It's what he said: Usain Bolt's 100m World Record of 9.58 seconds.

And what can you infer from that?

Given that it's a world record, you can infer that the majority of the species cannot run so quickly.

That is not a "powerful and useful inference", it's not an inference at all. It is simply the definition of a world record.

the point is that a "single datapoint" does not mean the same thing as a "single datapoint", depending on what that datapoint is and the context around it. The myth he was specifically busting in the original paragraph was that you cannot learn anything from a single datapoint, but that's only true if that datapoint is drawn from a random distribution.

A single datapoint can tell you a lot, if you know other things about it. In this case, knowing that it's a world record dramatically changes its utility. It's powerful in the sense that it tells you a lot about the overall dataset, not in the sense that it's a novel insight.

The global minimum of a distribution is not, in any way, a "single datapoint". It is a property of all the data points that ever were - in this case, it contains information about all humans that have ever run in the history of the species.

It is in no way comparable to knowing how many humans there are, which can't actually tell you anything about how many aliens there could be.

Sure, if you happen to know the global minimum value, then you know a lot. For aliens though, we don't have anything resembling that, so the conclusions we can draw are much less interesting.

The purpose was debunk that myth so that he can derive further (though less information) about aliens, and then the author goes on to do so.

That is, now that you've admitted that a single datapoint does in fact have the potential for providing extreme information in the extreme case, then you should expect to find (some) information in the other cases... so that's what he does. As long as you assume its not a purely random distribution, which he does not.

There is no such myth. Nobody is surprised that you can say something about a distribution if you know some facts about the distribution. Using "single data-point" in two completely different ways is just bait and switch.

Any data pertaining to humans would be a single data-point in the true sense, which in itself gives almost zero information. You can't compare that to knowing global properties of the sample or the distribution, it's completely different things.