AI, once a concept of science fiction, is now an undeniable part of our daily lives. From personalized recommendations on streaming services to powerful generative tools that write code and create art, its presence is ubiquitous. Yet, this rapid integration comes with a significant, often overlooked, cost: its environmental footprint. The immense power required to fuel AI’s development and operation is placing an unprecedented strain on the planet’s resources, particularly in terms of energy consumption and water usage.
The core of AI’s environmental impact lies in the data centers that house the massive computational infrastructure needed for training and running these models. Training a single large language model (LLM) can consume an astronomical amount of energy, with some estimates equating the carbon footprint of training one model to the lifetime emissions of multiple cars. This is due to the intensive, round-the-clock processing required for models with billions of parameters. Beyond the initial training, every user query, or “inference,” also consumes energy, with a simple AI prompt using significantly more power than a traditional search engine query. As millions of people use AI applications daily, the cumulative effect is staggering.
The energy demands of these data centers are so immense that they are causing grid reliability problems in some areas and even pushing back the planned retirement of coal-fired power plants. To make matters worse, the hardware in these data centers, such as powerful GPUs, generates a tremendous amount of heat. To prevent overheating, these facilities rely on vast quantities of water for cooling, with some large centers consuming millions of gallons daily—an amount comparable to the daily water needs of a small city. This water use is a growing concern, especially in regions already facing water scarcity.
The environmental impact extends beyond energy and water. The manufacturing of the specialized hardware required for AI, particularly the processors, involves the mining of rare earth elements and other materials, a process that is both energy-intensive and environmentally damaging. The rapid pace of technological innovation in the AI space also leads to a significant increase in e-waste, as older components become obsolete at an alarming rate, contributing to a global waste crisis. While AI can be a powerful tool for climate solutions—for instance, by optimizing renewable energy grids or improving disaster prediction—its own carbon footprint cannot be ignored. The paradox is clear: the technology that could help us combat climate change is also a major contributor to it. A balanced approach is needed to harness the benefits of AI while aggressively mitigating its hidden environmental costs.
