Computational protein structure prediction may be hitting a critical reliability threshold as artificial intelligence models begin to reflect the limitations of their training datasets rather than biological reality. This finding challenges the assumption that AI-driven structural biology tools can accurately capture the dynamic nature of enzyme function across all protein families. The research demonstrates that AlphaFold3's predictions of enzyme conformational changes upon ligand binding correlate more strongly with the abundance of similar structures in training databases than with actual biochemical binding events. When predicting how enzymes shift their three-dimensional architecture in response to substrate or cofactor binding, the AI system appears to default toward conformations that were heavily represented during its machine learning training phase. This systematic bias particularly affects enzymes with limited structural data, potentially leading researchers to incorrect assumptions about binding mechanisms and allosteric regulation. The implications extend beyond academic curiosity into drug discovery pipelines that increasingly rely on computational predictions to identify binding sites and optimize therapeutic compounds. While AlphaFold3 represents a remarkable achievement in structural biology, this analysis reveals that even sophisticated AI models remain constrained by the fundamental principle of machine learning: output quality depends on input data diversity. The bias suggests that experimental structural biology remains irreplaceable, particularly for understudied enzyme families where conformational flexibility plays crucial functional roles. This limitation may slow progress in areas like antibiotic resistance research, where novel enzyme targets often lack extensive structural databases, making computational predictions less reliable precisely when they would be most valuable.