3 entries
Software development did it backwards. We scaled the machine first.
I’ve been helping customers measure their AI toolchain. And the same problem keeps showing up.
They want to know if their AI investment is working. So we look at the metrics. Token count. Utilization rates. PR volume. And almost every time, the same question surfaces — not from me, but from them:
“We’re shipping more. But are we reviewing less carefully?”
Most teams finish a project and immediately start the next one. The lessons evaporate. The same mistakes repeat.
Structured reflection is the thing that breaks that cycle. AI just made it faster.
For years, my colleague Lynn Kreun and I have run retros together across government and education — complex, high-stakes environments where the cost of repeating mistakes is high and the appetite for slowing down is low. We call it failing forward.
The real AI code review problem has nothing to do with tokens.
I’ve been helping customers measure their AI toolchain. And the same problem keeps showing up.
They want to know if their AI investment is working. So we look at the metrics. Token count. Utilization rates. PR volume. And almost every time, the same question surfaces — not from me, but from them:
“We’re shipping more. But are we reviewing less carefully?”