How many programmers are there?

According to Evans Data, the worldwide developer community will reach 29M by 2019. The largest growth is expected to come from China and, to a lesser extent, other developing economies.

I tend to be very skeptical about quantitative analysis of the developer community, and more-so when it comes to global analysis and forecasting, but I have no prima facie reason to criticize those numbers.

As always, I turn my attention to questions of the distribution of developer productivity. Is the distribution of talent among these 29M more like:

curves

A normal distribution would imply that the most effective team structures would be fairly democratic.

The “superprogrammer” distribution, in which an elite (but not vanishingly small) population is vastly more productive than median would imply the most effective team structure as being one structured like a surgical team (the team is structure in service to the elite member).

The “incompetent” distribution, in which a good number of exceptionally bad programmers manage to stay employed, implies that instead of seeking out “rockstars” and “ninjas,” teams should take a satisficing approach. In this world, the median professional programmer is pretty darn good, but sees a lot of unacceptable crap.

A belief in the “superprogrammer” distribution is prevalent, but the “incompetent” distribution best explains the world I’ve seen over the past 30 years.

How Many Python Programmers Are There?

Giles Thomas makes the case that the Python programming community numbers in the low millions. This seems right to me: that’s a large community, but it’s not quite at the level of the most popular programming languages. That size is supported by this chart, which has impressed me as “feeling right” when it comes to the popularity of various languages.

One point, though, is that Python has made very significant inroads in the scientific community, which I believe is a key influencer and leading indicator: the libraries that scientists build become building blocks for future work. When you look at the history of programming languages, you see that scientists and engineers were clear driving forces behind FORTRAN and although C and C++ were broadly popular, their performance benefits made them extremely popular in labs as well.

I’m not sure that the popularity of Python in labs is going to be captured by metrics that focus on the professional programming community, so if anything, I suspect that the Python community might even be a moderate amount larger than Giles suggests.