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A median text prompt to Google’s Gemini Apps used an estimated 0.24 watt-hours of electricity, produced 0.03 grams of CO₂e and consumed 0.26 milliliters of water in Google’s May 2025 analysis. Those small per-prompt figures are real, but they do not describe AI’s full environmental toll. The larger concern is the expanding infrastructure behind billions of requests: data centers, power plants, cooling systems, chips and construction. AI is not best understood as a uniquely ruinous individual act; it is a fast-growing industrial system whose total costs remain difficult to measure.
What does a single AI prompt actually cost?
Google’s estimate is a useful example of why prompt-level numbers need a boundary. For a median text prompt in Gemini Apps, Google calculated 0.24 Wh of electricity, 0.03 gCO₂e and 0.26 mL of water in May 2025 using its comprehensive serving methodology. These are company-reported figures for a particular service, workload, infrastructure and geographic mix—not a universal average for AI. Google’s methodology and results also show how accounting choices matter: its narrower method estimated 0.10 Wh per median prompt, while the comprehensive method included host CPU and DRAM, idle machines and data-center overhead.
There is no dependable universal conversion such as “one AI query equals ten web searches.” A request’s footprint depends on the model and hardware, prompt and response length, reasoning workload, server utilization, cooling, location and electricity mix. Text, image, audio and video generation are different workloads; embodied emissions from manufacturing may also be excluded from an operational estimate. A small text prompt is not a proxy for a long response or a generated video.
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The International Energy Agency estimates that data centers worldwide used about 415 terawatt-hours (TWh) of electricity in 2024—roughly 1.5% of global electricity use—and projects approximately 945 TWh by 2030. These totals cover all data centers, not AI alone. AI is the most important driver of expected growth, alongside other digital services, but the IEA figures should not be relabeled as AI’s exact electricity use. The IEA’s outlook includes the facilities’ computing and supporting infrastructure, not just the energy used to train models.
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AI clusters can concentrate many specialized accelerators in one place, with high power needs for computing, networking and cooling. Facilities also need capacity for storage, non-AI cloud services and machines that consume power while idle. Training is only one part of the picture: models may be experimented on, fine-tuned, evaluated and updated, then serve requests continuously. For a widely used system, inference—the repeated work of answering users—can become a substantial part of lifetime energy demand; there is no single training-to-inference ratio that applies to every model.
Why local electricity impacts can be severe
A global share of 1.5% can obscure where demand lands. Data centers cluster around suitable land, fiber connections, power access and business hubs. The IEA says nearly half of U.S. data-center capacity is concentrated in five regional clusters. A large new load in one area can require transmission upgrades, complicate grid planning and intensify disputes over water, land, noise, reliability and who pays for infrastructure. In some places it can increase pressure to keep fossil-fuel plants running or add generation.
For the United States, Lawrence Berkeley National Laboratory’s 2025 update models data centers reaching 11.8% of national electricity use in 2030 in its reference case, equivalent to 649 TWh. Its modeled range is 9.5% to 15.3%; a sensitivity case reaches 782 TWh under changed assumptions about AI-server utilization, idle power, specialized chips and chip lifetimes. These are projections, not measured future consumption, and apply to data centers overall. LBNL’s report makes the uncertainty important: actual demand depends on how quickly facilities are built and how intensively their equipment is used.
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A data center physically draws electricity from a local grid. A company can also buy renewable-energy certificates or sign power-purchase agreements (PPAs) that support generation elsewhere or at another time. Those contracts can help finance clean energy, but annual accounting matches do not necessarily mean renewable electricity was available in the facility’s region during every hour it ran. Hourly matching, local grid carbon intensity, new versus existing generation, and backup power all affect the physical picture.
The IEA’s supply analysis uses the fuel mix of electricity physically consumed by data centers rather than operators’ contractual portfolios. It identifies natural gas as the largest current source for U.S. data-center electricity, at over 40%, followed by renewables, nuclear and coal. Globally, its base case expects renewables to meet nearly half of additional data-center electricity demand through 2030, while gas and coal together could supply more than 40% of that increase. The IEA’s supply outlook is a forecast, not a guarantee about any individual facility.
Google says it contracted more than 12 GW of net-new clean energy in 2025. That is meaningful procurement, but the company notes that contracted quantities may differ from actual generation because projects can change, terminate or perform differently than expected. Google’s 2026 Environmental Report, covering 2025, therefore supports a claim about contracts, not proof that each Gemini request was powered by newly generated renewable electricity at the moment it ran.
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Water has several different meanings
“AI water use” can refer to distinct things that should not be added together or compared without matching boundaries:
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- On-site withdrawal: Water taken into a facility, some of which may be returned.
- On-site consumption: Water not returned to the same watershed, often because evaporative cooling uses it.
- Indirect power-sector water: Water consumed or withdrawn to generate the electricity a data center uses.
- Manufacturing water: Water used to make semiconductors and other hardware, often outside the data-center site.
Google’s 0.26 mL-per-prompt figure is its estimate of water consumption for a median Gemini text prompt under its May 2025 methodology. It is not a measure of all water associated with AI supply chains or a general figure for other services. At the other scale, an Associated Press account of a United Nations University assessment reported about 1.2 trillion gallons of indirect water use through energy production for data centers in the assessment’s reported year. The AP noted that the assessment focused on energy-related impacts and did not fully examine the large amount of water used for cooling. The AP report also describes a projection of roughly 935 TWh of data-center electricity use by 2030, a different estimate from the IEA’s approximately 945 TWh outlook.
Those figures are not contradictory: one is a workload-specific estimate and the other an aggregate estimate of indirect water tied to data-center energy. Billions of small requests, plus training, image and video generation, cooling, power generation and manufacturing, can add up. Local water stress matters too: an amount that is modest in a global total can still compete with community needs in a dry region. Air cooling can reduce on-site water consumption, but may require more electricity for cooling; hydropower or nuclear power can lower operational carbon without removing all water, construction or supply-chain impacts.
Chips and buildings carry costs beyond electricity
AI infrastructure also requires accelerators, memory, storage, networking equipment and buildings. Their production entails semiconductor fabrication, critical minerals, water, energy, steel, concrete and other materials. The IEA flags critical-mineral demand as an energy-security concern associated with data-center expansion. Its analysis of AI and energy security places computing hardware within that wider supply-chain picture.
Hardware replacement rates and end-of-life handling affect the footprint as well. Faster upgrades can mean more manufacturing and discarded equipment; reuse and recycling can help but do not erase the impacts of making new chips. Operational emissions reports do not necessarily communicate the full embodied footprint. Scope 3 estimates—emissions across suppliers and products—depend on supplier data, allocation methods and assumptions about useful life, so corporate totals can differ materially without representing a simple like-for-like comparison.
Efficiency is improving, but total impact can still grow
Google reports that emissions per median Gemini text prompt fell 44-fold between May 2024 and May 2025. That is evidence of substantial per-task efficiency progress under Google’s methodology. It does not establish that Google’s total AI-related emissions fell. When each task becomes cheaper, organizations may add AI to more products, users may make more requests, and workloads may grow longer or shift to images, video and automated agents. This rebound effect means intensity and total consumption can move in opposite directions.
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Google’s 2026 environmental reporting describes a 37% annual increase in electricity demand in its 2025 reporting context, alongside emissions reductions associated with efficiency and procurement. The comparison underscores why per-prompt progress cannot stand in for system-wide results. Google’s account of its 2025 environmental performance is a company disclosure, not a standardized industry-wide measure.
Other reported corporate metrics need their own context. Google says it replenished 7.7 billion gallons of water in 2025, equivalent to about 78% of its reported total freshwater consumption, and diverted 88% of data-center operational waste from disposal. It also reports 58 million metric tons of avoided CO₂e across operations and its supply chain. The report defines avoided emissions against a counterfactual in which specified actions were not taken; they should not be subtracted from actual emissions as though they were physical reductions in the same place and time. Replenishment is not the same as eliminating water withdrawal or local consumption.
Can AI’s climate benefits outweigh its footprint?
AI can contribute to grid forecasting and demand management, building and industrial efficiency, materials discovery, weather and flood forecasting, routing, methane detection, renewable integration and agricultural optimization. Those applications may reduce environmental harm, but a claim of net benefit needs a fair comparison, not just a list of possible uses.
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To judge a benefit, ask whether it is additional or would have occurred without AI, whether the same time period and lifecycle boundary are used, and whether the result lasts. Also ask whether the task could have been done efficiently without AI, who receives the benefit, and whether lower costs trigger more consumption. AI used for a climate purpose is not automatically climate-positive.
What better AI environmental disclosure would show
Companies, cloud providers and regulators need comparable disclosures that let communities and customers see both per-task intensity and total system demand. Useful reporting would include:
- Energy use by model and workload, separating training, experiments and inference.
- Facility-level water withdrawal and consumption, with local water-stress context.
- Hourly electricity use and grid mix, alongside contracts and certificates.
- Embodied hardware emissions, supplier assumptions, equipment lifetimes and replacement rates.
- Data-center utilization, idle capacity and facility overhead.
- Scope 3 boundaries and methods, plus uncertainty ranges.
- Claimed climate benefits with explicit counterfactuals and the same accounting period as the footprint.
Without standardized, model-by-model and site-by-site reporting, public estimates should be read with their date, workload boundary, geography and method attached. Corporate averages can hide the impact of a particular campus, grid region or water basin.
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