Only for early adopters who know they'll be able to exploit late adopters. Users clearly become incentivized to market their free tokens as an opportunity at wealth, as they exit and sell them to late bag holders.

Satoshi could easily have designed the PoW to distribute more slowly, and favor long term growth as more users join the network. Instead only early adopters control the supply. The risk of this is catastrophic.

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...