Fundamental computational barriers prevent artificial intelligence from replicating human social cognition: A comprehensive theoretical and empirical analysis

Background: Despite unprecedented advances in artificial intelligence, the fundamental question of whether machines can truly replicate human social cognition remains unresolved, with critical implications for safety- critical applications and human-AI collaboration systems. Methods: We conducted a comprehensive meta-analysis of 47 peer-reviewed studies encompassing 23,847 human participants and 156 distinct AI systems across eight cognitive domains. We developed and validated the Social Cognition Complexity Theorem through advanced mathematical modeling, incorporating information theory and computational complexity principles. Crisis analysis included forensic examination of AI failures during the January 6th Capitol riots and Itaewon crowd disaster, with real-time performance tracking. Results: Statistical analysis reveals systematic performance gaps with Cohen’s d > 1.2 (p < 0.001) across all social cognitive domains. Our Social Cognition Complexity Theorem mathematically proves computational requirements grow exponentially C(t) = α ·eβ · I(t)· S(t)+γ · M(t) with contextual integration needs, creating theo- retical performance ceilings at 72.3% of human baseline. Crisis analysis demonstrates catastrophic failures: AI systems provided warnings 62 minutes late during Itaewon disaster while human observers warned 18 minutes early. Meta-regression analysis shows asymptotic performance plateaus despite exponential increases in computational resources. Conclusions: Fundamental mathematical limitations prevent AI from achieving human-level social cognition under current computational paradigms. Our novel Complementary Intelligence Framework achieves 23.4% performance improvement over pure AI systems and 5.2% over pure human approaches, suggesting collaborative architectures as the optimal path forward for safety-critical applications