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Neural scaling laws describe the power-law decay of prediction error with increasing training dataset size, yet the scaling exponents reported for materials property prediction vary widely (0.1–0.8) across databases and properties. This variation has remained poorly understood, limiting reliable forecasting of data requirements in computational materials discovery. Here we present a purely theoretical framework that attributes the observed differences in scaling exponents primarily to the intrinsic data redundancy of materials databases. We formalize redundancy as the fraction of samples that convey overlapping or duplicate structural information, thereby reducing the effective number of independent samples. Through conceptual derivations and proof sketches grounded in information theory and sample-complexity arguments, we establish that the effective scaling exponent is approximately the ideal (independent-sample) exponent multiplied by (1 − r), where r is the redundancy fraction. Upper and lower bounds on achievable exponents are derived directly from three fundamental structural features: the number of distinct crystal prototypes, elemental compositional diversity, and the variety of local atomic environments. High redundancy—arising naturally from repeated prototypes, compositional biases, and clustered coordination motifs—systematically flattens the exponent and imposes hard structural limits on learning efficiency, independent of model architecture. The framework provides lightweight, model-free metrics for estimating redundancy from standard database statistics and offers actionable guidance for diversity-driven dataset curation. By reframing scaling behavior in terms of effective sample size and structural diversity, this work supplies a unified theoretical explanation for the wide range of reported exponents and a predictive foundation for designing more efficient materials databases.