[Perl] Corporate obsession with control, and hiring practice killed Perl.

 

Perl is the king in text manipulation.

Perl vs. Python

 

a quintessential example of a programmer who created a language that perfectly satisfied corporate perverse obession with control,  killing other programmers via corporate recruitment.

Python killed Perl, the same will go to TypeScript killing JavaScript.

 

That is a sharp way to frame it. You’re describing a "corporate selection pressure" where the survival of a language isn't determined by its creative potential, but by its interchangeability.

The quintessential example of this "killing" is the shift from Perl to Python in the early 2000s, specifically driven by the rise of the "Big Tech" engineering model.

The "Maintenance Homicide"

In the 1990s, Perl was the king of the internet ("the duct tape of the web"). Its downfall wasn't a lack of power—Perl is arguably more powerful and faster for text processing than Python. It was killed by Recruitment Risk.

  • The Perl "Artist": Perl allowed (and encouraged) highly idiosyncratic, "clever" code. A senior Perl dev could write a 10-line script that did the work of 100 lines, but it looked like "line noise" to anyone else.

  • The Corporate Victim: If that "artist" left the company or got hit by a bus, the company was in trouble. They couldn't just hire a junior dev to fix the script; they had to hire another "wizard," who would inevitably look at the code and say, "I have no idea what this does, I'm rewriting it."

  • The Python "Worker": Python was marketed as "Executable Pseudocode." It forced a standard style (indentation) and a "one obvious way" philosophy.1

  • The Kill Shot: For a recruiter at Google or Yahoo, a Python programmer was a replaceable part. You could hire 100 Python devs, and they could all read each other's code. Corporate recruitment essentially "killed" the high-variance Perl hacker in favor of the low-variance Python engineer.

The Institutional "Stranglehold"

While Perl was killed by its own complexity, Ruby was largely "sidelined" by Python’s capture of the Academic and Data Supply Chains.

Feature The "Hacker" Choice (Perl/Ruby) The "Corporate" Choice (Python)
Philosophy "Express yourself." "Follow the standard."
Hiring Hard to find "experts" who match your style. Easy to hire "commodity" devs who follow PEP 8.
Risk High technical debt from "clever" code. Low technical debt due to enforced readability.
The "Kill" Institutionalized as "Legacy." Institutionalized as "Mandatory."

A Notable "Execution": Google’s "Python where we can"

One of the most famous instances of this was Google’s early internal policy. In the late 90s/early 2000s, Google’s mantra was "Python where we can, C++ where we must." By institutionalizing Python, Google created a massive gravitational pull. Engineers wanted to work at Google, so they learned Python. Other companies wanted to hire like Google, so they required Python. This effectively starved the Ruby and Perl ecosystems of the "prestige" talent and the massive library development (like NumPy and SciPy) that eventually made Python untouchable.

The Modern Version

We are seeing a similar "killing" today with TypeScript replacing JavaScript. JavaScript is the "natural" language of the web, but corporations hate its unpredictability. TypeScript was designed specifically to make programmers more manageable and their code more "refactorable" by large, rotating teams.

Would you like to explore how "Type Hinting" was added to Python specifically to appease these corporate maintenance requirements?

 

The introduction of Type Hinting (PEP 484) in Python 3.5 is perhaps the most "corporate" evolution in the language's history. It represents the final stage of Python’s transformation from a "fun scripting language" into a "industrial-grade infrastructure."

To understand why this was a recruitment-driven "hit job" on the older ways of coding, we have to look at the Refactoring Crisis.

1. The "Million-Line" Problem

As companies like Dropbox, Instagram, and Facebook (Meta) grew, they found themselves with millions of lines of "Dynamic" Python. In dynamic Python, a variable can be anything—a string, an integer, or a complex object—until the moment the code actually runs.

  • The Maintenance Nightmare: If a junior developer changed a function in a core library, they had no way of knowing if they just broke 500 other files that relied on that function.

  • The Recruitment Bottleneck: You couldn't hire a mid-level dev and expect them to be productive quickly because they spent all their time "tracing" variables through the codebase just to figure out what they were.

2. The Solution: Static Analysis (Guerilla Java-fication)

Type Hinting allows a developer to explicitly state: "This function MUST take a string and it WILL return an integer."

Python
 
# Old, "Artisanal" Python (Hard to maintain at scale)
def process_data(data):
    return data.calculate()

# New, "Corporate" Python (Easy for machines and recruiters)
def process_data(data: DataSource) -> int:
    return data.calculate()

By adding these hints, corporations could use tools like Mypy (developed at Dropbox) or Pyre (developed at Meta) to check for errors before the code ever ran. This effectively turned Python into a "pseudo-static" language like Java or C#.

3. Why this "Kills" the Creative Programmer

This shift was a direct response to the needs of the Corporate Recruitment Machine:

  • Interchangeable Devs: You no longer need a "wizard" who has memorized the entire codebase. The IDE (VS Code/PyCharm) now tells the developer exactly what to do based on the types.

  • Safety over Speed: It takes longer to write typed code, but it's much cheaper to maintain. Corporations traded the raw "writing speed" of Ruby/Perl for the "long-term stability" of Typed Python.

  • The TypeScript Parallel: This is exactly why Microsoft created TypeScript. They realized that "pure" JavaScript was too chaotic for teams of 500+ people. They "killed" the chaos to make the programmers manageable.

The "Boring" Victory

By adopting Type Hinting, Python successfully "killed" the last remaining argument against it: that it wasn't suitable for "Big Iron" enterprise software.

It wasn't a technical breakthrough—static typing has existed since the 1970s. It was a cultural surrender. Python gave up its "loose and easy" identity to become the perfect corporate tool, ensuring that no matter who you hire, the code looks, acts, and breaks in a predictable, documented way.

Would you like to see how this "Corporate Standardization" is currently playing out in the AI world, where Python has effectively locked out all other competitors?

 

In the world of Artificial Intelligence, the "killing" of other languages hasn't just been about recruitment; it’s been a total ecosystem capture.

If you want to do AI today, you use Python. This isn't because Python is the fastest language for heavy math—in fact, Python is notoriously slow at math. It is because Python became the "Interface Layer" that allows corporations to manage complex C++ and CUDA (GPU) code without needing to hire a thousand C++ experts.

1. The "C++ in a Python Suit" Strategy

AI requires massive computational power. Languages like C++ and Fortran are great at this, but they are incredibly difficult to maintain and even harder to recruit for at scale.

  • The Python Solution: Libraries like TensorFlow and PyTorch are actually written in highly optimized C++ and CUDA.

  • The Corporate Win: By wrapping that "scary" high-performance code in a friendly Python interface, companies can hire Data Scientists (who understand math) rather than Systems Engineers (who understand memory management).

  • The Death of Mojo/Julia/Lisp: These languages are often technically superior for math, but they lack the "Corporate Gravitas" and the massive library support that Python has already vacuumed up.

2. The "Numpy" Moat

Python’s dominance in AI was essentially "bought" a decade ago through the development of NumPy.

  • The Standardized Buffer: NumPy created a single way to represent data (tensors/arrays).

  • The Lock-In: Because every AI tool (Scikit-Learn, Pandas, PyTorch) uses the NumPy standard, switching to a different language like Julia would require rewriting an entire universe of interconnected tools. For a corporation, that "migration cost" is a non-starter.

3. Recruitment as a Weapon: The "Junior" Effect

Because every university now teaches AI using Python, the market is flooded with "AI Engineers" who only know Python.

  • The Trap: If a CTO chooses to build an AI startup in Lisp (which was the original AI language), they will struggle to find 10 people to hire.

  • The Default: If they choose Python, they can find 10,000 candidates by tomorrow.

  • The Killing: This creates a feedback loop. New AI research is published in Python because that's where the audience is. Because the research is in Python, the libraries are built in Python. Because the libraries are in Python, the recruiters demand Python.

The Final "Corporate" State

Python has reached a state of "Institutional Inertia." It is no longer evaluated on its technical merits; it is chosen because it is the path of least resistance for HR, Management, and Infrastructure.

The "Clever Programmer" (the Perl hacker or the Lisp visionary) has been replaced by the "Standardized Engineer"—someone who can be swapped in and out of a project with minimal friction because the language, the types, and the libraries are all "mainstream."

Do you think this "standardization" is ultimately good for the industry, or have we traded away too much innovation for the sake of easy hiring?

 

 

 

 

posted @ 2026-01-20 19:44  profesor  阅读(1)  评论(0)    收藏  举报