For the better part of a decade, the entire field has been a giant, spectacularly funded wrapper around a single operation: self-attention. The Transformer didn't win because it was the most elegant or the most brain-like design. It won because it had the best scaling story and it won the hardware lottery. Every token looks at every other token, the whole thing maps cleanly onto a GPU grid, and you train it all at once. Add data, parameters, compute, context ' and the loss curve cooperates. That smoothness is rare. Most clever ideas in deep learning never become industrial. This one did. But the tax was always there in plain sight. Self-attention buys you something genuinely valuable ' perfect, lossless recall over the entire context, with every token able to address every other token directly, and a training pass that parallelizes across the whole sequence at once. That's the benefit, and it's a real one. The cost is that attention scales quadratically with sequence length, and...
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