I found this article a nice read, which highlights the much faster growth of our collective knowledge (accelerated by computers and algorithms) than our individual brain capacities (constrained by biology and evolution). Each one of us knows a shrinking slice of the world, and this has profound implications on our society and civilization.
One, as mentioned in the article, is the increasing need of collaboration among researchers, especially for experimental science. (At this moment of writing, it is still feasible to single author a computer science paper.)
Even though the numbers of co-authors of my papers have not increased too much, I do find it increasingly harder to know exactly what is going on in every aspect of a project, notably detailed implementations and user studies.
Another timely topic is about politics.
The policies can become so complex that nobody really understands what is going on.
Thus, each voter knows only a tiny aspect of each political topic or candidate, and thus can form drastically different opinions from one another. This can be a scientific factor driving political polarization, even without other factors like social media. Fortunately, Monte Carlo sampling indicates that with enough (sufficiently independent) samples, the aggregate estimation can still be robust (low bias/variance).
For example, the US essentially has 100+ million votes for the presidency, which should give us confidence on the outcome, no matter how ridiculous it may look.