Google — Engineering Performance
Avg. perf / dev / mo (ETV)
+18.1%
1.19 → 1.41
Active contributors
−6.3%
126.0 → 118.0
Growth
−1.2pp
35.5% → 34.3%
Fixes
+0.2pp
12.5% → 12.7%
Google vs. 500 OSS Performance Index
Per-engineer ETV for Google plotted against the pooled 500 OSS Performance Index. Both series are 90-day trailing rolling averages scaled to a 30-day month, so the curves sit on the same scale (ETV / dev / mo) and can be compared point-for-point. Latest reading: Google is 28% below the index (1.41 vs 1.94 ETV/dev/mo). Baseline gap was 2% below.
Monthly reports
Highlights
- Introduced *SIMD-optimized flattening functions* for *Graphite's sparse strips*, significantly enhancing path processing performance [e659c958 · Thomas Smith].
- Enhanced the *BigFrames `ai.if_` function* to align with BigQuery ML SQL, providing more granular control over model inference [f3cb4ad0 · Shenyang Cai].
- Developed a *new capability* within the `aflow` module for *automated C program repair* driven by an *LLM agent*, alongside improved error reporting for crash reproduction [e4c25b20 · Alexander Potapenko], [2d02beca · Alexander Potapenko], [787e944f · Alexander Potapenko], [fdabd8b6 · Alexander Potapenko].
- Significantly improved the *Perfetto UI* with features like a *slice duration histogram* [be16f77f · Lalit Maganti], *SQL formatting* on the Query Page [6f6047ab · Steve Golton], and enhanced *plugin dependency visualization* [6f2f2e5e · Steve Golton].
- Implemented a *critical deadlock fix* in *`GeminiLlmConnection`* to unblock *tool calling functionality* for *Gemini 3.1 Flash Live models* [f57b05da · Sandesh Veerani].
- Addressed a *critical bug* in *Firestore `BulkWriter`* by enforcing backpressure, preventing Out-of-Memory crashes and data loss [820d0a24 · Baha Aiman].
Observations
- Commit volume for April 2026 was 875, a significant +35% increase compared to the 5-month average of 650 commits, indicating a period of heightened development activity.
- The Grow score of 36 was -13% below the 5-month average of 42, suggesting a relative shift in focus away from new feature development despite the overall increase in commits.
- Maintenance score was 75, a +15% increase compared to the 5-month average of 65, aligning with the observed focus on refactoring, documentation, and build system improvements.
- The Waste score remained stable at 14, matching the 5-month average, indicating efficient execution despite the high commit volume.
- A substantial effort was dedicated to standardizing documentation and metadata across numerous *Google Cloud client libraries*, evidenced by at least 10 commits related to `description_override` removal and updates (e.g., [ecef9645 · Jon Skeet], [ad721807 · Jon Skeet], [b39d3284 · Jon Skeet], [959d4136 · Jon Skeet], [2f955a0b · Jon Skeet], [010eeca2 · Jon Skeet], [7bfff06d · Jon Skeet], [ba514296 · Jon Skeet], [fadfbc52 · Jon Skeet], [5d85c029 · Jon Skeet], [7637a063 · Jon Skeet]).
- Active development and refinement were observed in the *Perfetto UI*, with multiple commits addressing new features, UI enhancements, and performance optimizations (e.g., [fde1f8d6 · Lalit Maganti], [be16f77f · Lalit Maganti], [a6f1f88a · Lalit Maganti], [a8f27c9e · Steve Golton], [6f6047ab · Steve Golton], [6f2f2e5e · Steve Golton], [0debe7dc · Samuel Wu]).
- The *Graphite GPU backend* received consistent attention, with commits focusing on performance improvements, refactoring, and resource adjustments (e.g., [836df02c · Michael Ludwig], [e659c958 · Thomas Smith], [e4012ccf · Michael Ludwig], [af089853 · Robert Phillips]).
- Several critical bug fixes were implemented across various core components, including *Firestore `BulkWriter`* [820d0a24 · Baha Aiman], *`GeminiLlmConnection`* [f57b05da · Sandesh Veerani], and *Perfetto's `TraceBufferV2`* [e024c8c8 · Lalit Maganti], indicating a focus on system stability.
- The *Guava cache module* saw significant activity related to *J2KT compatibility* and *test coverage expansion*, with specific APIs being hidden or tests enabled for J2CL/J2KT environments [07ce1e99 · cpovirk], [cf9c2dd8 · cpovirk], [0e86e61b · cpovirk].
Repositories
Active repositories ranked by average performance per developer per month (over the last 90 days). The chart shows monthly performance composition — each repo as a stacked layer, with the top of the stack representing total org performance per month. Top 9 repos shown; the remainder is aggregated as “Other”.
| Repository | ||||
|---|---|---|---|---|
| guava | 3 | 44 | 4.9 | +233%since Q2 2025 |
| syzkaller | 7 | 47 | 2.2 | +173%since Q2 2025 |
| zerocopy | 2 | 13 | 2.2 | +584%since Q2 2025 |
| perfetto | 24 | 148 | 2.1 | +102%since Q2 2025 |
| skia | 15 | 50 | 1.1 | −3%since Q2 2025 |
| google-cloud-python | 21 | 69 | 1.1 | +34%since Q2 2025 |
| google-cloud-go | 20 | 58 | 1.0 | +148%since Q2 2025 |
| adk-go | 5 | 14 | 1.0 | +616%since Q2 2025 |
| adk-python | 14 | 30 | 0.7 | +27%since Q2 2025 |
| go-github | 4 | 8 | 0.6 | +270%since Q2 2025 |
| flatbuffers | 2 | 4 | 0.6 | −92%since Q2 2025 |
| python-genai | 12 | 13 | 0.4 | −59%since Q2 2025 |
Company total12 repositories | 118unique devs | 498ETV total | 1.41ETV / dev / mo | +65%since Q2 2025 |
| Performance (ETV) is the sum of every repository above. Active devs at the company level counts unique contributors across all repos, so a contributor working in multiple repos is counted once here but appears in each repo's row (the per-repo column will sum higher). ETV / dev / mo = Company ETV ÷ unique devs ÷ 3 mo. The "Since start" column compares each repo's Q1 2026 quarterly performance to the first quarter it had any activity — for repos that existed in Q2 2025 (when this index began), that's Q2 2025; for younger repos it's the quarter they actually started. The company row uses Q2 2025 as the baseline since the index itself began then. | ||||
Performance Growth vs Active Contributors
Shows how engineering performance scales relative to team growth. Left axis shows total performance score, right axis shows active contributor count. The gap between curves represents productivity gains — more delivered per person, not just more people. Unit: Engineering Throughput Value (ETV).
Cost per Performance Unit
−40%
If performance per engineer grew 66%, each unit of engineering performance now costs approximately 40% less than at the baseline 90-day window (ending 2025-06-29). This is a directional estimate — the exact figure depends on fully-loaded engineer cost, but the direction is unambiguous.
Effective Capacity Added
+78 engineers
At today's productivity, the current 118-person team delivers the performance equivalent of 196 engineers at the baseline 90-day rolling window (ending 2025-06-29). That's roughly 78 engineers worth of capacity added through productivity gains, not hiring.
Performance Composition
Stacked bars show total complexity performance split into Growth (new value), Maintenance (sustaining systems), and Fixes (rework). The yellow line overlays performance per contributor — rising line means each engineer is delivering more, regardless of team size changes. Unit: Engineering Throughput Value (ETV).
CapEx vs OpEx
Monthly CapEx vs OpEx split. CapEx (capitalizable investment) is Growth — new features and capabilities. OpEx (operating expense) is Maintenance plus Fixes — keeping the lights on and reworking what's already shipped. The yellow line is the CapEx share, a quick read on how much of the month went into building new vs sustaining existing. 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 Growth vs Maintenance + Fixes mix.
| python-genai | 70.8% | 29.2% |
| perfetto | 49.8% | 50.2% |
| adk-go | 47.7% | 52.3% |
| google-cloud-go | 43.6% | 56.4% |
| syzkaller | 41.8% | 58.2% |
| zerocopy | 31.4% | 68.6% |
| adk-python | 22.9% | 77.1% |
| skia | 21.8% | 78.2% |
| google-cloud-python | 17.9% | 82.1% |
| go-github | 17.2% | 82.8% |
| flatbuffers | 2.9% | 97.1% |
| guava | 1.3% | 98.7% |
| Total | 34.3% | 65.7% |
Fix Burden Distribution
Monthly rework volume broken down by who did it. Top contributors carry their named slice; everyone else is rolled into Others. Use this to spot whether fix work is concentrated on a small group (bus-factor risk) or distributed across the team.
Fix authorship over time
Monthly fix activity in this scope, split by who fixed the bug. Darker emerald = the original author fixed their own bug; lighter emerald = someone else cleaned it up. A persistently high 'fixed by another author' share is a signal of bug-debt landing on the team rather than its author. X-axis is fix-time — bugs introduced but not yet detected don't appear.
- Self-fix share
- 26%
- Total bug impact
- 305.6
Quarterly Summary
The raw numbers behind the charts: commits analyzed, active contributors, total performance, performance per developer, and the Growth / Maintenance / Fixes split for each quarter.
| Quarter | ||||||||
|---|---|---|---|---|---|---|---|---|
| Q2'25 | 2,407 | 119 | 305.04 | 0.9 | 35.6% | 54.5% | 9.8% | — |
| Q3'25 | 2,916 | 126 | 351.99 | 0.9 | 32.4% | 57.8% | 9.8% | +15% |
| Q4'25 | 2,830 | 136 | 411.27 | 1 | 33.8% | 55% | 11.1% | +17% |
| Q1'26 | 3,763 | 130 | 503.95 | 1.3 | 34.5% | 53.3% | 12.1% | +23% |
Top Contributors
Contributors ranked by performance per month (Growth + Maintenance + Fixes), over the last 90 days normalized to a 30-day calendar month.
The best way to measure AI efficiency
SampleA preview of the Navigara engine running on a sample organization. The numbers below are illustrative, not part of the OSS500 benchmark above.
Measure
Score every commit by depth
GitHub commits are weighted by what it took to write them, not by lines of code. The result is ETV per developer per month.
SourceGitHub
Spend
Tie ETV to cost
AI token bills and seat costs are pulled per team and divided by the ETV produced. The result is your true cost per unit of work.
SourceToken usage + finance
Map
Tie work to objectives
Each ETV is mapped to your Jira epics and labels, so you can see what's key-aligned, aligned, or unmapped capacity.
SourceJira
Performance
ETV delivered per developer / month
9.4ETV / dev / month
5.6 below target- Non-AI 5.8
- AI 3.6
AI Efficiency
AI spend per ETV unit delivered
$4.20
$0.60 over target- Cost / ETV $4.20
Objective Alignment
Share of work mapped to key objectives
51%
24 pts below target- Key-aligned 28%
- Aligned 23%