Few — if any — technologies have been adopted as quickly as artificial intelligence (AI). Since that computation runs on electricity, discussions around AI often return to what this means for energy demand.
These concerns tend to take three forms. One is environmental: growing energy demand for artificial intelligence will drive increases in carbon emissions, and make it harder to decarbonize. Another is the impact on local communities: high electricity demand could strain local supplies and push up energy prices. The third is that AI’s energy consumption could be the technology’s bottleneck; for those who want to see it expand, this could be a key limiting factor.
How much energy, then, does AI consume?
We can look at this question on two levels. The first is the total amount of electricity AI uses. The second is about individual impact, or how much electricity each query consumes.
In this article, I try to answer both of these questions.
Before digging into the data, it’s worth clarifying what is included in AI energy consumption. It’s the electricity consumed for both training and running the models (called “inference”). Tech companies rarely publish data on how much energy is consumed when training their models, but based on the estimates we do have, it’s likely that energy demand is dominated by inference, not training.
The estimates we’ll look at include the electricity used specifically for the servers, plus additional energy used for cooling, lighting, and other things needed to keep the data centers running. They don’t include the energy used to power the device — a laptop, desktop, or phone — that someone is using to access AI. Importantly — and this matters when comparing to other sources — they do not include demand from cryptocurrency mining.
How much of the world’s electricity is used for data centers and artificial intelligence?
Let’s start with the big picture. How much electricity did data centers consume last year?
According to the International Energy Agency (IEA), around 485 terawatt-hours (TWh). That’s equivalent to the annual electricity generation of Germany. And for context, that’s around 1.5% of the world’s electricity generation.
Now, data centers are more than just AI: they’re facilities that contain the servers and IT infrastructure behind all of our digital services. That’s everything from email and Internet browsing to Netflix streaming, Google Maps, online banking, and messaging friends.
AI data centers are dedicated facilities for running AI models. The distinction between the two is not always clean-cut, but the chart below shows an estimated breakdown.
Non-AI data centers consumed two-thirds of the total, and AI-focused ones, the remaining third. Based on this data from the International Energy Agency, I estimate that AI consumed around 0.5% of the world’s electricity in 2025.
Electricity currently accounts for around one-fifth of the world’s primary energy consumption. Hence, AI likely consumed 0.1% to 0.2% of total primary energy in 2025.
Given how quickly demand for AI has been increasing, it might be surprising that AI-focused data centers consume less, on aggregate, than non-AI ones. Projections expect this gap to close quickly. In the chart, I’ve also included the IEA’s base-case projection of data center demand in 2030.
These projections are highly uncertain, and some have argued that the IEA is among the most conservative in its assessment of AI demand growth. Even so, most of the growth in data center demand will come from AI-focused facilities. In this scenario, data centers grow to 3% of global electricity in 2030, and AI centers then use about the same amount as non-AI ones.
Above, we looked at estimates of global electricity demand for data centers from the International Energy Agency, one of the most widely used sources on energy data.
However, other sources also publish estimates of data center demand. Another of these comes from S&P Global, which is used and reported by the Energy Institute. Its latest Statistical Review of World Energy included estimates of data center demand for the first time.
In the chart below, I’ve shown both sources from 2020 to 2025. This means we’re comparing historical estimates only, not projections, where differences between sources are far larger.
As you can see, there are large differences between them. S&P’s figures for 2025 are around 60% higher, but have been consistently higher for the entire time series.
Neither organization provides detailed and transparent breakdowns of their methodology, so it’s hard to give a definitive, complete explanation for the differences. But one major one is the inclusion of electricity for cryptocurrency mining. This is included in the EI/S&P data, but is not in the IEA data. Electricity consumption for crypto mining is likely to be in the range of 150 to 200 terawatt-hours (TWh), which would account for almost two-thirds of the difference.
Even taking this into account, there would still be a gap. This is likely explained by the differences in methodological approach. S&P models power demand based on bottom-up data on installed data center capacity. That relies on assumptions about how often this capacity is used, so the results are sensitive to them. The IEA attempts to estimate the electricity consumption of IT equipment, cooling, and infrastructure demand in data centers more directly. But again, this comes with significant uncertainty.
These estimates are not directly comparable, and measure different things, but it’s useful to keep this comparison in mind if you’re faced with quite different numbers for global power demand from data centers.
Electricity demand is concentrated in a few places 1.5% of the world’s electricity might not seem like much. But in some places, that share is far higher. This is really the key challenge with growing data center demand: it’s geographically concentrated, meaning the world’s demand is served by a small number of electricity grids.
In the chart, you can see the share of electricity used for data centers in different regions. 5% of electricity in the United States is used to power data centers. For AI-focused ones specifically, it’s probably around 2%.
Beneath Europe’s 1.6% figure, we have Ireland, where data centers account for more than 20% of electricity consumption.
But in fact, this demand is even more locally concentrated. Beneath Europe’s 1.6% figure, we have Ireland, where data centers account for more than 20% of electricity consumption. Beneath the 5% US figure, there are states where data centers make up more than 10% of demand. In states such as Virginia, it’s more than one-quarter.
This, combined with the rapid pace of AI growth, could put pressure on local grids, even if total electricity demand is not overwhelming for the world as a whole.
What’s the energy footprint of individual LLM queries?
I know many people who are conscious of their own use of large language models (LLMs) and AI tools for environmental reasons. Some abstain or use them as little as possible. Others continue to do so, but feel guilty for it.
How much energy do our individual queries consume? If someone asks an LLM — like ChatGPT, Gemini, or Claude — a question, how much additional electricity demand do they generate?
Again, accurate and up-to-date figures on this are hard to find because most technology companies have not released detailed analyses of the energy consumption of their models.
One of the first companies to do so was Google; in 2025, it released energy estimates for its Gemini model. It estimated the median text-based query (basically asking Gemini a text question) consumed around 0.24 watt-hours (Wh) of electricity. For context, that’s the amount of energy a microwave would consume in less than one second, or a television for ten seconds.
The CEO of OpenAI, Sam Altman, also previously wrote that an “average query” on ChatGPT consumed around 0.34 Wh (but without a detailed breakdown of where this number comes from). Epoch AI also provided its own independent estimate of around 0.3 Wh for a “typical” ChatGPT query.
So a number of sources tend to converge on a similar figure. However, most queries are simple, so the median query is probably small in both size and complexity.
For longer queries or requests that rely on AI agents or reasoning, the footprint is higher. Epoch AI estimated that a long query (7,500 words of input) could consume about 2.5 Wh, and a very long one (75,000 words) could consume 40 Wh. Those are far higher than the simple text query.
There are fewer estimates for other types of requests. In its 2026 report, the IEA provides estimates of GPU electricity consumption for agentic tasks with reasoning. A standard request to an AI agent — such as Claude — is estimated to consume around 1.1 Wh. An agentic request with reasoning, around 50 Wh. This is similar to the estimates for maximum-length text queries.
However, all of these are still relatively small compared to the average person’s daily electricity consumption, especially in high-income countries.
In the European Union, average electricity consumption per person is around 17,000 Wh per day. That’s equivalent to around 6,800 long-input queries (which consume 2.5 Wh each). Or 425 of the maximum-input queries.
The average person in the US consumes approximately twice as much electricity as the EU average, so the contribution of AI queries to someone’s footprint there is about half the size.
In the chart below, I’ve provided some comparisons to give a sense of how this energy consumption compares to other products or activities.
Future energy demand for AI is very uncertain In this article, I’ve focused on historical estimates of data center and AI demand. Getting reasonable recent estimates of how much electricity these are already consuming was already difficult. Predicting how this will change in the future is even more difficult.
There is a wide range of estimates for future demand, and the differences between them tend to grow the further into the future you go. This divergence is particularly clear after 2030, since much of the medium-term demand pre-2030 relies on infrastructure being built today.
Future demand will depend on a range of factors. Not just how user demand grows, but also how the efficiency of chips and other hardware grows, too. The IEA, for example, has four future scenarios: one projects far higher user demand growth, and another sees much faster efficiency gains.
For those worried about the climate impacts of data centers and AI, how that electricity is generated arguably matters more than how much it uses.
While we don’t know how much AI energy demand will grow in the future, the key points we learn from the data today are likely to hold true. For an individual worried about their own use of AI, the energy use of individual chatbot queries is small. For the world as a whole, data centers make up a relatively small share of total electricity consumption, and that won’t change in the coming years. The issue is that supply is so geographically concentrated: whether grids can meet this surge in demand while keeping emissions and local prices under control is the real test.
A final point that’s worth keeping in mind is that the figures we’ve looked at measure electricity consumption, not carbon emissions. For those worried about the climate impacts of data centers and AI, how that electricity is generated arguably matters more than how much it uses. A query served by a data center on a coal-dominated grid will have a much larger impact than one running on a renewables- or nuclear-heavy one. How the growth of AI affects emissions depends not just on its energy efficiency and demand, but also on how clean the grids are that supply it.
