Is there a limit in the number of layers for neural network?
The maximum number of layers that a network can have depends on many factors, such as the model architecture, activation function, optimization method, and others. For example, as you alluded to, ResNets is huge in increasing the number of layers possible, even up to 1000 layers for CIFAR-10. Another example is that when ReLU functions became more popular, Kaiming initialization allowed deeper networks to be trained by keeping the variance of each layer's activations at around 1, as the other popular method of Xavier initialization is more suitable for sigmoid activation functions.

So in general, there's no known limit except to keep experimenting and coming up with new techniques, including but not limited to finding better activation functions.