This chapter examines generative image synthesis through Jungian depth psychology, proposing that machine learning systems trained on vast visual repositories function as interpretive instruments for accessing archetypal content within the collective unconscious. Drawing from experimental work with Midjourney during Mythological Studies Journal editorial processes, the author develops a framework positioning these technologies as digital mediators capable of synthesizing archetypal imagery embedded within billions of artistic works. The investigation employs minimalist prompt methodology, utilizing concise archetypal inquiries such as “The Shadow Archetype” to generate visual interpretations. Systematic analysis across multiple archetypal categories—Hero, Trickster, Anima—reveals how systems trained on datasets like LAION-5B effectively aggregate and reinterpret humanity’s symbolic vocabulary while introducing novel interpretive dimensions. The research addresses critical limitations including neural network opacity, dataset biases, and ethical concerns regarding artistic appropriation. Drawing on Shannon Vallor’s mirror metaphor, the chapter argues that while these tools amplify and distort archetypal content, they provide unprecedented access to collective symbolic structures. Findings suggest human-machine creative collaboration represents emergent mythological expression, expanding traditional artistic boundaries while maintaining connection to fundamental psychological patterns. This hybridity challenges conventional authorship notions, opening new territories for both artistic exploration and depth psychological inquiry.