> ## Documentation Index
> Fetch the complete documentation index at: https://docs.weaveos.com/llms.txt
> Use this file to discover all available pages before exploring further.

# AI efficiency index

> A composite score measuring how effectively your team uses AI coding tools

## How it works

The AI efficiency index is a composite metric that combines four AI-related metrics into a single 0-100 score. It helps you understand how effectively your team is leveraging AI tools by balancing output, adoption, cost, and code quality.

A higher score indicates better overall AI tool usage - high output and adoption, reasonable costs, and low code churn.

## Calculation

The AI efficiency index uses **percentile-based normalization** to combine metrics with different scales. Each component is converted to a percentile rank (0-100) based on global benchmarks, then combined with weighted scoring.

**Formula:**

```
AI efficiency index = (AI output percentile x 0.28) +
                      (AI usage percentile x 0.28) +
                      (AI cost percentile x 0.16) +
                      (code turnover percentile x 0.28)
```

### Component metrics

| Component | Weight | Description | Direction |
| - | - | - | - |
| AI output volume | 28% | Code output attributed to AI tools | Higher is better |
| AI usage percentage | 28% | Percentage of total output that is AI-assisted | Higher is better |
| AI cost per user | 16% | Monthly spending on AI tools per user | Lower is better |
| Code turnover rate | 28% | Percentage of code reworked within 90 days | Lower is better |

<Note>These are the default weights.</Note>

### Percentile normalization

Since these metrics have different scales (percentages, dollars, lines of code), we convert each to a percentile rank before combining them:

1. Your value is compared against global benchmark data from all organizations
2. A percentile rank (0-100) indicates where you fall in that distribution
3. For "lower is better" metrics (cost and turnover), the percentile is inverted so that higher always means better

**Example:** If your AI cost is in the 20th percentile (low cost = good), it becomes the 80th percentile rank in the efficiency calculation.

## Technical details

<AccordionGroup>
  <Accordion icon="calculator" title="How are components calculated?">
    **AI output volume**

    * Source: Code output (PR estimates) from merged PRs that is attributed to AI tools
    * Normalized per day for fair comparison across time periods

    **AI usage percentage**

    * Formula: AI code output / Total code output
    * Displayed as 0-100%

    **AI cost per user**

    * Source: Aggregated spending from connected AI tools (Claude Code, Cursor, GitHub Copilot, etc.)
    * Normalized per month

    **Code turnover rate**

    * Measures how much code is changed or deleted within 90 days of being merged
    * See [code turnover](/metrics/code-turnover) for details
  </Accordion>

  <Accordion icon="circle-question" title="What if a component is missing?">
    All four component metrics must be present to calculate the efficiency index. If any component is missing for a given time period, that data point is skipped.

    This ensures the score is always based on complete data rather than partial calculations.
  </Accordion>

  <Accordion icon="chart-line" title="What data sources are used?">
    The AI efficiency index pulls data from:

    * GitHub, GitLab, and Bitbucket for code output and turnover
    * Connected AI tools for usage and cost data (Claude Code, Cursor, GitHub Copilot, Windsurf, etc.)
  </Accordion>
</AccordionGroup>


This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.