With the digitalization of most areas of human life and the expansion of quantitative measurements, primacy is increasingly given to knowledge arising from “Big Data” processes. Both the mechanisms and the ideology supporting machine cognition rely on principles alien to Design’s tacit ways of knowing (human cognition), bringing a critical shift in how our worlds are designed. Reification, the process of turning abstractions into things, participates in the creation of knowledge and reveals a profound dichotomy. By turning abstract ideas into code, machines operate a process of reverse reification, making the world utterly inexplicable to humans. This paper critically examines the mechanisms behind machine cognition, how quantified knowledge impacts design’s qualitative understanding and practices and proposes a relational approach to ways of knowing in order to bridge this gap. To illustrate this argument, a three-angled exploration of found images, from the human, machine and the relational cognition is presented.