AI research's one-step trap: why incremental improvements mislead

A researcher explores how AI papers often optimize for single-step improvements rather than solving real end-to-end problems, creating a false sense of progress. For indie builders shipping AI features, the takeaway is clear: benchmark against actual user workflows, not isolated metrics.