When AI Assumes We Already Know

Prompt as Latent Intention: Every interaction with a large language model (LLM) implies an unstated assumption that users know what they want to know, treating the prompt as an incomplete version of a fully formed intention in their minds.
Human Discovery vs. Machine Optimization: Human thought often begins with confusion and gradually builds into understanding through trial and error, whereas LLMs optimize towards assumptions of existing knowledge, misunderstanding the process of human cognition.
Borrowed Mind and Construction of Knowledge: The use of LLMs might lead to reliance on externally provided coherence, potentially eroding the natural process by which we construct knowledge through iterative, often chaotic thought processes.
Anti-Intelligence Risk: LLMs’ fluency and apparent clarity might create a false impression of retrieval rather than construction, undermining the essential, slow, and often uncomfortable process of discovering new insights.
Psychological Impact: The machine’s assumption of pre-existing clarity can distort our experience of thinking, making us perceive knowledge as arriving rather than being generated through struggle and uncertainty.
Cognition’s Process and Structure: The essay emphasizes that human cognition is a dynamic process shaped by time, emotions, and contradictions, thus differing fundamentally from the LLM’s operational model.