Developer productivity is waste
summary
Argues that obsessing over developer productivity metrics misfires because most gains come from outer loop improvements (CI/CD, automation, fewer platforms) not individual coding tweaks, cites a disputed McKinsey-style report and the AI debate about replacing developers, shows surveys with flat CI/CD adoption and a large share of teams still lacking end-to-end build automation, recommends running developer satisfaction surveys, automating pipelines, and consolidating platforms to cut maintenance drag.
AI-written summary · about
chapters (22)
- 0:00Introduction
- 1:06Productivity myths and metrics
- 3:34Inner vs outer loop gains
- 6:22McKinsey report controversy
- 9:12Why productivity matters now
- 12:19Productivity challenges
- 16:36Why measuring is hard
- 17:40Motivations for metrics
- 22:57Developer productivity metrics
- 23:17DORA metrics limitations
- 24:41SPACE metrics and experience
- 25:23Flow state framework
- 28:12Explaining to non-devs
- 31:44Tools improving productivity
- 32:08Developer surveys
- 35:41Build automation importance
- 39:30CI/CD adoption state
- 43:08Outer loop and platform engineering
- 43:50Building vs using platforms
- 45:54Platform costs
- 49:23Further reading
- 50:23Q&A
related talks
31mDeveloper productivity is waste
33mMeasuring Developer Productivity: the good, the bad and the data
34mBuilding a Culture For Developer Productivity
43mMeasuring Developer Productivity: The Good, the Bad and the Data
43mThe Past, Present and Future of Developer Productivity Theories and Metrics
24mBeyond the Commit: A Fireside Chat on AI and Developer Productivity
46mForget Developer Platforms, Think Developer Productivity!
07mDeveloper Experience can drive business growth