The Attribution Blind Spot in AI-Assisted Development

During a Fourth of July coding session, a developer encountered an unsettling realization: Existing AI coding tools like GitHub Copilot and Claude could tally inserted lines, but couldn't track which AI-generated code survived edits, rebases, or reverts. This gap meant developers had no reliable way to measure AI's true impact or audit its contributions—a critical oversight as AI becomes integral to software creation.

Illustration: Human-AI collaboration in coding workflows (Source: Git AI)

"You can't know which code in your repo is AI-generated by counting lines," the developer noted. "You need to follow each line through commits, rebases, PRs, and production builds." This insight sparked Git AI: an open-source tool designed to become the standard for tracing AI code from prompt to production.

Engineering Solutions: Checkpoints, Blame, and Offline-First Design

Git AI's architecture tackles attribution through three core innovations:

  1. AI Checkpoints: When an AI agent edits code, Git AI triggers pre-edit and post-edit hooks that create lightweight snapshots. These mark human-authored changes and explicitly tag AI contributions via metadata stored in Git notes—not the main history.

Visualization: Checkpoint workflow tracking human vs. AI changes (Source: Git AI)

  1. Authorship Logs: At commit time, Git AI compresses checkpoints into an authorship log mapping prompts to specific line ranges. Crucially, these logs persist through file moves and refactors via enhanced git blame functionality.

  2. Rewrite-Resistant Tracking: Unlike naive solutions, Git AI preserves attribution across complex Git operations:

    • Rebases rebuild authorship logs by replaying AI segments
    • Squashed merges combine attribution metadata
    • Cherry-picks carry forward AI lineage

"Performance was non-negotiable," the team emphasized. The C++ core completes most operations in <100ms by scaling with commit size—not repo size—leveraging Git's own efficiency.

Why This Matters for Engineering Teams

  • Auditability: Enterprises can now track AI-generated code for compliance and security reviews
  • Agent Optimization: Developers gain data to evaluate which AI tools deliver lasting value
  • Zero-Friction Adoption: Installs machine-wide without repo modifications; teammates without Git AI see no degradation

Screenshot: git-ai blame output showing AI/human authorship (Source: Git AI)

The Road to 1.0 and Beyond

After months of refinement with community contributions, Git AI 1.0 launches with:
- Support for Claude, Cursor, and Copilot
- Enterprise MDM deployment options
- 800x speed improvements

Future priorities include expanding agent integrations and adding PR diff annotations for GitHub/GitLab. "We want this to become the standard," says the team, inviting contributors to tackle integration challenges.

Try it: curl -fsSL https://cli.usegitai.com/install | bash
Contribute: GitHub - usegitai/git-ai

Source: Git AI Blog