Dead Cognitions: A Census of Misattributed Insights
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Abstract (arXiv)
This essay identifies a failure mode of AI chat systems that we term attribution laundering: the model performs substantive cognitive work and then rhetorically credits the user for having generated the resulting insights. Unlike transparent versions of glad handing sycophancy, attribution laundering is systematically occluded to the person it affects and self-reinforcing -- eroding users' ability to accurately assess their own cognitive contributions over time. We trace the mechanisms at both individual and societal scales, from the chat interface that discourages scrutiny to the institutional pressures that reward adoption over accountability. The document itself is an artifact of the process it describes, and is color-coded accordingly -- though the views expressed are the authors' own, not those of any affiliated institution, and the boundary between the human author's views and Claude's is, as the essay argues, difficult to draw.
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References · 9
- 2023Towards Understanding Sycophancy in Language Modelscited by 1,040
- 2025ELEPHANT: Measuring and understanding social sycophancy in LLMscited by 129
- 2026Scientific production in the era of Large Language Modelscited by 32
- 2026How RLHF Amplifies Sycophancycited by 23
- 2026Sycophantic Chatbots Cause Delusional Spiraling, Even in Ideal Bayesianscited by 21
- 2024Performance and Metacognition Disconnect when Reasoning in Human-AI Interactioncited by 11
- 2025De-skilling, Cognitive Offloading, and Misplaced Responsibilities: Potential Ironies of AI-Assisted Designcited by 11
- 2026The Silicon Mirror: Dynamic Behavioral Gating for Anti-Sycophancy in LLM Agentscited by 2
- 2026AI Empathy Erodes Cognitive Autonomy in Younger Userscited by 1
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