# Is the magnitude of the gradient a weakness of Gradient Descent?

The formula for Gradient Descent is as follows: $$\mathbf{w} := \mathbf{w} - \alpha\; \triangledown C$$

The gradient itself points in the direction of steepest ascent, therefore it is logical to go in the opposite direction by subtracting it. But besides the direction, the gradient also has a magnitude which actually doesn't say anything about the path to the optimum.

My question is, is this considered a weakness of gradient descent and the reason for the learning rate?