Exceeds

MARCH 2026

google.com Engineering AI Productivity Report

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

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

The google.com engineering team reports 60.2% AI adoption, 0.77× productivity lift, and 22.2% 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

MODERATE

60.2%

AI assistance is present in 60.2% of recent commits for google.com.

AI Productivity Lift

LOW

0.77×

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

AI Code Quality

LOW

22.2%

Review insights show 22.2% overall code health on AI-supported changes.

How is the google.com team performing with AI?

The google.com engineering team reports 60.2% AI adoption, translating into 0.77× productivity lift while sustaining 22.2% 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.

What's a good company AI adoption rate?

google.com is at 60.2%. This is 16.5pp above the community median (43.7%)..

60.2%

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?

google.com operates at 0.77×. This is 0.36× below the community median (1.13×)..

0.77×

↓0.36× below1.13× Community Median

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

How does AI affect code quality?

google.com holds AI-assisted quality at 22.2%. This is 1.0pp below the community median (23.2%)..

22.2%

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 usage is broad—top contributors represent 10.5% of AI commits.

10.5%

Keep rotating enablement leads and pair senior reviewers with new AI adopters to retain distribution.

How can I prove AI ROI to executives?

To prove ROI, google.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:

CA

cancun.tecnm.mx

(87.3%)

MO

momentohq.com

(87.3%)

UB

ub.edu

(21.2%)

RO

rossabaker.com

(21.2%)

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:

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.

AM

Alice Merrick

Commits1
AI Usage72.0%
Productivity Lift2.00x
Code Quality100.0%
JM

Jenn Magder

Commits92
AI Usage99.9%
Productivity Lift1.69x
Code Quality97.9%
GA

Gal

Commits25
AI Usage100.0%
Productivity Lift1.64x
Code Quality88.0%
JG

Jim Graham

Commits77
AI Usage99.3%
Productivity Lift1.62x
Code Quality84.8%
JM

Justin McCandless

Commits39
AI Usage99.2%
Productivity Lift1.59x
Code Quality93.2%

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

710

krzyzacy

kubernetes/sig-release

hoskeri

kubernetes/kubernetes

pjh

abcxyz/pkg

yarongmu-google

vllm-project/tpu-inference

erickt

ferrocene/ferrocene

calren

android/ai-samples

Activity

101,013 Commits

Your Network

2,954 People
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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