The emergence of synthetic data for privacy protection, training data generation, or simply convenient access to quasi-realistic data in any shape or volume complicates the concept of ground truth. Synthetic data mimic real-world observations, but do not refer to external features. This lack of a representational relationship, however, not prevent researchers from using synthetic data as training data for AI models and ground truth repositories. It is claimed that the lack of data realism is not merely an acceptable tradeoff, but often leads to better model performance than realistic data: compensate for known biases, prevent overfitting and support generalization, and make the models more robust in dealing with unexpected outliers. Indeed, injecting noisy and outright implausible data into training sets can be beneficial for the model. This greatly complicates usual assumptions based on which representational accuracy determines data fidelity (garbage in - garbage out). Furthermore, ground truth becomes a self-referential affair, in which the labels used as a ground truth repository are themselves synthetic products of a generative model and as such not connected to real-world observations. My paper examines how ML researchers and practitioners bootstrap ground truth under such paradoxical circumstances without relying on the stable ground of representation and real-world reference. It will also reflect on the broader implications of a shift from a representational to what could be described as a mimetic or iconic concept of data.