Detection is a guessing game
Statistical and LLM-powered AI code detectors don't work. UC Irvine researchers presented a peer-reviewed study at ICSE 2025 — the top software engineering conference — showing that existing AI code detectors score below 60% accuracy. Barely better than a coin flip. Results degraded further across different programming languages and models.
Detection makes sense in adversarial situations — students submitting AI-generated papers without disclosure, contractors misrepresenting their work. But modern AI-native engineering organizations are not adversarial. Engineers aren't hiding their AI usage. If you give them an easy way to mark AI-generated code, they have no problem doing it, especially if that data is being used to justify the spend on the Agents they like using.
Git AI is an open source Git extension that tracks AI-generated code at the source. Fortune 500 engineering leaders use it to measure which agents their teams rely on, how effective their AI-coding workflows are, and whether the generated code actually holds up in production.
From detection to attribution
When you stop trying to "detect" AI code and just start attributing it, everything gets easier.
Coding agents already know exactly what they wrote. Cursor knows. Claude Code knows. Copilot knows. They just never report it — until now.
By integrating directly with every major coding agent, Git AI doesn't guess which code was written by AI. Claude, Copilot, Cursor, and a dozen other Agents tell Git AI exactly which lines they generated. Then Git AI tracks that attribution in Git blame through the entire SDLC.
Git AI does not require developers to change their workflows. When they commit, thier AI-code is automatically attributed.
And those attributions are preserved through rebases, squashes, cherry-picks, etc. So you can track which AI code actually makes it to production:
That's the difference between detection and attribution. Detection asks "does this look like AI?" Attribution records "this was written by Claude Code, using claude-sonnet-4-5, because the developer asked the Agent to change how pagination works for this endpoint"
| AI Code Detectors | Git AI | |
|---|---|---|
| Attribution | No attribution — just a probability score | Line-level attribution in Git Blame |
| Accuracy | ~60%, degrades with each new model | Exact — agents report what they wrote |
| Stores intent | No — no context on why code was written | Each line of code is linked to prompt |
| AI effectiveness | Can only guess which code is AI | Measure generated:production ratio, parallel agents per engineer, and more |
| Production tracking | No visibility past the scan | Track AI-authored lines from commit through deploy |
| Durability and quality | Cannot measure — doesn't know which lines are AI | Measure durability of AI code, link alerts and incidents to the agent session that caused them |
| Open Source | No | Yes |
| Self-Host | Yes |
The open source standard for tracking
code from prompt to production.
curl -sSL https://usegitai.com/install.sh | bash