# The Cognivirus: Sociotechnical Feedback Loops and the Systemic Degradation of Artificial Intelligence Ecosystems

## 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: Synthetic feedback loops, model collapse, variance loss, 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 Cognivirus: Sociotechnical Feedback Loops and the Systemic Degradation of Artificial Intelligence Ecosystems Introduction: Defining the Cognivirus Paradigm The rapid proliferation of generative artificial intelligence (AI) and large language models (LLMs) has fundamentally altered the infrastructure of digital knowledge production. As these systems are integrated into global communication networks, enterprise software, corporate workflows, and social media platforms, a complex, self-reinforcing pathology has emerged across the sociotechnical landscape. This phenomenon is best understood through the conceptual metaphor of the "Cognivirus."

## 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.