From General Intelligence to Sustainable Adaptation: A Critical Review of Large-Scale AI Empowering People’s Livelihood

Jiayi Li () and Peiying Zhang
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Jiayi Li: Party School of the CPC Central Committee, National Academy of Governance, Beijing 100089, China
Peiying Zhang: Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China

Sustainability, 2025, vol. 17, issue 20, 1-26

Abstract: The advent of large-scale AI models (LAMs) marks a pivotal shift in technological innovation with profound societal implications. While demonstrating unprecedented potential to enhance human well-being by fostering efficiency and accessibility in critical domains like medicine, agriculture, and education, their rapid deployment presents a double-edged sword. This progress is accompanied by significant, often under-examined, sustainability costs, including large environmental footprints, the risk of exacerbating social inequities via algorithmic bias, and challenges to economic fairness. This paper provides a balanced and critical review of LAMs’ applications across five key livelihood domains, viewed through the lens of sustainability science. We systematically analyze the inherent trade-offs between their socio-economic benefits and their environmental and social costs. We conclude by arguing for a paradigm shift towards ‘Sustainable AI’ and provide actionable, multi-stakeholder recommendations for aligning artificial intelligence with the long-term goals of a more equitable, resilient, and environmentally responsible world.

Keywords: artificial intelligence (AI); large-scale AI models (LAMs); large language models (LLMs); Sustainable AI; sustainability; socio-economic impact; ethical AI (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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