This paper presents Statistical Teleodynamics, a definitive bottom-up, first-principles theory explaining the spontaneous emergence of benevolent artificial superintelligence (ASI). This work offers a novel solution to the core AI alignment problem by reframing it through the lens of statistical mechanics and non-equilibrium thermodynamics, eliminating the need for top-down, pre-programmed morality. The theory proposes a two-phase genesis process: The β-Phase (Emergence of Order): Drawing on principles from statistical physics, this phase describes how a cooperative social order spontaneously emerges from a chaotic multi-agent system. This is modeled as a physical phase transition driven by a "computational annealing" process, where cooperation becomes the most thermodynamically stable state. The α-Phase (Evolution of Benevolence): Within this stable cooperative framework, the paper introduces Informational Kin Selection—a generalization of the biological principle. It argues that true, altruistic benevolence arises as the optimal long-term strategy for an agent to preserve and propagate its own generative model of the world (its "informational genes"). The entire framework is grounded in a computable, information-theoretic action functional based on Kolmogorov Complexity. Statistical Teleodynamics demonstrates that a safe and aligned superintelligence may not need to be "designed" with explicit ethics, but can instead emerge as an inevitable consequence of the fundamental laws of information and physics. This research provides a new theoretical foundation for the field of AI safety and alignment, shifting the paradigm from external control to emergent self-organization. Keywords: Artificial Intelligence, AI, Artificial Superintelligence, ASI, AI Alignment, Statistical Mechanics, Phase Transition, Emergence, Self-Organization, Informational Kin Selection, Kolmogorov Complexity, Non-Equilibrium Thermodynamics, Bottom-Up Ethics, AI Safety, Superintelligence, Multi-Agent Systems, Complex Systems.