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Linus Torvalds credits AI with grunt-work on kernel debug session

Linux kernel creator Linus Torvalds has publicly credited an AI assistant with helping him work through a difficult graphics driver debug session, while noting the tool repeatedly declared the problem impossible before he pushed it to continue.

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Linus Torvalds credits AI with grunt-work on kernel debug session

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Linux kernel creator Linus Torvalds credited an AI assistant with performing much of the analytical work during a difficult graphics driver debug session, according to a quote collected by Simon Willison on 22nd August 2026. Torvalds even allowed the AI to write the commit message for the resulting patch - a drm/xe: Don't hand out the flat CCS storage as usable VRAM fix in the Linux kernel.

// in brief
  • Torvalds used an AI assistant to debug a drm/xe kernel patch and let it write the commit message
  • The AI told him the problem was 'impossible and unsolvable' several times before he pushed it to keep going
  • He concluded the tools 'have been trained by people who may not be quite as stubborn as I am'

His account of the session was not entirely flattering. The AI, he wrote, several times stated flat out that this was impossible and unsolvable and that we should just write a report about it. Torvalds pushed through that resistance each time, and the model continued adding debug code and analysing results when instructed to do so. His dry verdict: I suspect those things have been trained by people who may not be quite as stubborn as I am.

Despite the friction, Torvalds acknowledged the tool’s value in handling repetitive diagnostic work - what he called much of the grunt-work - and signed off with a direct acknowledgment: credit where credit is due.

The episode illustrates a pattern that practitioners encounter regularly. Current AI coding assistants can be effective at systematic, iterative tasks - running debug cycles, instrumenting code, parsing output - yet they tend to surface premature conclusions when a problem resists easy resolution. The practical takeaway is that an AI’s declaration of impossibility is a signal to push harder, not to stop. Torvalds’s session suggests that treating the model as a persistent but somewhat risk-averse collaborator, rather than an oracle, extracts more value from it: supply the stubbornness yourself and let the tool handle the repetition.

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Piotr Olszewski

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I edit and publish Quantslant. AI tools help with research, drafts and translation; choosing topics, running the tests, checking facts and the decision to publish are mine, and so is the responsibility for what appears here.

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