Securing U.S. Leadership in Agentic AI Literacy and Adoption: U.S. vs Chinese Government Policies and Initiatives

Satyadhar Joshi

This paper conducts a comparative analysis of U.S. and Chinese frameworks for AI literacy and adoption, with focus on agentic AI and Artificial General Intelligence (AGI) systems capable of autonomous reasoning and execution. We examine national policies, educational integration, governance structures, and technological roadmaps, employing both qualitative review and quantitative modeling. Mathematical formulations include multi-dimensional literacy scoring, Bass diffusion models for adoption dynamics, risk assessment functions, regulatory effectiveness indices, competitiveness metrics, and optimization frameworks for resource allocation. Our analysis reveals divergent paradigms: the U.S. Favors decentralized, innovation-driven approaches with emphasis on interoperability and public-private collaboration; while China pursues centralized, state-led strategies with comprehensive content labeling and rapid systemic integration. As both have their strength and weakness, we propose a hybrid governance architecture that synthesizes strengths from both models, supported by algorithmic implementations and sensitivity analyses. We have used recent publications (2021-2025), where we identify trends, challenges, and implication styles. The paper concludes with quantitative and algorithmic recommendations for policymakers, educators, and industry stakeholders navigating the evolving landscape of global AI competition.