<p>Since its public release in late 2022, ChatGPT has drawn global attention for its ability to simulate conversation, assist with complex tasks, and generate fluent, human-like text. While much of the debate has focused on issues such as privacy, bias, and automation, the emotional dimension of interacting with such systems remains underexplored. This essay argues that large language models (LLMs) function not only as tools for meaning-making but also as artificial communication partners with affective presence. Drawing on Elena Esposito’s extension of Niklas Luhmann’s systems theory, it reframes communication as a process of selection—utterance, understanding, and response—rather than one of transmission. From this perspective, LLMs are not mere sources of information but interlocutors that participate in emotional resonance, where understanding can transform into feeling. Their outputs do not arise in isolation; rather, they are shaped by layers of human expression embedded in training data and filtered through specific socio technical and socio-affective contexts. These dynamics give rise to phenomena such as AI-driven companionship, digital mourning, and emotional simulation, all of which challenge conventional boundaries between human and non-human agents. LLMs thus emerge as quasi-others—entities capable of eliciting genuine emotional responses despite lacking consciousness or inner life. This condition invites critical reflection on emotional dependency, the aesthetics of authenticity, and the commodification of affect. Overlooking these emotional architectures risks flattening the social and ethical stakes of artificial communication and obscures the ways in which LLMs are reshaping the affective fabric of contemporary life through interpretations of utterances that may evoke emotional responses and foster affective attachments.</p>