The latent space is useful because it reduces the dimensionality, in some cases to one-hot encoding. Yes, the "latent space" (it's not latent if it's in the input :) ) of a pixel map is of very little information (but not zero[1]). But neural nets can build very informative latent dimensions, which can correspond to abstractions: the network can abstract things like "Is this object a car?", "how red is this object?", "how large is it?", and so on, as latent parameters. Abstraction is one of the fundamental properties of (efficient) thought, that allow dealing with such large amounts of information efficiently by reducing its complexity, reducing it to essential data, that's essentially what latent parameters can be. Indeed I believe several experiments have shown emergence of such latent parameters (which confirms expectations). Also several papers do "latent space interpolation" for generative modeling, which allows mixing of abstract notions (such as gender, size, age, and so on) right in latent space to produce interpolated results (such as images or even text, etc.) that are not pixel-space averages but "conceptual averages" (conceptual interpolation) of some results. This possibility is also evidence of the validity of interpreting the latent space as abstracted data[2].
[1] For example, you can use the pixel space directly when comparing very similar images, say affected by additive noise, using Euclidean distance in pixel space is a pretty good distortion metric for human vision (and you can do classification in some cases as well of say digits using euclidean KNN in pixel space but depending on very large datasets).
[2] I think it's important to note that abstraction isn't necessarily quite 'dimensionality reduction', or removal of redundant data. I think abstraction is more like computational simplification, and in some cases you might need even more intermediary simplified data than you began with (consider the memory usage of some efficient algorithms that far exceeds the dataset size). So the abstract classes and dimensions of the data could be even more than the data size itself, but they tend to signal computationally independent characteristics, like say 'is there a car in the image?' and 'is there a person in the image?' -- you could have thousands of such classes each represented in a single dimension in binary encoding.