AI's Environmental Footprint: Sustainability Challenges and Employment Implications

By 2025, AI had affected many industries across the world including health care, finance, agriculture, and urban planning by enhancing productivity, accuracy, and efficiency. Breakthroughs in neural networks, transformers, and large-scale AI models-such as GPT and PaLM-enabled sophisticated language, image, and multimodal processing. However, these advances came with heavy environmental costs and economic considerations. Energy-intensive training and deployment, significant water use, and reliance on rare earth materials contributed to carbon emissions and ecological stress. The economic impact was felt in task automation and workforce reduction in major companies. Concerns were raised about AI increasing the social inequality gap, reducing job quality, and leading to generational mobility. Divergent regulatory responses-exemplified by the European Union's AI Act and the United States's innovation-driven strategy-shaped global outcomes. Potential future scenarios included unrestrained growth, sustainability-aligned AI, and consumer-driven adaptation. Ultimately, a trade-off was likely to occur between technological progress and environmental sustainability, as well as labour market transformation. Pitabas Mohanty is affiliated with XLRI-Xavier School of Management;Arpita Srivastava is affiliated with XLRI-Xavier School of Management