Boundary conditions between humans and AI: insights from Paul Virilio's philosophy

Abstract

Purpose We adopted a qualitative and conceptual design grounded in a phenomenological approach, using illustrative use cases to explore the evolving boundaries between humans and artificial intelligence (AI) machines. This article is conceptual in nature, and the methodology aims to deepen the analysis by drawing on Paul Virilio's theoretical framework, particularly his reflections on speed, technology and perception. This foundation enables a critical exploration of the human-AI interface beyond empirical observation, engaging with the philosophical implications of technological acceleration and its impact on human agency. Design/methodology/approach This research aims to provide an original and useful perspective on the changing boundaries between the domains of human capabilities and those of AI machines, drawing on the contributions of technology philosopher Paul Virilio. Our objective is to apply Virilio's philosophical concepts such as “dromology,” “perception prostheses,” “grey ecology” and the “accident of accidents” to better delineate the frontiers of human and machine interaction. Findings We provided a deep understanding of how Virilio's philosophy can help understand the evolving human-AI boundaries. This research emphasizes the need to ensure that the human-machine relationship is truly an “exchange relationship”. We have highlighted the necessity to conceive and imagine new forms of human-AI collaboration, particularly those related to attitudes, perceptions and actions towards AI. To prevent the spread of accidents, it is imperative that human-AI complementarity is guaranteed, which can only be achieved if humans preserve their self-identity and perceive AI machines as an integral part (and not a threat) of their work identity. Originality/value Unlike traditional studies that focus solely on the technical or ethical implications of AI, this research reconceptualizes the boundaries between human action and machine autonomy through the lens of existential risk and temporal compression. It offers a rich and alternative viewpoint, particularly for managers, enabling them to better understand the evolving roles of humans and AI machines in organizational contexts. This research is also original in that it proposes a “human-AI boundary condition matrix”, offering a conceptual tool to map and make sense of the shifting relational dynamics between human agency and machine decision-making.