AI will cause rises in e-waste — here’s what to do about it

The growing AI e-waste problem: Generative AI technologies are expected to contribute significantly to electronic waste (e-waste) by 2030, exacerbating an already pressing environmental issue.

• A new study published in Nature Computational Science projects that generative AI could add between 1.2 million and 5 million metric tons of e-waste by 2030, depending on adoption rates.
• While this represents a relatively small fraction of the current global total of over 60 million metric tons of e-waste produced annually, it highlights the need for proactive measures to address the environmental impact of AI technologies.

Understanding e-waste: E-waste encompasses discarded electronic devices and appliances, containing both valuable materials and hazardous substances that pose risks to human health and the environment.

• E-waste includes items such as air conditioners, televisions, cell phones, and laptops, which often contain toxic materials that can be harmful if not disposed of properly.
• Improper disposal of e-waste not only poses environmental and health risks but also results in the loss of valuable metals from the supply chain, including copper, gold, silver, and rare earth elements.

AI’s contribution to e-waste: The primary source of AI-related e-waste is the high-performance computing hardware used in data centers and server farms, which are frequently replaced to keep up with rapid technological advancements.

• AI-related e-waste primarily consists of servers, GPUs, CPUs, memory modules, and storage devices used in data centers and server farms.
• These components typically have lifespans of two to five years and are frequently replaced with newer versions to maintain optimal performance.

Strategies to reduce AI-related e-waste: Several approaches can be implemented to mitigate the environmental impact of AI technologies and reduce e-waste generation.

• Extending the lifespan of computing equipment is one of the most effective ways to reduce e-waste, according to Asaf Tzachor, a researcher at Reichman University and co-author of the study.
• Refurbishing and reusing components, as well as designing hardware for easier recycling and upgrading, can significantly reduce e-waste generation.
• Implementing these strategies could potentially reduce e-waste generation by up to 86% in a best-case scenario, according to the study’s projections.

Current e-waste management challenges: The global recycling and proper disposal of e-waste face significant obstacles, with only a small fraction of e-waste being formally collected and recycled.

• The 2024 Global E-Waste Monitor reports that only about 22% of e-waste is currently being formally collected and recycled.
• Informal e-waste collection and recovery systems, particularly in low- and lower-middle-income countries, often lack proper infrastructure for safe disposal of hazardous materials.
• Data security concerns pose a major barrier to reducing AI-related e-waste, as companies often prefer to destroy equipment to prevent information leaks rather than reuse or recycle it.

The need for policy intervention: Addressing the growing e-waste problem, including AI-related waste, will likely require the implementation of new policies and regulations.

• Recovering valuable metals from e-waste can provide economic incentives for recycling, but the process still comes with costs due to the safe handling of hazardous materials.
• Kees Baldé, a senior scientific specialist at the United Nations Institute for Training and Research, emphasizes that e-waste recycling will likely still come with a price due to the costs associated with safely handling hazardous materials.

Industry responsibility and future outlook: Companies and manufacturers must take responsibility for the environmental and social impacts of their products to ensure sustainable development of AI technologies.

• Tzachor stresses the importance of corporate responsibility in addressing the e-waste issue, stating that companies and manufacturers must consider the environmental and social impacts of their products.
• By implementing responsible practices and embracing circular economy principles, the AI industry can work towards minimizing its environmental footprint and ensuring that technological advancements do not come at the expense of human and planetary health.