MARCH 2026

magnopus.com Engineering AI Productivity Report

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

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

The magnopus.com engineering team reports 91.6% AI adoption, 1.21× productivity lift, and 20.0% 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

91.6%

AI assistance is present in 91.6% of recent commits for magnopus.com.

AI Productivity Lift

MODERATE

1.21×

AI-enabled workflows deliver an estimated 21% lift in throughput.

AI Code Quality

LOW

20.0%

Review insights show 20.0% overall code health on AI-supported changes.

How is the magnopus.com team performing with AI?

The magnopus.com engineering team reports 91.6% AI adoption, translating into 1.21× productivity lift while sustaining 20.0% 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?

magnopus.com is at 91.6%. This is 47.9pp above the community median (43.7%)..

91.6%

↑47.9pp above43.7% Community Median

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

Does AI actually make developers faster?

magnopus.com operates at 1.21×. This is 0.08× above the community median (1.13×)..

1.21×

Roughly in line1.13× Community Median

Instrument reviewer assignment and AI summaries to trim the slowest merge steps and edge past the median.

How does AI affect code quality?

magnopus.com holds AI-assisted quality at 20.0%. This is 3.2pp below the community median (23.2%)..

20.0%

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—88.7% of AI commits come from a few experts, raising enablement risk.

88.7%

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

How can I prove AI ROI to executives?

magnopus.com 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:

idesie.com

(2904.2%)

inngest.com

(1429.6%)

prefeitura.rio

(87.4%)

naduni.local

(87.4%)

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

Productivity Lift

Cycle-time improvement vs baseline

Companies in this quartile:

inngest.com

(4.82×)

u.nus.edu

(2.87×)

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:

gzgz.dev

(20.0%)

gwu.edu

(20.0%)

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

MAG-ChristopherAtkinson

Commits32

AI Usage92.0%

Productivity Lift1.64x

Code Quality20.0%

David Swift

Commits16

AI Usage92.0%

Productivity Lift1.36x

Code Quality20.0%

SandyMagnopus

Commits4

AI Usage26.0%

Productivity Lift1.27x

Code Quality20.0%

MAG-mv

Commits8

AI Usage92.0%

Productivity Lift1.27x

Code Quality20.0%

Elliot Morris

Commits77

AI Usage92.0%

Productivity Lift1.15x

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