Comment by danieltanfh95

AI models are fundamentally trained on patterns from existing data - they learn to recognize and reproduce successful solution templates rather than derive solutions from foundational principles. When faced with a problem, the model searches for the closest match in its training experience rather than building up from basic assumptions and logical steps.

Human experts excel at first-principles thinking precisely because they can strip away assumptions, identify core constraints, and reason forward from fundamental truths. They might recognize that a novel problem requires abandoning conventional approaches entirely. AI, by contrast, often gets anchored to what "looks similar" and applies familiar frameworks even when they're not optimal.

Even when explicitly prompted to use first-principles analysis, AI models can struggle because:

- They lack the intuitive understanding of when to discard prior assumptions

- They don't naturally distinguish between surface-level similarity and deep structural similarity

- They're optimized for confident responses based on pattern recognition rather than uncertain exploration from basics

This is particularly problematic in domains requiring genuine innovation or when dealing with edge cases where conventional wisdom doesn't apply.

Context poisoning, intended or not, is a real problem that humans are able to solve relatively easily while current SotA models struggle.