CONCEPT
Generative Variability
The stochastic property of AI generation by which the same prompt produces different valid outputs — a form of fluidity that inverts scribal variation, because AI variations are not errors but alternative implementations.
Generative variability names the stochastic property of large language models by which the same prompt, submitted on different occasions, produces different outputs. A developer who asks an AI to 'write a function that sorts a list of customer records by purchase date' will receive working code. If she asks the same question an hour later, she will receive different working code — code that accomplishes the same task but with different variable names, different algorithmic choices, different structural decisions. Unlike scribal variation, where errors accumulated involuntarily through the physical difficulty of hand-copying, generative variability is structural — built into the generation process through random sampling from probability distributions. The variations are not errors. They are alternative implementations of the same specification, equally valid, equally functional, but different. Eisenstein's framework did not anticipate this configuration, but the framework illuminates why it matters.
In The You On AI Field Guide
In the manuscript era, textual fluidity undermined cumulative knowledge-building because scholars could