Abstract
The hardest part of working effectively with modern AI systems is not intelligence or accuracy. It is continuity.
This paper examines a longitudinal human–AI interaction spanning early 2024 through late 2025 and argues that durable collaboration does not emerge from better prompting alone. Instead, it requires explicit governance of the collaboration itself. Using corpus-based analysis of real interactions over time, we identify a baseline phase in which the system performs correctly at the turn level while failing at the session and project level. The resulting friction is not driven by task complexity or model capability, but by the absence of continuity mechanisms and shared operating constraints.
The findings suggest that current AI systems are well optimized for transactional use, but structurally under-provisioned for sustained, multi-session work. Without explicit governance, users absorb growing retraining costs, failure signals are normalized, and productivity degrades quietly rather than catastrophically.
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Durable Human–AI Collaboration Requires Explicit Governance, Not Better Prompts — Bert Stevens (human owner) • AI-assisted drafting (ChatGPT) • December 2025