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In order to achieve this goal, ML systems need the greatest possible quantity of training data to allow the Artificial Intelligence (AI) model to develop an understanding of \u201cwhat it means to be human\u201d. We propose that the processes by which companies collect this data are problematic, because they entail extractive practices that resemble labour exploitation. The article presents four case studies in which unwitting individuals contribute their humanness to develop AI training sets. By employing a post-Marxian framework, we then analyse the characteristic of these individuals and describe the elements of the capture-machine. 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