Why GPUs Forced Deep Learning to Become Linear Algebra

 How hardware killed symbolic AI and made matrices king

Core thesis:
GPUs didn’t just accelerate ML — they defined what ML could be. Early deep learning succeeded not because it was smarter, but because it fit GPU execution models: wide SIMD, predictable memory access, and massive parallelism.

Hardware angle:

• GPUs hate branching, love dense math

• Backpropagation survives because it’s regular

• CNNs won because convolutions are cache-friendly

Key insight:

“Deep learning won not because it was biologically inspired, but because it compiled.”