Generative AI has a significant environmental footprint due to its high energy, water, and hardware demands. Training large models can consume several times more energy than typical computing tasks—sometimes enough to power over 100 homes for a year—while data centers also use substantial water for cooling. Rapid expansion often relies on fossil fuel-based electricity, increasing carbon emissions, and the production and frequent replacement of specialized GPUs contributes to electronic waste. Environmental impact varies by task, with image generation requiring more energy than simple text responses. Growing public concern has led experts to call for greater corporate responsibility, including renewable energy use, transparency, and smaller, more efficient AI models.