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Google — Engineering Performance

GoogleEngineering capacity
Navigara
+165Eff. engineering capacity added

94 engineers now deliver what 259 would have in Apr 2025.

+24 vs the previous 90 days

Effective engineersReal engineers
Apr 2025Dec 2025Sep 2026
Where the work wentBiggest shift: Features down 5 points
32%
Features
16%
Maintenance
32%
Tests
6%
Docs
14%
Fixes

Performance snapshot

Today's rolling 90-day reading for Google, compared with the start of the series. Pick a window to move that comparison point.

Avg. perf / dev / mo

+175.3%

0.94 → 2.58 ETV

Active engineers

−4.1%

98.0 → 94.0

Features

+4.0pp

28.3% → 32.3%

vs. 500 OSS index

0.80x

1.0x → 0.80x · −20% below

Engineering capacity

Effective engineers behind Google, against its pre-AI baseline. Each subject has its own: Google's is 0.94 ETV / dev / mo, its first reading in April 2025. Per-engineer ETV divided by that gives a capacity multiple, and that multiple applied to the engineers active in the trailing 90 days turns it into engineer-equivalents. The line is the real headcount, so the gap between line and area is what the leverage is worth. Because each baseline is its own, every subject opens at 1.0x on its first day: multiples measure improvement and are not comparable between subjects.

Effective engineers
259engineer-equivalents
+165 engineers above real headcount
Real engineers
94engineers
active in the trailing 90 days
Capacity vs pre-AI
2.8x
per engineer, vs 0.94 pre-AI

Google vs. 500 OSS Performance Index

Per-engineer ETV for Google against the pooled 500 OSS Performance Index. Both lines are 90-day rolling averages scaled to a 30-day month, so they share one axis and can be read against each other at any point. Pick a window to zoom the chart to it. Latest reading: Google is 20% below the index (2.58 vs 3.22 ETV/dev/mo). At the start of the tracked period the gap was 2% above.

Google
2.58ETV / dev / mo
+0.52 (+25.2%) past 90d
500 OSS Performance Index
3.22ETV / dev / mo
+0.99 (+44.4%) past 90d

Behind the numbers

Aug

Written summary of the work completed each month.

In August 2026, the team demonstrated standout output with major deliveries including context compaction across serving surfaces and Django 6.0 compatibility for django-google-spanner. Total output reached 210, marking a provisional candidate-material increase of +48% compared to the 5-month average of 142 and exceeding recent monthly run rates. A notable surge in quality and corrective activity occurred, with Tests (+55%) and Fixes (+160%) rising significantly above their 5-month baselines.

Highlights

Observations

  • Fixes score surged to 50, a +160% increase compared to the 5-month average of 19 (which also represents a candidate-material increase exceeding the 30% evaluation threshold), addressing stability issues in agent-to-agent communication 04be48b2 (George), Gemini context caching 8e30a308 (George), and OpenAPI timeouts 5d0bd5f6 (George)
  • Tests score reached 73 (+55% above the 5-month average of 47), driven by extensive compaction E2E test suites in cb612ec5 (João) and strict type validation testing across the tool subsystem dc99a581 (George)
  • Total commits decreased by 19% (602 vs 744 5-month average) while overall weighted output increased +48% (210 vs 142 5-month average), reflecting higher-impact architectural and refactoring commits
  • Documentation activity registered 11 points, a +50% change compared to the 5-month average of 7, supported by Javadoc restorations and ErrorProne cleanups in b3346e38 (cpovirk)

Based on 602 commits126.3 ETVUpdated Sep 8, 2026, 8:17 AM

Repositories

Where each repository stands: average performance per engineer per month over the last 90 days, with the rolling 90-day curve behind it. The range picks the window (past 90d): it sets how much of the curve you see, the Δ across it, and the span the work mix is measured over. Ranked highest first.

zerocopy

Avg performance

10.84 ETV

per engineer per month

Avg. dev performance / month (90-day MA)

+430.2%

Work mix

Features

67%

Maint

4%

Tests

2%

Docs

26%

Fixes

1%

2 devs65 ETV+297% since Q2 2025Repository report

guava

Avg performance

4.67 ETV

per engineer per month

Avg. dev performance / month (90-day MA)

+8.2%

Work mix

Features

0%

Maint

27%

Tests

65%

Docs

1%

Fixes

7%

3 devs42 ETV+1043% since Q2 2025Repository report

adk-python

Avg performance

3.76 ETV

per engineer per month

Avg. dev performance / month (90-day MA)

+354.1%

Work mix

Features

9%

Maint

12%

Tests

41%

Docs

5%

Fixes

33%

15 devs170 ETV+30% since Q2 2025Repository report

google-cloud-python

Avg performance

3.57 ETV

per engineer per month

Avg. dev performance / month (90-day MA)

-24.9%

Work mix

Features

42%

Maint

17%

Tests

35%

Docs

4%

Fixes

3%

15 devs161 ETV+353% since Q2 2025Repository report

perfetto

Avg performance

2.92 ETV

per engineer per month

Avg. dev performance / month (90-day MA)

+60.6%

Work mix

Features

42%

Maint

21%

Tests

22%

Docs

5%

Fixes

10%

18 devs159 ETV+44% since Q2 2025Repository report

adk-go

Avg performance

2.20 ETV

per engineer per month

Avg. dev performance / month (90-day MA)

+238.2%

Work mix

Features

29%

Maint

1%

Tests

62%

Docs

5%

Fixes

4%

2 devs13 ETV+203% since Q2 2025Repository report

syzkaller

Avg performance

1.52 ETV

per engineer per month

Avg. dev performance / month (90-day MA)

-32.3%

Work mix

Features

31%

Maint

17%

Tests

24%

Docs

1%

Fixes

26%

5 devs24 ETV+184% since Q2 2025Repository report

skia

Avg performance

1.23 ETV

per engineer per month

Avg. dev performance / month (90-day MA)

+7.2%

Work mix

Features

29%

Maint

23%

Tests

15%

Docs

2%

Fixes

32%

13 devs48 ETV−23% since Q2 2025Repository report

google-cloud-go

Avg performance

1.00 ETV

per engineer per month

Avg. dev performance / month (90-day MA)

+10.0%

Work mix

Features

35%

Maint

8%

Tests

46%

Docs

6%

Fixes

5%

13 devs39 ETV+305% since Q2 2025Repository report

go-github

Avg performance

0.28 ETV

per engineer per month

Avg. dev performance / month (90-day MA)

-55.8%

Work mix

Features

27%

Maint

23%

Tests

32%

Docs

5%

Fixes

14%

4 devs3 ETV+317% since Q2 2025Repository report

python-genai

Avg performance

0.19 ETV

per engineer per month

Avg. dev performance / month (90-day MA)

-53.3%

Work mix

Features

34%

Maint

3%

Tests

47%

Docs

5%

Fixes

11%

11 devs7 ETV−60% since Q2 2025Repository report

Performance Composition

Each month's output split by type of work: Features (new value), Maintenance (sustaining systems), Tests, Docs, and Fixes (rework). The yellow line is output per engineer, so when it rises each engineer is delivering more, whatever the team size did. Unit: Engineering Throughput Value (ETV).

Cost per Performance Unit

−64%

If performance per engineer more than doubled, each unit of engineering performance now costs approximately 64% less than at the baseline 90-day window (ending 2025-04-01). Treat this as a direction, not a price: the exact figure depends on fully-loaded engineer cost, but which way it moved is not in doubt.

Effective Capacity Added

+165 engineers

At today's productivity, the current 94-person team delivers the performance equivalent of 259 engineers at the baseline 90-day rolling window (ending 2025-04-01). That's roughly 165 engineers worth of capacity added through productivity gains, not hiring.

CapEx vs OpEx

Each month's work split two ways. CapEx (capitalizable investment) is the work that builds the asset: Features, plus the Tests that prove it works and the Docs that explain it. OpEx (operating expense) is keeping it running: Maintenance and Fixes. The yellow line is the CapEx share, so a rising line means more of the month went into building new rather than sustaining what exists. Unit: Engineering Throughput Value (ETV).

Hours per Repository

Trailing 90-day window (64 working days). Org-level capacity is allocated to each repo by its share of org performance, then split CapEx / OpEx by that repo's own Features + Tests + Docs vs Maintenance + Fixes mix.

adk-go95.7%4.3%
zerocopy94.9%5.1%
google-cloud-go86.5%13.5%
python-genai86.5%13.5%
google-cloud-python80.7%19.3%
perfetto68.6%31.4%
guava65.9%34.1%
go-github64.6%35.4%
syzkaller56.4%43.6%
adk-python54.9%45.1%
skia45.2%54.8%
Total70.0%30.0%

Feature Contribution

Who shipped each month's new feature work, as a share of that month's total. Bands of similar width mean new value is coming from across the team; one band that stays wide means most of it rests on the same person. Named engineers shipped the most Features over the period. Everyone else is grouped as Others.

Quarterly Summary

Engineers and the Features / Maintenance / Tests / Docs / Fixes mix for each quarter. Cost / Perf Unit is what one unit of performance costs against Q2'25. Eff. Capacity Added is measured against this org's own pre-AI baseline instead, its first reading in the index, so it agrees with the capacity tile and chart above.

Quarter
Q2'251190%−6 engineers
34% Features
Q3'25126−6%0 engineers
31% Features
+12%
Q4'25136−20%+26 engineers
34% Features
+28%
Q1'26130−50%+116 engineers
37% Features
+52%
Q2'26107−58%+134 engineers
35% Features
−2%