When some day some people (or some things) build an AGI, human-like or otherwise, it will at that time be extremely inferior to then-existing algorithms for any particular task (including any kind of learning or choice, including learning or choice of algorithms). Culture, including both technology and morality, will have changed beyond any of our recognitions long before that. Humans will already have been obsoleted for all jobs except, probably, those that for emotional reasons require interaction with another human (there's already a growth trend in such jobs today).
The phrasing suggests a level of certainty that's uncalled for for a claim that's so detailed and given without supporting evidence. I'm not sure there is enough support for even paying attention to this hypothesis. Where does it come from?
(Obvious counterexample that doesn't seem unlikely: AGI is invented early, so all the cultural changes you've listed aren't present at that time.)
All of these kinds of futuristic speculations are stated with false certainly -- especially the AGi-is-very-important argument, which is usually stated with a level of certainty that is incredible for an imaginary construct. As for my evidence, I provide it in the above "see here" link -- extensive economic observations have been done on the benefits of specialization, for example, and we have extensive experience in computer science with applying specialized vs. generalized algorithms to problems and assessing their relative efficiency. That vast amount of real-world evidence far outweighs the mere speculative imagination that undergirds the AGI-is-very-important argument.
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.