Researcher warns against optimizing for single-step improvements in AI

A post on the "one-step trap" highlights how focusing on incremental gains in AI research can lead you astray from meaningful progress. The insight matters for indie builders experimenting with language models and ML—it's easy to chase benchmark bumps that don't translate to real utility, wasting time and compute on false progress.