This talk summary discusses the impact of machine learning and artificial intelligence (AI) on the field of connectomics. It highlights that machine learning has revolutionized connectomics and is becoming a foundational technology in the field. The use of machine learning has accelerated progress in connectomics, with advancements in visualization and the ability to reconstruct brain tissue at the nanometer scale. However, there are doubts about the fidelity and detailed reconstruction of a human brain using static connectors or dynamic recordings enabled by brain-computer interfaces. The speaker emphasizes the importance of developing a principled way to reason about different models in the context of whole brain emulation. There is a need for a better understanding of failure modes, the implications of human cognition, and different paradigms beyond whole brain emulation. The summary concludes by emphasizing the need for a better understanding of relevant concepts and failure modes in the field and an improved discussion about different paradigms relevant to whole brain emulation.