Linus Torvalds: Linux is not anti-AI, AI is clearly a useful tool
Score
76首次明确Linux维护者立场，具有较高信息增量。
来自Linus本人邮件列表发言，真实可靠。
帮助开发者和企业判断Linux社区对AI的接纳程度，但未提供具体政策。
刚刚发布，反映最新态度。
转载自知名博客作者且链接原始来源，可信度高。
Base 84.5 × T2 Source weight ×0.9 = 76
Curation line 70Selected
Why selected
This information reveals the clear stance of an open-source leader on AI, serving as an industry weathervane.
AI commentary
「This news is crucial for the open-source community and AI practitioners. As the ultimate decision-maker for Linux, Linus Torvalds's stance directly affects the ecosystem's embrace of AI tools. If you were concerned about anti-AI sentiment in open-source projects, this is the most authoritative reassurance. It clearly shows that AI has gained top-level endorsement at the infrastructure level.」
Quick overview
Linus Torvalds, in a Linux media mailing list post, firmly stated that Linux is not an anti-AI project and that AI is a clearly useful tool, like any other. He argued that while it might not have been obvious a year ago, the usefulness of AI is no longer in question today, and those who doubt it clearly haven't used it. This decisive stance from the top-level maintainer signals a strong embrace of generative AI in the core open-source ecosystem, potentially influencing community norms and project policies.
- Linus Torvalds firmly states Linux is not anti-AI and AI is a tool.
- He asserts that AI's usefulness is beyond doubt; doubters haven't used it.
- This could reshape attitudes toward AI in the open-source community.
Full text
Showing the source text; the full translation is being generated.
I realize that some people really dislike AI, but this is an area where I'm willing to absolutely put my foot down as the top-level maintainer.
Linux is not one of those anti-AI projects, and if somebody has issues with that, they can do the open-source thing and fork it.
Or just walk away.
AI is a tool, just like other tools we use. And it's clearly a useful one.
It may not have been that "clearly" even just a year ago, but it's no longer in question today.
There are other questions around AI (like what the economy of it will actually look like in the end), but "is it useful" is no longer one of those questions. Anybody who doubts that clearly hasn't actually used it.
— Linus Torvalds, Linux Media Mailing List
Tags: open-source, linus-torvalds, linux, generative-ai, ai, llms
Related items
89
FeaturedOpenAI Research: AI Is Redefining Job Boundaries
OpenAI研究：AI正在重新定义工作边界
If you care about the real impact of AI on work, this research is a must-read. Instead of theory, it uses data to show how ChatGPT users expand their task scope, offering direct insights for HR, managers, and every professional looking to upgrade their skills.
87
FeaturedOpenAI CFO Shares Five Lessons for Building an AI-Native Finance Function
OpenAI CFO 分享构建 AI 原生财务职能的五大经验
As an editor, I find this article uniquely valuable because it comes from a finance chief at an AI leader, not an external consultancy, making it highly practical. I recommend it to finance digitalization leads, CFOs, and anyone interested in AI-driven transformation.
87
FeaturedScientific Computing in the Age of Agentic AI: OpenAI Releases New Field Report
智能体AI时代下的科学计算：OpenAI发布新领域报告
This report is one of the most practical analyses in the current AI agent wave, especially suitable for practitioners in computational biology and drug discovery. It answers the key question 'What can AI agents do besides writing code?' and directly demonstrates productivity gains in complex scientific computing.
87
FeaturedReal World VoiceEQ: Measuring the Human Quality of Voice AI
Real World VoiceEQ：量化语音AI的人类自然度
What's noteworthy is that this benchmark truly focuses on the soft metric of 'human-likeness', not traditional speech recognition rate or MOS scores. For teams working on speech synthesis, voice cloning, or voice interaction, this is a highly practical reference tool that quickly identifies model deficiencies in emotion, prosody, etc. However, language support is currently limited; looking forward to future expansion.
85
FeaturedOpenAI Introduces AI Age Scorecard: A New Way to Measure ROI
OpenAI 推出AI时代记分卡：衡量投资回报的新方法
This blog post hits a sore point in the current AI industry: how to quantify input-output. For enterprise decision-makers deploying or planning large-scale AI, it helps avoid herd mentality. My personal advice: try defining 'useful work' based on your own business scenario, and avoid designing metrics that are merely formality.
85
FeaturedHow to Manage AI Investments in the Agentic Era: Focus on Useful Work per Dollar and High-Value Workflows
代理时代如何管理AI投资：聚焦每美元有用功与高价值工作流
This article is especially important for enterprises scaling AI agents. It reminds us that focusing solely on falling API prices is a trap; what really matters is the return per unit of work. I recommend CTOs and CFOs read it together and establish quantitative evaluation mechanisms internally.
85
FeaturedDesigning Organisations That Can Keep Up With AI: Organisational Latency as the Biggest Barrier
设计能跟上AI的组织：组织延迟成为最大障碍
What makes this article truly worth following is that it raises a neglected bottleneck: no matter how strong the technology is, if the organisation can't keep up, it's useless. For CTOs, CIOs, and transformation leaders, this is a more important framework than tech selection.
84
FeaturedRuff v0.16.0 Released: Default Rules Jump from 59 to 413, CI Pipelines Need Attention
Ruff v0.16.0 发布：默认规则从59条激增至413条，CI工作流需关注
For Python developers, this is an unmissable update signal. The significant increase in Ruff's default rules means we can no longer ignore potential issues in our code, and AI fix tools can lower the upgrade cost. Notably, Astral (OpenAI) is deeply integrating AI coding capabilities with static analysis tools, which may become the norm in future engineering practices.