In software engineering, the tools you choose reflect your values. At our organization, we've made a deliberate decision: we don't hire developers who are dependent on Python. Not Python users— Python-dependent developers (being any developer whose largest or only proficient language is Python). There's a crucial distinction. This isn't about elitism or arbitrary language preferences. It's about engineering fundamentals, maintainable code, and the culture that different ecosystems foster. Let me explain.
Python's Popularity is Circular, Not Merit-Based
Python is popular because it's popular. The ML ecosystem uses Python because TensorFlow exists in Python. TensorFlow exists in Python because researchers were already using Python. Researchers were using Python because... and the circle continues. Strip away the ecosystem, and what technical advantages does Python actually have over modern C#, Java, or even Go? The answer is uncomfortable: almost none. The accessibility that made Python initially attractive— the low barrier to entry— has become its greatest weakness. It created a culture where fundamental engineering principles are optional, where "import library, do thing" replaces understanding, and where runtime errors are just part of the workflow.
The Technical Problems Are Real
Let's be objective about Python's flaws:
Dynamic Typing is a Time Bomb
Dynamic typing isn't a feature— it's a deferred debt payment. Every line of Python code without type hints is a potential runtime error waiting to happen. You discover typos in production. You discover type mismatches during customer demos. The "flexibility" of dynamic typing is actually rigidity in disguise: you can't refactor with confidence, and your IDE can only guess at what you meant. The fact that the Python community had to retroactively bolt on type hints (mypy, type annotations) proves the point. Dynamic typing wasn't a strength— it was a weakness they're desperately trying to patch. In C# or Java, entire classes of bugs are impossible. The compiler is your first line of defense. In Python, you need extensive unit tests just to catch what a compiler would catch for free. That's not a feature, it's technical debt.
The GIL: A Fundamental Architectural Flaw
Python's Global Interpreter Lock means true multi-threading is impossible. On a modern multi-core system, Python can only execute one thread of Python bytecode at a time. For CPU-bound tasks, you're forced into heavyweight multiprocessing or dropping to C extensions. This isn't a limitation— it's a design flaw that can't be fixed without breaking the entire ecosystem.
Performance Requires an Escape Hatch
When Python code needs to be fast, what do you do? You drop to C, Cython, or Rust. This proves that Python itself isn't the solution— it's just the glue holding together the actual solutions written in real systems languages. Pure Python code can be 50-100x slower than equivalent C# or Java for loop-heavy operations. The language's value proposition is "don't worry about performance until you have to rewrite it in something else." That's not engineering; that's procrastination.
No Compile-Time Checking of Anything
Typos, missing imports, attribute errors, type mismatches: all discovered at runtime. Modern IDEs help, but the language provides zero guarantees. Ship code with confidence? Not in Python. You ship and hope your test coverage caught everything. In statically-typed languages, an entire category of bugs simply cannot exist. The compiler won't let you. In Python, you're one typo away from a production incident.
Packaging Hell
pip, virtualenv, conda, poetry, pipenv, pyenv... Python's packaging ecosystem is fragmented chaos. Compare this to C#'s NuGet or Java's Maven/Gradle: one tool, one way, done.
Python has requirements.txt, setup.py, pyproject.toml, Pipfile, and probably three more by the time you read this. Even experienced Python developers struggle with dependency management. For newcomers, it's a nightmare.
The Python 2 to 3 Catastrophe
Python broke backward compatibility so severely that Python 2 lived on for 12 years after Python 3's release (2008-2020). No other major language has mismanaged a breaking change this badly. It split the ecosystem and showed catastrophic judgment in language stewardship.
Whitespace as Syntax
Indentation defines code blocks. This means:
- Copy-paste can silently break code
- Tabs vs spaces becomes a real problem (yes, PEP 8 says spaces, but...)
- No visual distinction between "end of block" and "continuing same block"
- Mixing indentation styles creates valid but incorrect code
Mutable Default Arguments
def append_to(element, target=[]):
target.append(element)
return target
This is a language design bug. The default list is created once and mutated across all calls. This footgun shouldn't exist in any language claiming to be beginner-friendly. It's not a feature— it's a trap.
No Real Encapsulation
_private is convention, not enforcement. __private is name mangling, not protection. There's no true private/protected like C# or Java provides. Python's philosophy of "we're all consenting adults" sounds nice until you're debugging why someone directly mutated internal state they shouldn't have touched.
"Magic" Behavior Makes Debugging a Nightmare
__init__.pymagically makes directories into packages- Import system searches
sys.pathin mysterious ways - Metaclasses and
__getattr__allow completely invisible behavior - Decorators can fundamentally change function behavior with zero visual indication at the call site You can't trust what you're reading in Python. The actual behavior might be defined three levels deep in metaclass magic.
async/await Bolted On
Python added async in version 3.5, but it's clearly retrofitted. You have sync libraries (requests) versus async libraries (aiohttp), and mixing them is painful. Compare this to C# where async/await is first-class and seamless throughout the entire ecosystem.
The Cultural Problem
Python's low barrier to entry has created a culture of bad practices:
Copy-Paste Programming is Rampant
Stack Overflow-driven development is endemic in Python. Developers import libraries they don't understand, copy code they can't debug, and ship it because "it works." The accessibility that made Python popular enables cargo cult coding.
"Import Library, Do Thing" Mentality
Why understand the algorithm when you can import sklearn? Why learn data structures when pandas exists? This isn't engineering— it's API consumption masquerading as development.
Python-dependent developers often struggle when forced to think about data structures, algorithms, or system design without a library to hide behind. They've learned to use tools, not to build them.
Runtime Errors as Workflow
In Python culture, runtime errors are just part of the process. Run it, see what breaks, fix it, repeat. This is acceptable for throwaway scripts, but it's catastrophic for production systems. Developers who've only worked in Python often don't internalize the discipline that static typing enforces: thinking about contracts, interfaces, and data flow before writing code.
What Python Dependency Signals
When a developer can only be productive in Python, it tells us:
- They prioritize "getting it working" over maintainability. Python rewards quick-and-dirty solutions. That's fine for prototypes, but production systems require different thinking.
- They may lack experience with compile-time safety. If you've never had a compiler force you to think about types, nullability, and contracts, you're missing a fundamental engineering discipline.
- They might struggle without library crutches. Can they solve problems when there isn't a library for it? Can they build custom solutions tailored to business needs? Or are they paralyzed when the exact tool they need doesn't exist on PyPI?
- They're comfortable with "good enough" code. Python's permissiveness allows shortcuts that wouldn't compile in stricter languages. Developers who thrive in Python might resist the discipline required for robust systems.
Addressing Common Objections
- "But Python is the industry standard for ML/data science!" This is circular reasoning. It's standard because it's popular, not because it's technically superior. TensorFlow could have been written for any language. The ecosystem exists because of momentum, not merit.
- "But rapid prototyping!" This is a skill issue. Modern C# and other statically-typed, compiled languages with top-level statements, var, LINQ, and minimal APIs prototypes just as quickly. The idea that Python is uniquely fast for prototyping was true in 2010, not 2025.
- "But the libraries!" Exactly. You're hiring for ecosystem access, not language strength. The libraries prove Python's popularity, not its quality. And what happens when you need performance, concurrency, or type safety? You rewrite it.
- "But it's easier to hire Python developers!" This is true, but it's not the argument you think it is. There are more Python developers because the barrier to entry is low. But do you want more developers, or better developers? We choose quality over quantity.
- "But the code is disposable anyway!" The "temporary" script becomes critical infrastructure. The "quick prototype" becomes the production system. The "we'll rewrite it later" never happens. If you build on the assumption that code is disposable, you'll be supporting disposable code for years.
What We Want Instead
We hire developers who:
- Are proficient in statically-typed languages. C#, Java, TypeScript, Go, Rust— languages that enforce discipline and catch errors before runtime.
- Think about data modeling, not just data manipulation. Engineers who design structures, not just query libraries.
- Understand that "scripts" become systems. Everything should be written with maintainability in mind from day one.
- Have strong fundamentals that transfer across languages. We want people who understand algorithms, data structures, and system design— not just API documentation.
- Value discipline even when no one is watching. The compiler won't save you in Python. Good developers write maintainable code regardless. But we'd rather hire people who've been trained by strict compilers to think correctly from the start.
The Bottom Line
Python has legitimate use cases: quick automation scripts, data exploration notebooks, glue code between systems. But these are narrow niches, not the foundation of a career. If your only tool is Python, you're not an engineer— you're a library consumer. And in a world where libraries change, frameworks evolve, and ecosystems shift, we need engineers who can adapt. We need people who think in systems, not scripts. Who design for maintainability, not convenience. Who understand that the compiler is your ally, not your enemy. Python's popularity will continue because momentum is powerful. But popularity doesn't equal quality. And we're hiring for quality. Tested, disciplined, adaptable engineers— not Python developers.