Measuring AI impact
What to count once agents write code, and what to ignore: delivery against coding speed, the evidence on Software Factories, and metrics that cannot drift.
- 6 articles, newest first
- Measuring AI impact on engineering: what to count and what to ignore Code volume, pull requests, usage: AI pushes up every number that is easy to count. A Stanford talk and DX data put the measured gain at around 10 percent, and it swings widely from company to company. What to measure instead, where the returns come from, and why things get worse before they get better.
- Report the daily delta, not the total that only goes up The Tweet Drafter in the software-factory.dev factory posts updates three times a day and reports the daily delta, not the cumulative total, because a total always looks like progress.
- A metric without a red line is decoration The weekly Performance Monitor in the software-factory.dev factory works because every metric it watches has a hard threshold written into it.
- Why faster coding does not guarantee faster delivery Productivity research measures task time, perceived savings, pull requests, and production flow, each in its own way. Read side by side, the measures show where the constraint moves.
- Software Factory evidence and its limits Public implementations show that agent-run delivery systems can build and ship software. Read their numbers with a source label, a scope, and careful limits.
- Daily metrics that cannot drift: subtract yesterday's total The Daily Metrics automation in the software-factory.dev factory never measures a daily change with a date-filtered query. It subtracts yesterday's cumulative total from today's, a method that cannot drift.