The point of the presentation is that staying close to your data - analyzing it, looking at it, and making decision about what to do with it - are what we are trained to do as cognitive neuroscientists (really, any scientific discipline). The reason I find AFNI to be superior is that it allows the user to do this in a relatively easy way. The only roadblocks are getting acquainted with Unix and shell programming, and also taking the time to get a feel for what looks normal, and what looks potentially troublesome.
Back in the good old days (ca. 2007-2008) we would simply make our scripts from scratch, looking through fMRI textbooks and making judgments about what processing step should go where, and then looking up the relevant commands and options to make that step work. Something would inevitably break, and if you were like me you would spend days or weeks trying to fix it. To make matters worse, if you asked for help from an outside source (such as the message boards), nobody had any idea what you were doing.
The recent scripts containing the "uber" prefix - such as "uber_subject.py", "uber_ttest.py", and so on - have mitigated this problem considerably, generating streamlined scripts that are more or less uniform across users, and therefore easier to compare and troubleshoot. Of course, you still need to go into the generated script and make some modifications here and there, but everything is pretty much in place. It will still suggest that you check each intermediate step, but that becomes easier to ignore once you have a higher-level interface that takes care of all the minor details for you. Like everything else, there are tradeoffs.
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