AI workloads projected to use >50% of data‑center power by 2028, paper warns
When I skimmed the newest white paper on AI infrastructure, the numbers caught my eye. A group of researchers mapped not only the spike in compute cycles but also the widening environmental toll of each fresh model. They point to a chain of side effects: cooling plants that drink ever more water, piles of discarded hardware growing faster, and a frantic hunt for the rare-earth minerals that feed the latest chips.
By linking these trends, the authors suggest the sector’s hunger for resources could soon outstrip what current supply chains and sustainability plans can handle. Their timeline is concrete, flagging a possible tipping point in just a few years. As the industry pushes to scale, the paper feels like a warning bell for operators, policymakers and investors alike.
*Researchers predict AI workloads will account for more than 50% of data centre power consumption by 2028. In addition to rising energy use, the paper highlights growing water consumption for cooling systems, e-waste generation and the extraction of rare-earth minerals for hardware production. "The r*
Researchers predict AI workloads will account for more than 50% of data centre power consumption by 2028. In addition to rising energy use, the paper highlights growing water consumption for cooling systems, e-waste generation and the extraction of rare-earth minerals for hardware production. "The resource consequences of AI's rapid growth and adoption are daunting, but the technology also can empower innovative solutions to the environmental problems it creates," David Costa, head of sustainability innovation headquarters at NTT DATA, said.
"AI's amazing capabilities can help manage energy grids more efficiently, reduce overall emissions, model environmental risks and improve water conservation. It's vital for organisations to recognise the challenge and build sustainability into AI systems from the start." The paper urges organisations to move beyond traditional performance metrics such as accuracy and speed, and to incorporate efficiency and sustainability as core design principles. Moreover, it calls for standard and verifiable metrics to quantify AI's environmental impact, including its energy use, carbon emissions and water footprint, with benchmarks such as the 'AI Energy Score' and 'Software Carbon Intensity for AI'.
NTT DATA's researchers advocate a lifecycle-centric approach to AI, incorporating sustainability from raw material extraction and hardware manufacturing to system deployment and eventual disposal.
The NTT DATA white paper sketches a worrying scenario: by 2028 AI might gobble up more than half of data-center power, cooling water use could rise, and e-waste is set to swell. It also points out that meeting the hardware demand will probably push rare-earth mining even higher. What the report skips, though, is any clear timeline for how fast mitigation steps could balance those trends, so there’s a noticeable gap.
The call to weave sustainability into every AI phase sounds reasonable, yet the road map stays fuzzy. If companies brush off the warning, the environmental toll could outstrip AI’s promised gains. On the flip side, the paper hints that AI might help climate work, but concrete cases are few.
Bottom line: the numbers flag real resource pressure; the suggested actions make sense but their execution is still uncertain. Stakeholders will have to turn the broad advice into specific, enforceable measures before the 2028 milestone turns into reality.
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Common Questions Answered
What percentage of data‑center power consumption are AI workloads projected to use by 2028?
According to the white paper, researchers predict AI workloads will account for more than 50% of data‑center electricity by 2028, marking a dramatic increase from current levels.
How does the rising AI compute demand affect water usage in data‑center cooling systems?
The report notes that as AI workloads grow, cooling plants must operate larger and more intensively, leading to a significant rise in water consumption for heat removal, which compounds the overall environmental footprint.
What environmental concerns are linked to the hardware required for expanding AI workloads?
The paper highlights three main concerns: an accelerating stream of e‑waste from discarded servers, increased extraction of rare‑earth minerals needed for advanced chips, and the associated ecological impacts of mining and disposal.
Does the NTT DATA white paper provide timelines for mitigation strategies to offset AI’s resource impact?
While the white paper stresses the need for sustainability across all AI stages, it does not quantify how quickly mitigation measures—such as renewable energy adoption or recycling programs—could counteract the projected increases in power, water, and material usage.