Understanding the Scale of AI
Boris Gamazaychikov
CEO of Sustainable AI Group
AI has scaled staggering amounts. Watch to find out just how much.
AI has scaled staggering amounts. Watch to find out just how much.
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Understanding the Scale of AI
2 mins 29 secs
Key learning objectives:
Understand AI as an environmental issue
Outline how environmental impact of AI differs between buyers and builders
Overview:
Generative AI has scaled massively across data, processing power, and energy. Since 2022, the industry has pushed this approach to extremes, causing the energy required to develop these models to double every year. Although data centres currently use a small portion of global electricity, their consumption is growing rapidly and is heavily concentrated in specific regions.
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Summary
Why has AI become such a significant environmental issue?
Generative AI has scaled massively across data, processing power, and energy. Since 2022, the industry has pushed this approach to extremes, causing the energy required to develop these models to double every year. This escalating demand for power has placed AI's environmental footprint firmly in the spotlight.
How is data centre infrastructure changing to support this scale?
Data centres are expanding dramatically, with 1-gigawatt facilities becoming standard (a 100-fold increase over older sites). Although data centres currently use a small portion of global electricity, their consumption is growing rapidly and is heavily concentrated in specific regions. This creates a distinct split where anyone with a phone can use these tools but there is a strain on local energy and water supplies that is felt entirely by the communities hosting the facilities.
How does the environmental impact of AI differ between buyers and builders?
Generative AI has scaled massively across data, processing power, and energy. Since 2022, the industry has pushed this approach to extremes, causing the energy required to develop these models to double every year. This escalating demand for power has placed AI's environmental footprint firmly in the spotlight.
How is data centre infrastructure changing to support this scale?
Data centres are expanding dramatically, with 1-gigawatt facilities becoming standard (a 100-fold increase over older sites). Although data centres currently use a small portion of global electricity, their consumption is growing rapidly and is heavily concentrated in specific regions. This creates a distinct split where anyone with a phone can use these tools but there is a strain on local energy and water supplies that is felt entirely by the communities hosting the facilities.
How does the environmental impact of AI differ between buyers and builders?
- Builders of AI: Concentrated local impacts create direct business risks, including local opposition, scarce power and water, budget overruns, and reputational damage.
- Buyers of AI: The footprint counts directly towards the buying organisation's Scope 3 emissions accounting, cost and reputational issues faced by builders can bubble up to affect the buyer and buyers are increasingly asking vendors for their environmental footprint.
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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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