Abstract As Large Language Models (LLMs) and diffusion models become ubiquitous tools for content creation, a paradoxical phenomenon has emerged: as the volume of synthetic output explodes, the diversity of that output appears to contract. This paper introduces the concept of "Algorithmic Convergence," whereby generative agents, trained on increasingly recursive datasets (datasets composed partially of previous AI outputs), begin to collapse the solution space of creativity. We argue that without intervention, the current trajectory of generative AI leads not to an infinite expansion of ideas, but to a polished, high-fidelity echo chamber where "hallucinations" are minimized, and novel outliers are systematically pruned.
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