A Coordination Layer for Global-Scale Problem Solving

Coordination failures are a dominant source of unrealized value and avoidable harm across development, supply chains, research, and public goods provision. This paper proposes a coordination layer—a second-order reasoning infrastructure for discovering, measuring, and implementing coordinated interventions that cannot be reliably produced by first-order institutions (markets, hierarchies, or consensus processes) acting alone. We present a Functional Model of Intelligence (FMI) as a practical framework for reasoning about coordination structure, coordination fitness, and the conditions under which coordination interventions are expected to succeed. Because ambitious coordination claims are prone to misinterpretation and rhetorical overreach, we explicitly separate (i) established facts, (ii) testable hypotheses, (iii) logical consequences conditional on hypotheses, and (iv) speculative strategic claims. In particular, large-scale economic implications are presented as conditional consequences of empirically falsifiable hypotheses about coordination multipliers and network sustainability. We provide worked examples illustrating why coordination interventions can plausibly yield order-of-magnitude gains in domains requiring discovery and synchronization of complementary activities, and we propose an experimental program to validate or falsify these hypotheses. Finally, we argue that building second-order coordination infrastructure is plausibly safety-relevant for AI deployment: it offers a pathway to institutionalized verification, auditability, and constraint-governed self-modification in contexts where AI systems amplify both capability and failure modes. This is not a claim of guaranteed risk reduction; rather, it is a claim that alignment and safety efforts are structurally constrained without scalable mechanisms for verification and governance under uncertainty.