Generative AI Could Generate Millions More Tons of E-Waste by 2030

Generative AI Is Poised to Worsen the E-Waste Crisis

By Saima S. Iqbal

Every time generative artificial intelligence drafts an e-mail or conjures up an image, the planet pays for it. Making two images can consume as much energy as charging a smartphone; a single exchange with ChatGPT can heat up a server so much that it requires a bottle’s worth of water to cool. At scale, these costs soar. By 2027, the global AI sector could annually consume as much electricity as the Netherlands, according to one recent estimate. And a new study in Nature Computational Science identifies another concern: AI’s outsize contribution to the world’s mounting heap of electronic waste. The study found that generative AI applications alone could add 1.2 million to five million metric tons of this hazardous trash to the planet by 2030, depending on how quickly the industry grows.

Such a contribution would add to the tens of millions of tons of electronic products the globe discards annually. Cell phones, microwave ovens, computers and other ubiquitous digital products often contain mercury, lead or other toxins. When improperly discarded, they can contaminate air, water and soil. The United Nations found that in 2022 about 78 percent of the world’s e-waste wound up in landfills or at unofficial recycling sites, where laborers risk their health to scavenge rare metals.

The worldwide AI boom rapidly churns through physical data storage devices, plus the graphics processing units and other high-performance components needed to process thousands of simultaneous calculations. This hardware lasts anywhere from two to five years—but it’s often replaced as soon as newer versions become available. Asaf Tzachor, a sustainability researcher at Israel’s Reichman University, who co-authored the new study, says its findings emphasize the need to monitor and reduce this technology’s environmental impacts.

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To calculate just how much generative AI contributes to this problem, Tzachor and his colleagues examined the type and volume of hardware used to run large language models, the length of time that these components last and the growth rate of the generative AI sector. The researchers caution that their prediction is a gross estimate that could change based on a few additional factors. More people might adopt generative AI than the authors’ models anticipate, for example. Hardware design innovations, meanwhile, could reduce e-waste in a given AI system—but other technological advances can make systems cheaper and more accessible to the public, increasing the number in use.

This study’s biggest value comes from its attention to AI’s broad environmental impacts, says Shaolei Ren, a researcher at the University of California, Riverside, who studies responsible AI and was not involved in the new research. “We might want these [generative AI] companies to slow down a bit,” he says.

Few countries mandate the proper disposal of e-waste, and those that do often fail to enforce their existing laws on it. Twenty-five U.S. states have e-waste management policies, but there is no federal law that requires electronics recycling. In February Democratic Senator Ed Markey of Massachusetts introduced a bill that would require federal agencies to study and develop standards for AI’s environmental impacts, including e-waste. But that bill, the Artificial Intelligence Environmental Impacts Act of 2024 (which has not passed the Senate), would not force AI developers to cooperate with its voluntary reporting system. Some companies, however, claim to be taking independent action. Microsoft and Google have pledged to reach net zero waste and net zero emissions respectively by 2030; this would likely involve reducing or recycling AI-related e-waste.

Companies that use AI have numerous options to limit e-waste. It’s possible to squeeze more life out of servers, for instance, through regular maintenance and updates or by shifting worn-out devices to less-intensive applications. Refurbishing and reusing obsolete hardware components can also cut waste by 42 percent, Tzachor and his co-authors note in the new study. And more efficient chip and algorithm design could reduce generative AI’s demand for hardware and electricity. Combining all these strategies would reduce e-waste by 86 percent, the study authors estimate.

There’s another wrinkle as well: AI products tend to be trickier to recycle than standard electronics because the former often contain a lot of sensitive customer data, says Kees Baldé, an e-waste researcher at the United Nations Institute for Training and Research, who wasn’t involved with the new study. But big tech companies can afford to both erase that data and properly dispose of their electronics, he points out. “Yes, it costs something,” he says of broader e-waste recycling, “but the gains for society are much larger.”