The Emergence of the Nighttime Artificial Intelligence-Robot-Driven Economy

Steve Lee (), Won-Yong Oh () and Irene Yi ()
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Steve Lee: Aprilli Design Lab
Won-Yong Oh: Lee Business School, University of Nevada
Irene Yi: Rotman School of Management, University of Toronto

Chapter 4 in Artificial Intelligence for Sustainability, 2024, pp 55-79 from Springer

Abstract: Abstract As digital technology advances, the emergence of an autonomous economy driven by artificial intelligence (AI) and robots is becoming a more realistic prospect. With highly capable corporate AIs assuming desk jobs and management roles and AI-driven robots performing physically demanding tasks, businesses may operate more autonomously in the future, requiring minimal human intervention. As AI-robot-driven businesses evolve, so too might self-sustaining systems customized to particular needs and conditions develop, accounting for various factors, such as the capabilities of AI and robots, optimal working conditions, cost-effectiveness, and resource access. In this scenario, AI-robot-driven businesses could establish collaborative networks within their own circular economy, facilitating sharing of resources, data, and infrastructure among AI systems to improve production, delivery, recollection, and recycling processes. Operations could be optimized by utilizing underutilized areas and peripheral locations for human activities, as well as taking advantage of nighttime operations during human rest periods. This article outlines how nighttime operations offer various advantages, including cooler temperatures, reduced energy costs, and improved infrastructure and road accessibility with minimal human intervention. We argue that the associated cost savings are likely to stimulate the development of an independent nighttime economy driven by AI and robots, separate from the human-centered daytime economy.

Keywords: AI-robot-driven economy; Nighttime economy; Circular economy (CE); Autonomous businesses; AI-robot-driven sharing economy (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-3-031-49979-1_4

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