a further 37% in 2025, and water consumption reached 10.9 billion gallons in 2025, up 34%. Its greenhouse-gas emissions rose 18% in 2025 — the largest annual increase it has reported. A textbook Jevons paradox: far more efficient per query, yet the total footprint still grows because usage grew faster than efficiency improved.
Zooming out: the aggregate numbers
Per-query numbers are small; the aggregate footprint is not. It is real, it is growing fast, and it is concentrated in particular places. These are the headline figures, stated plainly.
Where the growth is actually coming from
It is volume, not per-query cost
Energy per prompt is falling quickly. Total tokens processed is rising far more quickly, and increasingly from programmatic and agentic traffic rather than people typing.
How much of electricity demand growth is data centers?
The answer depends entirely on which system you are looking at — which is why this question produces so many contradictory headlines.
The short version
Capacity is not consumption
…and it is wrong
Why, in order of size
The transferable lesson
What this tool does not measure
Why it still matters
Why do the public numbers disagree so much?
Estimates for the same task differ by 10–30× in the public record. That isn’t (mostly) anyone lying — it’s six methodological choices that compound. Expand each one.
What we genuinely don’t know
Epistemic humility is the point of this section, not a footnote. Anyone quoting precise figures for the items below is estimating, not reporting.