Exceeds

JULY 2026

taosdata.com Engineering AI Productivity Report

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

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

The taosdata.com engineering team reports 0.1% AI adoption, 0.00× productivity lift, and 0.0% 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

LOW

0.1%

AI assistance is present in 0.1% of recent commits for taosdata.com.

AI Productivity Lift

LOW

0.00×

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

AI Code Quality

LOW

0.0%

Review insights show 0.0% overall code health on AI-supported changes.

How is the taosdata.com team performing with AI?

The taosdata.com engineering team reports 0.1% AI adoption, translating into 0.00× productivity lift while sustaining 0.0% 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?

taosdata.com is at 0.1%. This is 33.9pp below the community median (33.9%)..

0.1%

↓33.9pp below33.9% Community Median

Launch guided prompts, pairing sessions, and opt-in experiments to build confidence before scaling automation.

Does AI actually make developers faster?

taosdata.com operates at 0.00×. This is 0.98× below the community median (0.99×)..

0.00×

↓0.98× below0.99× Community Median

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

How does AI affect code quality?

taosdata.com holds AI-assisted quality at 0.0%. This is 19.7pp below the community median (19.7%)..

0.0%

↓19.7pp below19.7% 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—80.8% of AI commits come from a few experts, raising enablement risk.

80.8%

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

How can I prove AI ROI to executives?

To prove ROI, taosdata.com 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:

H-

h-its.org

(20.0%)

.G

.gns.cri.nz

(20.0%)

LG

lgsstudent.org

(-8578.7%)

LI

live.it

(-4657.1%)

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

Productivity Lift

Cycle-time improvement vs baseline

Companies in this quartile:

.I

.ieselrincon.es

(1.00×)

RO

rockstarwizard.ninja

(1.00×)

IN

inria.fr

(-1.30×)

EB

ebay.com

(-0.32×)

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:

GW

gwu.edu

(20.0%)

H-

h-its.org

(20.0%)

LI

live.it

(-2071.3%)

LG

lgsstudent.org

(-1866.4%)

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.

YI

yingzhao

Commits3
AI Usage40.0%
Productivity Lift1.02x
Code Quality20.0%
QC

Qin Chong

Commits15
AI Usage26.0%
Productivity Lift1.02x
Code Quality20.0%
YL

Yiqing Liu

Commits1
AI Usage0.0%
Productivity Lift1.00x
Code Quality0.0%
MJ

Minglei Jin

Commits2
AI Usage86.0%
Productivity Lift1.00x
Code Quality20.0%
PX

Ping Xiao

Commits1
AI Usage20.0%
Productivity Lift1.00x
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

2

jtao1735

No repositories listed

yihaoDeng

No repositories listed

Tony2h

No repositories listed

tjuzyp

No repositories listed

Unknown contributor

taosdata/tdengine-idmp-docs

Unknown contributor

taosdata/tdengine-idmp-docs

Activity

87 Commits

Your Network

12 People
localvar
Member
cqin@taosdata.com
Member
ethanliu@taosdata.com
Member
fztang@taosdata.com
Member
jtao1735
Member
stephenkgu
Member
xiaopingcs
Member
Tony2h
Member
yihaoDeng
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