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Generative AI's environmental impact in figures
PARIS (AFP) - The surge in generative artificial intelligence (AI) is being met with growing fears about the technology's ecological footprint, one of the top questions up for discussion at a global summit in Paris on February 10-11. Here are some key figures on the state of play in early 2025: Every request made to OpenAI's chatbot, which is able to generate all kinds of responses to natural-language queries, consumes 2.9 watt-hours of electricity. That is 10 times more than the equivalent figure for a Google search, according to the International Energy Agency (IEA). OpenAI claims that ChatGPT now has 300 million weekly users making a total of one billion requests every single day. Beyond ChatGPT, which fronted generative AI's emergence into public consciousness in 2022, there are thousands of chatbots. One survey by French pollsters Ifop found that 70 per cent of 18- to 24-year-olds in the country said they used generative AI. In America, a Morning Consult poll found that 65 per cent of 13- to 17-year-olds used generative AI, with the number close to half for the general population. Generative AI would not function without data centres hosting vast reserves of information and computing power. In 2023, data centres accounted for almost 1.4 per cent of global electricity consumption, according to a study by consultancy Deloitte. But with massive investments planned into generative AI, the figure is expected to reach three per cent by 2030 - or 1,000 terawatt-hours (TWh). Deloitte said that was comparable with the combined annual consumption of France and Germany. The IEA forecast a more than 75 per cent increase in data centre power consumption by 2026 compared with 2022's levels, to 800 TWh. American consultancy Gartner said the vast power demands meant that up to 40 per cent of data centres built for AI applications could face electricity shortages by 2027. Training one of the large language models (LLMs) that power chatbots generates around 300 tonnes of greenhouse gas carbon dioxide, researchers at the University of Massachusetts Amherst estimated in 2019. That is around the same output as 125 return flights between New York and Beijing. Two years later, Oxford University researchers put the figure at 224 tonnes for a single training session for OpenAI's GPT-3 model. Developers have to train thousands of models to push their technology forward. Despite such estimates, researchers say judging generative AI's overall greenhouse emissions is challenging. Experts and institutions have pointed to a lack of information on how models are produced, as well as an absence of global measurement standards. Beyond energy, generative AI also consumes water, especially for cooling computer hardware. GPT-3 requires around half a litre (one pint) of water to generate between 10 and 50 responses, according to a conservative estimate from researchers at the University of California Riverside and University of Texas at Arlington. Overall, increased AI demand for water is forecast to amount to between 4.2 billion and 6.6 billion cubic metres (155 billion - 233 billion cubic feet). That is four to six times the annual water consumption of Denmark, according to the same 2023 study. Around 2,600 tonnes of electronic waste such as graphics cards, servers and memory chips emerged from generative AI applications in 2023, according to a study from the Nature Computational Science journal. The researchers extrapolated that figure to 2.5 million tonnes by 2030 if current trends continue and nothing is done to limit waste. That would be the equivalent of around 13.3 billion discarded smartphones. And like much computer hardware, AI equipment including chips requires rare metals to manufacture. Mining for such metals, often in Africa, can involve heavily polluting processes.
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Generative AI's environmental impact in figures
Each interaction with OpenAI's chatbot uses 2.9 watt-hours of electricity. In 2023, data centres were responsible for nearly 1.4% of global electricity consumption, according to a Deloitte. With substantial investments in generative AI, this share is projected to rise to 3% by 2030, equating to 1,000 terawatt-hours (TWh) of energy.The surge in generative artificial intelligence (AI) is being met with growing fears about the technology's ecological footprint, one of the top questions up for discussion at a global summit in Paris on February 10-11. Here are some key figures on the state of play in early 2025: Ten times Google's power Every request made to OpenAI's chatbot, which is able to generate all kinds of responses to natural-language queries, consumes 2.9 watt-hours of electricity. That is ten times more than the equivalent figure for a Google search, according to the International Energy Agency (IEA). OpenAI claims that ChatGPT now has 300 million weekly users making a total of one billion requests every single day. Beyond ChatGPT, which fronted generative AI's emergence into public consciousness in 2022, there are thousands of chatbots. One survey by French pollsters Ifop found that 70 percent of 18- to 24-year-olds in the country said they used generative AI. In America, a Morning Consult poll found that 65 percent of 13- to 17-year-olds used generative AI, with the number close to half for the general population. Bigger than France and Germany Generative AI would not function without data centres hosting vast reserves of information and computing power. In 2023, data centres accounted for almost 1.4 percent of global electricity consumption, according to a study by consultancy Deloitte. But with massive investments planned into generative AI, the figure is expected to reach three percent by 2030 -- or 1,000 terawatt-hours (TWh). Deloitte said that was comparable with the combined annual consumption of France and Germany. The IEA forecast a more than 75 percent increase in data centre power consumption by 2026 compared with 2022's levels, to 800 TWh. American consultancy Gartner said the vast power demands meant that up to 40 percent of data centres built for AI applications could face electricity shortages by 2027. Hundreds of flights in CO2 Training one of the large language models (LLMs) that power chatbots generates around 300 tonnes of greenhouse gas carbon dioxide, researchers at the University of Massachusetts Amherst estimated in 2019. That is around the same output as 125 return flights between New York and Beijing. Two years later, Oxford University researchers put the figure at 224 tonnes for a single training session for OpenAI's GPT-3 model. Developers have to train thousands of models to push their technology forward. Despite such estimates, researchers say judging generative AI's overall greenhouse emissions is challenging. Experts and institutions have pointed to a lack of information on how models are produced, as well as an absence of global measurement standards. Rivers of water Beyond energy, generative AI also consumes water, especially for cooling computer hardware. GPT-3 requires around half a litre (one pint) of water to generate between 10 and 50 responses, according to a conservative estimate from researchers at the University of California Riverside and University of Texas at Arlington. Overall, increased AI demand for water is forecast to amount to between 4.2 billion and 6.6 billion cubic metres (155 billion - 233 billion cubic feet). That is four to six times the annual water consumption of Denmark, according to the same 2023 study. Heaps of electronic waste Around 2,600 tonnes of electronic waste such as graphics cards, servers and memory chips emerged from generative AI applications in 2023, according to a study from the Nature Computational Science journal. The researchers extrapolated that figure to 2.5 million tonnes by 2030 if current trends continue and nothing is done to limit waste. That would be the equivalent of around 13.3 billion discarded smartphones. And like much computer hardware, AI equipment including chips requires rare metals to manufacture. Mining for such metals, often in Africa, can involve heavily polluting processes.
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As generative AI usage surges, concerns about its ecological footprint are mounting. This story explores the environmental impact of AI in terms of energy consumption, water usage, and electronic waste.
Generative AI has seen a significant surge in popularity since ChatGPT's public debut in 2022. A survey by French pollsters Ifop found that 70% of 18- to 24-year-olds in France use generative AI, while in America, a Morning Consult poll revealed that 65% of 13- to 17-year-olds and nearly half of the general population use this technology 12. OpenAI claims that ChatGPT alone has 300 million weekly users, generating one billion requests daily 1.
The energy demands of generative AI are substantial and increasing. Each request to OpenAI's chatbot consumes 2.9 watt-hours of electricity, which is ten times more than a Google search 12. Data centers, crucial for AI operations, accounted for 1.4% of global electricity consumption in 2023 12.
With massive investments planned in generative AI, this figure is expected to reach 3% by 2030, equivalent to 1,000 terawatt-hours (TWh) - comparable to the combined annual consumption of France and Germany 12. The International Energy Agency (IEA) forecasts a more than 75% increase in data center power consumption by 2026 compared to 2022 levels, reaching 800 TWh 1.
Training large language models (LLMs) that power chatbots has a significant carbon footprint. Researchers at the University of Massachusetts Amherst estimated in 2019 that training one LLM generates around 300 tonnes of greenhouse gas carbon dioxide, equivalent to 125 return flights between New York and Beijing 12. Oxford University researchers later estimated 224 tonnes of CO2 for a single training session of OpenAI's GPT-3 model 12.
Generative AI also consumes substantial amounts of water, primarily for cooling computer hardware. GPT-3 requires approximately half a liter of water to generate between 10 and 50 responses 12. Researchers project that increased AI demand for water could amount to between 4.2 billion and 6.6 billion cubic meters by 2030, which is four to six times Denmark's annual water consumption 12.
The rapid advancement of AI technology is contributing to electronic waste. A study from Nature Computational Science journal reported that generative AI applications produced around 2,600 tonnes of electronic waste in 2023, including graphics cards, servers, and memory chips 12. If current trends continue, this could escalate to 2.5 million tonnes by 2030, equivalent to about 13.3 billion discarded smartphones 12.
Despite these estimates, researchers face challenges in accurately assessing generative AI's overall environmental impact. Experts point to a lack of information on how models are produced and an absence of global measurement standards 12. This uncertainty underscores the need for greater transparency and standardized reporting in the AI industry.
As AI applications continue to grow, there are concerns about potential resource shortages. American consultancy Gartner predicts that up to 40% of data centers built for AI applications could face electricity shortages by 2027 due to vast power demands 12. Additionally, the production of AI hardware requires rare metals, often mined in Africa through potentially polluting processes 12.
As the global AI summit in Paris approaches, these environmental concerns are set to be a key topic of discussion, highlighting the urgent need for sustainable practices in the rapidly evolving field of generative AI.
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