Nagarjuna, the 2nd-century Buddhist philosopher, built an entire logical method around one move: finding the hidden dependencies that make a concept unstable on its own. He called this *pratītyasamutpāda* — dependent origination, the idea that nothing has meaning or existence by itself, only in relation to something else. Most people assume this is mysticism. It's actually the sharpest diagnostic tool for understanding what's happening when AI-generated communication feels oddly hollow. A large language model produces text by predicting what words typically follow other words — which means it generates the *appearance* of meaning by recapitulating relationships from training data, without any of the originating dependencies: no stakes, no body, no relationship history, no silence before the sentence. The output reads as coherent because coherence is, technically, a pattern of dependencies — and those patterns can be simulated. What can't be simulated is the weight of what isn't said. The practical implication: when you're reading AI-generated summaries, drafts, or analyses, the question to ask isn't 'Is this accurate?' It's 'What relational context was this meaning dependent on — and is that context present here?' That one question cuts through a lot of confident-sounding noise.

What is the opposite of what you're currently doing when you evaluate AI-generated content — and would that opposite approach catch something your current one misses?

Drawing from Madhyamaka Buddhism — Nagarjuna