This lecture-performance introduces the concept of dissociative worldmaking; a methodological and aesthetic framework for understanding the interactions of queer/trans data with artificial intelligence. Grounded by LaRochelle's artistic practice – specifically the counter-mapping platform Queering The Map and its generative AI offspring QT.bot – they advocate for a "queer logic of data opacity” that resists the extractivism and standardisation inherent to Big Data.

Weaving together auto-theoretical reflections on ketamine, heartbreak and data moderation with writings by Édouard Glissant, Lauren Berlant, and Hannah Baer, LaRochelle reframes dissociation not as a pathology but as a generative strategy for processing the "too muchness" of an archive laden with both trauma and ecstasy. They argue that by embracing the failure of linear causality ("X does not equal Y"), machine learning can be appropriated to help us navigate the information overload of the present without resorting to reductionism. Ultimately, this lecture-performance proposes dissociative worldmaking as an aesthetic strategy that loosens attachments to normative time and subjecthood, allowing for new forms of sense-making within a world in crisis.