The main reason topography emerges in physical brains is because spatially distant connections are physically difficult and expensive in biological systems. Artificial neural nets have no such trade-off. So what's the motivation here? I can understand this might be a very good regularizer, so it could help with generalization error on small-data tasks. But hard to see why this should be on the critical path to AGI. As compute and data grows, you want less inductive bias. For example, CNN will beat ViT on small data tasks, but that flips with enough scale because ViT imposes less inductive bias. Or at least any inductive bias should be chosen because it models the structure of the data well, such as with causal transformers and language.