What's a good company AI adoption rate?
convex.dev is at 92.5%. This is 48.8pp above the community median (43.7%)..
92.5%
↑48.8pp above43.7% Community Median
Keep codifying prompts and monitoring adoption so the lead over peers is sustainable.
MARCH 2026
convex.dev Engineering AI Productivity Report
A focused summary of AI adoption, productivity lift, and code quality for the convex.dev engineering team.
See how AI-active teams rank this week on the Exceeds Leaderboards.
The convex.dev engineering team reports 92.5% AI adoption, 1.32× productivity lift, and 39.2% 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
92.5%
AI assistance is present in 92.5% of recent commits for convex.dev.
AI Productivity Lift
MODERATE
1.32×
AI-enabled workflows deliver an estimated 32% lift in throughput.
AI Code Quality
LOW
39.2%
Review insights show 39.2% overall code health on AI-supported changes.
How is the convex.dev team performing with AI?
The convex.dev engineering team reports 92.5% AI adoption, translating into 1.32× productivity lift while sustaining 39.2% 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.
Does AI actually make developers faster?
convex.dev operates at 1.32×. This is 0.19× above the community median (1.13×)..
1.32×
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?
convex.dev holds AI-assisted quality at 39.2%. This is 16.0pp above the community median (23.3%)..
39.2%
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?
55.2% of AI commits come from the most active contributors.
55.2%
Pair top AI practitioners with adjacent squads and capture their prompts/playbooks for reuse.
How can I prove AI ROI to executives?
convex.dev 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:
ID
idesie.com
(2904.2%)
IN
inngest.com
(1429.6%)
FM
fmease.dev
(87.5%)
DI
dimagi.com
(87.5%)
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.
Contributor
Commits
AI Usage
Productivity Lift
Code Quality
IK
Indrajit Khare
Commits38
AI Usage93.1%
Productivity Lift1.99x
Code Quality91.0%
SJ
Sujay Jayakar
Commits108
AI Usage97.6%
Productivity Lift1.73x
Code Quality66.5%
GS
Geoffry Song
Commits471
AI Usage91.7%
Productivity Lift1.49x
Code Quality20.0%
TB
Tom Ballinger
Commits383
AI Usage92.8%
Productivity Lift1.32x
Code Quality89.3%
NK
Nipunn Koorapati
Commits385
AI Usage92.0%
Productivity Lift1.27x
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
8Unknown contributor
get-convex/chef
get-convex/convex-backend
+2 more
Activity
1,627 Commits285
432
196
175
165
120
123
121
10
0
0
0
LessMore
Your Network
16 Peopledowski
Memberemmaling27
Membergautamg795
Membergoffrie
Memberianmacartney
Memberikhare
Memberjamescowling
Memberjamwt
Memberjordanhunt22
MemberWhy 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
