Engineering Intelligence treats understanding as a first-class engineering metric, alongside velocity and quality. As AI writes a larger share of production code, the risk is a team that ships more while comprehending less of what it ships. Engineering Intelligence is the discipline of closing that gap.
In practice it means grounding measurement in real repository evidence — the diffs, reviews, and changes a team actually produces — rather than self-report. From that evidence you can ask concrete questions: does an engineer understand the code merged under their name? Which concepts are they building durable mastery of, and which are they routing around? Where is understanding thinnest relative to how business-critical the code is?
The goal is not surveillance but growth: turning everyday AI-assisted shipping into a signal for where learning is needed, so speed and comprehension stop pulling in opposite directions.