{"id":107569,"date":"2026-09-28T09:30:56","date_gmt":"2026-09-28T05:30:56","guid":{"rendered":"https:\/\/techxmedia.com\/en\/?p=107569"},"modified":"2026-09-28T09:30:57","modified_gmt":"2026-09-28T05:30:57","slug":"ai-waste-could-reach-617-million-tonnes-by-2050","status":"publish","type":"post","link":"https:\/\/techxmedia.com\/en\/ai-waste-could-reach-617-million-tonnes-by-2050\/","title":{"rendered":"AI Waste Could Reach 617 Million Tonnes by 2050"},"content":{"rendered":"\n

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AI waste could become a major environmental consequence of the global artificial intelligence<\/a> boom, according to the Basel Action Network (BAN).<\/p>\n\n\n\n

The organisation, which monitors the global trade in toxic waste, has released Part 1 of The Coming AI Waste Wave<\/a>, a new four-part white paper series examining the electronic equipment that could become e-waste as AI infrastructure expands and becomes obsolete.<\/p>\n\n\n\n

AI infrastructure could generate millions of tonnes of waste<\/strong><\/p>\n\n\n\n

BAN’s analysis, titled “How Big is the AI Waste Wave?”, estimates that between 8.6 million and 13.1 million tonnes of AI-driven electronic equipment could be retired each year by 2030.<\/p>\n\n\n\n

That figure is roughly 40 to 60 times higher than the most widely cited academic projection.<\/p>\n\n\n\n

According to BAN, the difference is mainly due to the scope of the analysis.<\/p>\n\n\n\n

Previous quantitative studies have primarily focused on servers and graphics processing units (GPUs). BAN’s model instead considers the wider electronic infrastructure required to support AI data centers.<\/p>\n\n\n\n

This includes networking equipment, power distribution systems, data storage and backup systems, cooling infrastructure, servers and computing equipment.<\/p>\n\n\n\n

As a result, BAN says its model covers virtually all of the infrastructure that qualifies as electronic waste.<\/p>\n\n\n\n

AI build-out carries a $7 trillion price tag<\/strong><\/p>\n\n\n\n

McKinsey estimates that nearly $7 trillion in capital expenditure could be required between 2025 and 2030 to build the infrastructure needed to power AI.<\/p>\n\n\n\n

BAN’s model starts with the infrastructure that the industry says it intends to build. It then calculates the potential waste associated with that expansion.<\/p>\n\n\n\n

The report also considers what BAN calls the AI Waste Contagion.<\/p>\n\n\n\n

This refers to electronic equipment outside data centers that could be retired earlier as AI adoption expands across personal computers, smartphones, edge devices and telecommunications networks.<\/p>\n\n\n\n

Consequently, the impact of AI waste could extend well beyond data centers.<\/p>\n\n\n\n

Most data center infrastructure is included<\/strong><\/p>\n\n\n\n

BAN estimates that servers, accelerators and racks account for around 13% of a data center’s electro-mechanical infrastructure by mass.<\/p>\n\n\n\n

The remaining 87% includes networking, power distribution, storage and backup systems, and cooling infrastructure.<\/p>\n\n\n\n

BAN argues that excluding these components from previous estimates significantly understates the potential volume of electronic waste associated with AI infrastructure.<\/p>\n\n\n\n

The organisation also warns that AI equipment could have increasingly short lifespans as new technologies and infrastructure designs emerge.<\/p>\n\n\n\n

AI waste could reach 46 million tonnes annually<\/strong><\/p>\n\n\n\n

By 2030, BAN projects that 8.6 million to 13.1 million tonnes of AI-driven electronic equipment could be retired annually.<\/p>\n\n\n\n

By 2050, that annual volume could increase to between 31 million and 46 million tonnes.<\/p>\n\n\n\n

The cumulative figure is even larger.<\/p>\n\n\n\n

Between 2025 and 2050, BAN projects that 395 million to 617 million tonnes of AI-driven electronic equipment could be retired.<\/p>\n\n\n\n

The organisation estimates that this volume would fill approximately 15 million to 23 million 40-foot shipping containers.<\/p>\n\n\n\n

If placed end to end, BAN says the containers could circle the Earth approximately six times.<\/p>\n\n\n\n

AI’s impact could extend beyond data centers<\/strong><\/p>\n\n\n\n

The report also examines the potential impact of the AI Waste Contagion.<\/p>\n\n\n\n

BAN estimates that this downstream effect could add 16.2 million to 30.7 million tonnes of retired equipment annually by 2050.<\/p>\n\n\n\n

Under both scenarios, the organisation projects that downstream equipment retirement would eventually exceed the 15.5 million tonnes per year projected from inside data centers.<\/p>\n\n\n\n

Therefore, the potential AI waste problem could involve both dedicated AI infrastructure and conventional electronic equipment affected by the rapid adoption of AI technologies.<\/p>\n\n\n\n

AI could add to the global e-waste burden<\/strong><\/p>\n\n\n\n

BAN estimates that, when combined with the conventional e-waste trajectory, global electronic waste generation could reach 196 million to 211 million tonnes per year.<\/p>\n\n\n\n

That would represent an estimated 19% to 28% increase above BAN’s extrapolation of the UN baseline.<\/p>\n\n\n\n

It would also be more than three times today’s annual total.<\/p>\n\n\n\n

Meanwhile, the Global E-waste Monitor reports that the world currently manages only about a fifth of its e-waste responsibly.<\/p>\n\n\n\n

BAN’s own investigations, including Brokers of Shame<\/a>, have found that significant quantities of electronic waste are not properly recycled.<\/p>\n\n\n\n

According to BAN, some waste is exported to highly polluting operations in developing countries. The organisation says this can occur through companies certified as responsible recyclers, with waste ultimately affecting workers, communities and the local environment.<\/p>\n\n\n\n

Against this background, BAN warns that a significant increase in AI-driven electronic waste could place additional pressure on an already strained waste management system.<\/p>\n\n\n\n

PFAS contamination raises additional concerns<\/strong><\/p>\n\n\n\n

BAN also highlights concerns about potential contamination from PFAS, commonly referred to as “Forever Chemicals”.<\/p>\n\n\n\n

The organisation says much of the new AI waste could potentially contain PFAS.<\/p>\n\n\n\n

Regulators are increasingly seeking to restrict these substances because of their persistence and potential toxicity in humans and the environment.<\/p>\n\n\n\n

BAN plans to examine this issue in greater detail in Part 3 of its four-part white paper series.<\/p>\n\n\n\n

Future technology changes could accelerate retirement<\/strong><\/p>\n\n\n\n

The report also warns that AI infrastructure could have shorter operational lifespans than expected.<\/p>\n\n\n\n

BAN identifies three potential “rip-and-replace” transitions that could result in functional infrastructure being retired earlier than scheduled.<\/p>\n\n\n\n

These include potential shifts to superconducting power systems, liquid cooling and new battery chemistries.<\/p>\n\n\n\n

If these technologies are adopted at scale, existing infrastructure could be replaced before reaching the end of its originally expected service life.<\/p>\n\n\n\n

BAN calls for planning before the waste is created<\/strong><\/p>\n\n\n\n

Jim Puckett, Founder and Chief of Strategic Direction at the Basel Action Network, said the environmental discussion around AI has largely focused on electricity, carbon emissions and water consumption.<\/p>\n\n\n\n

He said the hardware supporting AI systems has received less attention.<\/p>\n\n\n\n

“AI may feel weightless, but every model depends on an enormous amount of highly specialized, cutting edge hardware,” Puckett said.<\/p>\n\n\n\n

He added that companies and governments need to plan for the potential waste generated by the expansion of AI infrastructure.<\/p>\n\n\n\n

Puckett also said the scale of the projected waste could create additional pressure on existing systems for managing toxic electronic waste.<\/p>\n\n\n\n

“We cannot ethically manage the e-waste we produce currently,” Puckett said.<\/p>\n\n\n\n

He called for technology companies to develop budgets, infrastructure and plans to address the potential environmental impact before the equipment becomes waste.<\/p>\n\n\n\n

The report argues that prevention and planning are important because waiting until large volumes of equipment have already been retired could leave waste management systems struggling to cope.<\/p>\n\n\n\n

As AI infrastructure continues to expand, AI waste is therefore becoming an increasingly important environmental consideration alongside energy, carbon emissions and water consumption.<\/p>\n\n\n\n

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