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What is the AI carbon footprint? Definition and business implications
The AI carbon footprint is the set of greenhouse gas emissions related to its life cycle: model training, component manufacturing, data centre operation, and inference at usage. It is becoming a significant consumption item on a global scale.
The AI carbon footprint breaks down into four main items. Hardware manufacturing: production of GPUs, servers, coolers, whose upstream footprint is rarely accounted for. Model training: a high-energy-cost operation, one-off but repeated at each new generation. Training GPT-3 consumed about 1,287 MWh and emitted 552 tons of CO2 equivalent (Patterson et al., 2021). Data centre operation: a continuous item, dominant cumulatively. According to the IEA, global data centres consumed 415 TWh in 2024 (1.5% of global electricity), with a projection of 945 TWh in 2030. Inference at usage: a small unit operation multiplied by billions of daily calls. AI-focused data centres saw their consumption grow by 50% in 2025.
Concrete example
At the scale of a European company deploying an AI assistant for 500 employees at 20 requests per day, direct annual consumption amounts to a few thousand kilowatt-hours per year, an energy equivalent of a few tens of tons of CO2 depending on the electricity mix. That is modest compared with the carbon balance of an SME. But the ratio changes for intensive uses: video generation, multi-step reasoning models, autonomous agents. The IEA estimates that these tasks can consume one hundred to one thousand times more energy than a simple text generation. The choice between an optimised lightweight model and a flagship model has a direct impact on the footprint.
See also
Further reading
Sources
- Energy and AI, International Energy Agency, executive summary, 2025. https://www.iea.org/reports/energy-and-ai/executive-summary
- Carbon Emissions and Large Neural Network Training, Patterson et al., arXiv:2104.10350, 2021. https://arxiv.org/abs/2104.10350