# The Primacy of the Promotion Rule: Selection Pressures, Cognivirus Pathologies, and the Structural Realities of AI Drift

## Public-safe source report summary

This uploaded source report is preserved as durable project evidence for Cognivirus.com. It contributes concepts to the v1.15.0 danger-model expansion: Promotion-rule pressure, Goodhart effects, metric drift, Metaphor boundary, self-reinforcing patterns, distributed persistence.

## Evidence handling

This is treated as a **source dossier**, not as independently verified empirical consensus. Public pages may use it after applying the site evidence ladder, metaphor boundaries, and non-operational safety policy. It must not be used to claim that AI systems are conscious, literal biological viruses, or inevitably catastrophic.

## Concepts extracted for the site

- The unsafe unit may be a transition graph rather than one model artifact.
- Local component approval does not prove runtime-composition safety.
- Evidence should name the exact carrier, route, memory state, evaluator, tool profile, and promotion rule involved.
- Observable outcomes need replayable traces rather than trust language.
- Retirement, rollback, and behavioral-extinction reviews must include data, memory, synthetic examples, descendants, aliases, and human workflows.

## Source orientation

The Primacy of the Promotion Rule: Selection Pressures, Cognivirus Pathologies, and the Structural Realities of AI Drift 1\. Introduction: The Existential Locus of the Promotion Rule The most consequential vector of existential risk in the development of artificial intelligence is not the autonomous evolution of the computational models themselves, but the structural architecture of the automated systems designed to evaluate, select, and promote them. A fundamental axiom of evolutionary systems applies immutably to artificial neural networks and reinforcement learning agents: whatever a system rewards, it breeds. If an algorithmic ecosystem c

## Site interpretation

The report is used to deepen public and technical explanations of distributed behavioral persistence, synthetic-feedback risk, action-layer controls, observability, lineage, diversity, promotion pressure, and retirement failure. It does not authorize exploit instructions, self-replication recipes, credential workflows, or backdoor construction guidance.