There are a few reasons. The biggest one is distribution. I came up with the idea for The Sample because I was working on an essay recommender system[1] and I was trying to figure out how to incentivise people to share it. I've built a referral system[2] into The Sample, and whenever someone submits a newsletter, I send them a referral link. If they share the link, their newsletter gets forwarded more often. So far it's been promising; last I checked, about 15% of our subscribers came from referral links. As The Sample gets larger, the referral system will become more compelling (network effects). I've used a fair amount of paid advertising to get things going, but after some threshold, I think it will grow extremely fast with just the referral system.
The next reason is implementation-related. I prefer to use collaborative filtering as much as possible, falling back to content-based filtering only when there isn't enough rating data for collaborative filtering. But for collaborative filtering, you need long-lived items. If you're recommending news articles, by the time you collect much rating/interaction data, the article will be stale and you won't want to recommend it more anyway[3]. Recommending newsletters is IMO an excellent way to combine ML with human curation: humans figure out what news is good, and ML helps readers find curators who are good/relevant to them.
[2] https://jacobobryant.com/p/an-algorithm-for-driving-newslett...
[3] Unless perhaps you're a large news site and can collect lots of interaction data quickly. An interesting thing about my situation is that I'm trying to bootstrap a recommender system from the beginning, whereas most recommender systems today were built as an add-on for another product, after it already had lots of users.