Artificial intelligence has become a general-purpose technology, capable of generating text and videos, accelerating scientific discovery, powering smarter automation and self-driving vehicles, and even managing critical infrastructure. Governments and businesses around the world are racing to adopt AI, channeling enormous investment into new data centers. Yet, as the IEA warns, “there is no AI without energy”. AI model training and inference run in massive, power-hungry data centers: a typical AI-focused data center uses about as much electricity as 100,000 households. This rising demand is testing electricity grids worldwide just as economies strive to meet net-zero climate targets.
The International Energy Agency (IEA) reports that data centers worldwide consumed roughly 415 terawatt-hours (TWh) of electricity in 2024 – about 1.5% of global power use. This share is set to swell dramatically as AI expands: in the IEA’s Base Case scenario, data center demand more than doubles by 2030 to around 945 TWh. This implies roughly 15% annual growth – far outpacing most other sectors. Today, the United States accounts for the largest share (≈45%) of data center electricity, followed by China (25%) and Europe (15%). By 2030, even Europe’s share will rise significantly: European data centers are expected to draw about 45 TWh more per year (an increase of ~70%). In practical terms, if this trajectory holds, data centers alone could consume nearly 3% of global electricity by 2030 – a substantial demand driver that must be met.
Even individual AI tasks can have surprisingly large energy footprints. For example:
* A single AI conversation can use around 2 Wh, comparable to leaving an LED bulb on for 10 minutes.
* Video generation via AI consumes about 50 Wh—the same as running a fan for an hour.
* Large-model training (e.g. GPT-4): ~42 GWh. This is equivalent to powering roughly 28,500 households in Europe in a day.
These figures illustrate the range: from fractions of a watt-hour for a simple chatbot query up to gigawatt-hours for model training. Scaling AI across billions of queries or ongoing data center operations thus translates into an immense electricity draw on the grid.
The rapid rise in AI-driven power demand underscores the urgency of expanding clean energy. To keep AI’s benefits without blowing past climate goals, electricity for these data centers and devices must come from sustainable sources. Solar photovoltaic (PV) is a key solution – especially distributed solar on rooftops and commercial buildings – because it can be deployed quickly and locally to meet surging demand. In Europe’s drive for net-zero emissions, every household and business rooftop equipped with solar panels helps offset the added load from AI and other digital services.
Meanwhile, as electricity demand surges across sectors—from digital infrastructure to AI-powered devices—clean energy will play an increasingly critical role. Scaling up solar, wind, and storage capacity is essential not only to keep grids resilient, but also to ensure climate targets remain within reach. Households, businesses, and policymakers alike have a role to play in this transformation. Distributed solar, in particular, offers a fast, scalable, and cost-effective solution—converting rooftops, carports, and underutilized surfaces into clean energy generators.
As Europe accelerates its renewable transition, proven and reliable technologies will be key to success. Since 2003, AESOLAR has been delivering high-quality solar solutions engineered in Germany for residential, commercial, and utility-scale applications—helping communities meet new electricity demands sustainably and with confidence.
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