Why Blamer
AI coding assistants are now responsible for 30–50% of commits in many engineering teams. Independent research consistently contradicts AI-vendor optimism. Five pains drive customer ROI.
Pain → Solution → Savings
Per medium customer (50 devs). Source citations below.
| # | Validated pain | Annual cost without Blamer | Savings with Blamer |
|---|---|---|---|
| 1 | AI code 1.7× bugs (CodeRabbit, 470 PRs) | $500K wasted on AI-induced bugs | $150–250K saved (drop worst tool) |
| 2 | Code churn 2× (GitClear, 211M lines) | $1.5M wasted rework | $375K saved (25% reduction) |
| 3 | EU AI Act Art. 50 non-compliance | up to €35M fine | de-risk for €30–100K/yr |
| 4 | AI tool procurement w/o data | $36K + 50% wasted spend | $12–24K saved |
| 5 | ADA/EAA litigation (4,605 lawsuits, 2024) | $20K/yr amortized | liability shift to AI vendor |
External evidence
- CodeRabbit AI vs Human Report (2025, 470 PRs): AI-authored code produces ~1.7× more issues; performance problems are 8× more frequent.
- GitClear 2025 Research (211M lines): Code churn doubled in 2024 vs. the 2021 pre-AI baseline; code cloning up 4×; refactoring down 60%.
- BlueOptima (2024): 88% of developers rework AI-generated code before committing.
- Uplevel Data Labs (2024): Developers with Copilot access saw a significantly higher bug rate.
- cURL maintainer (Daniel Stenberg, 2025): Shut down bug bounty — 20% of submissions were AI-generated slop.
- Ghostty project (2025): Banned AI-generated code entirely from contributions.
The visibility gap
git blame shows
"Pedro, 3 days ago"
whether Pedro typed the line by hand or pressed Tab on a
Copilot suggestion. There is no standard way to tell human-authored
code from AI-generated code at the commit level.
The consequences:
- VP Engineering cannot answer: "Are we getting worse because of Copilot?"
- CISO cannot answer: "Which AI tool is leaking our secrets?"
- Compliance Officer cannot answer: "Can we prove which code was AI-generated?" (EU AI Act Art. 50 mandates traceability)
- Developers cannot answer: "Which AI tool is best for this module?"
Why current tools don't solve this
| Tool | Finds issues | Attributes to AI tool | URL scanning | Compliance report |
|---|---|---|---|---|
| CodeRabbit | Yes | No | No | No |
| Snyk | Yes | No | Deps only | Partial |
| Semgrep | Yes | No | No | Partial |
| SonarQube | Yes | No | No | Partial |
| axe-core | A11y only | No | Yes (basic) | No |
| Lighthouse | Perf + a11y | No | Yes | No |
| Git AI | No | Yes (hooks only) | No | No |
| Blamer | Yes | Yes (Patent G) | Yes | Yes |
CodeRabbit has explicitly argued AGAINST tracking AI code percentage — "such metrics are too simplistic" (CodeRabbit Blog). This structural conflict (they sell AI; we audit AI) is Blamer's permanent competitive advantage.