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.
How Companies Can Mitigate AI’s Growing Environmental Footprint
Practical steps for reducing AI’s surging demand for water and energy.
July 04, 2024
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Summary.
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.