AI Data-Center E-Waste Estimates Jump When the Whole Facility Counts

AI data-center e-waste estimates become dramatically larger when the accounting includes the whole facility instead of only GPUs and servers. A September 15, 2026 analysis from the Basel Action Network (BAN) projects that 395 million to 617 million tonnes of AI-driven electronic equipment could be retired between 2025 and 2050. BAN's model includes power distribution, cooling, backup power and networking equipment in addition to compute hardware.

That number is a scenario from a nonprofit's model, not a measured future waste stream. Its importance is the accounting boundary: AI infrastructure is a physical system whose waste footprint extends far beyond the processors used to run models.

The accounting boundary changed

Earlier AI e-waste estimates commonly concentrated on servers and accelerators. BAN argues that this misses most of the electro-mechanical equipment required to operate a modern data center.

Its model adds:

• servers and AI accelerators;
• racks and associated hardware;
• networking equipment;
• cooling systems;
• power supply and distribution equipment;
• backup power systems;
• and a separate category BAN calls AI Waste Contagion, covering equipment outside data centers that could be retired sooner because of AI-driven performance requirements.

The resulting range is much larger than a server-only calculation.

The headline number is a model, not an observation

BAN estimates 395–617 million tonnes of AI-driven equipment retirements over 2025–2050. At the high end, the organization compares the mass with roughly 23 million 40-foot shipping containers, enough for a line around Earth several times.

The distinction between measurement and projection matters.

observed today
    |
    +--> installed equipment
    +--> replacement practices
    +--> published capacity forecasts
    |
    v
model assumptions
    |
    +--> equipment mass
    +--> useful life
    +--> capacity growth
    +--> replacement timing
    |
    v
2050 scenario range

The final number is therefore sensitive to assumptions that can change.

Replacement cycles are part of the waste problem

BAN's model assigns shorter lifetimes to some AI-era infrastructure than to traditional general-purpose computing. Independent recycling-sector reporting describes assumptions around roughly 2.5 years for accelerators, servers and racks, about 3.5 years for networking, and five years for backup power and cooling, with longer assumptions for power distribution.

Those are modeling inputs, not universal engineering laws.

A server can remain useful after its original owner upgrades. It can be redeployed, resold, refurbished or dismantled for material recovery. Conversely, a newer high-density workload can make an older system economically unattractive before its physical components fail.

The environmental outcome therefore depends on replacement plus reuse, not replacement alone.

Why power and cooling belong in the same conversation

AI infrastructure is unusually power- and cooling-intensive. The Observatory's earlier AI data-center flexible-load analysis examined the electricity side of this physical buildout: large AI facilities can become meaningful grid loads and may eventually participate in demand-response strategies.

The e-waste analysis adds the end-of-life side.

AI compute demand
      |
      +--> GPUs / accelerators
      +--> servers / racks
      +--> networking
      +--> power distribution
      +--> cooling
      +--> backup power
      |
      v
installation
      |
      v
replacement / reuse / retirement
      |
      v
e-waste stream

This is a useful systems boundary because optimizing electricity use does not automatically optimize material use.

The AI Waste Contagion idea is broader still

BAN uses AI Waste Contagion for equipment outside data centers that may become obsolete sooner because AI workloads raise performance requirements.

That could include computers, telecommunications equipment and other digital infrastructure that cannot economically keep pace with the demands created by AI services. Independent reporting describes this as a second waste pathway rather than part of the physical data-center inventory itself.

This is conceptually important but should be treated cautiously. It is harder to establish causality for a device retired because “AI advanced” than for a server replaced at a documented data-center refresh cycle.

The 2030 number may be more useful than the 2050 headline

BAN's model projects roughly 8.6 to 13.1 million tonnes of AI-linked equipment retirement per year by 2030 under its scenarios.

A near-term annual figure is operationally easier to reason about than a 25-year cumulative total. Recyclers, asset-disposition companies, data-center operators and policymakers can ask whether collection, refurbishment and material-processing capacity is expanding at a comparable rate.

That is where the report becomes more than a distant environmental warning: it points toward a capacity-planning problem.

Recycling is not the only lever

The most direct way to reduce waste is not necessarily to recycle more after retirement. It can be to keep equipment in service longer when doing so remains technically and economically sensible.

A circular strategy can be represented as:

new deployment
     |
     v
primary service
     |
     +--> workload migration
     |
     v
secondary use
     |
     +--> refurbishment
     |
     v
material recovery
     |
     v
responsible recycling

The useful policy question is therefore not simply “how do we recycle AI hardware?” It is “which hardware should be retired, which can be reused, and what recovery infrastructure is needed for the remainder?”

The connection to water and energy accounting

The Observatory's Software Water Intensity analysis tracks another emerging attempt to measure an environmental externality that software systems traditionally ignored.

The parallel is useful: the boundary of measurement determines the boundary of the problem you can see.

Counting only GPU electricity can hide cooling and grid infrastructure. Counting only server mass can hide power, networking and cooling equipment. Counting only direct data-center hardware can hide equipment retired elsewhere because of faster AI-driven performance requirements.

None of these boundaries is automatically “the correct one.” The important requirement is to publish the boundary so readers can reproduce or challenge the result.

What remains uncertain

BAN's 395–617 million tonne estimate is a scenario range produced from assumptions about future data-center growth, equipment mass and replacement cycles. It should not be presented as a forecast with a known probability.

The report's comparison with previous studies also depends on different accounting boundaries. A server-and-accelerator study and a full-facility study are not measuring exactly the same object.

Finally, the eventual waste stream could be smaller if equipment remains in service longer, is refurbished, moves into secondary markets or uses more durable infrastructure. It could also be larger if AI capacity growth and refresh cycles exceed the assumptions.

Why this matters

AI infrastructure is often described in terms of models, accelerators, power and cooling. The waste analysis adds a missing lifecycle stage: what happens to all of that hardware when the next generation arrives?

The durable observation is not that BAN has predicted an inevitable 617-million-tonne waste event. It is that AI infrastructure accounting needs a whole-facility lifecycle boundary if it is meant to describe the physical footprint of the system rather than only its most visible components.

Sources and further reading

• Basel Action Network — AI E-Waste Analysis
• The Verge — AI data-center e-waste analysis
• Resource Recycling — BAN e-scrap analysis
• ScrapMonster — AI e-waste analysis

Related Digital Observatory coverage: AI data centers as flexible grid loads, Software Water Intensity, and Semicon 2.0 and India's semiconductor ecosystem.