Join Turing Award laureate David Patterson for a look at the environmental considerations of AI. David will give tongue-in-cheek advice on how to make AI’s carbon footprint worse, and then how to make it better. He will dispel common fallacies about AI’s emissions. Learn key factors influencing AI's carbon footprint and gain valuable perspectives on building more sustainable AI systems.

Resources: 
The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink → https://goo.gle/3GMakKa 
Energy and Emissions of Machine Learning on Smartphones versus the Cloud: A Google Case Study → https://goo.gle/4k9Nlab 
Energy Is Physics, but Emissions Is Accounting: What’s Really Green? → https://goo.gle/459Tpeq 
Life-Cycle Emissions of AI Hardware: A Cradle-To-Grave Approach and Generational Trends → https://goo.gle/44vWPIo 

Speakers: David Patterson

Check out the AI session track from Google I/O 2025 → https://goo.gle/io25-ai-yt
Check out all the Dialogues sessions from Google I/O 2025 → https://goo.gle/io25-dialogues  
Check out all of the sessions from Google I/O 2025→ https://goo.gle/io25-sessions-yt  

Subscribe to Google for Developers → https://goo.gle/developers


Event: Google I/O 2025

Products Mentioned: AI/Machine Learning

Google for Developers

David Patterson explores the environmental impact of AI, focusing on how choices like model size, hardware, and data center location affect carbon emissions. He introduces the four Ms of emissions model and shares strategies for reducing AI's carbon footprint, including using efficient hardware and greener energy sources. Patterson also discusses misconceptions around the carbon footprint of newer technology and offers insights into better practices for sustainable AI development.

Turing winner David Patterson: how to give AI a bad carbon footprint