This, however, raises the issues of the normative concerns and values of researchers and/or forecasters. I have taken the view that Wikimedia Foundation provides public utility. Ideally, the per capita cost should remain the same. Thus, I still prefer the Foundation's income only grows in proportion to that of Internet users. It is a normative (not forecasting) statement with a value assumption that places more premium on membership. 

Here we enter a classic political economy question: Do we consider equality (or distribution of funds) more than growth, or the other way around? No matter which ideological position one has, we need to revisit Wikimedia Foundation's mission statement to ask the values of equality and growth. What happens when there is a conflict between pro-equality and pro-growth strategy? My interpretation of Wikimedia's mission statement will be thus: pro-growth in human knowledge and readership, pro-equality in fund raising and distribution. 

Thus, I agree with Oliver that contextless images or graphs may not be helpful. It is better for infographic designers to be aware of and then explain the underpinning theoretical assumptions (and thus concerns) of visualization. It does not mean my potential research agenda above is more valid than yours. It does suggest however different "ways of seeing" raise different concerns on metrics that are value-laden on topics such as development. It is always worthwhile to revisit the Foundation's value/mission statement when we measure the economic cost and values of Wikimedia Foundation.

As "[]a] statistical model is a set of assumptions concerning the generation of the observed data"(enWP), it is better to be explicit on the underlying assumptions (not just math, but also social science) for the model/theory to be examined and tested. There are many socio-economic indicators related to Internet will grow exponentially, will grow linearly, will hit an upper bound, or decrease. The selection of other variables is thus important to put things in meaningful contexts, in relative terms, and in relation to the environment where Wikimedia Foundation is in. 

I hope the message above is a plead for more and better research on meaningful forecasting. There are other innate research problems doing forecasting (such as overfitting, etc.)  Here I just raise one of the basic issues on the institutional values needed to construct a set of sensible metrics that fit well with institution's core values.