How Top Engineering Teams Actually Perform
A live index of the industry's leading engineering orgs. Every commit is weighted by the work it represents, not the lines it touched, and reported as ETV per engineer.
+1,435engineers' worth of capacity,
nobody hired
Rows sum to +1,414 against the +1,435 above. The index figure divides pooled output by the pooled engineer set, which is deduplicated across orgs and covers every team in the index, including ones without a public row.
Engineering capacity added since the Q1 2025 pre-AI baseline. Measured from shipped output, not from AI usage: teams also changed size, tooling and composition over this period.
How fast did it happen?
Engineering Throughput Value (ETV) per engineer, rolling 90 days. The dashed line is the pre-AI baseline. Distance above it is the capacity multiple.
Where does each org stand?
Same measure, one card per org. Headcount is not the story, the multiple per engineer is.
OpenAI
Capacity vs pre-AI
11.7x
+288 engineers
10.03 ETV / dev / mo
Avg. dev performance / month (90-day MA)
+60.9%
Work mix
Features
28%
Maint
7%
Tests
42%
Docs
5%
Fixes
18%
Microsoft
Capacity vs pre-AI
5.3x
+610 engineers
4.58 ETV / dev / mo
Avg. dev performance / month (90-day MA)
+51.0%
Work mix
Features
37%
Maint
13%
Tests
30%
Docs
3%
Fixes
18%
Vercel
Capacity vs pre-AI
4.8x
+216 engineers
4.12 ETV / dev / mo
Avg. dev performance / month (90-day MA)
+32.4%
Work mix
Features
34%
Maint
13%
Tests
28%
Docs
6%
Fixes
19%
Capacity vs pre-AI
2.4x
+139 engineers
2.08 ETV / dev / mo
Avg. dev performance / month (90-day MA)
+41.7%
Work mix
Features
30%
Maint
18%
Tests
32%
Docs
2%
Fixes
18%
Meta
Capacity vs pre-AI
1.6x
+108 engineers
1.36 ETV / dev / mo
Avg. dev performance / month (90-day MA)
+13.0%
Work mix
Features
33%
Maint
21%
Tests
27%
Docs
1%
Fixes
18%
Cloudflare
Capacity vs pre-AI
1.5x
+54 engineers
1.32 ETV / dev / mo
Avg. dev performance / month (90-day MA)
+26.8%
Work mix
Features
35%
Maint
12%
Tests
28%
Docs
11%
Fixes
15%
Speed tells you how much a team ships. It doesn't tell you what.
Direction
Faster at what?
The same amount of output can be new features, or fixes to work that already shipped. The mix is the decision.
Performance Composition
How each org's ETV splits across features, maintenance, tests, docs, and fixes.
Patterns
Where does the output come from?
The same measure one level down, at the codebases the output is actually made in. Ranked by per-engineer ETV, this week against the full quarter.
Top 5 Repositories · last 7 days
Highest per-engineer Engineering Throughput Value (ETV) shipped in the last 7 days. Not normalized to a month.
- 1
openai-agents-js
OpenAI · 1 dev
Features 23%Maintenance 0%Tests 58%Docs 10%Fixes 9%16.4
ETV / dev · 7d
- 2
turborepo
Vercel · 1 dev
Features 66%Maintenance 20%Tests 7%Docs 2%Fixes 5%10.8
ETV / dev · 7d
- 3
openai-agents-python
OpenAI · 1 dev
Features 23%Maintenance 0%Tests 31%Docs 31%Fixes 15%10.8
ETV / dev · 7d
- 4
TypeScript
Microsoft · 4 devs
Features 48%Maintenance 11%Tests 31%Docs 3%Fixes 7%7.3
ETV / dev · 7d
- 5
workflow
Vercel · 5 devs
Features 15%Maintenance 9%Tests 28%Docs 37%Fixes 11%5.4
ETV / dev · 7d
Top 5 Repositories · last 90 days
Highest per-engineer Engineering Throughput Value (ETV) over the trailing 90 days, normalized to a 30-day calendar month.
- 1
openai-agents-js
OpenAI · 1 dev
Features 20%Maintenance 2%Tests 49%Docs 9%Fixes 21%67.2
ETV / dev / mo
- 2
openai-agents-python
OpenAI · 2 devs
Features 18%Maintenance 2%Tests 44%Docs 12%Fixes 24%30.8
ETV / dev / mo
- 3
turborepo
Vercel · 5 devs
Features 42%Maintenance 22%Tests 12%Docs 3%Fixes 22%8.9
ETV / dev / mo
- 4
vscode
Microsoft · 64 devs
Features 37%Maintenance 13%Tests 31%Docs 2%Fixes 18%7.5
ETV / dev / mo
- 5
Agents-for-net
Microsoft · 3 devs
Features 44%Maintenance 12%Tests 37%Docs 2%Fixes 5%7.4
ETV / dev / mo
Your turn
Where does your team stand?
The index measures public repositories. The same measure runs on private ones.