Quantitative researcher Gregory Gundersen explores the mathematical foundations of backpropagation to explain why the algorithm must compute gradients in reverse order. By breaking down the underlying chain rule, he demonstrates how calculating derivatives from the output layer back to the inputs enables the efficient weight updates required for training neural networks.