A study from Oxford University reveals that deep neural networks (DNNs) excel in machine learning due to an inherent simplicity bias, akin to Occam's razor. Despite their complex architecture, DNNs prefer simpler solutions, aiding their generalization abilities even in overparameterized scenarios. This bias helps avoid overfitting, aligning well with structured data. The findings suggest parallels between DNNs and natural systems, enhancing our understanding of AI's decision-making and offering insights for future innovations.