From Text to Clay shows how ancient ceramic practices can teach us new ways to work with AI, using clay's slow, unpredictable transformations to develop more thoughtful approaches to machine learning that prioritize reflection over speed. Rather than letting AI accelerate creative work, the project demonstrates how ceramic temporality can slow down AI interaction, creating space for a deeper understanding of both materials, and suggesting more meaningful futures for creative practice with AI tools.
Contemporary AI creative tools prioritize speed and efficiency, promising instant results that fundamentally misalign with how creative practice actually develops. In From Text to Clay, we explore an alternative approach: using ceramic practice, with its inherent slowness, unpredictability, and temporal constraints as a framework for developing more reflective relationships with AI systems. By training an AI model on personal ceramic works and translating its outputs back into clay, we explore how ancient material practices can inform sustainable approaches to using AI in creative work.
