Tracking the carbon footprint of global generative artificial intelligence
Zhaohao Ding, Jianxiao Wang, Yiyang Song, Xiaokang Zheng, Guannan He, Xiupeng Chen, Tiance Zhang, Wei-Jen Lee, Jie Song
Abstract
In recent years, generative artificial intelligence (GAI) has gained unprecedented attention. Unlike conventional AI, GAI can generate innovative and meaningful content across texts, images, and videos. The success of OpenAI’s ChatGPT has driven global tech companies to develop high-performance models and integrate GAI into products.1 This AI arms race continues, as shown by OpenAI’s text-to-video model, Sora, and Anthropic’s new large language model, Claude 3. The release of DeepSeek V3/R1 has sparked a global AI cost revolution.
Topics & Concepts
Carbon footprintFootprintTracking (education)Generative grammarArtificial intelligenceCarbon fibersEnvironmental scienceComputer scienceGeographyPsychologyGeologyOceanographyGreenhouse gasAlgorithmPedagogyComposite numberArchaeologySpace Science and Extraterrestrial Life