AI emissions guide
AI Carbon Footprint
Artificial intelligence offers incredible capabilities, but it also relies on heavy compute infrastructure. Use this guide to understand what drives AI emissions and compare the tools you actually use.
Training and everyday use are different questions
Training headlines show the scale of model development, while most people really want to understand everyday inference: repeated use of tools like ChatGPT.
Hardware and cooling still matter
AI workloads sit on top of accelerator-heavy servers, networking, and cooling systems, so the visible product is only part of the footprint story.
Highest estimated emitters
Good pages to start with
Recommended pages
Explore related calculators and guides
Claude
Claude's carbon footprint involves large-scale inference processing in data centers. Each prompt contributes to electricity demand for both processing and cooling.
Midjourney
Midjourney requires massive GPU power to generate high-resolution images. Image generation models typically consume more energy per request than text models.
Gemini
Gemini's multimodal capabilities mean it processes text, images, and audio, all of which require significant data center compute power.
ChatGPT
ChatGPT's carbon footprint comes from every request, response, and supporting system behind the model. If you're asking how much CO2 ChatGPT produces, the answer depends on model size, response length, and how often you use it.
Side-by-side comparisons
Compare related apps and habits
ChatGPT vs Claude
See two related digital habits side by side.
ChatGPT vs Midjourney
See two related digital habits side by side.
ChatGPT vs Gemini
See two related digital habits side by side.
FAQ
Common questions
Why isn't there one simple AI number?
Because emissions vary with model size, hardware, prompt volume, and how often you use the tool.
What should I do after reading this guide?
Start with the biggest drivers, then open the calculator page that matches the AI tool you use.