A developer argues against using LLMs like GPT for research, comparing them to Google's 'I'm Feeling Lucky' button that delivers instant answers without the journey. The author contends that true intellectual growth comes from wrestling with conflicting sources, encountering diverse perspectives, and building mental models through struggle rather than receiving polished outputs.

The piece highlights how LLMs excel at producing plausible-sounding responses but lack the critical friction needed for deep understanding. Citing research on LLM overconfidence and generalization bias, the author notes that these systems often average out nuances, exaggerate claims, or confidently reproduce mistakes. The Gell-Mann Amnesia effect explains why users overestimate LLM performance in unfamiliar domains.

The core argument centers on experience versus information. While LLMs provide efficient answers, they bypass the intellectual development that comes from encountering bad takes, broken links, contradictory sources, and the epistemic smell that signals something is amiss. The author concludes that LLMs are intellectually corrosive not because they lie constantly, but because their smoothness hides uncertainty - a feature that undermines the very process of building robust understanding.