MARCH 2026

openearth.org Engineering AI Productivity Report

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

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

The openearth.org engineering team reports 94.1% AI adoption, 1.62× productivity lift, and 68.5% 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

94.1%

AI assistance is present in 94.1% of recent commits for openearth.org.

AI Productivity Lift

HIGH

1.62×

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

AI Code Quality

LOW

68.5%

Review insights show 68.5% overall code health on AI-supported changes.

How is the openearth.org team performing with AI?

The openearth.org engineering team reports 94.1% AI adoption, translating into 1.62× productivity lift while sustaining 68.5% 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?

openearth.org is at 94.1%. This is 50.3pp above the community median (43.8%)..

94.1%

↑50.3pp above43.8% Community Median

Keep codifying prompts and monitoring adoption so the lead over peers is sustainable.

Does AI actually make developers faster?

openearth.org operates at 1.62×. This is 0.48× above the community median (1.13×)..

1.62×

↑0.48× 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?

openearth.org holds AI-assisted quality at 68.5%. This is 45.2pp above the community median (23.3%)..

68.5%

Roughly in line23.3% 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—64.2% of AI commits come from a few experts, raising enablement risk.

64.2%

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

How can I prove AI ROI to executives?

openearth.org combines strong adoption, lift, and quality control—making the ROI story executive-ready.

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Link these metrics to deployment frequency and incident cost to convert engineering wins into business KPIs.

See how your full organization compares

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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:

idesie.com

(2904.2%)

inngest.com

(1429.6%)

pevesoft.ro

(87.6%)

vllmr.dev

(87.6%)

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

Productivity Lift

Cycle-time improvement vs baseline

Companies in this quartile:

konghq.com

(6.64×)

inngest.com

(4.82×)

acad.pucrs.br

(1.12×)

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:

konghq.com

(795.0%)

inngest.com

(701.7%)

gzgz.dev

(20.0%)

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.

Contributor

Commits

AI Usage

Productivity Lift

Code Quality

Mirco Rudolph

Commits304

AI Usage96.0%

Productivity Lift1.93x

Code Quality74.0%

Evan Prodromou

Commits435

AI Usage92.0%

Productivity Lift1.47x

Code Quality78.0%

isaacbello

Commits305

AI Usage94.0%

Productivity Lift1.41x

Code Quality76.0%

Milan Gruner

Commits222

AI Usage92.0%

Productivity Lift1.27x

Code Quality20.0%

Marina Olivella

Commits218

AI Usage94.0%

Productivity Lift1.17x

Code Quality78.0%

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