Computational hermeneutics: why study canonical figures like David Hume in the age of AI?

This article argues that figures like David Hume (1711–1776) should be studied in the age of AI not despite computation but through it. Drawing on Enlightenment studies and computational history, I sketch a triadic model of the book – as artefact, data object, and vehicle of meaning – and show how metadata, text and image reuse, translation mining, and model-assisted workflows let us trace reception, canon formation, and semantic change at scale. However much we may wish to move beyond canonical figures, the canon remains one of the main structures through which visibility, transmission, and exclusion are historically organised; understanding how it was made is often the most effective route to recovering those figures left outside it. The aim is not to automate interpretation, but to relocate it: canonical authors become testing-grounds for reproducible, data-aware intellectual history with broader relevance in an AI-saturated world.