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In the rapidly evolving field of materials artificial intelligence (AI), the prevailing emphasis on scaling data volumes and computational resources has driven significant advancements in predictive modeling and discovery processes. However, this conceptual manuscript interrogates the implicit assumption that larger scales invariably yield superior outcomes, positing instead that unchecked expansion introduces intricate interaction dynamics that undermine the integrity of materials informatics. Through an integrative analysis, we explore how escalating data scales interact with inherent biases, leading to amplified distortions in representational fidelity and epistemic reliability. The framework delineates trade-offs wherein quantitative abundance may erode qualitative depth, fostering feedback structures that perpetuate homogeneity in material explorations at the expense of diversity. Ethical reasoning underscores the epistemic implications, revealing how scale-driven approaches can inadvertently prioritize dominant paradigms, marginalizing underrepresented material classes and contexts. Systems-level insights highlight steering logics that balance scale with interpretive nuance, advocating for calibrated integrations that preserve domain-specific insights. This argument reframes scale not as an unequivocal virtue but as a contingent factor within broader conceptual interpretations, urging a reevaluation of priorities in applied AI for materials science to foster sustainable and equitable progress.