Carbon Footprint

The carbon footprint of LLMs not only affects our environment, but also our economy. It is important to recognize carbon impacts are externalized to the commons—not factored into the operational costs of the platform, nor considered the responsibility of the platform. However, the expense of high energy consumption drives a publicly acceptable case for privatizing and commodifying these platforms, driving them away from public, open-source resources to subscriber-based models for corporate/investor profit. As such, while the negative carbon impact is unevenly but publicly shared, the benefit and profit of use is privatized.

The carbon footprint spans all three temporalities of design and development, operationalization, and future legacy.

Read more: 

• Is generative AI bad for the environment? A computer scientist explains the carbon footprint of ChatGPT and its cousins
• The Internet’s Invisible Carbon Footprint
• How to Make Generative AI Greener
• As the AI industry booms, what toll will it take on the environment?
• The mounting human and environmental costs of generative AI
• Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model
• The Staggering Ecological Impacts of Computation and the Cloud
• Environmental Impact of Ubiquitous Generative AI (Paywall)
• Training a single AI model can emit as much carbon as five cars in their lifetimes: Deep learning has a terrible carbon footprint
• The Generative AI Race Has a Dirty Secret: Integrating large language models into search engines could mean a fivefold increase in computing power and huge carbon emissions
• Ethical and social risks of harm from Language Models
• Machine Learning CO2 Impact calculator
• How Big is the CO2 Footprint of AI Models? ChatGPT’s Emissions
• On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?