This paper investigates the phenomenon of Model Collapse in artificial intelligence through the lens of psychoanalytic theory. Model Collapse is observed when Large Language Models (LLMs) are trained recursively on AI-generated data, after which models progressively lose the ability to generate meaningful text. This reveals an empirical difference between human and machine-generated discourse that psychoanalytic theory may help explain. While AI systems can produce text often indistinguishable from human writing, they are fundamentally unable to produce what French psychoanalyst Jean Laplanche terms "enigmatic signifiers": the productive failures of comprehension that paradoxically enable human subjects to generate genuinely new meanings.

Laplanche's framework uniquely explains both clinical phenomena and the qualitative difference between human and machine-generated discourse. His account centers on how children's incomplete comprehension of messages from caregivers creates repressed signifiers that constitute the unconscious. These mistranslations are not deficits but productive failures that generate from the perspective of LLM Model Collapse we might term "surplus variance," a mathematical property present in human discourse but absent in synthetic text.

This analysis reveals that AI models are paradoxically too successful at integrating training data. They lack the unconscious dimension created by primary repression, where enigmatic signifiers remain partially untranslated and continue to affect discourse production through slips, errors, and creative transformations. This has implications for AI development and theoretical psychology, suggesting that more sophisticated language models may require incorporating controlled imperfections that mirror the generative role of repression in human psychology: productive misunderstandings rather than improved pattern recognition.