Outsourcing Our Epiphanies

Traditionally, being an author has been considered to involve having the capacity to think. This has raised questions about the attribution of authorship to AI. In this essay, I first explain how large language models (LLMs), the technology behind popular chatbots, produce their output. I then survey recent literature which defines what thinking is with particular comparison to the way LLMs operate. This literature argues that that thinking involves opening oneself to new ideas and evaluating those ideas, always keeping alert to the possibility of unexpected discoveries. While LLMs can have new ideas put into their training data, they currently cannot evaluate those ideas correctly without human intervention and they cannot have epiphanies. I finally argue that both of these concepts are necessary for authorship.