CAPPAA: A Multiplicative, Domain-Pointed Framework for Human Enablement and Domain Intelligence Production

Abstract

We propose CAPPAA — a multiplicative, domain-pointed framework for measuring and predicting the capacity of any human-enabler pair to produce executable domain intelligence. CAPP (Curiosity × Attitude × Passion × Persistence) captures irreplaceable human qualities measured via behavioral proxies, not self-report. A(domain) captures authentic lived domain knowledge. A(enabler) captures amplification — which may be a school teacher, mentor, community, book, or AI system. All axes are domain-pointed: the same human may have CAPPAA=648,000 in one domain and CAPPAA=600 in another. The formula is multiplicative — zero in any axis collapses output. Enablement is a mesh, not a chain: each new enabler raises the value of all existing nodes — bidirectional edges, dormant nodes that activate when the mesh reaches sufficient density, emergent nodes, and cycles. CAPPAA is measurable before and after enablement; the delta is the Transformation Score — quantifiable proof that an enabler moved the needle. We demonstrate the framework through TraitOS, show that expertise can reduce CAPPAA (the Expert Paradox), prove that the 90% of humanity outside current AI systems have high domain-specific CAPPAA, and identify CAPP as the structural boundary between human and AGI intelligence. AGI cannot have authentic CAPP because it cannot give up — and persistence is only meaningful when stopping is a real option.