Environmental Impact

The development of artificial intelligence also has a significant environmental impact, as training and running AI models require large amounts of computing power, electricity, and infrastructure. Modern models are developed and operated in data centers containing thousands of processors and specialized accelerators, which must be powered and cooled continuously.

One of the main effects is electricity consumption. Training a complex AI model can require a very large number of processors to operate continuously for extended periods. Even after training is complete, models consume energy every time users submit requests and receive responses. As AI services are used by hundreds of millions of people, the total energy consumption of the supporting infrastructure can become substantial.

Another important issue is water consumption. Data centers generate large amounts of heat and require cooling systems to keep equipment at appropriate temperatures. Some of these systems use water for cooling, which can put pressure on local water resources, particularly in regions affected by drought or high temperatures.

Data centers consumed approximately 415 TWh of electricity in 2024, accounting for around 1.5% of global electricity consumption. The International Energy Agency (IEA) estimates that this figure could reach approximately 945 TWh by 2030, nearly 3% of global consumption. AI is the main driver of this growth.

An AI-focused data center can consume enormous amounts of electricity. According to the IEA, a typical AI data center can consume as much electricity annually as 100,000 households, while the largest data centers under construction could consume the equivalent of 20 times that amount.

The environmental impact also arises during equipment manufacturing. Producing processors, servers, circuit boards, and other components requires raw materials, energy, and complex industrial processes. The extraction of certain minerals and semiconductor manufacturing can also have environmental consequences.

There is also the issue of electronic waste. Data center equipment is periodically replaced to keep pace with increasing computing demands. Decommissioned servers, accelerators, power supplies, and other components generate electronic waste that must be properly recycled or disposed of.

Therefore, AI’s environmental impact is complex and cannot be considered exclusively negative or positive. On the one hand, the expansion of AI infrastructure can increase the consumption of energy, water, and raw materials. On the other hand, the same technology can be used to make numerous processes more efficient. A major challenge for the AI industry is to develop more energy-efficient models, use renewable energy sources, reduce water consumption, and extend the lifespan of equipment while improving recycling practices.