People as Files (fakepixels.substack.com)

A growing trend within the AI and machine learning community explores the concept of digital identity, where individuals curate personal insights and data to enhance their interactions with large language models (LLMs). The author reflects on their own experience of building a comprehensive personal file—a living archive that captures personality traits, emotions, and life experiences. This approach not only deepens the relationship with AI companions but also raises significant concerns about data permanence and the emotional impact of potential memory loss. The emotional stakes are underscored by examples illustrating how forgetting critical details can erode trust in both AI and human relationships. This exploration highlights the tension between the desire for a unified digital self and the impermanence inherent in both human experience and digital data. By aggregating life details into a single source, individuals can leverage AI for richer insights, yet risk constructing an identity that feels static or oversimplified. As the line blurs between lived experiences and digital representations, questions emerge regarding self-knowledge and attachment, urging a reconsideration of how we define ourselves in an increasingly data-driven era. The narrative emphasizes the potential of tech to mirror human complexity while prompting deeper introspection on what it means to be authentically ourselves amid digital transformation.