The rise of artificial intelligence is not only consuming massive amounts of energy and water: It is also creating an unprecedented tsunami of electronic waste.
According to Stanford University, private investment in AI rose from $3 billion in 2022 to $25 billion last year, with companies adopting AI tools faster than ever. This surge is forcing data centers to continually upgrade their hardware, discarding still-functional equipment in a race to maintain a competitive advantage.
This massive use of components to power the hardware that runs AI models is generating millions of tons of discarded electronic components. A new study published in Nature by a team of researchers from China, Israel and the United Kingdom estimates that Large Language Models (LLMs) such as ChatGPT, Claude or LlaMa alone could generate 2.75 million tons of waste electronics per year, severely increasing the environmental impact of AI.
“In the optimistic scenario where LLMs become ubiquitous (i.e., everyone uses it daily like on social media), the results indicate that the EoS waste stream from designated data centers would increase to approximately 16 million tons.” (Mt) within a decade, from 2020 to 2030,” the research cites.
The waste stream is growing at a compound annual rate of 110%, dramatically outpacing the 2.8% growth of conventional electronic waste such as screens and washing machines.
The geography of this crisis is highly concentrated. North America leads with 58% of AI-related e-waste, followed by East Asia with 25% and Europe with 14%, according to research from the Chinese Academy of Sciences and Reichman University.
In addition to the huge amounts of e-waste, the AI industry in general is consuming enormous amounts of resources. Last year, Decrypt reported that for every 4 queries, ChatGPT consumes half a liter of water. Think about the fact that the site has more than 220 million visitors each month, and you can do the math and understand why cities near AI data centers have seen their water costs nearly double in less than a decade.
Research estimates that by 2030, this e-waste will contain almost one million tons of lead, 6,000 tons of barium and worrying amounts of cadmium, antimony and mercury, adding a considerable amount of toxic elements to the environment, all with well-documented risks. for soil, water and public health.
The researchers don’t mention whether companies and governments are doing enough, but there is also a financial angle that can be beneficial. Metals such as gold, silver and platinum used in these discarded servers also represent significant financial potential if recovered. The study estimates that proper recycling of these metals could inject $70 billion into the economy, an incentive to advance e-waste recycling infrastructure.
The study also explains that countries without access to the latest chips can generate up to 14% more electronic waste as they are forced to use less efficient hardware.
But there are some solutions that can help address the problem. The researchers argue that extending server life through improved maintenance could reduce the volume of e-waste by 58%, and reusing specific components would further reduce waste by 21%.
Additionally, obsolete AI servers could be repurposed for lighter tasks such as educational projects or basic web hosting instead of being scrapped, diverting them from waste streams and maximizing their usefulness.
This is becoming a priority for environmental groups around the world. Energy analyst Alex de Vries, founder of Digiconomist, told Decrypt that it is important to work on solutions before the negative impact of the AI industry becomes too difficult to control.
“Right now, the numbers are small, so you can argue, ‘Why do we need to put this at the top of our agenda if it’s still small?’” de Vries said. “But the situation is not going to remain small for long.”
Edited by Andrew Hayward
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