we can consider "AI" at different time depths — deep learning (2010s), cybernetics (1950s), automation (1800s), but in this paper I argue that to understand its *interactive* appeal we must further broaden our outlook It's not a big jump from divination to deep learning — they are united by the generative use of chance. People have always been eager to ascribe meaning to random processes, and that's where we must start to understand the appeal of present-day LLMs https://doi.org/10.5281/zenodo.19452872