Google — Engineering Performance
94 engineers now deliver what 259 would have in Apr 2025.
▲ +24 vs the previous 90 days
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.
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.
Behind the numbers
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
- Introduced an end-to-end context compaction architecture spanning sliding-window summaries and mid-invocation token-threshold compaction across REST, Pub/Sub, and Agent Engine surfaces via 96f795c5 (João), 664cec63 (João), e9da6686 (João), and 5b62af7b (João)
- Delivered django-google-spanner compatibility for Django 6.0 with support for composite primary keys, covering indexes, and DML
THEN RETURNclauses in 207d9472 (Sakthivel) - Hardened authentication and workflow security by preventing OAuth client secret exposure in credential payloads and session storage in fdfa4b11 (George) and 393b3b0d (George)
- Optimized the Skia Graphite rendering pipeline by canonicalizing color transfers, eliminating redundant raster pipeline operations, and adding sparse strip geometry across 93ac1e63 (Michael), 194d7ba4 (Michael), a38371cb (Michael), and bc17ad02 (Thomas)
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%
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%
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%
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%
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%
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%
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%
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%
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%
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%
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%
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-go | 95.7% | 4.3% |
| zerocopy | 94.9% | 5.1% |
| google-cloud-go | 86.5% | 13.5% |
| python-genai | 86.5% | 13.5% |
| google-cloud-python | 80.7% | 19.3% |
| perfetto | 68.6% | 31.4% |
| guava | 65.9% | 34.1% |
| go-github | 64.6% | 35.4% |
| syzkaller | 56.4% | 43.6% |
| adk-python | 54.9% | 45.1% |
| skia | 45.2% | 54.8% |
| Total | 70.0% | 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 | |||||
|---|---|---|---|---|---|
| Q2'25 | 119 | 0% | −6 engineers | 34% Features | — |
| Q3'25 | 126 | −6% | 0 engineers | 31% Features | +12% |
| Q4'25 | 136 | −20% | +26 engineers | 34% Features | +28% |
| Q1'26 | 130 | −50% | +116 engineers | 37% Features | +52% |
| Q2'26 | 107 | −58% | +134 engineers | 35% Features | −2% |