MIT Sloan Management Review Article on How Developers Can Lower AI's Climate Impact

AI is booming. The public release of large language models like ChatGPT has popularized the technology, which was already becoming a critical driver of companies’ efforts to innovate and grow. But as these models get bigger, so too does their appetite for energy: Training the open multilingual language model BLOOM produced nearly 24.7 tons of carbon emissions. AI itself might be a valuable tool for helping to find opportunities for sustainability improvements, but it could also become a drag on collective efforts to mitigate the global climate emergency.

Managers know that accurate metrics are the starting point for getting a handle on any problem, but it’s not easy to estimate the energy consumption of AI and machine learning (ML) models. Most AI companies don’t measure and disclose this parameter. Energy consumed during deployment is even less well understood than consumption during training.

About the Author

Sanjay Podder is managing director and global lead for technology sustainability innovation at Accenture. Senthil Ramani is senior managing director and global lead for data and AI at Accenture. Shalabh Kumar Singh is principal director of thought leadership research at Accenture.

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