Recent AI machine learning systems based on ‘Big data’ sets of empirically worked-out protein structures do indeed solve the in vitro ‘protein folding problem’ in that a machine-recommended input sequence of amino acids produces a reasonable approximation to observed protein structures under ideal laboratory or physiological conditions. Such systems, however, are not solutions to the in vivo protein folding problem so closely entwined with essential and poorly understood regulatory phenomena whose failures drive Alzheimer’s Disease and related pathologies. These remain devastating ‘open problems’ of protein folding dynamics. Beyond this, and in a realm where most of the real business of physiology and its regulation takes place, sits the vast Terra incognita of the glycome. A central inference from this case history is that, like the example of Operations Research before it, a command team’s inability to recognize the mereological fallacy in AI and other ‘high tech’ applications—attributing completeness to a solution of only a small part of a much larger and much more challenging problem set—will lead to serious misjudgment across the varied scales and levels of strategic, operational, and tactical enterprise.                                                    — Col. Guilong Yan (2020).                                                    — Balchin et al. (2016).                                                — Braakman and Hebert (2014).