AI will add to the #e-waste problem. Here’s what we can do about it.
Equipment used to train and run generative AI models could produce up to 5 million tons of e-waste by 2030, a relatively small but significant fraction of the global total.
Generative AI could account for up to 5 million metric tons of e-waste by 2030, according to a new study.
That’s a relatively small fraction of the current global total of over 60 million metric tons of e-waste each year. However, it’s still a significant part of a growing problem, experts warn.
E-waste is the term to describe things like air conditioners, televisions, and personal electronic devices such as cell phones and laptops when they are thrown away. These devices often contain hazardous or toxic materials that can harm human health or the environment if they’re not disposed of properly. Besides those potential harms, when appliances like washing machines and high-performance computers wind up in the trash, the valuable metals inside the devices are also wasted—taken out of the supply chain instead of being recycled.
Depending on the adoption rate of generative AI, the technology could add 1.2 million to 5 million metric tons of e-waste in total by 2030, according to the study, published today in Nature Computational Science.
“This increase would exacerbate the existing e-waste problem,” says Asaf Tzachor, a researcher at Reichman University in Israel and a co-author of the study, via email.
The study is novel in its attempts to quantify the effects of AI on e-waste, says Kees Baldé, a senior scientific specialist at the United Nations Institute for Training and Research and an author of the latest Global E-Waste Monitor, an annual report.
The primary contributor to e-waste from generative AI is high-performance computing hardware that’s used in data centers and server farms, including servers, GPUs, CPUs, memory modules, and storage devices. That equipment, like other e-waste, contains valuable metals like copper, gold, silver, aluminum, and rare earth elements, as well as hazardous materials such as lead, mercury, and chromium, Tzachor says.
One reason that AI companies generate so much waste is how quickly hardware technology is advancing. Computing devices typically have lifespans of two to five years, and they’re replaced frequently with the most up-to-date versions.
While the e-waste problem goes far beyond AI, the rapidly growing technology represents an opportunity to take stock of how we deal with e-waste and lay the groundwork to address it. The good news is that there are strategies that can help reduce expected waste.
Expanding the lifespan of technologies by using equipment for longer is one of the most significant ways to cut down on e-waste, Tzachor says. Refurbishing and reusing components can also play a significant role, as can designing hardware in ways that makes it easier to recycle and upgrade. Implementing these strategies could reduce e-waste generation by up to 86% in a best-case scenario, the study projected.
Only about 22% of e-waste is being formally collected and recycled today, according to the 2024 Global E-Waste Monitor. Much more is collected and recovered through informal systems, including in low- and lower-middle-income countries that don’t have established e-waste management infrastructure in place. Those informal systems can recover valuable metals but often don’t include safe disposal of hazardous materials, Baldé says.
Another major barrier to reducing AI-related e-waste is concerns about data security. Destroying equipment ensures information doesn’t leak out, while reusing or recycling equipment will require using other means to secure data. Ensuring that sensitive information is erased from hardware before recycling is critical, especially for companies handling confidential data, Tzachor says.
More policies will likely be needed to ensure that e-waste, including from AI, is recycled or disposed of properly. Recovering valuable metals (including iron, gold, and silver) can help make the economic case. However, e-waste recycling will likely still come with a price, since it’s costly to safely handle the hazardous materials often found inside the devices, Baldé says.
“For companies and manufacturers, taking responsibility for the environmental and social impacts of their products is crucial,” Tzachor says. “This way, we can make sure that the technology we rely on doesn’t come at the expense of human and planetary health.”
▻https://www.technologyreview.com/2024/10/28/1106316/ai-e-waste