The article's spin is that since so little of the program uses complex features, we don't really need those features in our language. However this conclusion is not justified. The right question is how much more work it would take to write the program without those features.

The real use of this kind of work is studying how to best optimize dynamic programs. The conclusion is that a Python JIT optimizer can assume the first data type you see is likely to be what you'll always see, add a small sanity check for that case then goes down a fast path. This will correctly optimize 98% of the code. And there is nothing good to be done with the remaining 2%.

This is a concrete data point. JIT optimizers for other dynamic languages (particularly JavaScript) have discovered and taken advantage of similar things.