The intelligence of AI driving today’s business world may come at a tremendous natural cost. Gartner data predicts that by 2028, half of all greenhouse gas emissions in the IT sector will originate from AI models alone—a sharp rise from just 10% in 2025. The key question is how cutting-edge innovation can continue without leaving an "environmental footprint" that burdens the planet’s future.
AI’s environmental footprint extends far beyond the electricity used for processing or running models. It also includes hidden costs often overlooked by many organizations, such as the massive amounts of water needed for data center cooling systems, electronic waste from shorter hardware life cycles, and pollution within supply chains that are difficult to track. Truly managing these impacts must begin with transparent and comprehensive "measurement" across all dimensions.
Accurate measurement of environmental footprints is the crucial first step. Modern organizations need a holistic approach to assess efficiency in energy use (PUE), water use (WUE), and emissions across the supply chain—known as Scope 3—to gain a full picture of AI’s lifecycle impact. Although pinpointing emissions per individual model is challenging since providers rarely disclose detailed data, using component-based emission tracking tools helps target reductions more effectively.
Beyond environmental factors, social impacts are also key variables. Many countries are starting to face opposition to expanding data centers due to concerns about the stability of local power grids and community water supplies. Organizations must turn this challenge into an opportunity by designing infrastructure that supports communities—for example, by implementing heat recovery systems to power nearby buildings, recycling water for local agriculture, or investing in clean energy farms that provide sustainable electricity access to residents.
Ultimately, sustainable AI strategies must be embedded at every stage of development—from choosing energy-saving distributed model architectures (Sparse Architecture), opting for smaller specialized models instead of oversized ones, to deciding on cloud or on-premises infrastructure based primarily on energy sources. This is not just about reducing carbon emissions; it is about creating responsible innovation that allows AI to grow hand in hand with the planet’s sustainability.
Source: Gartner