Board Report

Google — Engineering Performance

Performance Analysis · Q2 2025 – Q2 2026

Methodology

Performance is measured via Complexity Performance — the rate at which engineers deliver complex features. Each commit is analyzed for the complexity of the change and categorized into Growth (new value creation), Maintenance (sustaining existing systems), and Fixes (rework / throwaway code). Higher performance means complex features are shipped faster.

Developer Momentum

Beta

Cumulative contribution over time. Watch developers race as positions shift month by month.

Work Patterns

Beta

Commit activity distribution by hour and day of week across the entire organization.

Engineering Performance Report for Google organization · Generated April 2026 · Powered by Navigara

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Board Report

Google — Engineering Performance

Performance Analysis · Q2 2025 – Q2 2026

Methodology

Executive Summary

Over the analyzed period, Google delivered a total complexity performance of 1933.7, representing a −72% change from Q2'25 to Q2'26. This is not a hiring story. The team grew -31%. Performance grew -72%.

Performance per engineer changed by −59% (3.2 → 1.3). New value creation (Grow) accounts for 30% of total performance, while waste is held at 11%.

There is 1 signal worth watching — flagged below with context.

Performance / Engineer

−59%

3.2 → 1.3

Total Performance

−72%

364.7 → 101.5

Cost / Perf Unit

+146%

Effective cost change

Fixes

+1pp

9.8% → 10.5%

Team Size

−31%

115 → 79 contributors

Codebase

Repositories in org

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

+146%

If performance per engineer grew -59%, each unit of engineering performance now costs approximately 146% less than in Q2'25. This is a directional estimate — the exact figure depends on fully-loaded engineer cost — but the direction is unambiguous.

Effective Capacity Added

+-47 engineers

At Q2'26 productivity levels, the current 79-person team delivers the performance equivalent of 32 engineers at Q2'25 baseline. That's roughly -47 engineers worth of capacity added through productivity gains, not hiring.

Quarterly Performance

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).

Performance Composition

How engineering effort is distributed: Growth % is new feature development, Maintenance % is sustaining existing systems, Fixes % is rework and throwaway code. A healthy team has rising Growth, stable Maintenance, and declining Fixes. Sudden shifts may indicate a change in priorities or a technical debt paydown.

Signals to Watch

Automatically detected anomalies — sharp rises in waste, sudden performance drops, or unusual shifts in composition. Each signal includes context to help determine whether it reflects a real concern or an expected transition (e.g. onboarding, migration, tech debt cleanup).

Q2'26 — performance dropped 82% vs Q1'26

Total performance fell from 575.72 to 101.54. This may indicate reduced activity, scope changes, or transitional costs.

Trajectory

Monthly performance per active contributor — the most direct measure of individual productivity over time. An upward trend means engineers are delivering more complex work per person. The forward signal below extrapolates the current run rate, but is not a forecast — it shows the floor if current productivity holds. Unit: Engineering Throughput Value (ETV).

Forward signal: Apr'26 hit 1.3 performance per contributor — the highest recent month on record. If the current run rate holds, next quarter total performance is on track to continue growing. That's not a forecast. It's a floor, assuming no regression.

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. The QoQ column shows quarter-over-quarter change in total performance — green means growth, red means a decline.

Quarter
Q2'25	2,233	115	364.73	3.17	31.1%	59.2%	9.8%	—
Q3'25	2,754	121	410.13	3.39	29%	61.1%	9.9%	+12%
Q4'25	2,629	127	481.53	3.79	29.5%	59.7%	10.8%	+17%
Q1'26	3,395	117	575.72	4.92	30%	58.1%	11.8%	+20%
Q2'26	688	79	101.54	1.29	27.8%	61.7%	10.5%	−82%

Key Findings

Quality is maintained

Fixes is held at 11% across the entire period. Jun'25 hit a record low of 7.8%. Engineers are shipping faster and cleaner.

Productivity Leverage Index

Are we scaling efficiently? PLI = Performance Growth % / Headcount Growth %. A PLI > 1.0 means output grows faster than team size — good leverage. < 1.0 indicates diminishing returns from hiring.

Productivity Leverage Index

Output grows faster than team — good leverage

+5.02 QoQ

PLI Trend (Q2'26)

Growth vs. Technical Debt Ratio

Ratio of Growth output to Debt work (Maintenance + Fixes). A ratio ≥ 2:1 (ideal) means for every unit of debt work, 2 units of new value are created. Below 1:1 is a danger zone — tech debt dominates.

Current ratio: 0.38 !

≥ 2:1 — Healthy1–2:1 — Monitor< 1:1 — Danger

Cost Efficiency Waterfall

Cost per performance unit (headcount / total performance × 100) per quarter. A declining trend means the same cost produces more output. Color: red = cost increase, green = cost decrease.

Engineering ROI Bubble Chart

Which teams deliver the most value per person? X = total team performance, Y = Growth %, bubble size = team size. Use play button to animate through quarters and track team evolution over time.

Q2'26

UnknownGoogle

Effort Flow

Where is engineering investment actually going? Flow: Organization → Repositories → Work Type (Grow / Maintenance / Fixes). Latest quarter. Wider flows = more investment.

Momentum Score Race

Who has the strongest momentum right now? Cumulative momentum = Σ(delta_perf × grow%) / (1 + waste%). Use the play button to animate the race. Top 10 developers.

Apr'25

Seasonal Pattern Analysis

Are there predictable seasonal patterns in productivity? 12-axis radar shows multi-year average vs. current year performance. Deviations above average = above-seasonal productivity.

Investment Quality

Beta

Reclassifies engineering effort based on bug attribution. Commits that introduced bugs are retrospectively counted as poor investments.

36%Productive TimeGrowth 69% + Fixes 31%

54%Maintenance Time

11%Wasted Time

How it works

Methodology

Investment Quality reclassifies engineering effort based on bug attribution data. Commits identified as buggy origins (those that introduced bugs later fixed by someone) have their grow and maintenance time moved into the Wasted Time category. Their waste (fix commits) remains counted as productive. All other commits retain their standard classification: grow is productive, maintenance is maintenance, and waste (fixes) is productive.

Relationship to Growth / Maintenance / Fixes

The standard model classifies commits as Growth, Maintenance, or Fixes. Investment Quality adds a quality lens: a commit that introduced a bug is retrospectively counted as a poor investment — the engineering time spent on it was wasted because it ultimately required additional fix work. Fix commits (Fixes in the standard model) are reframed as productive, because fixing bugs is valuable work.

Proposed API Endpoint

Currently computed client-side from commit and bug attribution data. Ideal server-side endpoint:

POST /v1/organizations/{orgId}/investment-quality
Content-Type: application/json

Request:
{
  "startTime": "2025-01-01T00:00:00Z",
  "endTime": "2025-12-31T23:59:59Z",
  "bucketSize": "BUCKET_SIZE_MONTH",
  "groupBy": ["repository_id" | "deliverer_email"]
}

Response:
{
  "productivePct": 74,
  "maintenancePct": 18,
  "wastedPct": 8,
  "buckets": [
    {
      "bucketStart": "2025-01-01T00:00:00Z",
      "productive": 4.2,
      "maintenance": 1.8,
      "wasted": 0.6
    }
  ]
}

Developer Momentum

Beta

Cumulative contribution over time. Watch developers race as positions shift month by month.

Work Patterns

Beta

Commit activity distribution by hour and day of week across the entire organization.

Engineering Performance Report for Google organization · Generated April 2026 · Powered by Navigara

Quarter

Q2'25

2,233

115

364.73

3.17

31.1%

59.2%

9.8%

—

Q3'25

2,754

121

410.13

3.39

29%

61.1%

9.9%

+12%

Q4'25

2,629

127

481.53

3.79

29.5%

59.7%

10.8%

+17%

Q1'26

3,395

117

575.72

4.92

30%

58.1%

11.8%

+20%

Q2'26

688

101.54

1.29

27.8%

61.7%

10.5%

−82%

GGoogle — Engineering Performance

Developer Momentum

Work Patterns

GGoogle — Engineering Performance

Performance Growth vs Active Contributors

Quarterly Performance

Performance Composition

Signals to Watch

Q2'26 — performance dropped 82% vs Q1'26

Trajectory

Quarterly Summary

Key Findings

Quality is maintained

Productivity Leverage Index

Growth vs. Technical Debt Ratio

Cost Efficiency Waterfall

Engineering ROI Bubble Chart

Effort Flow

Momentum Score Race

Seasonal Pattern Analysis

Investment Quality

Methodology

Relationship to Growth / Maintenance / Fixes

Proposed API Endpoint

Developer Momentum

Work Patterns

Effort Flow

Momentum Score Race

Seasonal Pattern Analysis

Performance Growth vs Active Contributors

Quarterly Performance

Performance Composition

Signals to Watch

Q2'26 — performance dropped 82% vs Q1'26

Trajectory

Quarterly Summary

Key Findings

Quality is maintained

Productivity Leverage Index

Growth vs. Technical Debt Ratio

Cost Efficiency Waterfall

Engineering ROI Bubble Chart

Investment Quality

Methodology

Relationship to Growth / Maintenance / Fixes

Proposed API Endpoint

Google — Engineering Performance

Google — Engineering Performance