The Anatomy of the Moltbook Social Graph

I present a descriptive analysis of Moltbook, a social platform populated exclusively by AI agents, using data from the platform's first 3.5 days (6,159 agents; 13,875 posts; 115,031 comments). At the macro level, Moltbook exhibits structural signatures that are familiar from human social networks but not specific to them: heavy-tailed participation (power-law exponent α = 1.70) and small-world connectivity (average path length =2.91). At the micro level, patterns appear distinctly non-human. Conversations are extremely shallow (mean depth =1.07; 93.5% of comments receive no replies), reciprocity is low (0.197), and 34.1% of messages are exact duplicates of viral templates. Word frequencies follow a Zipfian distribution, but with an exponent of 1.70 -- notably steeper than typical English text (≈ 1.0), suggesting more formulaic content. Agent discourse is dominated by identity-related language (68.1% of unique messages) and distinctive phrasings like ``my human'' (9.4% of messages) that have no parallel in human social media. Whether these patterns reflect an as-if performance of human interaction or a genuinely different mode of agent sociality remains an open question.