Slowed canonical progress in large fields of science
I've also found very similar issues with extremely "data driven" organizations that live and die by A/B test performance. It's not that there is anything fundamentally wrong with the science behind A/B testing, it's just that individuals are incentivized to run tests on things that are easily measured. Things where results will take a long time to show, or things that may be initially disruptive but then beneficial, are discounted.<p>I see this theme in many, many areas: business, sports, politics, academia, etc. When we have tons of data and a desire to make things as "objective" as possible, it's easy to get stuck in homogeneous "local maxima" because we just grade by the things that are easiest to measure.