The problem of epistemic opacity in Artificial Intelligence (AI) is often characterised as a problem of intransparent algorithms that gives rise to intransparent models. However, the dichotomy in the pertinent literature between a perceived necessity of making AI algorithms transparent and the countervailing claim that they give rise to an 'essential' epistemic opacity of AI models might be false. Instead, epistemic transparency is primarily a function of the degrees of an epistemic agent's perceptual or conceptual grasp of the relevant elements and relations in a model, which might vary in kind and in accordance with the pertinent level of modelling. In order to elucidate this hypothesis, I first contrast computer models and their claims to algorithm-based universality with cybernetics-style analogue models and their claims to structural isomorphism between model and target system (Black 1962). I then undertake a comparison between contemporary AI approaches that variously align with these modelling paradigms: Behaviour-based AI, Deep Learning and the Predictive Processing Paradigm. I conclude that epistemic transparency of the algorithms involved in these models is not sufficient and might not always be necessary to meet the condition of recognising their epistemically relevant elements.