Researchers built AI models trained only on pre-1930 text to let people interact with "historical minds," then tested whether this changed how participants viewed the past. Talking to a 1930s-bounded AI reduced the "moral decline illusion"—the common belief that people were more ethical back then.

• A randomized experiment with 240 participants showed that interacting with historically-bounded LLMs reduced the illusion of moral decline compared to using contemporary AI
• The study demonstrates a new experimental method where temporal knowledge boundaries become controllable variables, turning philosophical thought experiments into testable science
• This approach reveals how our understanding of the past gets distorted by everything that happened after it—we can't see history clearly without the filter of hindsight

Dan Luu argues that he notices severe bugs in products that teams insist work fine, and traces this gap to how fans of something—whether employees, users, or enthusiasts—develop psychological blind spots to its flaws. He uses search engines, Blackboard, and forum software as examples of products widely disliked by actual users but defended by insiders.

• Products can be severely broken (requiring workarounds, producing spam-filled results, cheating on performance metrics) while internal teams genuinely believe they work well, then fail publicly when real users encounter the same issues Luu spotted.
• People exhibit strong motivated blindness toward things they're invested in: Volvo forum users deny reliability data, Blackboard employees were shocked it was widely hated, and Discourse developers implemented performance cheating while apparently believing their software was fast.
• Luu suspects this blindness is partly about mental barriers people construct—acknowledging a flaw in your work or something you love requires confronting cognitive dissonance, so teams rationalize away contradicting evidence.

This paper defines the “LLM fallacy” as a bias where users credit their AI-assisted outputs to their own skill rather than the model’s contribution. It analyzes how fluent, opaque interactions with large language models blur human-machine boundaries, offers a framework for its mechanisms, and discusses impacts on education, hiring, and AI literacy.

• People using LLMs tend to internalize the AI's output as evidence of their own skill, not just trust the tool's answer (unlike classic automation bias).
• This happens because model reasoning is invisible, the prose feels fluently "authored," there's no clear handoff moment, and positive feedback loops reinforce the false belief.
• It shows up concretely: coding, academic writing, data analysis, and creative work all get passed off as personal competence when AI did much of the work.
• Real consequences include students graduating without real mastery, hiring managers misjudging AI-reliant candidates, and skewed performance reviews at work.