The thing I like most about this post is that it's falsifiable. We will know in ten years whether C and Java are still popular, and whether Go succeeds in the sense this data suggests. So thank you for being concrete and clear, even if it's all in fun and other people don't like it :)
> whether Go succeeds in the sense this data suggests
An interesting thing about this methodology is that it is extremely sensitive to the age of a language. It's possible to switch from an old language to a new language, but not the other way around -- so if you happen to do your measurements after a language has had some uptake but before it's been around for long enough that people have built significant projects on it and subsequently gotten sick of it, the future distribution by this method can only be 100% New Language. (Because sometimes people switch to New Language, but no one ever switches away.)
Actually, to predict the future distribution of language use, you also need to know the rate of people moving from nothing ("I just had a brilliant idea!") to each language. If everyone eventually transitions to Go, but everyone starts in Ruby, then the division of market share between Go and Ruby depends in part on how frequently people start new projects.
The sorted stochastic matrix shows that C contradicts your assumption that it's not possible to switch from a new language to an old one. Or, at least, it shows that portions of new language code are occasionally rewritten in C.
What's missing from the matrix is "no language" language or null language. That is, a column and row that represents people who start projects from scratch in a given language.
I agree that the analysis makes it abundantly clear people move to older languages, but the question is what new projects are started in, and how many projects represent new versus transitioned projects.
This analysis is interesting, and gives a rough idea of what people are moving from and to when they decide to do that, but not necessarily popularity.
What the author is indexing in the end isn't really predicted overall language use, it's predicted transition target frequency.
I have defined "new language" in my comment as one so new that no significant projects exist in that language, not as one which is newer than C but still arbitrarily old.
> if you happen to do your measurements after a language has had some uptake but before it's been around for long enough that people have built significant projects on it and subsequently gotten sick of it
By this definition, it is not possible to switch from a new language to anything.
It's stated as a binary, but really this defines a continuum of newness, and the metric of the OP is very sensitive to it.
I didn't interpret the results in this post as "predicting the future distribution of language use." Rather, I interpreted the ranking as an indicator of a qualitative trend in that distribution. I think the author also made this very clear.
And of course, all things trend toward newness, so your objection there seems more about time or human psychology than the methodology of the post.
Nevertheless, it seems quite obvious the author did not literally interpret the eigenvector as "x% of future projects will be written in Go." Rather, the conclusions he drew were along the lines of "Oh wow look Go is on top, C and Java are still relevant."
I reserve the right to respond to what people say. Commenting on the accuracy of a label is worthwhile regardless of whether the label was meant to be precise or loose.
This is a great point. It's a model that predicts something about the future. I could have backtested it on historical Google stats to figure out if it's a good model :)
