Understanding LLMs as Bullshitters: Why AI's Lack of Truth Matters
By
speckx
Hot, fresh, and worth queueing round the block for.
Summary
This personal essay by Matt Ranger, Kagi's head of ML, explores how Large Language Models (LLMs) engage in 'bullshitting' as defined by philosopher Harry Frankfurt - producing statements without regard for truth, unlike lying which requires knowledge of truth. The author argues that while LLMs are fundamentally bullshitters that generate plausible-sounding but often incorrect information, they remain useful tools when their limitations are understood. The essay examines examples like Gemini 2.5 Pro's incorrect responses to simple questions, discusses the philosophical implications of AI bullshitting, and suggests that recognizing LLMs as bullshitters rather than truth-tellers is key to using them effectively.
Key quotes
· 5 pulledI'm not the first to note that LLMs are bullshitters, but I want to delve into what this means.
Lying means you have a concept of what is true, and you deliberately say something else. Bullshitting means you don't care about truth at all - you just say whatever sounds plausible.
This is some decent bullshit!
The bearded surgeon mother
Gemini 2.5 pro was Google's strongest model until yesterday. At launch it was showered with praise to the point some questioned if humanity itself is now redundant.
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