Ok, maybe that make sense. We should continue to take data for longer period. And I think we should ask developers to add historical graphs as in your post to steemd.com itself in order to make this information more transparent.
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Yes, that'll definitely make it easier. :)
But you see, you are making the same point. There are 2 more users who were previously dolphins become 100+MV hit 1000MV. You can see in my graphs there's an uptick 3-4 days back, with only 45 of them a small change makes a big difference. That's why it is important to collect data for a long period of time. And over 2 weeks, it's clear what is happening. Also, the All Users shows a different perspective with many, many new users with miniscule stakeholding joining, which skews things. Active 24 Hours eliminates the effect of inactive users.
Simply put: "How do we know the dolphins aren't just aliases of the whales?" Simple answer: "If they are; than they are acting incorrectly for maximizing monetary withdraw from Steem."
Have you determined by what means the wealth is being transferred from the 1000 MV ($millionaire) whales to the upcoming 100 MV ($100,000) orcas? When money supply is 100% in STEEM POWER (SP), the debasement to pay for blogging comes out of SP holders relative value, but when the ratio of SP to STEEM is 9:1 or lower then the debasement is coming only from STEEM, so in this case the whales must be cashing out and the debasement is charged to the STEEM held by the exchanges for liquidity. If the latter (which likely to be at least partially the case if the STEEM on exchanges is not insignigicant % of the money supply), then we have the whales upvoting their gfs and friends to transfer the wealth debased from those holding STEEM on exchanges, while they themselves cash out. The groupthink is diversifying far too slowly due to the iron lock that the upper 1% has on rewards earned.
The attrition rate of signups becoming permanently inactive appears to be about 80%. Replacement by new signups (especially if rate of those are growing exponentially) masks the attrition rate. I hope you can shed some light on this, even if it is not favorable for Steem.
There we can see as percentage of all the money supply including abandoned accounts that the GINI coefficient may not be improving much. The very high attrition rate coupled with the voting reward algorithm which quadratically favors whales inhibits rapid redistribution to a normal power-law distribution of wealth.