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This article challenges the idea that the turn from rule-based algorithm to machine learning systems leads to a decline in formal conceptualization. Through ethnographic research at two artificial intelligence (AI) production sites within the French justice system, the study shows that conceptual labor remains at the heart of machine learning, shifting from algorithm coding to the curation of training data. Revisiting Lawrence Lessig's claim 'code is law,' the article argues that in AI, the influence of formal code has waned, but a new form of structured conceptual framing has emerged in the form of ground-truth datasets—where 'ground-truth is law.' These datasets, shaped by a range of actors across the AI production chain, subtly guide algorithmic operations under the guise of neutrality. This study applies Anselm Strauss' 'arc of work' framework to identify five critical stages in AI production: goal setting, databasing, taxonomy construction, labeling, and monitoring—and demonstrates how conceptual understandings of the world are embedded within algorithmic systems in each phase of the process. The article then examines two key mechanisms that obscure this work: first, the fragmentation and distribution of AI tasks across a broad range of actors; and second, the shift of conceptual labor away from coding toward data preparation and algorithmic monitoring. This article lays the foundation for an investigative method aimed at tracking ethnographically the entire algorithmic production chain and the diverse actors involved, in order to better document how conceptual labor is integrated into machine learning systems.

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