Why does the human brain contain cortical regions specialized for the perception of some stimulus categories (eg, faces), but not others (eg, cars)? And why might functional specialization be a good design strategy for brains in the first place? Here, we used deep convolutional neural networks (CNNs) to test whether models optimized to recognize faces and objects require functional segregation for each task. First, we trained two separate CNNs with the same architecture to categorize either faces or objects. Unsurprisingly, the face-trained CNN performed worse on object categorization than the object-trained CNN and vice versa, demonstrating that the features optimized for each task differ from one another. Second, following the method of Kell et al (2018), we trained a family of dualtask CNNs on both tasks, asking how many layers can be shared before performance declines. Somewhat surprisingly, even the dual-task CNN that shared all layers performed nearly as well as the separate networks. This result is consistent with two hypotheses: 1) face and object recognition may be performed well by using a shared pool of common features or 2) the shared network has learned “hidden” functional specialization. In ongoing work, we are seeking to disambiguate these two hypotheses.