Exceeds

MARCH 2026

alum.mit.edu Engineering AI Productivity Report

A focused summary of AI adoption, productivity lift, and code quality for the alum.mit.edu engineering team.

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

The alum.mit.edu engineering team reports 86.1% AI adoption, 1.13× productivity lift, and 27.6% 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

86.1%

AI assistance is present in 86.1% of recent commits for alum.mit.edu.

AI Productivity Lift

MODERATE

1.13×

AI-enabled workflows deliver an estimated 13% lift in throughput.

AI Code Quality

LOW

27.6%

Review insights show 27.6% overall code health on AI-supported changes.

How is the alum.mit.edu team performing with AI?

The alum.mit.edu engineering team reports 86.1% AI adoption, translating into 1.13× productivity lift while sustaining 27.6% 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?

alum.mit.edu is at 86.1%. This is 42.4pp above the community median (43.7%)..

86.1%

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?

alum.mit.edu operates at 1.13×. This is 0.00× below the community median (1.13×)..

1.13×

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?

alum.mit.edu holds AI-assisted quality at 27.6%. This is 4.3pp above the community median (23.3%)..

27.6%

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—86.5% of AI commits come from a few experts, raising enablement risk.

86.5%

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

How can I prove AI ROI to executives?

alum.mit.edu 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:

DI

dimagi.com

(87.5%)

PO

postgresql.org

(87.5%)

BL

bloq.com

(21.4%)

DA

daimond113.com

(21.4%)

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.

NM

Niko Matsakis

Commits95
AI Usage93.0%
Productivity Lift1.87x
Code Quality81.1%
PF

Paul Fitzpatrick

Commits104
AI Usage90.3%
Productivity Lift1.13x
Code Quality20.0%
AF

Abraham Flaxman

Commits34
AI Usage92.0%
Productivity Lift1.04x
Code Quality20.0%
TH

Tim Hill

Commits3
AI Usage20.0%
Productivity Lift1.04x
Code Quality20.0%
MS

msalib

Commits1
AI Usage92.0%
Productivity Lift1.02x
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

46

rajiv

Orange-OpenSource/hurl

akf

18F/handbook

sepiatone

UKGovernmentBEIS/inspect_evals

msalib

open-telemetry/otel-arrow

emina

cedar-policy/cedar-spec

vbro

getsentry/relay

getsentry/sentry-docs

Activity

212 Commits

Your Network

29 People
aflaxman
Member
sepiatone
Member
akf
Member
roberto-bayardo
Member
billcai
Member
bi-ran
Member
dgulotta
Member
emina
Member
lin-erica
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