LLM planning is better characterized as pattern matching than genuine planning: when facing a problem (e.g., planning a trip), the model recognizes it as similar to thousands of trip-planning guides in its training data and uses those patterns to generate a plausible response.
causalpending
Speaker
Michael WaldridgeEvidence Quote
“when it's looking at planning a trip you've seen thousands of trip planning guides and trip agendas and so on and it's doing patent matching to pick up on that uh and help you plan the trip but is it actually planning from P first principles”
Source
Don’t Believe AI Hype, This is Where it’s Actually Headed | Oxford’s Michael Wooldridge | AI History— Johnathan BiCreated: 8/11/2026, 7:08:35 AM
My Notes
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