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Metric definition

AI commit rate

AI commit rate shows how often detected AI assistance appears in committed work. It is an adoption signal—not a productivity score and not a measure of how many lines an assistant wrote.

By José Pedro Nunes
Merge commits excluded
Calculated by time window
Available by repo and contributor
Detection method remains visible
AI commit rateTempo · last 30 days
63%AI commits
Formula
1,099
detected AI commits
÷
1,745
non-merge commits
=63%
CLI matched
69%
High confidence
Pattern found
31%
Medium confidence
Merge commits
0
Excluded
The rate uses detected non-merge commits; missing evidence is never labeled as definitively manual.
01

How Tempo calculates AI commit rate

Tempo first removes merge commits because they are repository operations rather than a useful signal of code creation. It then counts commits with a positive AI detection and divides that count by the remaining commits.

Organization and team rates are calculated from the underlying commit counts, not by averaging repository percentages. This prevents a small repository from carrying the same weight as a high-volume repository.

02

How to interpret the number

A rising rate means detected AI assistance is appearing in a larger share of commits. It does not, by itself, show that delivery is faster, quality is higher, or cost has fallen.

Interpret adoption alongside PR cycle time, PR size, review behavior, change failure signals, and team context. The useful comparison is whether the delivery system changes as adoption changes.

03

Detection coverage matters

AI commit rate is only as complete as the evidence available. CLI file matching provides stronger coverage than commit-message forensics alone. An undetected commit should be read as “no supported signal found,” not “proven manual.”

04

Useful ways to segment AI commit rate

The organization-wide number is a starting point. Repository, contributor, tool, and time-window breakdowns explain where adoption is happening and whether it is sustained.

  • Compare repositories with similar work profiles.
  • Track adoption before and after enablement or policy changes.
  • Separate CLI-attributed work from pattern-detected work.
  • Compare AI-assisted and manual PR outcomes rather than ranking people.
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