Computational structuralism: Toward a formal theory of meaning in the age of digital intelligence

The discovery that “next-token predictor” language models can fluently produce text has important but underappreciated theoretical implications. Most notably, their success demonstrates that fully relational models with no access to external referents or human actors are sufficient to generate contextually appropriate discourse. Building upon this insight, this article proposes computational structuralism, a perspective that synthesizes insights from deep learning, information theory, and French structuralism to interpret the success of large language models and provide a vocabulary for rigorous and formalized inquiry into systems of meaning. Computational structuralism conceptualizes the world as an information system rife with pattern and redundancy. Patterned observations can be compressed into latent structures that enable both interpretation and the generation of contextually appropriate responses. Large language models achieve this with web text, suggesting that discourses can be sufficiently modeled as systems of transformations over a set of relationally defined elements. Today’s AI models thus provide an empirical “proof of concept” for what structuralist theorists sought to achieve decades ago, a distillation of social life’s complexity into the latent forms that render its content meaningful.