Why Your Favorite Programming Language Sucks (And Why That's Okay)

Let me be honest with you: your favorite programming language is terrible. So is mine. So is everyone else’s. And you know what? That’s perfectly fine. We’re living in an era where developers treat programming languages like sports fans treat their teams. “Python is for data scientists,” someone declares. “JavaScript will rot your brain,” mutters a C++ programmer. “Go is the future,” insists a DevOps engineer. Meanwhile, all these languages are out there just doing their jobs, blissfully unaware that they’ve each inherited a dedicated tribe of defenders and detractors. The truth is more nuanced—and frankly, more interesting. Every programming language is fundamentally a series of trade-offs, compromises made by people who had to decide what matters most. Speed? Readability? Ecosystem? Community? Safety? The fact that we have different answers to these questions isn’t a bug; it’s a feature.

The Universal Truth About Trade-Offs

Before we dive into dunking on specific languages, let’s establish something important: perfection in programming languages is impossible. It’s thermodynamically forbidden, like teaching a goldfish quantum mechanics. Consider what happens when you design a language. You’re immediately faced with impossible choices:

• Make it fast like C++, and you’ll spend half your life debugging memory leaks
• Make it easy like Python, and watch performance suffer on CPU-intensive tasks
• Make it concurrent like Go, and you’ll need to think about race conditions
• Make it safe like Rust, and you’ll fight the borrow checker for the first three months Every decision ripples outward. Every feature added for flexibility makes static analysis harder. Every optimization for performance makes the code less readable. This isn’t laziness or poor design—it’s physics.

Let’s Talk About Python (My Language, Actually)

Python holds an impressive 25.87% share in the TIOBE Index as of 2025, which means roughly one in four developers worldwide are out there writing Python code right now. That’s a lot of responsibility for a language that has some… let’s call them “personality quirks.” The Good: Python reads like English. Seriously. If you’ve never programmed before, Python is genuinely welcoming. It excels at data science, machine learning, web development, and rapid prototyping. The standard library is practically a Swiss Army knife. The community? Fantastic. The Brutal Truth: Python is slow. Not “slightly slower,” but “interpreted vs. compiled” slow. If you’re building something that needs to process millions of operations per second, Python is going to embarrass itself. Plus, it’s dynamically typed, which means you catch errors at runtime instead of compile time. Want to increment a string variable? Sure, Python will let you try, then explode in production. Here’s a practical example of Python’s performance problem:

import time
def fibonacci_recursive(n):
    """Classic slow Python: no memoization"""
    if n <= 1:
        return n
    return fibonacci_recursive(n - 1) + fibonacci_recursive(n - 2)
def fibonacci_optimized(n):
    """Better: using memoization"""
    cache = {}
    def fib(num):
        if num in cache:
            return cache[num]
        if num <= 1:
            return num
        result = fib(num - 1) + fib(num - 2)
        cache[num] = result
        return result
    return fib(n)
# Test the difference
start = time.time()
result1 = fibonacci_recursive(35)
time1 = time.time() - start
start = time.time()
result2 = fibonacci_optimized(35)
time2 = time.time() - start
print(f"Recursive: {time1:.4f}s")
print(f"Optimized: {time2:.6f}s")
print(f"Speedup: {time1/time2:.0f}x faster")

Run that and watch Python calculate fibonacci(35) the naive way. You’ll age slightly. The optimized version? Almost instantaneous. This is Python’s story: it’s not always slow, but it makes it easy to write slow code. The memory consumption is also no joke. Python objects carry a lot of overhead—each object needs reference counting, garbage collection tracking, and metadata. In resource-constrained environments like IoT devices or embedded systems, this can be genuinely problematic. And then there’s the Global Interpreter Lock (GIL). This is a famous Python problem that sounds academic but has real consequences. The GIL prevents true multithreading at the bytecode level. While you can use multiprocessing to work around it, true concurrent programming in Python is always somewhat… awkward.

The JavaScript Paradox

JavaScript is everywhere. It’s literally running in your browser right now, reading this article. But here’s the thing—JavaScript exists because of an accident of history. In 1995, Brendan Eich was given 10 days to create a language for Netscape Navigator. The result? JavaScript. Now, JavaScript has evolved dramatically. Node.js brought it to the server-side. Frameworks like React and Vue made frontend development genuinely pleasant. The ecosystem is ridiculous—there’s a package for literally everything, including packages that check if numbers are prime. But JavaScript is weird. Genuinely, deeply weird. Type coercion is a nightmare fuel festival:

// These are all true in JavaScript. Really.
console.log([] == false);           // true
console.log('' == 0);               // true
console.log('0' == false);          // true
console.log(null == undefined);     // true
console.log(NaN === NaN);           // FALSE
console.log(typeof NaN);            // "number" (WHAT?)
// And my personal favorite:
console.log(0.1 + 0.2 === 0.3);    // false (IEEE 754 floating point nonsense)

This isn’t JavaScript’s fault entirely—it’s a byproduct of trying to be flexible and accommodating. But it means every JavaScript developer has PTSD from debugging something that worked yesterday and doesn’t work today because of implicit type conversion. The async/await model is genuinely excellent once you understand it, but getting there involves understanding callbacks, then promises, then the event loop. It’s like needing to earn a master’s degree just to properly handle concurrent operations.

C++: The Masochist’s Dream

If Python is the welcoming grandmother of programming languages, C++ is the Zen master who’ll hit you with a stick every time you make a mistake. Or every time you breathe. Possibly both. C++ is phenomenally powerful. It gives you raw performance, direct memory access, and the ability to control almost every aspect of your program’s execution. It’s used in game engines, trading systems, and embedded systems because when you need speed and efficiency, C++ delivers. It’s also absolutely capable of ruining your week. Memory management in C++ is a constant tightrope walk. You’re responsible for allocating and deallocating memory. Forget to deallocate? Memory leak. Deallocate too early? Dangling pointer. Use-after-free bugs are invisible killers that might not manifest until production. Here’s a simple example of C++ memory management pain:

#include <iostream>
using namespace std;
void leaky_function() {
    int* data = new int;
    // Do something with data
    // Oops, forgot to delete[] data;
    // Memory leak created
}
void safe_function() {
    // Modern C++17+ approach using unique_ptr
    unique_ptr<int[]> data(new int);
    // Do something with data
    // Automatically cleaned up when data goes out of scope
}
int main() {
    leaky_function();  // Memory leak
    safe_function();   // Safe
    return 0;
}

Modern C++ (C++17, C++20) has helped a lot with smart pointers and RAII patterns, but it’s still a language that demands respect and expertise. The learning curve isn’t a curve; it’s a vertical cliff.

Go: The Deliberate Minimalist

Then there’s Go, which took a different approach entirely. Created by Google to solve their infrastructure problems, Go is deliberately minimalist. No inheritance hierarchies. No generics (until recently). No exceptions. It’s designed to be simple, concurrent, and fast. The Good: Go is genuinely fast. It compiles to native code. The concurrency model with goroutines is brilliant—you can spawn thousands of them with minimal overhead. The standard library is comprehensive. Build times are quick. The syntax is straightforward, and code written by different teams looks remarkably similar. The Bad: Go is aggressively minimalist. This means you’ll often write more boilerplate than you’d expect. Error handling involves lots of if err != nil, which some find tedious:

package main
import (
    "encoding/json"
    "fmt"
    "io/ioutil"
    "net/http"
)
func main() {
    resp, err := http.Get("https://api.example.com/data")
    if err != nil {
        fmt.Println("Request failed:", err)
        return
    }
    defer resp.Body.Close()
    body, err := ioutil.ReadAll(resp.Body)
    if err != nil {
        fmt.Println("Reading failed:", err)
        return
    }
    var data map[string]interface{}
    err = json.Unmarshal(body, &data)
    if err != nil {
        fmt.Println("Parse failed:", err)
        return
    }
    fmt.Println(data)
}

Notice all those if err != nil checks? That’s Go philosophy: explicit is better than implicit. Some people love this. Others find it repetitive.

The Pattern: Every Language is a Personality Type

Let me visualize the landscape of programming language trade-offs:

Every language has made certain bets and chosen to win in specific areas. Python bets on developer productivity and ecosystem richness. C++ bets on raw performance and control. Go bets on simplicity and concurrency. Rust bets on memory safety without a garbage collector.

So How Do You Choose?

Here’s my hot take: you don’t choose a language because it’s perfect. You choose it because it’s the best tool for this specific job.

Step 1: Define Your Constraints

What matters for your project? List them honestly:

• Performance Requirements: Will milliseconds matter?
• Team Expertise: What does your team actually know?
• Ecosystem Needs: What libraries are critical?
• Deployment Environment: Where will this run?
• Development Speed: How fast do you need to move?

Step 2: Rank Your Trade-Offs

Some people will tell you Python is bad. They’re right if your constraint is “must process 100,000 requests per second.” They’re wrong if your constraint is “must prototype a machine learning model in two weeks.”

Project: IoT Device with ML Detection
├─ CRITICAL: Must use <50MB RAM
├─ CRITICAL: Must respond in <100ms
├─ IMPORTANT: Need ML library support
├─ NICE: Easy to maintain
└─ Result: C++ or Rust (not Python)
Project: Rapid Startup MVP
├─ CRITICAL: Speed to market
├─ CRITICAL: Fast iteration
├─ IMPORTANT: Rich ecosystem
├─ NICE: Performance
└─ Result: Python or JavaScript (not C++)

Step 3: Accept the Trade-Offs

Once you’ve chosen, stop complaining about what it can’t do. Every language sucks at something. You’ve made a choice; now optimize within your chosen constraints.

The Real Issue: Tribal Thinking

Here’s what actually bothers me about programming language discourse: the tribalism. We’ve created an industry where people’s language choice becomes part of their identity. “I’m a Python developer.” “I’m a Go engineer.” “I code in Rust.” It’s become a badge, a tribe marker, almost a personality type. And this causes real problems:

1. Premature Language Adoption: Teams choose languages based on what’s trendy, not what solves their problem
2. Stubbornness: When a language doesn’t work well for a task, defenders blame the task instead of considering alternatives
3. Knowledge Gaps: Developers only learn one or two languages deeply and remain ignorant about what others can do
4. False Debates: “Python vs. Go” isn’t a real question—it’s context-dependent

What I Actually Believe

After years of working with different languages, here’s my genuine opinion: Learn multiple languages deeply. Not superficially—understand their philosophy, their trade-offs, and their culture. When you truly understand C++, you gain perspective on why it makes the choices it does. When you’ve used Go’s concurrency model, Python’s event loop suddenly makes more sense. Choose languages based on problems, not preferences. This is harder than it sounds because we develop preferences. But the best engineers I know are pragmatic. They’ll pick Python when it’s right, C++ when it’s necessary, Go when it’s elegant. Defend your language’s weaknesses honestly. Don’t claim Python isn’t slow. It is. But explain why that matters less for your use case. Don’t pretend C++ is easy. It isn’t. But explain why the control it gives you is worth the complexity. Respect different approaches. The Rust community’s obsession with memory safety comes from real pain. The Go community’s minimalism comes from experience with complexity. The Python community’s pragmatism comes from understanding that shipping quickly matters.

The Uncomfortable Truth

Here’s something nobody wants to hear: the language is rarely the bottleneck. Poor architecture will slow you down more than the language choice. Bad algorithms will destroy performance. Terrible APIs will frustrate developers more than syntax quirks. A language that’s “slow” in benchmarks but clear in intent beats a “fast” language that nobody understands. The best code I’ve ever seen wasn’t written in the “best” language. It was written by people who:

• Understood their constraints
• Chose appropriate tools
• Mastered those tools
• And stopped complaining about what they couldn’t do

Moving Forward

Stop ranking languages. Stop defending your tribal choice so aggressively. Instead:

• Learn what each language excels at. Python for data science and web frameworks. Go for microservices. Rust for systems where memory safety is critical. JavaScript for frontend and Node.js servers.
• Understand the trade-offs you’re making. When you choose Python, you’re optimizing for developer happiness and ecosystem. Accept that you’re giving up raw performance.
• Use multiple languages in your career. Most full-stack developers will use JavaScript and Python and maybe Java. That’s healthy. Each language teaches you something.
• Evaluate honestly. “Is Python slow for this task?” Yes, sometimes. “Is that a problem for our actual requirements?” Maybe not. The programming language that “sucks the least” is the one that solves your problem while respecting your constraints. That’s different for everyone. And that’s beautiful. Your favorite language does suck. So does mine. So does everyone’s. And we’re still here, building incredible things, because we’ve learned to work with our tools instead of against them. Now stop reading blog posts and go write some code. In whatever language makes sense for what you’re building. Because that’s what actually matters.