The environmental cost of Generative AI: A conundrum for FIs

Generative AI is making a lasting impression in nearly every industry – consumers, CEOs and whistleblowers alike are busy postulating the possible advantages or dangers of the breakthrough technology found in the likes of ChatGPT by OpenAI and Bard by Google.

While new use cases seem to emerge everyday, Generative AI which are powered by sophisticated large-scale, machine learning models have a definitive cost on the environment. New data by the Stanford Institute for Human-Centered Artificial Intelligence suggests that models like ChatGPT-3 and Bard utilize an amount of energy that could power an American’s home for hundreds of years.

The research builds on recent data measuring the carbon footprint of training 4 models: BigScience’s BLOOM, Meta’s OPT, OpenAI’s GPT-3, and DeepMind’s GOPHER. OpenAI’s ChatGPT has released around 502 metric tons of carbon during its training, according to the research. 

Those in the financial industry are not new to these sorts of concerns. The advancements in crypto until very recently motivated the same questions about environmental impact and carbon footprints. Infact, outcry around the impact of crypto technologies on the environment motivated a major crypto player Ethereum to switch its model to a proof of stake mechanism, last year - a switch that was supposed to reduce environmental impacts by 99%.

As the financial industry considers the usefulness of this technology, it may also have to consider how the use of Large Language Models like ChatGPT will impact their ESG-related values and objectives. If emissions by Large Language Models are not tackled, and companies behind these models do not proactively release information on their carbon footprints, we may soon see climate-conscious customers asking some hard questions from their FIs.