I have been using Python for a decade, and intensively for 7 years. I am a domain expert (finance) but I do also have 4 years of CS so consider myself non-idiotic, if not haskell-genius, on programming languages. I adopted Python (before it was popular - it was behind ruby / PHP / perl at the time) because it was pragmatic above all. That last statement is being violated with 3.x, and that is the seed of my concern.

Before answering your questions 1 thru 3, some context. It is my strong belief that the authorities are constantly looking at golang and JS as their competitors, in other words, the web world, whereas the real hardcore advantage of Python is in science and numerical computing. As evidence, witness numerous Python books which advise new users to hit the Continuum Analytics or Enthought sites for their full-stack Python installations, even texts which are not about numerics.

On your questions:

1) I don't care about threading. I care about pushing as many compute bits through the Xeon as I can in a given amount of seconds (using Numpy). But as I am a data scientist, I need the REPL. C is out. Why can I still not do this easily? Multiprocessing is there, sure, but it's been unch for years, while Cuda, OpenCL etc are far too hard for guy like me whose intellectual bandwidth is occupied with the domain, not the CS. Isn't that what Python was supposed to be about? Getting stuff done? Why isn't Python vectoring my data through the CPU and GPU yet, 15 years after numeric was first introduced?

2) Continuum Analytics is doing an awesome job and I don't see why they, or Enthought, couldn't take up the mantle, 10gen/Datastax style to use a database analogy. They really know their customers, and the Continuum stack delivers real new value every 6 months, and not only for a scientific audience. More generally, real users in real domains should be driving the project.

3) I am less concerned about Python 3 happening as planned, than I am about the focus of the project. Type annotations? This is an intellectual indulgence if it does not increase performance. Asyncio? We've had async libraries for years! Even when I started Python we had async libraries (not as good but they were there). How is async something fantastic and new? It's nothing but polishing an existing capability a little bit further. Unicode. fine. But again, web focused. Nobody else cares. Xrange laziness. Okay. Leaves me cold. Print(). No CS benefit, but huge marketing loss as you can't go out to newbies anymore and say "hey, check this out... Python hello world?"

>>> print "hello world"
"hello world"

It just doesn't get any simpler, and yet Python wants to throw out that unique hook for new people who care little about coding but a lot about domain. It's zero, genuinely zero, boilerplate, whereas there are a dozen languages where you can do print("hello world"). Seems trivial, but in print vs print() we have the key difference in philosophy (get-stuff-done-now vs take-me-oh-so-seriously). If you're so serious at a computer science level, you're not going to do Python.

So. What is Python. A serious language? NO. A wonderfully malleable, not too serious, friendly language, into which you can insert some real hardcore stuff (Numpy, ML, website parsing, database transformations, game scripting, image processing....the list is endless) really easily? Yes.

Where is Python genuinely way ahead of everyone else? Only on numerical computing.

It strikes me that Guido and co are embarrassed by their weekend hack of 1.x and 2.x, when that is precisely what the user base loves about it. Their attempt to make Python serious, is killing Python's original spirit. There is nothing wrong with 2.7. Nobody wants Python to morph into Java.

So, am I a Luddite wanting 2.7 to live forever? no. What I want is vectorization plus DAG-like workflows. These are the most important pieces of computer science that actually dovetail with real world use cases, today. Yes async is cool, but golang now owns that space. What I'd really like is for Python to give us a good framework for the Big Data world which is in almost everybody's use case now, and that means, Python needs to talk multiprocessor, Python needs to talk GPU, Python needs to talk cluster, and Python should long ago have been addressing this directly. Python needs to "go vectorized". That should be the project's obsession. SIMD, in a word, where the parallel granularity can go from GPU kernels,to CPU, to multi-machine clusters, and DAG-like workflows (with possible recursion) built in. This is not a nice-to-have capability anymore. It is what the next wildly popular mainstream programming language will have, built into the language. The signals from everything we're seeing added to 3.x is NOT this. It's web-like stuff. That battle is over. JS won.

Let's not gift the opportunity of huge data to Java (Spark) and Cuda, or a new language that will see the future better than us. It should be Python!

There you go. My view.