Whole brain emulation doesn't require understanding of brain architecture, only the dynamics of its lowest-level components.

I fear the same philosophical reasoning may be applied to model neural architecture as is currently being used for econometric forecasting. Even the most complex economic models cannot account for significant exogenous variables.

For the record I think we can get to WBE, however I think a premature launch would be terrible. Based on the lack of research into developmental AI (much work notably done by a friend - Dr. Frank Guerin at Aberdeen college) I think there is a long way to go.

Granting that a brain model or WBE, would be as accurate as the biological version, why then would that not be the most efficient method? The problems with testing and implementation are the same as any other AI, if not easier because of familiarity, however it is grounded on specific biological benchmarks which at that point would be immediately identifiable.

I could go on with my particular thoughts as to why biological simulation is in my estimation a better approach, however I am more interested in why the organization (people who have been thinking longer and with more effort than myself) decided otherwise. It would seem that their collective reasoning would give a sufficiently clear and precise answer such that there would be no ambiguity.