Environmental impacts of artificial intelligence

The environmental impacts of artificial intelligence (AI) may vary significantly. Many deep learning methods have significant carbon footprints and water usage. Some scientists have suggested that artificial intelligence may provide solutions to environmental problems.

Carbon footprint of AI
AI has a significant carbon footprint due to growing energy usage, especially due to training and usage. Researchers have argued that the carbon footprint of AI models during training should be considered when attempting to understand the impact of AI. One study suggested that by 2027, energy costs for AI could increase to 85–134 Twh, nearly 0.5% of all current energy usage. Training one deep learning model may use up to the same carbon footprint as the lifetime emissions of 5 cars. Training and running large language models (LLM) and other generative AI generally requires much more energy compared to running a single prediction on the trained model. Operating a model, though, may easily multiply the energy costs of predictions. The computation required to train the most advanced AI models doubles every 3.4 months on average, leading to exponential power usage and resulting carbon footprint.

BERT, a generative AI model trained in 2019, consumed "the energy of a round-trip transcontinental flight". GPT-3 released 552 metric tons of carbon dioxide into the atmosphere during training, "the equivalent of 123 gasoline-powered passenger vehicles driven for one year". Much of the energy cost is due to inefficient model architectures and processors. One model named BLOOM, from Hugging Face, trained with more efficient chips and only released 25 metric tons of CO2. Incorporating the energy cost of manufacturing the chips for the system doubled the carbon footprint, to "the equivalent of around 60 flights between London and New York." Operating BLOOM daily was estimated to release the equivalent carbon footprint as driving 54 miles.

Algorithms which have lower energy costs but run millions of times a day can also have significant carbon footprints. The integration of AI into search engines could multiply energy costs significantly, with some estimates suggesting energy costs rising to nearly 30 billion kWh per year, an energy footprint larger than many countries. Another estimate found that integrating ChatGPT into every Google search query would use 10 tWh each year, the equivalent yearly energy usage of 1.5 million European Union residents.

AI has caused both increased water and energy usage, leading to significantly more demands on the grid. Due to increased energy demands from AI-related projects, a Kansas City and a West Virginian coal-fired plant pushed back closing. Other coal-fired plants in the Salt Lake City region have pushed back retirement of their coal-fired plants by up to a decade. Environmental debates have raged in both Virginia and France about whether a "moratorium" should be called for additional data centers. In 2024 at the World Economic Forum, OpenAI executive Sam Altman gave a speech in which he said that the AI industry can only grow if there is a major technology breakthrough to increase energy development.

In 2024, Google failed to reach key goals from their net zero plan as a result of their work with AI, and had a 48% increase in greenhouse gas emission attributable to their growth in AI. Microsoft and Meta had similar increases in their carbon footprint, similarly attributed to AI. Carbon footprints of AI models depends on the energy source used, with data centers using renewable energy lowering their footprint. Many tech companies claim to offset energy usage by buying energy from renewable sources, though some experts argue that utilities simply replace the claimed renewable energy with increased non-renewable sources for their other customers. Analysis of the carbon footprint of AI models remains difficult to determine, as they are aggregated as part of datacenter carbon footprints, and some models may help reduce carbon footprints of other industries, or due to differences in reporting from companies.

Some applications of ML, such as for fossil fuel discovery and exploration, may worsen climate change. Use of AI for personalized marketing online may also lead to increased consumption of goods, which could also increase global emissions.

Energy efficiency
AI chips, (i.e. GPUs) use more energy and emit more heat than traditional CPU chips. AI models with inefficiently implemented architectures, or trained on less efficient chips may use more energy. Since the 1940's the energy efficiency of computation has doubled every 1.6 years. Some skeptics argue that improvements of AI efficiency may only increase AI usage and therefore carbon footprint due to Jevons paradox.

Water usage of AI
Cooling AI servers can demand large amounts of fresh water which is evaporated in cooling towers. By 2027, AI may use up to 6.6 billion cubic meters of water. One professor has estimated that an average session on ChatGPT, with 10–50 responses, can use up to a half-liter of fresh water. Training GPT-3 may have used 700,000 liters of water, equivalent to the water footprint of manufacturing 320 Tesla EVs.

One data center that Microsoft had considered building near Phoenix, due to increasing AI usage, was likely consume up to 56 million gallons of fresh water each year, equivalent to the water footprints of 670 families. Microsoft may have increased water consumption by 34% due to AI, while Google increased its water usage by 20% due to AI. Due to their Iowa data center cluster, Microsoft was responsible for 6% of the freshwater use in a local town.

Other environmental impacts of AI
E-waste due to production of AI hardware may also contribute to emissions. The rapid growth of AI may also lead to faster deprecation of devices, resulting in hazardous e-waste. Some applications of AI, such as for robot recycling, may reduce e-waste.

Climate solutions from artificial intelligence
AI has significant potential to help mitigate effects of climate change, such as through better weather predictions, disaster prevention and weather tracking. Some climate scientists have suggested that AI could be used to improve efficiencies of systems, such as renewable-energy systems. Google has claimed AI could help mitigate some effects of climate change such as predicting floods or making traffic more efficient. Some algorithms may help predict the impacts of more severe hurricanes, measure the melting of polar ice, deforestation, and help monitor emissions from sources. One machine learning project, the Open Catalyst project, has been used to identify "suitable low-cost electrocatalysts" for battery storage of renewable energy sources. AI may also improve the efficiencies of supply chains and productions for environmentally detrimental industries such as food and fast fashion.