AI won’t cause climate change, it will solve it
Richards et al. 23 (Blake Richards is an associate professor of computer science and neuroscience at McGill University, and a Canada CIFAR AI Chair at Mila, the Quebec AI Institute. Blaise Agüera y Arcas is a vice president and fellow at Google Research, where he leads an organization working on basic research, product development and infrastructure for AI. Guillaume Lajoie is an associated professor in the Mathematics and Statistics Department of the Université de Montréal, and a core member of Mila, The Québec AI Institute. Dhanya Sridhar is an assistant professor of computer science at the Université de Montréal, a core member of Mila, the Québec AI Institute, and a Canada CIFAR AI Chair. “The Illusion Of AI’s Existential Risk” 7/18/2023. Accessed 5/30/2024. https://www.noemamag.com/the-illusion-of-ais-existential-risk/) wtk
However, the carbon emissions of AI training and inference today are minuscule compared to the computing sector as a whole, let alone the more carbon-intensive activities of our industrial civilization, such as construction and transportation, which together account for more than a third of our total carbon emissions.
While the use of AI may continue to grow dramatically over the coming years, its energy efficiency will also likely continue to improve dramatically, making it unclear whether AI will ever be among the most significant carbon emitters on a global scale.
It is also worth noting that the current best approach to developing generalist AI is “pre-training” large foundation models like GPT-4 or PaLM, which are not specific to any one task but can be quickly adapted to a variety of uses. While pre-training may be energy-intensive, it need only be done once, replacing the narrow, per-task training required by previous generations of AI.
The emerging generation of general-purpose, multimodal AI will be capable not only of modeling human language, but many other complex systems; such AI will likely play an important role in clean power generation, climate analysis and climate change mitigation. More to the point, any expansion of AI infrastructure, or effectively AI’s energy footprint, is another checkpoint under human control. After all, data centers do not build themselves.