>The reason Python 3 is on the back burner is that there simply isn't a large enough audience right now.

Funnily enough the reason I haven't adopted using PyPy is down to their lack of Python3 support. I think you're underestimating the amount of people using Python3.

Pip statistics tells us they're not that many.

Do you have any recent statistics?

I'm guessing that most people who use Python 3 are using recent versions of pip that have caching built in, so that may skew the results a bit.

From this (last year), Python 2.6 is bigger than 3:

https://caremad.io/2015/04/a-year-of-pypi-downloads/

>I'm guessing that most people who use Python 3 are using recent versions of pip that have caching built in, so that may skew the results a bit.

Since pip updates itself too, why wouldn't people using Python 2 also have newer versions?

I'm guessing a lot of people who are stuck on Python 2.6 are also stuck on old versions of pip.

I think he means not enough companies willing to sponsor python 3 support.

> After they get momentum by attracting people with their speed improvements, it will be a lot easier to attract talent and money to tackle Python 3

Well, this is a catch-22, because folks like me and my team won't touch it unless it's Python 3. We're not a Python shop, but have flirted with Python before. It wouldn't make sense for us to write new code in Python 2.

After they get momentum? But pypy has existed for a long time. They are in a maturity phase now, surely.

There are major bug fixes and compatibility and performance improvements in this release. They're also asking for a large amount of money to start a push to add Py 3 support. And NumPy support is still improving - they'll gain a lot of users as that becomes complete. None of this seems like "maturity phase", especially in terms of userbase or funding.

You don't get many cases of 20% memory consumption improvements in mature projects...

You should read through PostgreSQL release notes some day.

"20% consumption" is more a factor of how much memory you have installed and how much you intend to use than PyPy's maturity. I frequently use malloc()/mmap() to consume much more than 20% of system memory.

EDIT: strike this, I misunderstood.

I think they're talking about a 20% improvement in memory consumption release-over-release, not complaining that it's consuming 20% of their memory.