Large language models can produce poems that look correct while lacking any feeling of vitality or charge. This project takes that disjunction as its subject by staging an interview with an “AI poet,” then displaying and annotating the transcript to show how competence and emptiness coexist in machine-generated writing. The work argues that the fascination of AI poetry lies less in its aesthetic achievement and more in the reader’s oscillation between amazement and disappointment, between “wow! It can write!” and “Argh! Why is it so bad?” By treating the AI as if it were a real poet we begin to see the gaps and to ask what those gaps teach us about how humans write and how readers attribute intention, sincerity, and authority to language.
David Braziel (2026) studied this question.
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