Policy-Level Learning Myopia in AI Governance: Why Cultural Dimensions Fail to Explain Strategic Divergence

This article challenges the prevailing use of Hofstede's cultural dimensions to explain divergence in national AI governance strategies (Leiden & Kayworth 2006; Myers & Tan 2002). This paper does not test policy effectiveness, but narrative rationality under uncertainty. Authors agree that cultural factors influence acceptance of technology (Venkatesh et al. 2003; Brauner et al. 2025; Alshakhsi et al. 2026). In the same time, organizational learning theory (Shrivastava 1983; Langer 2023) offers a model for the study of public policies, since many experts consider the firm and the state analogous entities. The state, like the firm, must act under the constraints of bounded rationality (Simon & Bendor 2010). The most popular conceptualization of the influence of culture on management and politics has been that of Hofstede (Leiden & Kayworth 2006; Hofstede 2001; Hofstede & Minkov 2010). Through lexical analysis of 26 national AI strategies (2018-2025), the research demonstrates that March's exploration-exploitation framework provides superior explanatory power for understanding systematic differences in policy orientations. The central theoretical contribution lies in demonstrating the inadequacy of cultural dimensions alone: power distance exhibits contradictory relationships (high PDI has a negative effect on innovative idea generation, but a positive effect on innovative idea implementation, Luo et al. 2020), and uncertainty avoidance shows similarly inconsistent patterns across contexts (Cardon & Marshall 2008). Instead, the article proposes that national strategies encode distinct "policy grammars" privileging either exploitative control (E1: governance, regulation, risk management) or exploratory innovation (E2: research, collaboration, foresight). Empirical analysis using 150 keywords (83 E1 terms versus 66 E2 terms—deliberate structural asymmetry) reveals that strategic orientations cluster into four coherent configurations rather than distributing along cultural dimension predictions. Orientation scores range from +6.76 (China, exploration-dominant) to -7.00 (Saudi Arabia, exploitation-dominant), with E1 and E2 trajectories remaining systematically non-crossing within national strategies. This polarization cannot be adequately explained by cultural values but reflects deeper epistemological commitments about how states learn under uncertainty. The article introduces the concept of policy-level learning myopia—systematic distortions in collective sense-making that parallel organizational competence traps and search traps—and proposes the Intercultural Mirroring (ICMIRR) framework as an institutional corrective mechanism. The findings have direct relevance for the UN Independent International Scientific Panel on AI (announced February 2026), suggesting that effective global governance requires explicit attention to culturally embedded learning pathologies rather than assuming convergence toward universal best practices. Theoretically, the analysis extends organizational learning theory to state-level policy-making, treats national strategies as public rationality artifacts subject to bounded rationality constraints, and demonstrates that cultural context operates as probabilistic epistemic filter rather than deterministic cause.