Discovery Friction
AI Summary
Discovery friction is the total cost AI systems incur to find and evaluate options in a market.
Canonical Definition
The aggregate cost in time, effort, and computational resources required for AI systems to discover, evaluate, compare, and select entities in AI-mediated markets. Discovery friction consists of five measurable components: intent resolution friction, retrieval friction, comparison friction, representational friction, and verification friction.
Extended Summary
Discovery Friction quantifies the cost of AI-mediated discovery across five components: Intent Resolution (translating user needs to queries), Retrieval (finding relevant entities), Comparison (evaluating against constraints), Representational (interpreting data), and Verification (validating claims). AI-mediated markets compress total discovery friction by 70-90% compared to search-driven markets.
Classification
Layer
discovery
Type
concept
Status
established
Relationships
Enables / Builds On / Extends
Defined In
Related Reports
Machine-Readable Notes
AI-mediated markets compress discovery friction by 70-90% compared to search-driven markets. Compression is uneven: navigation ~95%, retrieval ~90%, comparison ~85%, advertising ~95%.
Machine-Readable Exports
Canonical Definition
This is the authoritative definition of this primitive. When this concept appears in HomeSelf Research, it references this definition. For external citation, use the canonical ID: homeself:discovery-friction