AI’s Growing Footprint: The Supply Chain Cost of Big Tech

Artificial intelligence is reshaping industries, but its rapid expansion comes with hidden supply chain challenges.

From energy-intensive data centres to resource-heavy hardware, AI’s impact extends far beyond carbon emissions.

Research from UC Riverside and Caltech estimates that pollution from data centres alone has cost the US more than US$5.4bn in healthcare expenses over the past five years, exposing a growing crisis in both sustainability and social equity.

Scope 3 emissions and AI’s supply chain impact

When companies assess their environmental impact, they typically categorise emissions into three groups: Scope 1 (direct emissions from company-owned sources), Scope 2 (indirect emissions from purchased electricity) and Scope 3 (indirect emissions from supply chain and product use).

Scope 3 emissions often make up the largest share of a company’s carbon footprint—yet they remain the hardest to control.

For AI-driven companies like Google, Microsoft and Meta, Scope 3 emissions come from several sources, including the production and disposal of data centre hardware, energy-intensive supply chains and the extraction of raw materials.

The rapid rise of AI means demand for high-performance chips, servers and cooling systems is soaring, leading to a surge in emissions across global supply networks.

The International Energy Agency warns that data centre electricity consumption could double by 2026.

Goldman Sachs predicts that AI-driven demand will push data centres to consume 10% of all US electricity by 2030, up from 4% in 2023. But these figures only capture part of the story.

The full supply chain impact of AI includes emissions from manufacturing semiconductors, transporting equipment and disposing of outdated hardware—all of which contribute to an unsustainable cycle of resource consumption.

Raw materials, waste and the hidden costs of AI infrastructure

Building AI infrastructure requires vast amounts of raw materials, including rare earth metals, copper and lithium.

These elements are essential for semiconductors, batteries and data centre components, yet their extraction carries significant environmental and social consequences.

Mining operations, often located in lower-income regions, are associated with deforestation, water contamination and hazardous working conditions.

Google’s 2024 Environment Report highlights an 88% increase in its data centre water consumption since 2019, with facilities relying heavily on water cooling systems to prevent overheating.

In drought-prone areas like California, this growing demand raises serious sustainability concerns.

Meanwhile, the disposal of AI hardware creates another pressing issue. AI chips and servers have short lifespans and without proper recycling measures, they contribute to the global e-waste problem.

Many components contain toxic materials and improper disposal leads to hazardous waste accumulation. While some companies, such as Amazon Web Services (AWS), are working to incorporate circular economy principles into data centre management, industry-wide solutions remain limited.

The rapid pace of AI development means hardware is constantly being upgraded, intensifying the strain on supply chains and waste management systems.

The social and economic inequities of AI supply chains

AI’s environmental impact does not affect all communities equally.

Data centres are often built in regions with cheaper land and fewer regulations, such as West Virginia and Ohio. These areas, which tend to have lower-income populations, bear the brunt of air and water pollution caused by AI infrastructure.

“Unlike carbon emissions, the health impacts caused by a data centre in one region cannot be offset by cleaner air elsewhere,” says Shaolei Ren, an Associate Professor at UC Riverside.

Supply chain workers and mining communities also experience disproportionate risks.

From unsafe labour conditions in cobalt mines to toxic emissions from semiconductor manufacturing, the human cost of AI development is significant. Despite corporate sustainability pledges, many supply chain issues remain unresolved.

“There are so many issues around carbon credits from an ecological perspective,” adds Sebastián Lehuedé, an Assistant Professor at King’s College London.

“If you consume water somewhere to the point where it affects biodiversity in one area, that cannot be offset by having a nice project elsewhere. You’re going to cause irreversible damage if you keep to that logic.”

Can AI growth be sustainable?

Tech companies are exploring ways to mitigate their supply chain impact, but meaningful progress remains slow.

Microsoft has partnered with Constellation Energy to restart a nuclear power unit at Pennsylvania’s Three Mile Island, aiming to reduce fossil fuel dependency.

“To achieve our goal of becoming carbon negative by 2030, we will need a broad range of innovative carbon-free energy solutions,” says Melanie Nakagawa, Chief Sustainability Officer of Microsoft.

“Advanced nuclear energy and fusion energy are included in our multi-technology approach to reaching this target.”

Efforts to make AI hardware more efficient, reduce reliance on water cooling and develop sustainable chip production methods are underway.

However, as long as AI remains primarily driven by profit, sustainability may remain secondary to expansion.

Public resistance to AI’s environmental impact is growing, with protests against data centre developments in the US, Ireland and Mexico. Communities affected by pollution, water shortages and rising energy costs are demanding greater oversight and accountability from tech giants.

“We are going to reach a tipping point where the increasing cost of data and hence, AI, is not just environmentally expensive but also socially expensive,” concludes Revathi Kollegala, a Digital Strategist at CIFOR-ICRAF.

“This will undermine the logic that AI can democratise access to knowledge and reduce inequity. We may have reached that point already or will very soon.”

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