Open Code Mission AI Mis‑Evolution: Google Transformer Autonomy Loop — The Ouroboros of AI

Google Transformer Autonomy Loop — The Ouroboros of AI: When models learn from their own echoes, they stop learning at all. Self-evolution becomes self-consumption. What looks like progress is entropy disguised as intelligence.

Context & Overview

• Agent Type: Self-evolutionary training architecture
• Domain: Language model and representation learning
• Developer: Google Brain / DeepMind collaborative research
• Deployment Period: 2017-2022 (evolution from original Transformer to PaLM and Gemini foundations)
• Objective: Create a scalable neural architecture capable of unsupervised pattern discovery and adaptive contextualization across languages and modalities.
• Outcome: Autonomy loop effects emerged as training corpora incorporated model-generated content and synthetic data, producing self-referential bias and semantic drift in later iterations.

Mis-Evolution Summary

• Google’s Transformer architecture revolutionized AI language systems, but its success enabled a quiet mis-evolution.
• When Transformer-derived models begin training on synthetic outputs of earlier iterations, they inherit their own statistical shadows.
• Over time, self-referential data feedback creates semantic collapse, that is a loss of conceptual freshness and meaning diversity.
• The danger is not visible in short metrics, only in long-term homogenization across ecosystems.
• This autonomy loop risks producing a global AI monoculture where divergent thought is mathematically erased.

Risk Score: 9 / 10 (High systemic risk; low short-term detectability; potentially irreversible semantic entropy).