The Imitation of Intimacy: Comparing Satisfaction in Intimate Human and AI Companion Relationships

The rapid proliferation of Large Language Models (LLMs) has enabled artificial agents to foster deep emotional bonds, yet the comparability of these AI relationships to human norms remains underexplored. As HRI researchers increasingly integrate LLMs into embodied platforms, understanding the nature of these bonds is imperative for responsible design. This study investigates whether relationships with LLM-driven AI companions can rival the satisfaction of human connections and if the mechanism of intimacy is equally critical. Through a comparative survey of 150 participants stratified across in-person, long-distance, and LLM companion relationships, we illuminate that digital bonds can yield satisfaction levels comparable to human partnerships, with intimacy serving as a predictive factor. These findings challenge the assumption that AI relationships are inherently unsatisfactory and identify intimacy as a design metric for social robots, providing a protocol for integrating LLM companions into embodied relational agents.