Exceeds

MARCH 2026

daocloud.io Engineering AI Productivity Report

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

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

The daocloud.io engineering team reports 41.4% AI adoption, 0.70× productivity lift, and 10.7% 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

MODERATE

41.4%

AI assistance is present in 41.4% of recent commits for daocloud.io.

AI Productivity Lift

LOW

0.70×

AI-enabled workflows deliver an estimated 0% lift in throughput.

AI Code Quality

LOW

10.7%

Review insights show 10.7% overall code health on AI-supported changes.

How is the daocloud.io team performing with AI?

The daocloud.io engineering team reports 41.4% AI adoption, translating into 0.70× productivity lift while sustaining 10.7% 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?

daocloud.io is at 41.4%. This is 2.3pp below the community median (43.7%)..

41.4%

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?

daocloud.io operates at 0.70×. This is 0.43× below the community median (1.13×)..

0.70×

↓0.43× below1.13× Community Median

Pilot AI-assisted grooming, ticket triage, or incident retros to create visible productivity wins.

How does AI affect code quality?

daocloud.io holds AI-assisted quality at 10.7%. This is 12.6pp below the community median (23.3%)..

10.7%

↓12.6pp below23.3% Community Median

Add structured AI code review rubrics and require human sign-off for critical surfaces.

How evenly is AI use distributed across our team?

AI impact is concentrated—91.8% of AI commits come from a few experts, raising enablement risk.

91.8%

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

How can I prove AI ROI to executives?

To prove ROI, daocloud.io needs steadier adoption, measurable lift, and consistent quality. The ingredients are forming but not yet executive-grade.

Start with a lighthouse project, measure cycle improvements end-to-end, and harden quality guardrails.

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:

DI

dimagi.com

(87.5%)

PO

postgresql.org

(87.5%)

BL

bloq.com

(21.4%)

DA

daimond113.com

(21.4%)

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

Productivity Lift

Cycle-time improvement vs baseline

Companies in this quartile:

RO

rockstarwizard.ninja

(1.00×)

.I

.ieselrincon.es

(1.00×)

DR

draad.nl

(-9.59×)

WG

wgu.edu

(-0.41×)

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:

GZ

gzgz.dev

(20.0%)

GW

gwu.edu

(20.0%)

DR

draad.nl

(-82634.9%)

IN

inria.fr

(-2424.6%)

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.

XI

xin.li

Commits667
AI Usage91.9%
Productivity Lift1.85x
Code Quality23.4%
CA

carlory

Commits166
AI Usage87.0%
Productivity Lift1.23x
Code Quality20.2%
LS

Linghao Su

Commits13
AI Usage83.0%
Productivity Lift1.15x
Code Quality20.0%
JT

Jared Tan

Commits33
AI Usage66.4%
Productivity Lift1.13x
Code Quality20.0%
BO

bo.jiang

Commits12
AI Usage28.7%
Productivity Lift1.10x
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

47

ErikJiang

kubernetes/autoscaler

ray-project/kuberay

+3 more

vivian-xu

codefresh-io/argo-cd

linghaoSu

codefresh-io/argo-cd

argoproj/argo-cd

yankay

LMCache/LMCache

mistralai/llm-d-inference-scheduler-public

+10 more

Unknown contributor

kubernetes/website

WillardHu

DaoCloud/DaoCloud-docs

Activity

1,290 Commits

Your Network

36 People
carlory
Member
flpanbin
Member
bzsuni
Member
ErikJiang
Member
tukwila
Member
windsonsea
Member
lsq645599166
Member
honglian.you@daocloud.io
Member
daocloudpublic
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"

Learn more

Prove ROI

Export executive snapshots and benchmarks

Learn more