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Linus Torvalds’s point is not that AI should take over software engineering. He finds it useful for work that suits the tool and for exploring ideas, but draws a clear line around consequential development: programmers still need to understand, check and maintain what AI produces.
What Torvalds means by using AI for things he is bad at
The headline captures a task-specific approach: use AI where it can help with a weakness or make experimentation more approachable, rather than handing it responsibility for an entire engineering job. The available reporting supports personal or exploratory use, not blanket delegation of critical software work.
At Open Source Summit Europe in Prague on October 9, 2026, Torvalds reportedly urged caution when using AI for “something real and important,” in The Stack’s account of his remarks. That short phrase is reported event coverage, not a verified transcript. The Stack’s October 9, 2026 report also describes AI as a possible entry point for young developers, while noting the strain that low-quality AI-generated submissions can put on kernel maintainers.
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Useful assistance does not remove responsibility
Torvalds has described AI as a useful tool. In a July 2026 Linux Media mailing-list post, he wrote: “AI is a tool, just like other tools we use. And it’s clearly a useful one.” Simon Willison reproduced the statement and its context on July 16, 2026. Willison’s post also quotes Torvalds saying Linux is not an anti-AI project and that people who object can fork it.
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That position is compatible with caution. In a May 2026 interview reported by TechRadar, Torvalds stressed that developers should understand both their prompts and the resulting code, because that is necessary to maintain it over time. TechRadar’s report frames the key test as whether the person responsible can explain and support what the tool created—not simply whether the output appears to work.
Why critical software calls for a higher bar
For a personal experiment, a flawed suggestion may be an inconvenience or a learning opportunity. In software that others depend on, an unnoticed defect can create consequences beyond the original author. That makes review, testing, and the ability to maintain the result essential parts of using AI responsibly.
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The same distinction matters for open-source maintainers. AI can help someone investigate a bug or learn how a project works; it can also make it easier to send maintainers random, duplicative, or poorly prepared patches. The Stack’s October 2026 coverage describes AI-generated kernel submissions as disruptive and burdensome. Torvalds’s criticism of that burden does not contradict his view that AI can be useful: the value depends on the quality and relevance of the work reaching the project.
A practical way to apply the distinction
- Use AI to explore: It may help with unfamiliar concepts, idea generation, or personal projects where the user can inspect the outcome.
- Review before relying: Treat generated code as a proposal. Check that it does what is intended and fits the surrounding project.
- Keep ownership of maintenance: Do not merge or ship code you cannot explain well enough to debug and support.
- Respect other people’s time: Before submitting a patch or bug report to an open-source project, verify that it is relevant, non-duplicative, and supported by useful evidence.
These are practical implications of Torvalds’s reported position, not a checklist he is reported to have issued. His broader July 2026 stance, as reproduced by Willison and discussed in The Register’s coverage, is that Linux should judge contributions on technical merit rather than adopt a blanket anti-AI rule.
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What the remarks do—and do not—establish
The reporting supports a nuanced view: Torvalds enjoys using AI and sees potential in it, while warning against trusting it with important engineering without human understanding and oversight. It does not establish that he endorses a specific AI product, recommends a particular coding assistant, or claims AI reliably finds a given number of bugs or makes developers more productive by a measurable amount.
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