Rapid progress in generative AI comes with a hidden environmental cost: mountains of obsolete hardware.
What’s new: A study projects that servers used to process generative AI could produce millions of metric tons of electronic waste by 2030. Extending server lifespans could reduce the burden substantially, according to author Peng Weng and colleagues at the Chinese Academy of Sciences and Reichman University.
How it works: The study extrapolated from publicly available data to model accumulation of electronic waste, or e-waste, between 2023 and 2030. The authors examined four scenarios: One scenario assumed linear growth in which hardware manufacturing expands at the current rate of 41 percent annually. The other three assumed exponential growth of demand for computing: conservative (85 percent annually), moderate (115 percent annually), and aggressive (136 percent annually). The study evaluated each scenario with and without measures taken to reduce waste.
- In the linear-growth scenario, e-waste could add up to 1.2 million metric tons between 2023 and 2030. In the aggressive scenario, the total could reach 5 million metric tons, or roughly 1 percent of total electronic waste during that period. (These figures don’t account for mitigations, which would improve the numbers, or ongoing manufacturing of earlier, less efficient technology, which would exacerbate them.)
- The study assumed that servers typically would be discarded after three years. Upgrading servers more frequently, when improved hardware becomes available, would reduce overall server numbers because fewer servers would deliver greater processing power. However, because servers would be discarded more quickly, it could add a cumulative 1.2 million metric tons in the linear scenario or 2.3 million metric tons in the aggressive scenario, assuming no mitigation measures are taken.
- U.S. trade restrictions on advanced chips are also likely to exacerbate the problem. They could push affected countries to rely on less-efficient hardware designs and thus require more new servers to reach a competitive processing capacity. This could increase total waste by up to 14 percent.
- The authors explored several approaches to reducing e-waste. Repurposing equipment for non-AI applications and reusing critical components like GPUs and CPUs could cut e-waste by 42 percent. Improving the power efficiency of chips and optimizing AI models could reduce e-waste by 16 percent.
- The most promising approach to reducing e-waste is to extend server lifespans. Adding one year to a server’s operational life could reduce e-waste by 62 percent.
Why it matters: E-waste is a problem not only due to its sheer quantity. Server hardware contains materials that are both hazardous and valuable. Discarded servers contain toxic substances like lead and chromium that can find their way into food water supplies. They also contain valuable metals, such as gold, silver, and platinum, that could save the environmental and financial costs of producing more of them. Proper recycling of these components could yield $14 billion to $28 billion, highlighting both the economic potential and the urgent need to develop and deploy advanced recycling technologies.
We’re thinking: Humanity dumps over 2 billion metric tons of waste annually, so even comprehensive recycling and repurposing of AI hardware and other electronic devices would make only a small dent in the overall volume. However, the high density of valuable materials in e-waste could make mining such waste profitable and help recycle waste into valuable products, making for a more sustainable tech economy.