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    What does a kernel engineer do if AI can generate CUDA kernels?

    Standard Kernel says the job is shifting toward hardware expertise and systems that verify AI-generated kernels.

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    TLDR

    Standard Kernel says kernel engineers are moving from hand-writing every kernel toward helping AI agents learn new hardware, improving search and feedback, and verifying correctness. It warns that a generated kernel can look faster in a benchmark but fail in production because of race conditions, stream interactions or output reuse.

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    Combined views

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    3 Sources

    @Standard_KernelIf AI can generate CUDA kernels, what does a kernel engineer do now? The work is shifting from hand-writing every kernel toward building the systems that make AI-generated kernels useful in practice — helping agents learn new hardware, improving search and feedback, and building better ways to verify correctness. Generation is getting cheaper, but correctness is becoming a critical problem. A generated kernel can look faster on a benchmark but fail in production because of race conditions, stream interactions, output reuse, or other subtle bugs. That makes verification and deep hardware expertise more central to the job. At Standard Kernel, we’re building that infrastructure. Our cofounder @anneouyang spoke with Business Insider about how kernel engineering is changing. https://www.businessinsider.com/cuda-engineers-adapt-ai-reshapes-nvidia-chip-expertise-2026-104h
    @anneouyangRT @Standard_Kernel: If AI can generate CUDA kernels, what does a kernel engineer do now? The work is shifting from hand-writing every ker…4h

    3 Sources

    @Standard_KernelIf AI can generate CUDA kernels, what does a kernel engineer do now? The work is shifting from hand-writing every kernel toward building the systems that make AI-generated kernels useful in practice — helping agents learn new hardware, improving search and feedback, and building better ways to verify correctness. Generation is getting cheaper, but correctness is becoming a critical problem. A generated kernel can look faster on a benchmark but fail in production because of race conditions, stream interactions, output reuse, or other subtle bugs. That makes verification and deep hardware expertise more central to the job. At Standard Kernel, we’re building that infrastructure. Our cofounder @anneouyang spoke with Business Insider about how kernel engineering is changing. https://www.businessinsider.com/cuda-engineers-adapt-ai-reshapes-nvidia-chip-expertise-2026-104h
    @anneouyangRT @Standard_Kernel: If AI can generate CUDA kernels, what does a kernel engineer do now? The work is shifting from hand-writing every ker…4h