The most valuable engineering work can leave the smallest footprint.

A production incident can consume six hours of investigation and end with a ten-minute fix. An engineer can spend an afternoon reviewing an AI-generated implementation that took 20 minutes to produce, experimenting with five approaches before finding the one that works, or helping a teammate make a critical architectural decision. Yet the system may record only a ticket moving from “In Progress” to “Done.”

I saw this repeatedly while building AI-native systems for enterprises at my previous organization, leading a team of talented developers.

The consequence is bigger than poor reporting: companies misjudge capacity, overlook contributions, and reward visible output over difficult work. AI coding agents are making that mismatch impossible to ignore.