I think people will change their minds as this gets more adoption, or if they don't tools to track price changes over time & notify will become more popular and price-sensitive people will buy when their ideal price target is reached.
On a separate note, I assume from the post and the copy on the landing page (that I see) that this tool doesn't help you price discriminate on an individual basis but instead changes the price across a cohort of n segments (A/B/C...) in a round-robin way, doing this is very different from predicting someone's preferences and real-time adjusting the price to a predicted max they would pay. I think people would be right to get really pissed at that, but this seems to just figure out what aggregate pricing should be based on demand (or am I wrong about this OP?)
Addressed this a bit in another comment, but if you think about it, non-public/targeted discounts are pretty much unorganized price a/b testing. Also, the alternative is having sub-optimal pricing which isn't going to cut it pretty soon with Amazon (+ other big companies) dynamic pricing tech + in-house brand expansion.
I'd argue there's a qualitative difference between A/B testing regular pricing vs. testing via targeted discounts.
If your friend finds out you got a "private sale" price, this doesn't seem to violate the "fairness" principle because you were a part of something or got targeted for a reason. It might actually end up increasing the friends FOMO/jealousy and get them to find ways to join in on the discount via mailing lists, insider clubs, etc.
A/B testing pricing might come off as arbitrary and not easily reasoned away thus violating a sense of fairness (at least during testing).
I don't see this as a huge issue as A/B testing is usually limited time/scope but something to think about, I guess.
Could that problem be solved by changing the price at different times, rather than for different people in the same time interval? I wonder if that's why Amazon is making 2.5m price changes a day like the OP says above.
Unfortunately this wouldn't be a proper a/b test since it'd be comparing 1 day of 1 price to another day of 1 price. Only by splitting traffic 50/50 do you properly isolate the price variable.
Most likely, although that solves a different pricing aspect. I would guess most of those are the result of automated pricing optimizations to influence your offers position among other sellers for the same product. There are services that automatically do this for amazon and other price comparison services. It can also result in a book priced at $23,698,655: http://www.michaeleisen.org/blog/?p=358 :-)
On a separate note, I assume from the post and the copy on the landing page (that I see) that this tool doesn't help you price discriminate on an individual basis but instead changes the price across a cohort of n segments (A/B/C...) in a round-robin way, doing this is very different from predicting someone's preferences and real-time adjusting the price to a predicted max they would pay. I think people would be right to get really pissed at that, but this seems to just figure out what aggregate pricing should be based on demand (or am I wrong about this OP?)