In the aftermath of renewed trade tensions and geopolitical realignments—exemplified by the 2025 trade war under President Trump 2.0—the dominant policy discourse posits that globalization went “too far,” sacrificing resilience and national security at the altar of cost efficiency. This paper challenges that narrative by unpacking the implicit assumptions that undergird it, notably the belief that global trade and value chains were ever efficient in the first place. Drawing on international business literature, economic geography, and trade theory, we argue that global supply chains, far from representing optimal configurations, were largely shaped by bounded rationality, cognitive biases, and incomplete information—what we term the streetlight post bias. Contrary to the Heckscher-Ohlin-Samuelson model’s idealized vision, firm-level decisions rarely reflect first-best equilibria; instead, trade patterns have followed the more constrained logic of the gravity model and regional familiarity. The paper contends that neither globalization nor its retrenchment (via reshoring, nearshoring, or friend-shoring) guarantees a move toward a more resilient or efficient trade architecture. Instead, both may reflect alternative second-best equilibria. We propose a forward-looking framework in which big data analytics and machine learning—grounded in an economic geography perspective— can help firms and policymakers identify robust, diversified, and efficient global value chain configurations. By addressing information asymmetries and reducing decision-making bias, such tools offer a path toward a closer approximation of the first-best equilibrium. We conclude with implications for trade policy, calling for evidence-based interventions that move beyond reactive deglobalization toward intelligent, data-driven integration.