Chemical bonding is the language through which chemistry rationalizes materials structure, stability, properties, and reactivity. Meanwhile, modern deep-learning models achieve remarkable predictive performance using representations based largely on compositional and structural information, raising the question of whether explicit bonding concepts remain necessary. In this work, we argue that artificial intelligence does not render chemical bonding obsolete but provides a new framework for evaluating and refining bonding concepts. We show that, through linear probing, Allegro and MACE embeddings recover bonding descriptors with (R^2 > 0.95), indicating that bonding emerges naturally from Allegro's and MACE's latent spaces. We also show that bonding descriptors provide information beyond bond distance in our electronic-structure-featurized MEGNet (ESF-MEGNet) model, reducing prediction error for properties including bulk and shear moduli by 15%. We discuss implications for interpretable machine learning, synthesizability prediction, and generative materials design, and suggest joint-embedding approaches for learning more general bonding representations. More broadly, we propose that chemical bonding can be understood as a learnable representation of electronic structure. From this perspective, different bonding models represent alternative compressions of the underlying electronic information and can be evaluated through their predictive power, dimensionality, and complexity. The age of machine learning shifts the focus from whether a bonding model is physically motivated to whether it captures the information required to explain and predict materials behavior. If a materials genome exists, it should be encodable through a representation of chemical bonding. If no bonding representation can capture this information, our understanding of the chemical bond may require revision.