PCA first dimension do not not capture enough variance
You don't state how many original features there are? where there 10, 50, 50 million? The above 10 principle components capture 62.3% of the variance and so we can be sure that there are more that ten original features. However we will assume then that there aren't many more (lets say circa twenty), lets consider what is going on.

With PCA you are hoping to separate the useful information from the noise. By taking only the the first two components in you example you are discarding all other information as noise. 

It depends upon the application but I would say that 25% is never enough (I think serious questions would be raised as why you had). So the question becomes then what is going on? Well lets consider the two extreme cases: A set of uncorrelated features and a set of fully correlated features. Consider first three uncorrelated features:

1 0 0 
0 1 0 
0 0 1


The total variance is 3. The eigenvalues are 

1.000
1.000
1.000


Each component therefore represents 1/3 of the variance and they are equal. Now let use consider the other extreme, all features are highly correlated, i.e. a correlation matrix of

1.000 1.000 1.000
1.000 1.000 1.000
1.000 1.000 1.000


The eigenvalues are

3.000
0.000
0.000


Therefore the first principle component contains all the information and these three features could be characterised by a single latent variable. 

So why is this relevant? Well in your data set your eigenvalues are all of similar magnitude. This suggests that the features in your original data are not very correlated (like the first example above, look at the off diagonal elements your original correlation matrix they are likely close to zero) and therefore it is expect that many principle components would be required since there is little shared information in the original feature space.

This of course assume that we only have a small number of original features. If you have 50 million features then capturing 62.3% in only ten latent variables is pretty good,