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As artificial intelligence (AI) becomes increasingly ubiquitous in business and governance, its substantial environmental impact — from significant increases in energy and water usage to heightened carbon emissions — cannot be ignored. By 2030, AI’s power demand is expected to rise by 160%. However, adopting more sustainable practices, such as utilizing foundation models, optimizing data processing locations, investing in energy-efficient processors, and leveraging open-source collaborations, can help mitigate these effects. These strategies not only reduce AI’s environmental footprint but also enhance operational efficiency and cost-effectiveness, balancing innovation with sustainability.
By 2026, computing power dedicated to training AI is expected to increase tenfold. As more power is expended, more resources are needed. As a result, we’ve seen exponential increases in energy and perhaps more unexpectedly, water consumption. Some estimates even show running a large AI model generates more emissions over its lifetime than the average car. A recent report from Goldman Sachs found that by 2030, there will be a 160% increase in demand for power propelled by AI applications.
