Nagarjuna, the 2nd-century Buddhist philosopher, built his entire career on demonstrating that things have no fixed, inherent nature — that every concept derives its meaning relationally, from what it is not. His method was not mysticism but rigorous logical dismantling: show that any property you claim is 'essential' to a thing actually depends on a web of relations that could be otherwise. Most people read this as abstract metaphysics. It has an almost embarrassingly direct application to how we currently think about AI-generated output. We tend to evaluate what an AI produces against a fixed standard — is this accurate? is this good writing? — as though quality were a stable property of the artifact. Nagarjuna's śūnyatā (emptiness — the idea that no thing has properties independent of its relations and context) suggests the more useful question is always relational: accurate relative to what purpose, good relative to whose judgment, for which audience, in which moment? The trouble with offloading output to a system trained on aggregate patterns is not that it produces bad answers — it's that it produces answers optimized for the average context, and then hands them back to you in a form that feels complete. Completeness suppresses the relational questions. The output lands, and you stop asking 'relative to what?' A useful discipline: before accepting any AI-generated framing of a problem or summary of a conversation, name the specific context that framing was not built for. Not as a critique of the tool — as a maintenance of your own relational thinking.

Name one AI-generated output you accepted last week — a summary, a draft, a recommendation. What specific context of yours was invisible to the system that produced it, and how would the output have differed if that context had been visible?

Drawing from Madhyamaka Buddhist philosophy — Nagarjuna (Mūlamadhyamakakārikā, c. 2nd century CE)