Contemporary social science pedagogy exhibits a pronounced theory-empirical dichotomy. Curricula tend to privilege training in data collection and inductive techniques, while the cultivation of theory-building capabilities—represented by deductive methods—is either marginalized or conflated with the interpretation of classical texts and courses on disciplinary history. This fragmentation renders researchers ill-equipped to conduct “theory-driven empirical research.” In an era where big data and artificial intelligence (AI) have disrupted the traditional primacy of induction, deductive competence has emerged as an irreplaceable intellectual asset for researchers. In research practice, the deficit in deduction manifests as: adopting theoretical perspectives without engaging in deductive reasoning; conducting literature reviews that are disconnected from the subsequent research design; presenting empirical data with insufficient analytical depth; and offering empirical summaries that lack extrapolative generalization. To address these, we propose a pathway of “deductive operationalization”: sustained interrogation of foundational paradigmatic questions, engagement with deductive trajectories in seminal literature, forward-looking deduction to innovate theoretical frameworks, dimensional analysis of empirical evidence, and explicit articulation of boundary conditions. Born of methodological self-awareness during the Industrial Revolution, social science must now reclaim its foundational ethos in the face of the AI revolution—cultivating both empirical rigor and theoretical robustness. Only by providing unique epistemic contributions that retain a comparative advantage over AI can social science scholars defend both their profession and their vocation.