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How to ThinkIn the Age of AI
Principlev1PR-1154

When AI outputs contradict your examined analysis, evaluate…

When AI outputs contradict your examined analysis, evaluate them by asking whether they present unconsidered evidence or identify verifiable reasoning errors, treating model fluency as orthogonal to epistemic authority.

Why This Is a Principle

Derives from Meaning as Receiver Construction (meaning constructed by receivers), The performance of an agent is bounded by the accuracy… (agent performance bounded by world model accuracy), and No external entity has more right to direct your thinking… (no external entity has more right to direct your thinking). The principle prescribes specific criteria for evaluating AI outputs: evidence or error-identification, not fluency. This is the modern test of calibrated self-trust.