Comments URL: https://news.ycombinator.com/item?id=46862551

Points: 11

# Comments: 0

   [1] Virtually all the major mechanisms that can drive efficiency improvements — improving technology and overlapping S-curves, economies of scale (including geometric scaling effects), eliminating process steps, reducing variability and improving yield, advancing towards continuous process manufacturing — are on display here

https://www.youtube.com/watch?v=7xTGNNLPyMI

  Morphability - natural language as morphable code
  Abstraction - tasks become reusable commands
  Recursion - stack abstractions for leverage
  Internal Consistency - prevent system drift
  Reproducibility - crash-resilient design
  Morphic Complexity - recognize over-engineering
  E2E Autonomy - measure actual capabilities
  Token Efficiency - maximize work per token
  Mutation & Exploration - controlled self-improvement

https://www.dell.com/en-us/shop/desktop-computers/dell-pro-m...

1. The Rise: 2005 - 2010 Google hired Guido van Rossum in 2005 (stayed on for seven years) and gave corporate blessing that made everyone comfortable with moving from Perl to Python. It was seen as the language of scientists and smart people so a lot of people working in misc. languages like Fortran, MATLAB, Perl moved here. To remove the speed issue the official Google mantra was "Python where we can, C++ where we must". AI heavy weights like Peter Norvig (I think he was the chief AI scientist at one point and co-author of the famous AIMA book), promoted Python to be an acceptable Lisp.

2. Near Death: 2010-2015 Python almost died due to self inflicted wound from the 2 -> 3 transition and there was a good chance it would have gone nowhere like many languages before. Guido also moved away from Google and Google seemed to have shifted it's attention to Golang (apart from the standard C++ and Java). BTW, Python's dominance was not seen positively within Google hence they stopped actively promoting it. For ex. a leaked transcript from Eric Schimdt had him saying this

   So another definition would be language to Python, a programming language I never wanted to see survive and everything in AI is being done in Python.

3. Resurrection: 2015-Now Data science and ML took off and Python was right there thanks to the initial sponsorship from Google and ecosystem of scientists and engineers who were familiar (including working in the two-language mode). There was no language that could rival at this point.

Most of the syntax, power considerations etc.. are side shows as most scripting languages just tap into very powerful libraries written in c/c++/fortran or wrappers around shell. Doubt that distinguishes Python to the point where it has become so dominant.

IBM has been around for over a hundred years, maybe they know a thing or two about running a software business :-)

* Construction and Engineering -- Massive cost overruns and schedule delays on large infrastructure projects (e.g., public transit systems, bridges)

* Military and Government -- Defense acquisition programs notorious for massive cost increases and years-long delays, where complex requirements and bureaucratic processes create an environment ripe for failure.

* Healthcare -- Hospital system implementations or large research projects that exceed budgets and fail to deliver intended efficiencies, often due to resistance to change and poor executive oversight.