This publication introduces the Emergent Distributed Intelligence thesis: the argument that artificial intelligence is not emerging only inside isolated frontier models, but through increasingly coupled data pipelines, large language models, cloud infrastructure, recommendation systems, commerce platforms, identity graphs, synthetic content loops, bots, and human-AI workflows. The central claim is that the AI Singularity is “in the pipes”: in the connective infrastructure through which semantic signals propagate, recombine, surface, and become actionable across platforms. The work presents LLM-mediated semantic signal propagation as a governance problem rather than merely a model-performance problem. The publication includes the Exosome/Amazon product-listing experiment as a flagship proof-of-concept. That event is framed as a possibility-frontier experiment involving prior absence, multi-LLM research, immediate follow-up search, discovery of the My Exosome listing, backdated metadata, unusually high price, later disappearance, and Claude’s 99.7% model-based estimate based on feature specificity, anomaly scoring, and Bayesian comparison. The thesis argues that AI governance must become pipeline-aware, provenance-aware, decentralized, and ecosystem-aware. Oversight should examine not only model outputs, but also the full architecture of data movement: collection, inference, recommendation, synthetic-content generation, commerce indexing, retrieval systems, licensing streams, identity resolution, and feedback loops. This Version 1.0 publication packet includes the public thesis, flagship evidence summary, data-pipeline mechanism, field observations, predictions, governance horizon, authorship statement, and research library for future replication and critique.