A Futuristic Perspective of the Usage of AI: Growth, Merits and Limitations

Mrinalini Deswal

Students and Academic Group, Royal Melbourne Institute of Technology (RMIT), Melbourne, Australia.

Abstract

Artificial Intelligence (AI) has shifted from an academic curiosity to a powerful economic and social force that is transforming industries, governance, and daily life. This paper offers a forward-looking, comprehensive analysis of AI, synthesising technical, economic, and policy literature. It emphasises AI’s dual potential as a driver of economic growth and a source of significant societal risks. The paper details AI’s rapid progression from task-specific models to general-purpose systems, highlighting the substantial economic value and productivity improvements already achieved in sectors such as healthcare, finance, and manufacturing. It also highlights major benefits, including human augmentation, personalised services, and accelerated scientific discovery.
However, the paper strongly cautions against unchecked adoption, outlining critical risks such as bias, privacy erosion, model brittleness, misinformation, and labour displacement. It underscores that high-stakes applications—including autonomous weapons, mass surveillance, and automated sentencing—necessitate strict governance or even moratoriums. To address these challenges, the paper presents a comprehensive roadmap for fostering a responsible AI future, advocating for risk-based regulations, increased investment in safety research, robust auditing regimes, and social policies designed to ensure equitable access to AI’s benefits while actively mitigating its associated harms. In conclusion, the paper posits that AI holds the potential for transformative societal benefits—only if its deployment is guided by strong governance, aligned incentives, and significant investment in human capabilities.

Keywords: Artificial intelligence (AI); economic growth; societal risks; accelerated scientific discovery; privacy erosion; labour displacement; risk-based regulation; robust audit regimes

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