It can be summarized as follows: for basic reasons of economics and computer science, specialized algorithms are generally far superior to general ones.
I don't understand your reasoning here. If you have a general AI, it can always choose to apply or invent a specialized algorithm when the situation calls for that, but if all you have is a collection of specialized algorithms, then you have to try to choose/invent the right algorithm yourself, and will likely do a worse (possibly much worse) job than the general AI if it is smarter than you are. So why do we not have to worry about "extreme consequences from general AI"?
Skill at making such choices is itself a specialty, and doesn't mean you'll be good at other things. Indeed, the ability to properly choose algorithms in one problem domain often doesn't make you an expert at choosing them for a different problem domain. And as the software economy becomes more sophisticated these distinctions will grow ever sharper (basic Adam Smith here -- the division of labor grows with the size of the market). Such software choosers will come in dazzling variety: they like other useful or threatening software will not be general pu...
Nick Szabo on acting on extremely long odds with claimed high payoffs:
Beware of what I call Pascal's scams: movements or belief systems that ask you to hope for or worry about very improbable outcomes that could have very large positive or negative consequences. (The name comes of course from the infinite-reward Wager proposed by Pascal: these days the large-but-finite versions are far more pernicious). Naive expected value reasoning implies that they are worth the effort: if the odds are 1 in 1,000 that I could win $1 billion, and I am risk and time neutral, then I should expend up to nearly $1 million dollars worth of effort to gain this boon. The problems with these beliefs tend to be at least threefold, all stemming from the general uncertainty, i.e. the poor information or lack of information, from which we abstracted the low probability estimate in the first place: because in the messy real world the low probability estimate is almost always due to low or poor evidence rather than being a lottery with well-defined odds.
Nick clarifies in the comments that he is indeed talking about singularitarians, including his GMU colleague Robin Hanson. This post appears to revisit a comment on an earlier post:
In other words, just because one comes up with quasi-plausible catastrophic scenarios does not put the burden of proof on the skeptics to debunk them or else cough up substantial funds to supposedly combat these alleged threats.