A layer with bigger number of nodes than previous one is something very common. Some examples are:
strategies encoder-decoder (autoencoders) where the encoder typically has layers with a decreasing number of nodes (until the compressed/encoded data) and the decoder has layers increasing in number of nodes.
bidirectional recurrent networks where in the ...
A very wide but shallow neural network is going to be harder to train.
You can check that with the playground of tensorflow or with the MPG example in Google Colab.
The relationship between architecture and learning capabilities is not fully understood, but, empirically, thats what you see.
But making the network too deep creates more problems: