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The Factors That Make AI Sustainable (Or Not)
3 mins
Key learning objectives:
Outline the problem with AI and power grids
Explain the relationship between AI and water use
Understand what sustainable cooling approaches look like
Outline additional environmental impacts of AI
Overview:
Historically, emissions depended on whether the local power grid ran on clean energy. However, AI developers need so much electricity that grids cannot safely support them. Consequently, developers are installing off-grid gas turbines directly at data centres, which are often dirtier than standard power plants. Likewise, AI relies on rare minerals from deforestation and open-pit mining, while short chip lifespans (1–2 years versus 5 for standard chips) generate unrecycled e-waste. These issues drive social impacts and a growing backlash.
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Summary
What other environmental and social issues contribute to the growing backlash against AI?
AI relies on rare minerals from deforestation and open-pit mining, while short chip lifespans (1–2 years versus 5 for standard chips) generate unrecycled e-waste. These issues drive social impacts and a growing backlash:
What is the problem with AI and power grids?
Historically, emissions depended on whether the local power grid ran on clean energy. However, AI developers need so much electricity that grids cannot safely support them, and developers are unwilling to wait for grid interconnection. Consequently, developers are installing off-grid gas turbines directly at data centres, which are often dirtier than standard power plants.
What is the relationship between AI and water use?
Data centres use water to cool chips heating up from complex calculations. This is a nuanced issue: water cooling increases energy efficiency, making it a sensible choice where water is plentiful. However, many facilities are built in water-stressed regions. In these areas, consuming vast amounts of fresh water strains limited supplies, directly competing with essential residential and agricultural needs.
What do sustainable cooling approaches look like?
Newer methods avoid constantly drawing in fresh water lost to evaporation:
Historically, emissions depended on whether the local power grid ran on clean energy. However, AI developers need so much electricity that grids cannot safely support them, and developers are unwilling to wait for grid interconnection. Consequently, developers are installing off-grid gas turbines directly at data centres, which are often dirtier than standard power plants.
What is the relationship between AI and water use?
Data centres use water to cool chips heating up from complex calculations. This is a nuanced issue: water cooling increases energy efficiency, making it a sensible choice where water is plentiful. However, many facilities are built in water-stressed regions. In these areas, consuming vast amounts of fresh water strains limited supplies, directly competing with essential residential and agricultural needs.
What do sustainable cooling approaches look like?
Newer methods avoid constantly drawing in fresh water lost to evaporation:
- Closed-loop systems: Function like household refrigerators, continuously circulating the same coolant.
- Zero-water approaches: Use specialised fluids and sealed systems needing little to no water, though these remain the exception across the industry.
What other environmental and social issues contribute to the growing backlash against AI?
AI relies on rare minerals from deforestation and open-pit mining, while short chip lifespans (1–2 years versus 5 for standard chips) generate unrecycled e-waste. These issues drive social impacts and a growing backlash:
- Local community risks: Gas turbines degrade air quality, facilities generate noise, and infrastructure can raise consumer energy costs.
- Stakeholder opposition: Local US communities are blocking projects, while employees, customers, and investors express concern.
- Variable outcomes: These impacts are not fixed—they depend entirely on how AI is built, powered, and deployed.
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Boris Gamazaychikov
Boris Gamazaychikov is an AI Sustainability Leader and environmental engineer by training, focused on aligning AI development with climate action. He is the Co-Founder and CEO of Sustainable AI Group, a research and advisory firm helping enterprises understand and act on the environmental impacts of AI. Previously, he served as Head of AI Sustainability at Salesforce, where he built the company's AI sustainability strategy from the ground up and co-created the AI Energy Score - the first standardised benchmark for measuring AI model energy use.
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