Environmental Impact & Sustainability

Training and running AI systems consumes significant energy, water, and hardware resources, and the environmental footprint is growing as models get larger and adoption increases. Training a single large language model can consume as much electricity as several hundred homes use in a year, and the water used for cooling data centres is substantial. The hardware supply chain involves mining rare minerals, energy-intensive manufacturing, and electronic waste. The major AI companies have made sustainability commitments, but their data centre energy consumption is growing rapidly - in some cases, outpacing their renewable energy procurement. The full lifecycle environmental cost of AI is difficult to calculate because it depends on the energy mix of the data centres, the efficiency of the hardware, and how intensively models are used after training. Inference (running a trained model to generate outputs) is less energy-intensive than training per query, but the sheer volume of queries means aggregate inference costs are significant and growing. For businesses deploying AI, the environmental dimension is increasingly relevant to stakeholders, customers, and regulators. Understanding the carbon footprint of your AI usage, choosing efficient models and providers, and being transparent about environmental impact are becoming standard expectations rather than nice-to-haves.