This work explores the mismatches between the way ML and humans understand everyday objects and their potential impact on the future of design and fabrication. We present an experiment in which a generative ML model is trained on a dataset of 6k+ objects and a random output is selected to be fabricated. The fabrication, the translation of the volumetric digital data into a physical form, serves as an speculative experiment into the shifts in design and authorship by embedding Machine Learning (ML) in design. We discuss our process and the challenges encountered in representing 3D data in a digital form, as well as the reciprocal challenge of transforming ML-generated 3D digital data into physical objects. The result of this experiment is a chair designed by a computer and built by a designer. The material and technique decisions made in the fabrication process highlight discrepancies between human and computer perceptions of the essential features of an object. This leads to a contemplative artwork that raises questions about the future of design and the role of humans and machines in the creative process. The experiment demonstrates the potential, but also the limitations, of machine learning models to generate not just digital models, but also physical objects that are both functional, visually appealing and that encode the history of design.