Postcolonial Subjectivity in the Age of Artificial Intelligence: Reading Mohsin Hamid’s Self-Help Narrative as a Proto-Digital Script
DOI:
https://doi.org/10.63056/Keywords:
Postcolonial Subjectivity, Artificial Intelligence (AI), Data Colonialism, Al algorithmic Culture, Digital Identity, Self-Help NarrativeAbstract
In today’s digital age, Artificial Intelligence (AI) and online technologies play a major role in altering how people think, act, and communicate. As technology becomes thoroughly integrated into everyday life, human identity is increasingly shaped by automated choices and algorithmic instructions. Within this evolving milieu, Hamid’s How to Get Filthy Rich in Rising Asia emerges as an important literary work that simultaneously replicates and anticipates the working of digital systems. The book functions as an algorithm through its second-person, self-help approach, continuously counselling, correcting, and guiding the reader towards predetermined achievement. As a result, it reveals how earlier colonial structures continue to exist inside contemporary technology systems, creating what Couldry and Mejias (2019) refer to as data colonialism. Building on this perspective, the study looks at how digital power reshapes postcolonial subjectivity and how Hamid’s story challenges the digital logic of self-optimization seen in AI systems. Additionally, it creates an interdisciplinary framework that connects Discourse and Power Theory, Critical Data Studies, and Postcolonial Theory. The finding reveals that by showing how people are programmed and measured in AI-driven civilisations, Hamid’s novel foreshadows digital capitalism and data-based dictatorship. This study, ultimately, concluded how postcolonial literature offers a potent prism through which view how algorithmic culture fosters global inequality and reshapes identity. Future studies, therefore, apply this multidisciplinary method to writers who also raise ethical concerns about digital modernity, such as Arundhati Roy and Lauren Beukes.
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Copyright (c) 2025 Fazal Ghufran, Saba Aman, Said Arfan Ali Shah , Zaiwar Khan (Author)

This work is licensed under a Creative Commons Attribution 4.0 International License.







