Machine-readable humanity is an evocative idea, and it is this idea which Hanley et al. spell out and critically discuss in their contribution. They are interested in exploring the technological as well as the moral side of the meaning of machine-readability. They start by differentiating between various ways to collect (and read) data and to develop classification schemes. They argue that traditional top-down data collection (first the pegs and then the collection according to the pegs) is less efficient than more recent machine readability, which is dynamic, because of the successive advances of data and predictive analytics (“big data”), machine learning, deep learning, and AI. Discussing the advantages as well as the dangers of this new way to read humans, they conclude that we should be especially cautious vis-à-vis the growing field of digital biomarkers since in the end they could not only endanger privacy and entrench biases, but also obliterate our autonomy. Seen in this light, apps (like AdNauseam) that restrict data collection as a form of protest against behavioral profiling also constitute resistance to the inexorable transformation of humanity into a standing reserve: humans on standby, to be immediately at hand for consumption by digital machines.