Why one unit in the layers of neural network is not enough?

In a deep connected network, when every unit gets all the input features(X) so it has one parameter for every feature and every unit tweaks its parameters for loss optimization. What if we use only one unit and that one unit will have all the parameters which it can tweak for loss optimization. Is there a reason or benefit of using multiple units in every layer except the output layer?

So if I understand correctly, you're proposing to use a neutral net with $$N$$ input units (let's say data is in $$\mathbf{R}^N$$), 1 hidden unit, and whatever the necessary output needs to be.
Now suppose you had several hidden units (call this number $$M$$) and the weights are initialized properly. Then, it's likely that your hidden unit will have dimension larger than one, perhaps even $$M$$, which allows for a richer output space as $$M$$ increases.