DX recently published results from a 16-month longitudinal study across 400+ engineering organizations: AI tool usage rose by 65%, while median PR throughput rose by just under 8%. Most organizations landed in the 5-15% range. Meaningful, but well short of the 3x or 10x being promised by some vendors.
Brian Houck, who leads developer productivity research at Microsoft, offered an explanation: coding accounts for around 14% of a developer's workweek. Even significant gains in the coding portion only recover so much.
The other 86%, things like review, planning, debugging, context-switching, stakeholder back-and-forth, the rework that happens when something gets built before it's understood, doesn't get faster because writing the code did.
This isn't an argument against coding tools, I use them daily. But "roll out coding tools" isn't a strategy, and I've recently moved into a new area of focus at Mintel: optimising how engineering teams adopt AI tools. If my job is to help teams move faster, I need to know which tools are helping which teams, where the real bottlenecks are, and how to target investment at problems teams actually have. That's a measurement problem before it's a tooling problem.
