If you don't opt out by Apr 24 GitHub will train on your private repos
TL;DR Highlight
Starting April 24, GitHub changed its policy to use Copilot users' private repo interaction data for AI training by default. You need to know exactly where the opt-out link is and what data is actually in scope.
Who Should Read
Developers and teams who own private repos on GitHub or are currently using GitHub Copilot. Especially development team leads who need to manage code data security at the organizational level.
Core Mechanics
- Starting April 24, 2025, GitHub changed its policy to include interaction data from Free, Pro, and Pro+ Copilot users in AI model training by default (opt-out basis). Headlines were somewhat exaggerated, causing confusion — the policy does not use entire private repos for training, but rather the 'interaction data' generated while using Copilot.
- Business and Enterprise plan subscribers are not affected by this change. GitHub has officially stated that 'usage data from Business/Enterprise subscribers will not be used for training.'
- People who do not use Copilot at all are not directly affected by this change. However, if you plan to use Copilot in the future, opting out now will preserve that setting.
- The opt-out setting is located on the github.com/settings/copilot/features page, under the Privacy section at the bottom — toggle off 'Allow GitHub to use my data for AI model training.' It takes about 30 seconds to configure.
- There are concerns that the method for bulk disabling at the organization level is unclear. The currently confirmed setting is per individual account, and it remains ambiguous whether repo data could be included if even one team member fails to opt out.
- Users belonging to Enterprise accounts have reported that the opt-out option disappears from their personal Copilot Pro subscription settings. Enterprise policies override individual settings, causing confusion.
- GitHub stated that it had been continuously notifying users of this change via banners, but many users reported only becoming aware of it after seeing the HN post, indicating that very few actually read the banners.
- This policy change is interpreted as an extension of the industry trend that 'any data a company can freely read will eventually be used for AI training.' The view that ToS changes can enable this at any time — unless end-to-end encrypted — resonated widely in the community.
Evidence
- "A commenter believed to be a GitHub employee directly disputed the headline, stating it was inaccurate. They clarified — with a link to the official GitHub blog (github.blog) — that entire private repos are not used for training; only interaction data generated during Copilot usage is collected, and Business/Enterprise subscribers are not affected. An org admin expressed concern about whether one team member failing to opt out could expose the entire repo's code via their Copilot usage, and the lack of a clear official response heightened anxiety — the inability to control this at the org level, with only per-account settings available, was flagged as a problem. A humorous comment — 'my private repo is such a mess that training on it would hurt GitHub more than me' — got a lot of upvotes, while a parallel observation noted that messy, uncommented code could degrade training data quality. Some users said they didn't mind, noting their repos contain no client data or credentials and that they actually appreciate AI learning their code style. Others expressed distrust of GitHub/Microsoft, arguing that even with policies in place, accidents like accidentally ignoring private flags could happen. There was significant criticism of GitHub designing the policy as opt-out rather than opt-in. Concrete alternatives were proposed — such as switching to opt-in with participation incentives like increased token quotas to rebuild trust — and some users said this was a good reason to reduce their dependence on GitHub."
How to Apply
- "If you are a developer using GitHub Copilot (Free/Pro/Pro+), go to github.com/settings/copilot/features right now and disable 'Allow GitHub to use my data for AI model training' under the Privacy section at the bottom. This must be done before April 24 to take effect. If you are a team lead responsible for managing code security at the organizational level, instruct all team members to opt out from their individual accounts, and consider upgrading to a GitHub Enterprise/Business plan, as this policy does not apply to those plans. Even if you are not currently using Copilot, it is worth opting out in advance if there is any chance you will use it in the future — GitHub states that opt-out settings are preserved even after Copilot is later activated. When storing sensitive code on cloud services like GitHub/Microsoft, design with the assumption that service ToS can change at any time. For critical business logic and secrets, consider separating them into self-hosted Git solutions (such as Gitea or GitLab) or end-to-end encrypted storage."
Terminology
Related Papers
Is One Layer Enough? A Single Transformer Layer Matches Full-Parameter RL Train
LLM의 RL 후처리 학습(post-training)에서 성능 향상의 대부분이 중간 레이어 소수에 집중되며, 단 하나의 레이어만 학습해도 전체 파라미터 학습과 비슷하거나 더 나은 결과를 낼 수 있다는 연구 결과. 이는 RL 학습 비용을 대폭 줄일 수 있는 가능성을 시사한다.
Knowledge Distillation of Black-Box Large Language Models (2024)
GPT-4 같은 내부 구조에 접근할 수 없는 독점 LLM에서 작은 모델로 지식을 효과적으로 전달하는 Proxy-KD 기법을 소개하는 논문으로, 전통적인 White-Box 방식보다 성능이 높다는 점에서 주목할 만하다.
Show HN: NanoEuler – GPT-2 scale model in pure C/CUDA from scratch
PyTorch나 autograd 없이 C와 CUDA만으로 GPT-2 수준의 LLM을 처음부터 구현한 교육용 프로젝트로, 역전파·BPE 토크나이저·FlashAttention까지 직접 손으로 작성했다.
Show HN: Neural Particle Automata
고정된 격자 대신 움직이는 파티클 위에서 동작하는 Neural Cellular Automata의 확장 버전으로, 형태 생성·포인트 클라우드 분류·텍스처 합성 등 다양한 작업에서 자기조직화 동작을 학습할 수 있다.
The annotated PyTorch training loop
PyTorch 학습 루프의 각 코드 줄이 왜 그 위치에 있어야 하는지, 순서를 바꾸거나 빠뜨렸을 때 어떤 문제가 생기는지를 단계별로 설명한 심층 가이드다.
When Good Verifiers Go Bad: Self-Improving VLMs Can Regress on New Tasks
VLM 자가학습 루프에서 verifier가 특정 태스크에 맞지 않으면 학습할수록 오히려 성능이 떨어지는데, DPO 손실값은 멀쩡히 내려가서 눈치채기도 어렵다.