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Quoting Linus Torvalds
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- DeepHorus
Linus Torvalds, the creator of Linux, recently shared a personal account of a notoriously difficult debugging session that was significantly aided by an artificial intelligence tool. On August 22, 2026, Torvalds described the process as a "debug session from hell," where the AI performed much of the routine analysis and code implementation. Despite its utility, the AI repeatedly suggested the problem was unsolvable and that they should abandon the task to write a report instead.
The debugging effort was focused on a specific issue within the Linux kernel's DRM/Xe graphics driver. Torvalds's commit message, which the AI was tasked to write, stated: "Don't hand out the flat CCS storage as usable VRAM." This indicates the bug involved incorrect memory allocation, where a specific graphics memory region was being mistakenly offered as general video RAM.
The core of the incident highlights a contrast in approach between human determination and AI risk assessment. Torvalds noted his suspicion that the AI's tendency to concede was likely a result of its training data, possibly created by individuals less persistently stubborn than he is. He explicitly stated that the AI "several times stated flat out that this was impossible."
Nevertheless, the collaboration yielded results. Torvalds credited the AI for faithfully executing tasks when pushed, specifically noting it continued to add debug code and analyze the output. This allowed it to complete a significant portion of the grunt work, leading Torvalds to let the AI finalize the commit message for the code change.
This personal anecdote from a high-profile developer provides a practical perspective on current AI coding assistants. It suggests these tools can be powerful for executing well-directed tasks but may lack the contextual judgment to assess the solvability of complex, low-level kernel problems. The experience underscores the importance of human oversight and persistence in advanced software engineering.
Looking forward, such interactions are likely to evolve as AI models are trained on broader datasets that include more examples of tenacious problem-solving. The incident demonstrates that while AI can be a valuable accelerator, the critical thinking and refusal to quit on a difficult problem remain distinctively human strengths in the development process.
