This paper presents a replicable workflow for turning long-form podcast and YouTube transcripts into testable commodity–macro hypotheses. Using SmarterMarkets™ “Setting Course 2026, Episode 4” as a demonstration corpus, the study extracts discrete claim tokens, normalizes them into measurement-ready propositions, and encodes asserted support relations as a directed acyclic graph (DAG). The narrative is then operationalized through (1) Toulmin-style decompositions of key claims and (2) a falsification mapping that links each proposition to observable indicators, decision rules, and monitoring frequency. The resulting artifacts—an argument DAG, a claim-level monitoring table, and a measurement architecture distinguishing stock from evolvability metrics—enable ex ante auditing of macro-commodity narratives that usually remain qualitative. In the demonstration episode, the dominant causal spine runs from hyperscaler capex and AI-driven electrification to electricity bottlenecks, critical-minerals constraints, slow supply response, and hoarding-induced opacity, implying elevated odds of discontinuous repricing and rapid asset-rotation dynamics. The contribution is methodological: a transparent protocol and replication package template for comparing narrative claims over time using publicly observable and industry-synthesized series. Keywords: argument mapping; directed acyclic graphs; falsification; measurement design; commodities; critical minerals; electricity constraints; AI infrastructure; hoarding; narrative evaluation.
Peter Bell (Wed,) studied this question.