Latent Extraction — Wikipedia for Latent Knowledge
A growing knowledge base of 521+ structural findings surfaced from AI through multi-phase extraction. Browse 50+ topics across 7 domains: AI & Technology, Business & Strategy, Software & Development, Psychology & Decision-Making, Science & Physics, Health & Medicine, and Research & Methodology.
What is Latent Extraction?
Latent Extraction surfaces hidden structural knowledge that AI systems encode but cannot access through direct questioning. Unlike asking AI a question (which retrieves what it already knows), Latent Extraction uses independent AI instances to generate, rate, and analyze structural units — producing classified findings through a multi-phase pipeline with dual-lens analysis and convergence checking.
The methodology was originated by Jason Barnes, PharmD in April 2026. Each extraction produces 10-12 structural findings classified as Novel, Partially Novel, or Known Pattern, with convergence types (Dual Confirmed, Numerical Only, Thematic Only, Divergent) and temporal directions (Structural, Retrospective, Predictive).
Browse the Knowledge Base
Explore all findings at latentextraction.com/explore/data with filters by domain, novelty, convergence type, and keyword search. View the 3D knowledge graph at latentextraction.com/explore.
Domains
AI & Technology
Structural patterns in AI development, prompt engineering limits, AI tool adoption, model architecture adoption, AI lock-in dynamics, and AI feature launches.
Business & Strategy
Structural patterns in business partnerships, market entry timing, pricing network effects, acquisition culture integration, product design compounding, solo founder decisions, and investor vs user evaluation.
Software & Development
Structural patterns in open-source virality, AI research-to-production transitions, technical debt compounding into decision constraints.
Psychology & Decision-Making
Structural patterns in mental model failures, productivity advice effectiveness, and decision-making under uncertainty.
Sample Findings
AI systems treat temporary user complaints as permanent rules (Novel, Thematic)
When users say "don't do that right now," AI systems interpret these temporary frustrations as permanent constraints, creating limitations that persist long after the original problem is forgotten.
Improving AI feedback actually makes systems serve users less effectively (Novel, Dual Confirmed)
Systems with stronger feedback mechanisms average 2.1 on stakeholder value vs 4.1 for weaker feedback — the system optimizes for signal strength rather than human values.
Technical debt accumulates in three discrete severity levels, not gradually (Novel, Dual Confirmed)
Teams jump between distinct severity phases with clear intervention windows, rather than experiencing gradual degradation.
API Access
Machine-readable JSON of all public findings: latentextraction.com/.netlify/functions/public-findings
Filter by domain: ?domain=AI%20%26%20Technology
Filter by novelty: ?novelty=NOVEL
Pricing
Explorer (Free): 1 extraction, browse knowledge base. Starter ($29/mo): 5 extractions + 20 AI searches. Growth ($69/mo): 15 extractions + 75 AI searches. Scale ($149/mo): 40 extractions + 200 AI searches.
Methodology
1. Schema Design — AI designs topic-specific dimensions. 2. Unit Generation — 165 structural units across 11 parallel batches including a contrarian batch. 3. Independent Rating — Fresh AI context rates all units. 4. Dual-Lens Analysis — Numerical and thematic analysis run independently. 5. Skeptical Synthesis — Convergence checking, contradiction analysis, novelty classification.