Why this report exists
There is a great deal of marketing copy about AI making engineers faster. There is much less work that holds up to scrutiny on what kind of faster, on which kinds of tasks, and at what cost in regression and defensibility.
This is our attempt to start that work seriously.
−38%
Median time-to-fix
AI-assisted vs solo, debugging tasks
2.1×
Regression rate
Bottom-tercile engineers, 30-day window
612
Engineers measured
Across 14 production-derived codebases
4,200
Tasks scored
Blind panel-graded sessions
Time savings, by task category
The median speedup is dramatic on closed-form debugging and shrinks fast as ambiguity rises. Feature-build tasks see the smallest gain — most of the time goes to spec questions AI cannot resolve.
The regression cost
Speed comes with a regression tax that scales inversely with engineer experience. Top-decile engineers actually reduce their regression rate when paired with AI; bottom-tercile engineers more than double it.
| Band | Solo | AI-paired | Δ |
|---|---|---|---|
| Top decile (D10) | 3.2% | 2.1% | −1.1pt |
| Upper quartile (Q4) | 5.8% | 6.1% | +0.3pt |
| Median (Q3) | 7.4% | 11.9% | +4.5pt |
| Lower quartile (Q2) | 9.1% | 18.7% | +9.6pt |
| Bottom tercile (T1) | 11.3% | 23.8% | +12.5pt |
Defensibility lags speed
When asked to defend their decisions on replay, candidates lose ground fastest exactly where AI helped most. The fastest fixers are not always the clearest explainers.
What we'll do next
Codritium re-runs this benchmark every six months. The raw scoring rubric is in the Defensibility Rubric, 2026. We invite independent replication.