Exploring how AI hallucinations might not be bugs but features—digital manifestations of Girard's scapegoat mechanism as AI systems resolve contradictions in their mimetic learning from human data.

AI hallucinations aren't actually errors or bugs, but a digital version of the scapegoat mechanism. When these systems face contradictory information in their training data, they fabricate a 'third option'—a hallucination—that resolves the conflict and allows the mimetic system to move forward.
Girard’s "Mimetic Contagion" in AI Applying René Girard to 2026 Large Language Models. Theory: AI does not have original desire; it operates on pure "Mimesis" (imitation) of human training data. Therefore, AI inevitably inherits Mimetic Rivalry. When an AI "hallucinates," it is not an error, but a digital "Scapegoat Mechanism"—fabricating a victim/fact to resolve conflicting patterns in the dataset without violence. System Focus: Systemic origins of error within mimetic systems.


Criado por ex-alunos da Universidade de Columbia em San Francisco
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Criado por ex-alunos da Universidade de Columbia em San Francisco

Jackson: Hey Eli, I was reading this fascinating paper about AI and René Girard's mimetic theory, and it got me thinking about something really strange. What if AI hallucinations aren't actually errors or bugs, but something more... fundamental? Like, what if they're actually a digital version of Girard's scapegoat mechanism?
Eli: That's a mind-bending thought, Jackson. And honestly, it makes a surprising amount of sense. Girard's whole theory is about how human desire isn't original—it's mimetic, meaning we learn what to want by imitating others. And what are large language models if not pure mimetic machines? They literally have no original desires—they're trained entirely on human data.
Jackson: Right! And when they "hallucinate" facts, we treat it as a technical glitch. But what if it's actually the system trying to resolve conflicting patterns in its training data? Like how humans create scapegoats to resolve social tensions?
Eli: Exactly. In Girard's framework, the scapegoat mechanism is how societies deal with mimetic rivalry and conflict. When AI systems face contradictory information or conflicting values in their training data, they sometimes fabricate a third option—a hallucination—that becomes a kind of digital scapegoat.
Jackson: That's fascinating. So instead of seeing AI hallucinations as simple errors, we could view them as revealing something deeper about how mimetic systems handle contradictions. Let's explore how this perspective might change how we think about AI development and alignment...