Exceeds

MARCH 2026

debian.org Engineering AI Productivity Report

A focused summary of AI adoption, productivity lift, and code quality for the debian.org engineering team.

See how AI-active teams rank this week on the Exceeds Leaderboards.

The debian.org engineering team reports 86.0% AI adoption, 1.62× productivity lift, and 28.0% code quality across recent work.

These metrics track how AI integrates into delivery pipelines, how throughput changes when assistance is used, and the health of AI-supported code review outcomes.

What this report measures

We analyze commits and diffs to estimate AI adoption, productivity lift, and code quality for your engineering organization.

How to interpret these metrics

Use these signals to understand how AI assistance fits into day-to-day development, where enablement efforts drive throughput, and how review practices keep quality steady.

AI Adoption Rate

HIGH

86.0%

AI assistance is present in 86.0% of recent commits for debian.org.

AI Productivity Lift

HIGH

1.62×

AI-enabled workflows deliver an estimated 62% lift in throughput.

AI Code Quality

LOW

28.0%

Review insights show 28.0% overall code health on AI-supported changes.

How is the debian.org team performing with AI?

The debian.org engineering team reports 86.0% AI adoption, translating into 1.62× productivity lift while sustaining 28.0% code quality. These outcomes suggest AI-supported reviews are embedded in day-to-day delivery without trading off reliability.

Manager Questions Answered

Real questions engineering leaders ask about AI productivity, with live benchmarks and company-specific data.

What's a good company AI adoption rate?

debian.org is at 86.0%. This is 42.3pp above the community median (43.7%)..

86.0%

Roughly in line43.7% Community Median

Spot squads sitting below the median and pair them with high-adoption champions to share workflows.

Does AI actually make developers faster?

debian.org operates at 1.62×. This is 0.49× above the community median (1.13×)..

1.62×

↑0.49× above1.13× Community Median

Double down on automation around QA and release prep to compound the gains already in flight.

How does AI affect code quality?

debian.org holds AI-assisted quality at 28.0%. This is 4.8pp above the community median (23.2%)..

28.0%

Roughly in line23.2% Community Median

Invest in AI-specific test checklists and shadow reviews to keep quality slightly ahead of peers.

How evenly is AI use distributed across our team?

AI impact is concentrated—97.2% of AI commits come from a few experts, raising enablement risk.

97.2%

Run prompt-sharing sessions, codify AI review checklists, and incentivize broad participation.

How can I prove AI ROI to executives?

debian.org has a solid ROI signal with room to strengthen either adoption, lift, or quality before presenting to executives.

Document case studies where AI accelerates delivery while maintaining quality, and expand playbooks across teams.

See how your full organization compares

Unlock personalized insights across all your repositories, teams, and contributors.

Securely connect Exceeds with your codebase to get commit-level insights on AI adoption and performance.

How Your Company Ranks

See how top engineering organizations compare across AI adoption, productivity lift, and code quality.

AI Adoption

% of commits with AI assistance

Companies in this quartile:

CA

cancun.tecnm.mx

(87.3%)

MO

momentohq.com

(87.3%)

UB

ub.edu

(21.2%)

RO

rossabaker.com

(21.2%)

Top 25% of teams adopt AI in 65-75% of their commits.

Productivity Lift

Cycle-time improvement vs baseline

Companies in this quartile:

IN

inngest.com

(4.82×)

U.

u.nus.edu

(2.87×)

AC

acad.pucrs.br

(1.12×)

MC

mcornholio.ru

(1.12×)

Top performers sustain 1.5× cycle-time improvements over six months when embedding AI into workflows.

Code Quality

Post-merge defect rate

Companies in this quartile:

IN

inngest.com

(701.7%)

ID

idesie.com

(649.2%)

GZ

gzgz.dev

(20.0%)

GW

gwu.edu

(20.0%)

Top 25% maintain quality above 92% while expanding AI usage, pairing automation with rigorous guardrails.

Rankings based on aggregated Exceeds AI dataset of 1.2M commits across open-source and enterprise engineering teams (Q4 2025).

Top contributors

Top contributors combine high AI adoption and quality output. Encourage internal sharing of best practices.

KM

Kunal Mehta

Commits193
AI Usage93.6%
Productivity Lift1.86x
Code Quality76.6%
SL

Sylvestre Ledru

Commits710
AI Usage90.8%
Productivity Lift1.68x
Code Quality20.0%
SP

Stuart Prescott

Commits81
AI Usage70.0%
Productivity Lift1.11x
Code Quality20.0%
CB

Christoph Berg

Commits5
AI Usage20.0%
Productivity Lift1.08x
Code Quality20.0%
DE

Dirk Eddelbuettel

Commits19
AI Usage23.6%
Productivity Lift1.07x
Code Quality20.0%

Encourage knowledge transfer from top AI users to others through internal mentoring or recorded "AI coding walkthroughs." Balanced adoption across the team typically improves overall performance by 12-15%.

Cross-Organization Network

Shared Repositories

54

toddy15

gohugoio/hugo

smcv

libsdl-org/SDL_ttf

libsdl-org/SDL_mixer

anbe42

JuliaGPU/pocl

Unknown contributor

FreeCAD/FreeCAD

asarhaddon

rust-lang/gcc

WeepingClown13

geerlingguy/linux

Activity

791 Commits

Your Network

30 People
anarcat
Member
anbe42
Member
bluca
Member
maitbot
Member
carnil
Member
cjwatson
Member
dpaleino
Member
eddelbuettel
Member
hefee
Member

Why these metrics matter for engineering managers

Faster delivery

1.4x lift → predictable roadmaps

Safer velocity

93% quality → lower rollback risk

Equitable gains

AI less dependency on heroes

Governance

Depth monitoring audit-ready

ExceedsExceeds AI

Turns these insights into daily coaching and automatic alerts, helping managers balance speed with sustainability.

See the truth of AI impact

Adoption + lift + quality in one view

Learn more

Know where to act first

Repo and role level "lift potential"

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Prove ROI

Export executive snapshots and benchmarks

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