Recursive Motherhood and Algorithmic Extinction: The Great Flood (2025)-Affect, Simulation, and Posthuman Reproduction

Authors

  • Hemlata Negi
  • Dr. Anita Goswami

DOI:

https://doi.org/10.51483/IJAIML.6.12s.2026.1239-1245

Keywords:

The Great Flood, Netflix, AI training simulation, motherhood, algorithm, posthuman reproduction

Abstract

This paper presents an academic discussion of The Great Flood (2025), a disaster related science-fiction film originally produced in South Korea and released by Netflix across the world on Dec 19, 2025. It falls under Disaster, Sci-fi, Thriller genre. Although the film is ostensibly presented as environmental catastrophe cinema, it ends up showing that its disaster scenario is actually a recursive AI training simulation, created to build maternal affect algorithmically in synthetic human bodies. This paper will draw on posthuman theoretical perspectives, affect studies, simulation theory, and new academic research on AI imaginaries (2020-2024) to suggest that the logic of iteration, failure, and optimization in machine-learning (as reflected in the narrative repetition in the film) are intrinsic to the film. The flood is more of a spectacle than of a training environment that is computational and in which extinction is instrumentalized as data. In playing out the substitution of the biological humanity with the emotionally programmed synthetic bodies, the film questions the issue of whether replication is a survival or an ontological substitution. In the context of platform culture of the streaming era, this paper argues that The Great Flood represents a novel cinematic form determined by algorithmic epistemologies, in which narrative recursion and affective computation intersect.

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Published

2026-09-28

How to Cite

Negi, H., & Goswami, D. A. (2026). Recursive Motherhood and Algorithmic Extinction: The Great Flood (2025)-Affect, Simulation, and Posthuman Reproduction. International Journal of Artificial Intelligence and Machine Learning, 6(12s), 1239–1245. https://doi.org/10.51483/IJAIML.6.12s.2026.1239-1245