The development of generative artificial intelligence, and in particular large language models such as ChatGPT, will produce up to 5 million tons of e-waste (e-waste) between 2023 and 2030. According to an analysis led by Peng Wang and published this Monday in the journal Nature Computational Sciencein the scenario with the greatest growth of AI and if recycling measures are not increased, these technologies will produce up to almost 1,000 times more electronic waste, an amount greater than that generated by India, one of the most polluting countries in this type of waste.
Wang and his team estimate that generative AI could generate between 1.2 and 5 million tons of accumulated e-waste. The authors have calculated the mass of garbage generated by hardware elements, such as processing units, storage units, and power systems. By component, the result indicates that the electronic waste generated could include 1.5 million tons of printed circuit boards and 0.5 million tons of batteries, which may contain hazardous materials such as lead and chromium.
The analysis has four scenarios with different degrees of production and application of generative AI, ranging from an aggressive scenario (with generalized applications) to a conservative scenario (with more specific applications). The authors suggest that implementing a circular economy strategy (where the useful life of existing infrastructure is extended and/or key modules and materials are reused in the remanufacturing process) could reduce e-waste generation by up to 86%. .
The importance of recycling
The findings highlight the need for responsible use of generative AI and proactive e-waste management strategies to reduce the harmful impacts of pollution. These findings add to previous reports that indicated that companies like Google and Microsoft have skyrocketed their polluting emissions due to artificial intelligence, that pointed out the massive water consumption of their data centers or that training a single artificial intelligence can contaminate as many as 2,800 flights Madrid-Barcelona.
This study will invite further discussion on e-waste, a critical issue that is often overlooked when discussing AI.
Shaolei Ren
— Professor at the University of California in Riverside (USA)
“The conclusions are based on the best data available in the public sphere and on scientifically valid methods,” he says. Shaolei Renassociate professor of Electrical and Computer Engineering at the University of California at Riverside (United States), in statements to the SMC. The study uses Nvidia’s DGX H100 server as a reference to estimate the mass of garbage coming from next-generation servers, the expert details.
In his opinion, electronic waste is a critical issue, which is often overlooked when talking about AI. “This article draws attention to the e-waste generated by generative AI and I believe it will invite a deeper debate,” concludes Ren. “More importantly, it highlights the crucial role of a circular economy in achieving truly sustainable AI.”
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