> Otherwise we would see social networks and other free services replaced on the regular.

Don't we? I can think of a dozen or more social networks of one sort or another that I've belonged to in the last decade or so.

And how many of those have the market cap of Facebook?

From 2007: "Will MySpace Ever Lose Its Monopoly?"[1]

Past performance does not beget future success.

[1]: https://www.theguardian.com/technology/2007/feb/08/business....

MySpace prospered for four years, that's it.

And by prospered I mean, never made money, never became self-sustaining at all.

Facebook is still expanding in its 15th year.

MySpace never earned a profit pre Fox acquisition (the Google deal gave it a one time bump).

In historical terms, MySpace is barely a bump in the road, about the size of an Ask.fm type service (ie trivial in today's hyper scale). It's like looking back and thinking Excite was a juggernaut and therefore a supporting piece of evidence that Google is going to die soon.

Facebook will earn ~$20 billion in 2018 and will end the year with $50 billion in cash. That's 86 times what MySpace sold to Fox for.

MySpace peaked in size at a mere 75 million monthly active users. Facebook is nearly 30 times larger.

MySpace then is to Facebook now, what AltaVista at its peak is to Google today.

2007 was 11 years ago. That's glacial on tech timescale.

That doesn't mean that creating a new social network automatically besets the network effects of the most dominant platforms. I realize that platforms sometimes get replaced. It is early days for crypto, to be sure. It certainly hasn't reached maturity yet. However, there are examples of other monopolies (AT&T, Microsoft, GE, etc) that have lasted for many decades.

How many of those do you still use on a regular basis?

Network effects, yes like how BTC is owned by a tiny pool of oligarchs who need other users to transfer real capital in for their zero sum pyramid scheme.

  One important point: if we actually include all 7 billion 
  people on the earth, most of whom have zero BTC or 
  Ethereum, the Gini coefficient is essentially 0.99+. And  
  if we just include all balances, we include many dust 
  balances which would again put the Gini coefficient at 
  0.99+. Thus, we need some kind of threshold here. The 
  imperfect threshold we picked was the Gini coefficient 
  among accounts with ≥185 BTC per address, and ≥2477 ETH 
  per address. So this is the distribution of ownership 
  among the Bitcoin and Ethereum rich with $500k as of July 
  2017.


  In what kind of situation would a thresholded metric like 
  this be interesting? Perhaps in a scenario similar to the 
  ongoing IRS Coinbase issue, where the IRS is seeking 
  information on all holders with balances >$20,000. 
  Conceptualized in terms of an attack, a high Gini 
  coefficient would mean that a government would only need 
  to round up a few large holders in order to acquire a 
  large percentage of outstanding cryptocurrency — and with 
  it the ability to tank the price.

  With that said, two points. First, while one would not 
  want a Gini coefficient of exactly 1.0 for BTC or ETH (as 
  then only one person would have all of the digital 
  currency, and no one would have an incentive to help boost 
  the network), in practice it appears that a very high 
  level of wealth centralization is still compatible with 
  the operation of a decentralized protocol. Second, as we 
  show below, we think the Nakamoto coefficient is a better 
  metric than the Gini coefficient for measuring holder 
  concentration in particular as it obviates the issue of 
  arbitrarily choosing a threshold.


  ...However, the maximum Gini coefficient has one obvious 
  issue: while a high value tracks with our intuitive notion 
  of a “more centralized” system, the fact that each Gini 
  coefficient is restricted to a 0–1 scale means that it 
  does not directly measure the number of individuals or 
  entities required to compromise a system.


  Specifically, for a given blockchain suppose you have a 
  subsystem of exchanges with 1000 actors with a Gini 
  coefficient of 0.8, and another subsystem of 10 miners 
  with a Gini coefficient of 0.7. It may turn out that 
  compromising only 3 miners rather than 57 exchanges may be 
  sufficient to compromise this system, which would mean the 
  maximum Gini coefficient would have pointed to exchanges 
  rather than miners as the decentralization bottleneck.


  Conversely, if one considers “number of distinct countries 
  with substantial mining capacity” an essential subsystem, 
  then the minimum Nakamoto coefficient for Bitcoin would 
  again be 1, as the compromise of China (in the sense of a 
  Chinese government crackdown on mining) would result in 
  >51% of mining being compromised.

https://medium.com/@balajis/quantifying-decentralization-e39...