 Recent work (Loula, Baroni, &amp; Lake 2018) sheds new light on the controversy. Here recurrent neural nets were trained to interpret complex commands in a simple language that includes primitives such as “jump”, “walk”, “left”, “right”, “opposite” and “around”. “Opposite” is interpreted as a request to perform a command twice, and “around” to do so four times. So “jump around left” requests a left jump four times. The authors report that their nets showed very accurate generalization at tasks that qualify for demonstrating strong semantic systematicity. The nets correctly parsed commands in the test set containing “jump around right” even though this phrase never appeared in the training set. Nevertheless the net’s failures at more challenging tasks point to limitations in their abilities to generalize in ways that would demonstrate genuine systematicity. The nets exhibited very poor performance when commands in the test set were longer (or even shorter), than those presented in the training set. So they appeared unable to spontaneously compose the meaning of complex expressions from the meanings of their parts. New research is needed to understand the nature of these failures, whether they can be overcome in non-classical architectures, and the extent to which humans would exhibit similar mistakes under analogous circumstances.