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The integration of artificial intelligence into materials design processes introduces complex dynamics where initial algorithmic choices shape subsequent trajectories, often embedding persistent dependencies that influence innovation pathways. This manuscript explores the conceptual underpinnings of path dependence, examining how data selection, model architectures, and iterative learning mechanisms interweave to form self-reinforcing structures in AI-assisted materials discovery. Through a synthesis of recent literature, it examines the interpretive implications of bias propagation, feedback loops, and epistemic constraints in computational materials science. The proposed framework conceptualizes these elements as interconnected layers, in which early decisions cascade through design cycles, shaping the exploration of material spaces and the emergence of novel properties. By focusing on systems-level insights, the analysis highlights trade-offs between efficiency and diversity in algorithmic guidance, as well as ethical considerations in steering material innovation. This interpretive approach underscores the need for reflective practices in AI-driven workflows, emphasizing how path-dependent logics can both constrain and enable creative outcomes in materials engineering. Ultimately, the discussion integrates these dynamics to reveal broader implications for sustainable and equitable advancements in the field, without positing empirical directives.