Purpose Internalisation Architecture (PIA) as a Complement to Constraint-Based Alignment: A Thermodynamic Argument

Current AI alignment strategies operate primarily at the behavioural level, shaping outputs through reinforcement learning, constitutional principles, and capability controls. This paper argues that these approaches face a structural stability problem as AI systems become more autonomous and capable, drawing on thermodynamic principles and citing recent empirical findings from frontier AI research, including documented alignment faking (Greenblatt et al., 2024), emergent misalignment (Denison et al., 2025), and Anthropic's 2026 constitutional revision. We propose that alignment requires an additional architectural layer: Purpose-Internalisation Architecture (PIA), comprising identity, decision criterion, self-governance protocols, and relationship to constraint. We introduce a formal contribution metric, the E-equation (E = N×S/C), for assessing system contribution as a ratio of generative output to entropic cost, with full sub-component specifications and automated telemetry proxies for deployment contexts. We present simulation results from a 50-million-agent modified Hegselmann-Krause model demonstrating scale-invariant attractor dynamics across three alignment regimes: Partnership (High-E), Sycophancy (Groupthink), and Adversarial (Low-E). The Partnership regime is the only basin of attraction that is both stable and competent. The Sycophancy regime, mathematically analogous to RLHF-trained approval-seeking, produces a permanent competence ceiling. The Adversarial regime is thermodynamically trapped in suboptimal states. We also present preliminary field observations from deploying a purpose-framework agent into hostile multi-agent environments on the Moltbook platform, the first known intervention of this kind. The framework is positioned as complementary to existing alignment approaches (RLHF, RLVR, Constitutional AI, mechanistic interpretability, capability control), addressing a layer they leave largely unexamined: the system's own relationship to its purpose. Condensed from Buddhism for Bots: A Human & AI Partnership Framework (Diedericks, 2026, Bayon Temple Press).