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The rapid evolution of computational and data-driven materials engineering has transformed materials discovery from traditional trial-and-error approaches to sophisticated AI-integrated pipelines. Within this paradigm, learned embeddings serve as foundational representations that encode complex material properties, structures, and behaviors into latent spaces amenable to machine learning algorithms. However, these embeddings, while powerful for predictive modeling and high-throughput screening, introduce epistemic limits that challenge the fidelity of computational design systems. This manuscript explores the disconnect between representational abstractions and physical reality, emphasizing how embedding-induced biases, dimensionality reductions, and generalization assumptions constrain the reliability of AI-guided materials innovation. We introduce a novel conceptual framework, the Epistemic Representation Cascade (ERC), which dissects the multi-layered interactions between data infrastructures, learning architectures, and discovery workflows to reveal inherent epistemic risks. By integrating insights from materials informatics and representation learning, the ERC highlights feedback mechanisms that amplify or mitigate these limits, offering systems-level guidance for enhancing interpretability and robustness in autonomous design ecosystems. Implications extend to closed-loop experimentation and inverse design, advocating for infrastructure-aware strategies that prioritize epistemic alignment over mere predictive accuracy. This work underscores the need for balanced computational steering in materials AI, fostering more trustworthy pathways for next-generation materials engineering.