AI-Enhanced Cyber and Information Warfare: A Comparative Analysis of United States and People's Republic of China Capabilities, Doctrine, and Strategic Implications (2020–2026)

The increasing convergence of artificial intelligence (AI) and cyber warfare capabilities represents one of the most consequential developments in contemporary military affairs, fundamentally altering the strategic landscape of great power competition between the United States and the People's Republic of China (PRC). This dissertation presents a comparative analysis of AI-enhanced cyber and information warfare capabilities, doctrine, and strategic implications across the 2020–2026 timeframe. The study addresses a critical gap in the scholarly literature: the absence of a comprehensive framework for evaluating how AI transforms both nations' approaches to cyber operations, information warfare, and cognitive domain competition. Employing a comparative case study methodology grounded in qualitative content analysis of open-source intelligence (OSINT), the research examines official doctrine, policy documents, government reports, academic literature, and think tank analyses to investigate four interconnected research questions concerning doctrinal conceptualization, operational integration, strategic stability, and international normative implications. Key findings reveal a fundamental doctrinal divergence between the United States' "Defend Forward" and persistent engagement approach and the PRC's "Intelligentized Warfare" framework, with the United States demonstrating advantages in defensive AI automation while the PRC excels in cognitive warfare operations at scale. The study identifies the 2024 PLA Information Support Force reorganization and the Volt Typhoon/Salt Typhoon critical infrastructure pre-positioning campaigns as pivotal developments. Analysis reveals fundamental norm divergence between the Tallinn Manual framework and China's cyber sovereignty model, with AI accelerating escalation risks through compressed decision timelines and attribution challenges. The dissertation advances four original theoretical contributions—Algorithmic Deterrence Instability, Cognitive Asymmetric Advantage, Institutional Velocity Mismatch, and Deterrence Opacity—that extend existing deterrence and strategic competition frameworks. Implications for defense policymakers, military planners, and international security scholars are discussed, with recommendations for adaptive deterrence architectures, allied cooperation frameworks, and normative engagement strategies.