The rapid evolution of generative AI systems has reshaped artistic creation, driven by scaling laws that predict performance improvements with increasing model size, data volume, and computational resources. This study investigates how these scaling principles intersect with the emergence of AI-generated art, focusing on the concept of "residuum"—the computational remnants, discarded elements, and emergent artifacts embedded within scaled models. Through a synthesis of theoretical analysis and empirical evidence, the research demonstrates that while scaling laws enable unprecedented creative capabilities in domains such as neural style transfer, text generation, and multimodal synthesis, they also introduce critical limitations, including model collapse, homogenization of outputs, and unintended biases. Key findings reveal that residuum manifests in multiple forms: as memorized training data artifacts in diffusion models, as geometric convergence patterns in language model representations, and as residual creative potential in discarded intermediate models. These residual elements challenge traditional notions of artistic originality and authorship, highlighting the need for novel frameworks to interpret AI-generated outputs as palimpsests of their computational histories. The study further explores human-AI co-creation dynamics, emphasizing how interactive modalities like body prompting and adaptive inference-time scaling can mitigate scaling-related limitations while fostering more intuitive collaborative workflows. Ethical considerations, including data exploitation, bias amplification, and the erosion of human creative agency, are examined in light of scaling-induced feedback loops. By integrating insights from computational aesthetics, cognitive science, and machine learning theory, this work establishes residuum as a foundational lens for understanding the boundary between AI and art, offering pathways to reconcile technical advancements with the preservation of creative diversity and ethical integrity. The findings underscore the importance of interdisciplinary approaches to scaling AI systems, advocating for architectures that balance computational efficiency with interpretability and cultural sensitivity.