‘Incited and Inclined but Not Impelled’: Distinguishing Productivity from Creativity in Artificial Intelligence

Discussions about the encroachment of Large Language Models (LLMs) on the domain of human creativity often conflate productivity with creativity; they conflate the ability to generate new structures according to rules or other systems (productivity) and the ability to use one’s productive capacity without fixed reliance on identifiable stimuli while nevertheless producing structures appropriate to the situation (creativity). For a behavior to be “creative” is to use the productivity of a capacity like language in ways that are stimulus-free, frequently novel, yet appropriate to situations. This paper argues that, because of this conflation, improvements in the productive capacities of LLMs have been mistaken for improvements in this richer notion of creativity. Although LLMs – underpinning both chatbots and agentic systems – exhibit remarkable productive capacities, their relation to their environment does not exhibit this “creative” aspect. Where the appropriateness of human linguistic behavior does not appear governed by a causal relationship between the individual and their environment, LLMs exhibit a functional appropriateness that sees their productive capacities tethered to identifiable stimuli in the local environment. They are, in the Cartesian sense, impelled to act, but not inclined, contrasting with the human who is ‘incited and inclined but not impelled.’