{"results":[{"id":"amortization-argument","text":"EEM construction is expensive but amortizes. ~$300 Sonnet for 13,511 beliefs. Each query costs ~$0.01. Breakeven at 100-250 queries. After that, every query is cheaper than re-reading source documents from scratch.","truth_value":"IN","justification_count":2,"dependent_count":0,"challenges":[],"last_reviewed":"2026-05-30T07:02:40","review_result":"invalid","source_type":""},{"id":"automated-overnight-construction","text":"The derive-review-research cycle is mechanical enough to run unattended. Three of six substrate validations were run by an autonomous Claude session with no human intervention, producing consistent results. The target: kick off expert-build derive-review-repair in the evening, wake up to a reviewed knowledge base. Learn while you sleep, build while you're awake.","truth_value":"IN","justification_count":2,"dependent_count":0,"challenges":[],"last_reviewed":"2026-05-30T07:02:40","review_result":"invalid","source_type":""},{"id":"construction-cost-measured","text":"EEM construction cost measured for enterprise scale (6 departments, 5,366 sources, 13,511 beliefs): ~$300 at Sonnet pricing, ~$1,500 at Opus pricing. Dominant cost is the summarize step (~98M tokens). Per-query breakeven at 100-250 queries — after that, every query is cheaper than re-reading source documents.","truth_value":"IN","justification_count":0,"dependent_count":2,"challenges":[],"last_reviewed":null,"review_result":null,"source_type":""},{"id":"construction-vs-retrieval","text":"Construction cost dominates: O(chunks) + O(beliefs x rounds). But it amortizes across all queries O(queries). Expensive to build, cheap to query at scale","truth_value":"IN","justification_count":0,"dependent_count":1,"challenges":[],"last_reviewed":null,"review_result":null,"source_type":""},{"id":"training-finetuning-cost-comparison","text":"Model fine-tuning costs $10K-$100K+ for a single domain adaptation, requires ML expertise, and produces a model locked to one provider. Training from scratch costs millions. EEM construction costs ~$300 (Sonnet) to ~$1,500 (Opus) for enterprise scale (13,511 beliefs, 6 departments), requires no ML expertise, and produces a portable knowledge artifact usable by any model. EEM is 10-100x cheaper than fine-tuning and works across providers.","truth_value":"IN","justification_count":0,"dependent_count":1,"challenges":[],"last_reviewed":null,"review_result":null,"source_type":""}],"count":5,"limit":20,"offset":0}