Sure, most anyone can throw some data into an SVM and get a result out, maybe even a good one. The problem comes when someone like this has to answer questions beyond a simple 90% accuracy rate. What does the computed separation direction tell me? Could I improve accuracy by using some a priori information like how often one class occurs in relation to the other? What 10% of the population am I failing on? Is it an important part? Is there some easy way I could do better? Is my data so high dimensional that I'm getting some trivial separation and not anything driven by the data itself?
And what happens when this person gets a new data set and they are suddenly getting garbage out of some standard SVM? Is it just a matter of the data not being well-separated using a linear model but throwing some simple kernel at the SVM will do the trick?
Even something as simple as taking a mean can fall apart when you are dealing with data which doesn't live in a Euclidean space, let alone something like PCA or SVM which also make assumptions of linearity.
The point is, it isn't just about being able to invent new methods. Things like SVM make assumptions about your data and applying them in cases when these assumptions don't hold can give completely worthless information, even if it looks good on the surface. Using something you don't understand, even if it is at a (much) more basic level than someone with a PhD in statistics, is just asking for trouble.