Beyond Alignment: Rethinking Control in Goal‑Pluralistic AI Megasystems (A Response to Susan Schneider's _From LLMs to the Global Brain_)

The dominant paradigm in AI safety treats the central problem as one of alignment: ensuring powerful AI agents pursue goals consistent with human values. This framing presumes a singular, bounded agent with a coherent utility function and a legible objective. Yet, as AI systems are increasingly embedded across cloud platforms, social media, sensors, and human-computer interfaces, we face something different: the instantiation of AI megasystems – vast, decentralized, and emergent networks in which humans, organizations, and heterogeneous models are coupled through feedback loops. Building on and extending Susan Schneider’s “Global Brain” argument, we contend that alignment is conceptually mismatched to this setting. The megasystem is goal‑pluralistic and often non‑agentic at the macro-scale; “aligning” it is like aligning a market or an ecosystem. Instead, we propose a shift toward regulatory thermodynamics: a systems-theoretic approach that treats stability, modularity, and resilience – not utility maximization – as the primary design and governance goals. Using tools from non‑equilibrium thermodynamics and complex systems (entropy, information bottlenecks, phase transitions, critical slowing down), we outline diagnostics and interventions suitable for planetary‑scale AI. The piece closes with a policy program emphasizing structural constraints, coupling “speed limits,” and real-time early‑warning indicators to manage tipping points, together with responses to key objections. Schneider is right that we face a new kind of control problem; our claim is that megasystems cannot be aligned – only stabilized.