This essay explores the growing anxiety around undetectable AI-generated content, questioning whether pre-AI content was ever truly “authentic” given algorithmic curation. It examines the real shift from content scarcity to abundance, the limitations of detection solutions, and suggests that the human-AI boundary is already dissolving through collaboration—forcing us to develop new frameworks for trust and verification that focus on claims rather than authorship.

This essay explores Catherine Olsson’s observation that language models seem to have an intuitive sense of “what they’re supposed to say,” drawing parallels to how human children learn social performance through modeling adult expectations. It argues that both human and machine cognition may be fundamentally constituted by layers of contextual performance rather than expressing some authentic core, and examines what this means as human and AI systems increasingly co-evolve.