Synthetic Collapse: The Global Risk of Information Inbreeding in AI Ecosystems

Synthetic Collapse: The Global Risk of Information Inbreeding in AI EcosystemsCivilization Physics — Entropy Dynamics Series This whitepaper examines synthetic collapse—a civilizational-scale failure mode in which AI ecosystems recursively train on AI-generated content, triggering a degenerative informational feedback loop analogous to biological inbreeding. As the proportion of synthetic text, code, and images entering global training pipelines increases, the “human signal” becomes diluted, and successive model generations begin to lose diversity, accuracy, epistemic grounding, and long-tail knowledge structure. The paper synthesizes emerging evidence from multiple fronts:• Model collapse research showing irreversible degradation when models train on their own outputs• Open-source contamination cycles in China’s rapidly expanding LLM ecosystem• Western benchmark erosion, where evaluation datasets are polluted by prior model outputs• Developer reliance on synthetic instruction data such as GPT-distilled datasets• SEO-spam proliferation, flooding the public internet with low-integrity AI-generated pages Building on the Entropy Law (R) from the Civilization Physics framework, the paper argues that information inbreeding accelerates entropy in the global knowledge substrate. Once key thresholds are crossed—such as the human-signal ratio falling below critical levels or multiple frontier models beginning to regress despite increased scale—collapse becomes systemic and difficult to reverse. The societal risks are profound: misinformed governance, corrupted scientific literature, degraded education, model monocultures, compromised public trust, and the collapse of reliable evaluation metrics. To avert collapse, the paper proposes a suite of negative-entropy interventions:• Benchmark reconstruction and dynamic evaluation• Training-data provenance auditing and content labeling• Retrieval-anchored architectures (RAG) to reconnect models with external human knowledge• Frame-coherent reinforcement to restore Presence × Integrity in model oversight• Institutional safeguards, including expert oversight networks and clean-corpora consortia Ultimately, the whitepaper argues that AI must remain open to human reality—not recursively trapped within its own synthetic reflections. Preventing information inbreeding is essential to preserving epistemic integrity, scientific progress, and the stability of global AI ecosystems. Keywords: Synthetic Collapse · Information Inbreeding · Entropy Law (R) · Model Collapse · Data Contamination · Benchmark Erosion · Synthetic Data Drift · Frame Theory · Presence × Integrity · Civilization Physics · Epistemic Risk · AI Governance