AI’s Hidden E-Waste Burden Could Reach 617 Million Tons by 2050, Report Warns

The artificial intelligence boom could generate as much as 617 million metric tons of electronic waste by 2050, according to a new report from the Basel Action Network. The nonprofit says earlier estimates have badly underestimated the problem because they focused on servers and chips while ignoring the full data-center footprint that AI requires.

The warning matters because the hardware behind AI systems does not last forever. Power gear, cooling systems, backup batteries, networking equipment, and related infrastructure all wear out, get replaced, or become obsolete as companies race to expand model training and inference capacity.

Why the AI e-waste problem is bigger than it looks

The core finding from Basel Action Network, or BAN, is that AI’s waste stream is far wider than the industry typically acknowledges. Instead of counting only the visible computing hardware, the group included the surrounding systems that make AI data centers run.

That broader accounting shifts the scale of the issue dramatically. BAN says AI-related electronic equipment retired between 2025 and 2050 could produce between 395 million and 617 million metric tons of waste. In annual terms, that works out to roughly 8.6 million to 13.1 million metric tons per year by mid-century.

For comparison, the organization says the world already generates about 68.3 million tons of e-waste a year, but less than a quarter is formally collected and recycled. Much of the rest is discarded through informal channels, where burning, dismantling, and dumping expose workers and nearby communities to hazardous substances.

“AI may feel weightless, but every model depends on an enormous amount of highly specialized, cutting edge hardware,” BAN founder Jim Puckett said in a statement. “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.”

How did BAN calculate the scale of the waste?

BAN says its estimate is higher than previous research because it counts the full stack of hardware needed to support AI data centers, not just servers and GPUs.

That includes:

• servers and accelerators
• power supply and distribution systems
• cooling equipment
• backup power infrastructure
• networking gear
• related devices likely to be retired early because of AI-driven upgrades

The group also introduced a broader concept it calls “AI Waste Contagion,” which covers equipment outside the data center itself. BAN argues that faster AI adoption could accelerate replacement cycles for telecommunications infrastructure and even consumer devices, creating a wider downstream waste effect.

According to the nonprofit, prior estimates missed about 87% of a data center’s electro-mechanical infrastructure because they concentrated on computing hardware alone.

What is the projected waste per gigawatt?

BAN estimates around 70,000 metric tons of e-waste for every gigawatt of data-center capacity. That projection becomes more significant when paired with McKinsey’s forecast that global data-center capacity could reach as much as 219 gigawatts by 2030.

That growth matters because AI workloads are driving a wave of new server construction, expansion of existing campuses, and enormous demand for cooling, electricity, and backup systems. The more facilities are built, the more equipment will eventually need to be replaced and disposed of.

How does this compare with earlier estimates?

Earlier studies have pointed to a much smaller but still substantial problem. A 2024 analysis estimated AI-related e-waste could reach between 1.2 million and 5 million tons by 2030. Another study published in February projected annual AI server waste of roughly 131,000 to 225,000 tons by the end of the decade.

Those numbers are far below BAN’s new projection, but they are not in conflict so much as they are based on narrower assumptions. BAN’s report takes a more expansive view of what counts as AI-linked hardware, which is why its long-term totals are so much larger.

Why is e-waste such a serious environmental and health issue?

Electronic waste is one of the most difficult waste streams to manage safely because it contains valuable materials mixed with toxic ones. When equipment is dumped, burned, or broken apart without proper controls, it can release lead, chromium, and other dangerous substances into soil, air, and water.

The problem is often worst in informal recycling networks. BAN points to the global reality that most e-waste is not handled through regulated systems. Instead, much of it ends up in low-income countries where workers, including children, may dismantle electronics with little protection.

The World Health Organization has warned for years that informal waste processing can put children at serious risk. In the United States, meanwhile, the country has more data centers than any other nation, but has not ratified the Basel Convention, the international treaty designed to curb hazardous waste trade.

Investigations have also shown that some U.S. recyclers export e-waste abroad, where it can be absorbed into “backyard recycling” systems that lack safety oversight. Those realities make the AI hardware boom not just a climate and industrial issue, but also a public-health and trade concern.

What does the data-center boom mean for the waste stream?

The rapid expansion of data centers is central to the story. AI development depends on huge clusters of specialized servers, but those clusters are only one part of the physical buildout. Every facility requires electrical substations, cooling plants, backup generators or batteries, network switches, cabling, and other supporting systems.

As demand for AI services rises, companies are building out more capacity and refreshing equipment more frequently. That can shorten replacement cycles and increase the amount of retired hardware entering the waste stream.

There is also an efficiency paradox. The industry often emphasizes software breakthroughs and model improvements, but those gains can still trigger more physical infrastructure construction. In practice, each leap in capability may require more compute, more electricity, and more material throughput.

What is “AI Waste Contagion”?

BAN uses the term to describe secondary waste effects caused by AI’s growth beyond the data center itself. The concept includes infrastructure that may be upgraded or abandoned earlier than it otherwise would have been because AI pushes the broader digital ecosystem to change faster.

That could mean telecommunication hardware, edge devices, personal electronics, and other systems that are replaced in response to AI-enabled product cycles. In other words, the waste burden may not stop at the server room door.

Who is most exposed to the consequences?

The people most directly exposed are often not those building the AI systems, but workers and communities at the end of the disposal chain. Informal recyclers, waste pickers, and residents near dumping or burning sites face the highest risk from toxic exposure.

Countries with large-scale import of used electronics often bear much of the environmental and health burden. That makes the issue deeply unequal: the benefits of AI may be concentrated in a relatively small number of wealthy companies and countries, while the waste is handled elsewhere.

BAN argues that without better planning, the AI boom could intensify an already dangerous system in which hazardous electronics are shipped, dismantled, and dumped in ways that harm people far from the companies that created them.

What should governments and companies do now?

Experts in waste management and environmental policy have long argued that electronics should be designed for longer life, easier repair, and safer recycling. BAN’s new report adds urgency to that debate by showing how quickly AI infrastructure may overwhelm current waste systems.

Possible responses include:

1. designing data-center hardware for modular replacement and reuse
2. requiring manufacturers to fund take-back and recycling programs
3. tightening rules on export of hazardous e-waste
4. improving transparency around disposal practices
5. extending the life of equipment through repair and refurbishment

None of those steps will eliminate the waste problem entirely, but they could reduce the most harmful outcomes if adopted before the largest wave of AI infrastructure reaches end of life.

Why this report matters now

The AI industry has spent much of the past two years focused on compute shortages, energy demand, and the race to build larger models. BAN’s report adds another layer to that conversation: the mounting physical residue of the AI buildout.

That matters because the environmental footprint of AI is not limited to carbon emissions from electricity use. It also includes extraction of raw materials, manufacturing of specialized electronics, and the disposal of outdated equipment. If the industry keeps expanding at current speed, the waste burden could become one of its largest hidden costs.

The report does not claim every projected ton of e-waste will be dumped unsafely. But it does argue that the scale is likely to be far larger than policy makers, companies, and the public have been told. If that estimate is anywhere near correct, the question is no longer whether AI creates a waste problem. It is whether existing systems can handle it at all.

AI e-waste at a glance

The following table summarizes the main figures from BAN’s report and the broader context around electronic waste.

In the end, the report’s message is straightforward: AI is not just an invisible software revolution. It is a massive material buildout with a disposal bill that could rival the size of the industry’s biggest promises.