What does RMSE points about performance of a model in machine learning?
There are multiple factors to consider, but the first thing to realize is that in regression, you don't want to think about whether an example is "correct" or "incorrect" but rather how close it was to the true target value.  Therefore you can ignore your original intuition about "80% of predictions are 'correct'." 

Second remember that RMSE is in the same space as your target values.  So it is relative to the variance in your target values.  The benchmark of random guessing should get you an RMSE = standard_deviation.  So lower than this, your model is demonstrating some ability to learn; above that number, you haven't even learned to guess the mean correctly.

There isn't a cutoff for "my model is doing well" in RMSE space, just like with other metrics.  Everything is relative to a naive solution/benchmark or the state-of-the-art.