Why does a Convnet not work for sequence modeling?

Based on the above slide, I understand that inputs/outputs may be of varying size, but assuming we ignore this issue, I don’t understand the second bullet point…

Could we not make a convolutional network with input dimensions of (m x Tx_max x 10000 x 1)? If so, what prevents this Convnet from sharing features across different positions, and in that case why do normal Convnets succeed in sharing features across positions of an image?

The standard NN uses the linear combination of the examples and features in each layer - so the order of the examples does not matter.

For a sequence model, the order of the examples is critical.