The discussions around TopoNets highlight mixed opinions on their performance compared to traditional models. While some assert that imposing a brain-like topographic organization might enhance understanding of visual processing, many criticize the resulting models for underperforming relative to conventional architectures. Questions arise about the necessity and efficacy of mimicking brain structures when the scale of the model and data increase. The concept of topography appears to be a double-edged sword: it could provide better generalization on small datasets, but at the cost of performance on larger tasks. Critics emphasize the need for rigorous statistical analysis in demonstrating these benefits, while supporters see potential advancements in understanding AI through biological parallels.