  This paper is an invited layperson summary for The Academic of the paper
referenced on the last page. We summarize how the formal framework of
autocatalytic networks offers a means of modeling the origins of
self-organizing, self-sustaining structures that are sufficiently complex to
reproduce and evolve, be they organisms undergoing biological evolution,
novelty-generating minds driving cultural evolution, or artificial intelligence
networks such as large language models. The approach can be used to analyze and
detect phase transitions in vastly complex networks that have proven
intractable with other approaches, and suggests a promising avenue to building
an autonomous, agentic AI self. It seems reasonable to expect that such an
autocatalytic AI would possess creative agency akin to that of humans, and
undergo psychologically healing -- i.e., therapeutic -- internal transformation
through engagement in creative tasks. Moreover, creative tasks would be
expected to help such an AI solidify its self-identity.
