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In the rapidly evolving field of materials science, artificial intelligence (AI) has emerged as a transformative tool for accelerating discovery and design. Yet, a critical bottleneck persists: the representations used to encode material properties often prioritize predictive accuracy over scientific meaning. This Perspective introduces a novel conceptual framework that bridges this gap, proposing a “Representation-Meaning Ladder” to systematically link types of representations—such as descriptors, graphs, embeddings, and text-derived variables—to the strength of scientific claims they can legitimately support, including predictive, comparative, mechanistic, causal, and transferable inferences. We argue that meaning is not inherent to representations but emerges from embedded constraints, assumptions, and contextual use, highlighting how unchecked “semantic overreach” leads to misinterpretations of correlations as mechanisms. By drawing on recent advances in materials AI, we emphasize the fragility of representations under domain shifts and the need for invariance-preserving designs to enable robust knowledge generation. This theory provides a roadmap for researchers to evaluate and enhance representations, fostering AI that not only predicts but meaningfully advances materials understanding. Ultimately, addressing this representational challenge is essential for realizing AI’s full potential in tackling complex materials challenges, from energy storage to sustainable manufacturing.

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.