Taste in the age of LLMs

Two pieces this month land on the same point from different angles. Raj Nandan Sharma argues that AI has made competent output abundant and cheap, so the scarce skill is now discernment — recognising what's generic versus what's genuinely valuable — paired with real accountability and contextual depth that "the model could not have added on its own." Matthew Sinclair frames the same shift through the lens of DJing: curation as a creative act, the same way a conductor's selection and sequencing is the work, even though they didn't write the music. Two phrases — "taste as moat" and "taste as art" — that aren't synonyms but rhyme: in a world of statistically average output, the human contribution is the willingness to choose against the average for reasons the model cannot have.

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