That is a bit of an old chestnut around here. It is like saying "the rule" for computer software is to crash or go into an infinite loop. If you actually look at the computer software available, it behaves quite differently. Expecting the real world to present you with a random sample from the theoretically-possible options often doesn't make any sense at all.
Of course it doesn't make sense, but that isn't the argument.
Most computer programs more or less work after a lot of time is spent debugging. The problem is that once it is bug free enough to get into the subspace of mind designs that are capable of 'FOOM', then it has to work exactly on the first try. Keep in mind that mind design space itself is a small target surrounded by a bunch of crash/infinite loops.
The idea isn't that we'd be throwing darts with a necessarily uniform distribution over the dartboard- and that we better quit forever because the eternal payoff calculation comes out negative. The idea is that if an inner bullseye wins big, but an outer bulls eye kills everybody, you don't play until you're really really really good.
Or at least, unless you are better than the average player capable of hitting the board.
Here is another example of an outsider perspective on risks from AI. I think such examples can serve as a way to fathom the inferential distance between the SIAI and its target audience as to consequently fine tune their material and general approach.
via sentientdevelopments.com
This shows again that people are generally aware of potential risks but either do not take them seriously or don't see why risks from AI are the rule rather than an exception. So rather than making people aware that there are risks you have to tell them what are the risks.