Microsoft says it will use GitHub Copilot interaction data, like inputs, outputs, and code snippets, to train its AI models from April 24, unless users opt out
The generative AI models powering ChatGPT, Copilot Gemini, and other assistants were created with mountains of training data.
Context & Ripple Effects
GitHub has steadily broadened Copilot from code completion into conversational and multimodal workflows, including general availability of Copilot Chat in Microsoft’s IDEs and later Vision features that can work from images and diagrams. That expansion creates more types of user interaction around software development.
The policy shift matters because Copilot was already deeply embedded in coding activity: GitHub previously said its tool accounted for as much as 30% of new code on its network. Interaction data therefore becomes a potentially material feedback source, while making user control over code-sharing settings more consequential.
First-order effects
- GitHub Copilot users who do not opt out will have their inputs, outputs, and code snippets available for Microsoft’s AI-model training starting April 24.
- Microsoft gains an opt-out data pipeline from real Copilot usage, spanning both developer prompts and the assistant’s generated responses.
Second-order effects
- Organizations using Copilot with proprietary code will need to review user settings and internal AI-use policies, since individual interaction data—not only source repositories—are implicated.
- The move increases pressure on AI coding-tool providers to make training-data controls legible and usable for teams deciding whether workflow data can leave their environment.
Third-order effects
- If opt-out collection becomes common across developer tools, interaction data may become a core competitive asset for improving workflow-native AI, concentrating an advantage with platforms that already sit inside daily development work.
- The resulting trade-off between model improvement and code confidentiality is likely to make consent, retention, and enterprise controls a more central part of AI developer-tool procurement.
The trend: AI coding platforms are turning everyday workflow interactions into governed training corpora, with user controls becoming part of the product’s trust model.