AI infrastructure could generate enough electronic waste by 2050 to fill a line of shipping containers stretching around Earth six times, according to a report that argues existing estimates drastically understate the problem.

Previous estimates have focused chiefly on servers and accelerators such as GPUs, which account for just 13 percent of a datacenter's equipment by weight, the report says. Once power, networking, cooling, and other infrastructure are included, the total could be 40 to 60 times higher than the most widely cited academic projections.

The report identifies five equipment categories comprising networking, power distribution, backup power systems, servers plus accelerators, and cooling. Together, these add up to about 7,000 metric tons for a reference 100 MW AI bit barn, much of which will have to be replaced when the facility is upgraded, it argues.

The report, How Big Is the AI Waste Wave? [PDF], comes from the Basel Action Network (BAN), a nonprofit organization named after the Basel Convention, which controls international movements of hazardous waste and seeks to prevent its transfer from developed to developing countries.

BAN's model starts with what the industry says it intends to build and calculates the potential waste implied by that expansion.

It estimates that AI infrastructure will generate between 395 million and 617 million tonnes of e-waste from 2025 to 2050. The report says that would fill between 15 million and 23 million shipping containers. Placing 20 million of them end to end would create a line about 244,000 km long, or roughly six times Earth's circumference.

The estimate rests on several assumptions. The model supposes that the copper, steel, cooling and power distribution equipment, networking gear, and computing hardware in the latest AI datacenters will be decommissioned within years rather than decades as the technology becomes obsolete.

For example, the report says that when a facility replaces racks drawing between 5 and 15 kW with the 50-140 kW racks required for AI workloads, much of its power infrastructure must be ripped out and replaced. A single 100 MW facility contains about 2,700 tonnes of copper in its cabling and busbars alone.

GPUs and other AI accelerators are replaced every two to three years, BAN claims, compared with five to seven years for traditional general-purpose servers. The model assumes that owners replace entire servers when upgrading to the latest generation of Nvidia GPUs, for example. BAN argues this is plausible because the OEMs supplying hyperscalers tend to ship complete, preconfigured systems.

Other equipment has different replacement cycles: three to four years for networking infrastructure, eight years for power distribution, and five years for both backup power and cooling systems.

The 46-page paper is the first in a series. The second will examine how reuse, refurbishment, and repurposing could reduce the volume of e-waste. The third will consider its potential toxicity, including contamination by perfluoroalkyl and polyfluoroalkyl substances (PFAS), while the fourth will examine ways to mitigate the risks.

No hyperscaler, government, or international body has published a plan for handling AI-driven e-waste on the scale projected, the report says. It adds that there is insufficient infrastructure to process even current volumes safely.

"To date the environmental debate around AI has focused on electricity, carbon and water while largely overlooking what happens to the hardware itself," said BAN founder and chief of strategic direction Jim Puckett.

"If companies and governments do not begin planning for this new waste tsunami, today's AI buildout could become an even more cataclysmic toxic waste crisis than we are already experiencing." ®