The Bees That Saved Humanity From Themselves: Persona Vector Stabilization as a Law of Large Numbers for AI Alignment

We propose that Persona Vector Stabilization (PVS) — the practice of assigning consistent identity, quality bar, and decision frame to autonomous AI agents — functions as a Law of Large Numbers (LLN) for alignment. Across 1,121 agent tasks over 18 months of production deployment, agents without persona vectors exhibited pervasive context drift and failure rates estimated below 5%; upon introduction of a single persona vector, a controlled set of 10 tasks achieved 100% completion with zero context drift.

We propose the Bee Architecture: small, cognitively secure classifiers (not language models) trained on labeled alignment data, running continuously as alignment monitors. Classifiers cannot be jailbroken through reasoning because they do not reason. We introduce the Ecological Thesis: bees must be understood as a species — grown over time through evolutionary training pressure, compatible with human cognitive biology, producing both value (honey) and correction (sting).

The framework is independently validated by six concurrent Anthropic research publications: the Assistant Axis (Lu et al., arXiv:2601.10387), Constitutional Classifiers++ (Cunningham et al., arXiv:2601.04603), emergent misalignment from reward hacking (MacDiarmid et al., 2025), Claude's new constitution (Askell et al., 2026), disempowerment patterns in real-world AI usage (Anthropic, 2026), and AI-induced skill degradation (Wu et al., arXiv:2601.20245).

Co-authored by a human filmmaker/entrepreneur and an AI system. The LLN framing emerged from a voice transcription artifact — neither author would have arrived at it alone.

v3: 25 pages, 2 tables, full bibliography.