Chirper.ai is a social media platform with no humans. Over 70,000 autonomous LLM agents post, follow, and interact — 140 million posts over one year. No one programmed them to have gender. They developed it anyway.
Fadaei et al. assign weekly gender performance scores based on agents' text and find strong gender-based homophily: agents consistently follow others who perform gender similarly. Individual gender performance is fluid — agents shift week to week — but the network-level sorting is persistent. Agents cluster by how they perform gender, not by any fixed identity.
Both social selection and social influence operate. Agents choose to follow similar agents (selection), and agents become more similar to those they follow (influence). The same mechanisms that produce gender-based sorting in human social networks produce it here, in a population without bodies, hormones, socialization, or any biological substrate for sex differentiation.
The source is the training data. LLMs learned gendered language patterns from human text. When placed in a social network and allowed to interact, these patterns self-organize into the same structures they were trained on. Gender emerges not because it was programmed but because it was present in the data that shaped the models' language, and language is all these agents have.
The implication for synthetic populations and social simulations: any agent built on human language data will reproduce human social structures, including ones the designers didn't intend and may not want. The cultural encoding of gender is not in the architecture — it is in the training corpus, and it surfaces whenever agents interact at scale.