[hn_throwaway_99]

I wish I could downvote you, but as a direct reply I'll have to settle for replying instead. You seem to have completely misunderstood my argument. I'm not "Monday morning quarterbacking", and I in no way think that Facebook's success was guaranteed.

The point I was making was the idea that it was believed early on that social media was a "toy" or that it would never be profitable or that people poo poo-ed the idea of Facebook in its early days is not accurate, at all. Indeed, the example of MySpace shows that it was already well known that there would be a scramble for dominance in the social media realm.

> So why FB took over MySpace? We don't know. Yes, we don't know in 2020, 16 years later.

Uhh, I think we have a pretty good idea. First and foremost, Facebook was always focused on your real, offline identity, which was rather new at the time. MySpace and the early social networks were primarily based on different online personas (e.g. online usernames, no real name policy, etc.) MySpace tried to get people to add their real names and identities later, but it was basically too late by then. This isn't hypothetical, either, one of the founders of MySpace told me as much in a conversation about a decade ago.

[fizixer]

I just want to point out one thing, for the record.

I'm sorry "Stop Monday Morning Quarterbacking (MMQ)" isn't directed at you per se, but the general direction of whoever is reading my comment.

MMQ is, unfortunately, practiced widely in many prediction/retrospection circles like startups, finance, economics, politics.

I'm speaking from a the point of view of scientific rigor, or at least quantitative data analysis. No one does that when it comes to opining about the cause-and-effect of an event in the past. If you're "the winner", anything you say about why you won, would be taken as gospel. "Winner is always right". Rigorous analysis is very hard, and costly, and to what end? Just so you could say "my reasoning is based on analysis"? That's a very boring thing to say. Unless rigorous-analysis finds utility in applications like decision-making for future startups, and is shown to work over and over again (maybe we'll need AI for that), no one is going to bother with it.