So are people. People are trained on existing data and learn to reproduce known solutions. They also take this to the meta level—a scientist or engineer is trained on methods for approaching new problems which have yielded success in the past. AI does this too. I’m not sure there is actually a distinction here..
Of course there is. Humans can pattern match as a means to save time. LLM pattern match as the only mode of communication and “thought”.
Humans are also not as susceptible to context poisoning, unlike llms.
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.