Artificial intelligence (AI) is becoming a significant contributor to pollution and carbon emissions, mainly due to its massive energy consumption. Training a single AI model can emit over 626,000 pounds of CO2, equivalent to the emissions of five cars over their lifetimes. For instance, a study on GPT-3's energy consumption revealed that it used 1,287 MWh of electricity, resulting in 502 metric tons of carbon emissions, comparable to driving 112 gasoline-powered cars for a year.
The environmental impact of AI is multifaceted. Data centers, which house AI servers, consume significant amounts of energy, contributing to greenhouse gas emissions. It's estimated that data centers could account for nearly 35% of Ireland's energy use by 2026. Furthermore, data centers use substantial amounts of water for cooling, which can strain local water supplies. For every kilowatt-hour of energy consumed, a data center needs approximately two liters of water for cooling.
The rapid growth of AI infrastructure also leads to increased electronic waste, containing hazardous substances like mercury and lead. AI's carbon footprint varies depending on the energy source used to power data centers. In places with clean energy grids, like France, AI's carbon footprint is lower than in areas reliant on fossil fuels.
To mitigate AI's environmental impact, companies can transition to 100% renewable energy for data centers, which can significantly reduce AI's carbon footprint. For example, Microsoft aims to run on 100% renewable energy by 2025. Developing more computationally efficient AI models can also reduce energy consumption. Techniques like power capping can lower energy use by 15-20% without significantly impacting performance. Implementing sustainable practices in data centers, such as direct-to-chip cooling or immersion cooling, can improve energy efficiency.
