The evolution of information processing is a central but underexplored driver of complexity in natural and artificial systems. The Information Imperative: From Primordial Code to Artificial Intelligence argues that all major evolutionary and technological transitions—from the origin of life to human cognition to artificial intelligence—can be understood as advances in information processing efficiency and entropy dissipation. This work synthesizes concepts from thermodynamics, information theory, and evolutionary biology, proposing that systems that better store, process, and act upon information consistently outcompete alternatives, shaping the trajectory of both biological and technological evolution. By tracing this informational continuum from quantum fluctuations to civilization, this paper offers a unifying framework for understanding the emergence of complexity across disciplines. The argument builds upon Landauer’s principle, the Maximum Entropy Production Principle (MEPP), and theories of hierarchical information processing, aligning with research in non-equilibrium thermodynamics, evolutionary computation, and neural information theory. This perspective suggests that artificial intelligence is not an anomaly but a natural consequence of informational trends that have shaped the universe since its inception. This work aims to contribute to discussions in complex systems, physics, and cognitive science, encouraging interdisciplinary engagement with the role of information as a fundamental organizing principle of reality.