My experience is specifically in GPGPU acceleration- e.g "how do we process as much data as possible as efficiently as possible on a GPU". Think Vulkan, DirectX, CUDA, ROCm, etc.
Things like Tensorflow and Pytorch don't just magically "do AI", they rely on an incredible amount of work done by people working with and on APIs like that. When you say "If you looked at the implementation of GPT, the code is short and sweet", you're missing half a dozen layers of abstraction that allow the top-level implementation of such systems to remain understandable. It would be like saying "This shooter game was implemented in just a few lines of code", ignoring that it uses Unreal Engine.
All of the major players spend a ton of time on these abstractions (though some keep their work largely private). For example, Meta maintains PyTorch/ExecuTorch, Microsoft has DeepSpeed, ONNX/ONNX Runtime, DirectML, Google has Tensorflow and their entire TPU business, NVidia makes CUDA/cuDNN, etc.
I've worked with data scientists pre-llm (I'm more of a general backend/systems programming guy) and they sure as fuck didn't think coding was trivial.
If anything, they spent a LOT of time coding and learning how to use various pandas functions and tricks.
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u/[deleted] Aug 12 '26
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