Intimate Strangers by Design: A Uses and Gratifications Analysis of AI Companionship

TL;DR

This study explores user motivations and evolving gratifications in AI companionship through interviews, revealing unique interactive benefits and shifts from instrumental to emotional engagement over time.

Contribution

It introduces new gratification categories specific to AI companions and analyzes how user needs change with ongoing interaction, extending Uses and Gratifications theory.

Findings

AI companions offer gratifications like persistent availability and personalization.

New gratification types include creative collaboration, relational simulation, and romantic reclamation.

User engagement shifts from instrumental to emotional and self-regulated over time.

Abstract

Conversational AI companions have grown prominent in public discourse, yet scholarly understanding of user experiences remains limited, with existing research organized around evaluative poles of harm and benefit rather than examining what users seek, how affordances mediate need fulfillment, or how use evolves over time. Drawing on interviews with 20 users of AI companionship platforms and qualitative content analysis informed by Uses and Gratifications (U&G) theory, this study offers three contributions. First, participants reported gratifications mapping onto established U&G categories but qualitatively inflected by conversational AI's distinctive affordances, such as persistent availability, personalization, and absence of social judgment. Second, several gratifications, creative collaboration as relational co-production, relational simulation as interpersonal training, and sexual/romantic satisfaction as reclamation, do not map onto existing typologies, instead emerging through interactive processes in which users actively simulate experiences with AI. Third, gratifications shifted over time, moving from instrumental entry points toward emotional engagement and, in some cases, self-regulated moderation after therapeutic functions were fulfilled. These findings extend U&G by identifying gratification processes unique to interactive AI and suggest governance efforts would benefit from an empirically grounded understanding of how and why users engage with AI companions.