Foundations of AI-Mediated Discovery: From Exploration to Resolution, Reuse and Default Formation

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This working paper establishes the first-principles foundation of the Elsewhere Systems Framework, examining why AI-mediated discovery is structurally different from traditional search and ranking. It explores the underlying mechanics of the transition from exploration to resolution: how uncertainty creates evaluation cost, how successful resolution creates reusable pathways, how reuse generates structural trust, and how repeated reuse can lead to defaults. The paper develops a series of connected concepts including resolution as the unit of discovery, trust as reuse capacity, default formation, Default Answer Collapse, trust networks, authority without assertion, the Default Economy, replacement cost, exit velocity, agentic procurement, bleed and category-boundary failure. It examines how defaults can move beyond individual decisions to become infrastructure, and how connected defaults can reshape markets around reusable pathways rather than visibility, ranking or conventional category boundaries. The paper also establishes the conceptual foundation for the wider Elsewhere Systems architecture: resolution → reuse → trust → default → dependency → infrastructure. The Foundations layer sits beneath the subsequent AI-Mediated Discovery, Resonance Layer, Agentic Mechanics and Coherence Web papers, providing the first-principles explanation for why these mechanisms emerge. This document synthesises the Foundations series into a single open reference paper and presents the framework as a set of theoretical propositions that can be tested, challenged and refined through further research.