20 June 20264 min read
AI helps most where you need it least
A Stanford study of 100,000+ developers found AI delivers big gains on clean, greenfield work and almost none on the complex, legacy code where most teams actually live. The lever isn't the model. It's the state of your codebase, and that's something you can change.
Short version: the AI productivity pitch is built on the easy case. New project, clean slate, simple feature. That's where the tools shine, and that's almost never where your team spends its day. Stanford went and measured the hard case, the messy existing codebase, and the gains mostly evaporate. Some teams come out behind. The uncomfortable read: AI pays off in proportion to how good your codebase already was.
What the Stanford study measured#
A team led by Yegor Denisov-Blanch at Stanford studied AI's real effect on developer productivity using private-repository data from over 100,000 developers across 600+ companies. Not a survey of how fast people felt. Actual code, measured. The split that matters:
- Greenfield, low-complexity work: 35-40% gains. This is the demo, and the demo is honest, on this kind of work.
- This is most real work.