AI/Machine Learning Being Linear Algebra Meme (Always Has Been)

When you finally peek behind the curtain of "revolutionary AI" and discover it's basically just matrix multiplication with extra steps. The entire field of machine learning—neural networks, deep learning, transformers, all of it—fundamentally boils down to linear algebra operations. Those fancy gradient descent algorithms? Matrix calculus. Backpropagation? Chain rule applied to matrices. Your ChatGPT? Massive matrix multiplications happening billions of times per second.

The y = mx + b is hilariously reductive but not entirely wrong—linear regression is literally the simplest form of machine learning, and even the most complex neural networks are essentially stacking thousands of these linear transformations (with some non-linear activation functions sprinkled in to keep things spicy). Every CS student who thought they escaped math by going into "AI" eventually has this exact moment of existential dread when they realize they're just doing very expensive linear algebra on GPUs.