I think your comment is an example of the point of view that the author was talking about. The Google Prediction API won't automate the process of grouping comments or stories by content. Someone has to do the work of collecting the data and preparing the corpus, determining the best way to analyze it and prepping the inputs and outputs. There are levels of understanding and effort between having an idea involving machine learning and getting accurate predictions.

The google prediction api takes care of the code for algorithmic computation. While that's handy, it's only one step of a much larger process. The scale of that process is something that many people don't fully understanding about machine learning (yet).

There have been readily available machine learning toolkits available for decades--if you want to use a SVM in your project, you just need to grab an implementation and get going. The trick, as others have described, is getting your data into a usable format, choosing features, and experimenting to find the method that gives the best results. As far as I can tell, the Prediction API doesn't do much to make any of that easier.

I think his point is that it's just a tool that makes it a little easier for startups to incorporate machine learning into their products - like he said, it may be appropriate for some types of problems, but not all. But I'm sure we'll start to see more tools like that become more widely used.

When AJAX first came out, not everyone knew how to do it - but now, everyone can drop in jQuery and do all sorts of complex things relatively easily.