Is Python Coding dying too?
Will it be replaced by faster languages like Rust or more modern tools like Mojo?
That’s what many new developers—and even seasoned ones—are asking in 2025.
But here’s the twist:
👉 Python is still the most used programming language in the world.
👉 It powers over 70% of AI/ML tools and frameworks.
👉 And despite its speed limitations, demand for Python developers is still growing.
Crazy, right?
I had the same doubt last semester. But here is what I say,
“Python won’t win every race. But in the right lanes, it’s still miles ahead.”
But every real-world ML tutorial, API backend, or even agent framework I tried—was still running on Python.
That’s when I realized:
Python may not be the fastest or flashiest.
But it’s quietly becoming the operating system of AI.
Let’s break down where Python actually stands, what’s changing fast, and whether it’s still a smart bet for your career or startup.
Where Python Dominates Right Now (And Why)
Python is everywhere—but not by luck. It dominates because it makes hard things easy and boring things beautiful.
When I first started with automation tasks in my second semester, I wrote scripts in Python that would’ve taken me hours in Java. That simplicity is why it’s still the go-to language in 2025 for so many high-impact fields.
In data science and machine learning, Python is the king. Period.
Over 80% of ML frameworks—from TensorFlow to PyTorch, pandas, scikit-learn, spaCy, and HuggingFace Transformers—are Python-first or only available in Python.
Even Google’s DeepMind, OpenAI, and Meta’s FAIR use Python for prototyping.
Why?
Because Python reads like pseudocode.
When I trained my first neural net, I didn’t care about memory pointers—I cared about results. Python let me focus on that.
In web development, Python powers millions of backends through Django, Flask, and now the ultra-fast FastAPI.
What I think cool is that FastAPI gives the feel of TypeScript’s static typing without losing Python’s flexibility.
I built an API last month with FastAPI that was live in two days—blazingly fast to write, but not as lightweight as Go in terms of performance.
That’s always been Python’s thing—developer speed over machine speed.
In automation, data wrangling, and orchestration, Python feels like magic.
Over 75% of DevOps engineers use Python for scripting tasks (Stack Overflow Developer Survey 2023).
Even YouTubers automate their content workflows with Python scripts.
I recently met a guy on Reddit who automated his entire content pipeline—from scraping ideas to generating thumbnails—using Python and ffmpeg. Wild stuff 🤯
Then there’s education. Python’s beginner-friendly syntax makes it the #1 teaching language in universities worldwide.
According to IEEE Spectrum, it’s been ranked the top language for several years in a row.
But critics argue this makes devs too dependent on Python’s “easy mode,” leading to poor understanding of low-level concepts.
I’ve seen that too—people (including me, early on) getting stuck when they step into C++ or Rust land. 🐢
Python’s dominance is not accidental. It’s the glue of modern tech stacks.
But behind that glue are speed tradeoffs, dependency on C-extensions, and runtime bottlenecks.
Still, in real-world jobs, deadlines matter more than raw speed. And Python wins when building something that works now.
Key Challenges Python Is Facing in 2025 and Beyond
Python is not perfect.
It’s elegant and readable, yes—but when you push it into performance-critical systems, it starts coughing.
The biggest problem? Speed.
Python is up to 60x slower than C++, according to The Benchmarks Game.
That’s not just a fun stat.
When I tried building a real-time ML model for video frame analysis, it froze at just 5 FPS while the C++ version flew.
I had to use Python for orchestration and rewrite all heavy logic in C++.
It was frustrating, like riding a bike next to a Tesla 🥲.
Another major roadblock is the Global Interpreter Lock (GIL).
It blocks true multi-threading, meaning even with 8 cores, Python runs one thread at a time.
Languages like Go and Rust eat Python alive in parallel processing.
I remember building a web scraper that needed thousands of threads—Python turned it into a snail.
I rewrote parts of it in Node.js with async/await just to make it bearable.
And I’m someone who loves Python. That hurt.
Python is also almost useless in mobile development.
Sure, you can try Kivy or BeeWare, but good luck building something stable or native-feeling.
I once tried creating a small Android utility with Kivy.
It crashed, lagged, and the final APK was over 80MB for a tiny app.
Play Store didn’t accept it because of missing NDK configurations.
Meanwhile, React Native builds it in a few minutes. Painful comparison.
And then comes the dependency hell.
Have you ever installed something with pip and suddenly five other things break?
It’s like playing Jenga with package versions.
I had a backend app crash after a minor update in a sub-dependency I wasn’t even aware of.
Python’s package ecosystem is massive, but fragile.
Languages like Rust solve this with static linking and lockfiles that just work.
One more bitter truth: Python is not the powerhouse behind AI.
It’s the interface.
TensorFlow, PyTorch, Numpy—all run their cores in C/C++.
Python is just the smooth UI for calling those libraries easily.
I used to think Python was doing all the math in my models.
Turns out, it’s just giving the orders—C++ is doing the heavy lifting 💪.
To be fair, Python is evolving.
There are efforts like nogil, Pyjion, and Mojo trying to fix its flaws.
But most of them are still in experimental phases.
So if you’re doing intense systems work, high-frequency trading, gaming, or mobile apps—Python isn’t gonna be the best friend.
It’s still one of the best tools for scripting, orchestration, data science, and prototyping.
But the myth of Python being “good for everything” I think needs to go.
It’s a sharp tool for the right job.
But in the wrong place, it cuts productivity—not code.
How Python Is Evolving to Stay Relevant
Python isn’t standing still. It knows it’s slow, bloated, and not great for concurrency—but it’s fighting back.
Python 3.12 brought serious performance upgrades thanks to the Faster CPython Project. Benchmarks show up to 50% speed boosts in some workloads (source).
And the upcoming Python 3.13 will double down on memory efficiency and even better startup times. I tried running a REST API with FastAPI on both 3.10 and 3.12, and the newer version literally felt snappier without me touching a single line of logic.
But the biggest shift? The long-awaited GIL-free Python.
Yes, the Global Interpreter Lock—Python’s biggest flaw—is finally being challenged. Facebook engineer Sam Gross’s nogil branch achieved near-linear scaling in multi-threaded workloads (source).
It’s not production-ready yet, but it proves Python is at least trying to leave its old limits behind. If this gets merged into core Python, we’ll finally be able to build real concurrent systems without feeling punished.
Meanwhile, extensions like Cython and Numba are helping Python cheat its way to performance.
I once used Numba for an ML-heavy feature extraction pipeline—and with one @jit decorator, a 12-minute loop became a 24-second job.
That was pure magic, until I had to debug it. The stack traces were a mess, and standard Python tools didn’t work well inside the JIT context. So yes, it’s faster, but it’s also painful.
The new kid on the block? Mojo.
It’s built by Chris Lattner (the creator of Swift and LLVM) and combines Python’s ease with C++-level performance. Mojo claims it’s 35,000x faster than vanilla Python for certain benchmarks (source).
That number sounds fake. Until I rewrote a matrix multiplication example that took 3.4s in NumPy and watched Mojo finish it in under 0.0001s. I didn’t believe my own stopwatch 😵.
But Mojo’s not open-source yet, and you can’t build production systems on something that’s still behind a waitlist. Its tooling, documentation, and community are still baby-sized.
Another huge shift is how Python is embracing type safety.
Back in 2020, most devs hated type hints. Now, with tools like Pyright, mypy, and Pydantic, static typing is becoming essential.
I once joined a messy legacy codebase where type hints helped us eliminate over 40 hidden bugs before launch. It wasn’t just safer—it made our team communicate better through code.
Still, Python isn’t perfect. It’s slow to change. Backward compatibility often blocks innovation. And every time you want native speed, you need to borrow from C, C++, or Rust.
Languages like Go and Rust are beating Python in areas like CLI tools, serverless compute, and embedded systems. They compile fast, ship small, and run like rockets.
But here’s the thing—Python isn’t trying to win every war. It’s evolving exactly where it dominates: AI, data, and automation.
For those domains, Python is not just surviving. It’s mutating into something better.
Python’s Role in AI, ML, and LLM Ecosystem Going Forward
Python isn’t just surviving the AI boom—it’s powering it.
Almost every major AI framework—from TensorFlow and PyTorch to HuggingFace Transformers and LangChain—is built primarily in Python.
Why? Because Python lets researchers test ideas fast, build prototypes in hours, and connect different systems like LEGO blocks.
When I built my first LLM-powered chatbot, I assumed the cool stuff happened in C++ or Rust.
But nope—the heart of the logic, the data loading, the embeddings, the orchestration—all Python.
Even tools like OpenAI’s API clients, Vector DB connectors (like FAISS, Chroma), and agent frameworks are Python-first.
It almost feels like Python has become the “unofficial operating system of AI.” 🤯
And this isn’t just my personal experience.
According to Stack Overflow’s 2023 Developer Survey, Python is the most wanted language in Data Science and ML, with over 50% of developers preferring it for AI work.
That tracks when you realize that even in massive production systems, Python isn’t doing the heavy lifting—but it’s still running the show.
Training and inference happen in CUDA, C++, or custom silicon, but Python handles the orchestration, logic, and model pipelines.
And it’s brilliant at that.
Let’s talk tools.
LangChain, the fastest-growing framework in the LLM space, is Python-first.
So is AutoGPT, LlamaIndex, Haystack, and even DeepEval for evaluation.
I remember trying to rebuild an agent loop in Node.js just for fun.
I gave up halfway—90% of the community, GitHub issues, and plugin ecosystem lived in Python-only repos.
Even business-focused AI platforms like Cohere, Anthropic, and OpenAI prioritize Python SDKs over any other language.
And the new kid on the block, Mojo, despite the hype for C-level speed, still clings tightly to Python’s syntax and compatibility.
Mojo isn’t trying to replace Python—it’s trying to supercharge it.
But here’s a real criticism.
Python’s dominance in AI is creating a single point of failure.
If the ecosystem breaks or Python’s performance becomes a real bottleneck, we hit a wall.
That’s why many startups are wrapping their critical logic in Rust or C++, using Python just as an outer interface.
Still, that interface is gold.
Python is where the innovation starts.
It’s where models get tested, fine-tuned, visualized, and shipped.
That’s what makes Python irreplaceable in the current AI/LLM landscape—not speed, but flexibility, interoperability, and community velocity.
And that’s exactly why, even in 2025, I still write all my AI pipelines, agent workflows, and experiments in Python.
Because every time I try something else… I end up right back here. 💻
Future Job Market and Industry Demand for Python
Is Python still in demand?
Absolutely.
Despite the rise of Rust, Go, and TypeScript, Python’s job market remains strong and expanding in 2025.
According to the 2024 Stack Overflow Developer Survey, Python is in the top 3 most wanted languages, with over 48% of developers interested in learning or continuing it. (source)
This isn’t just survey talk.
From AI startups to Fortune 500 companies, Python is still heavily used in hiring—especially in data science, automation, backend APIs, and ML pipelines.
I remember when I applied for a part-time ML role last semester, and the entire stack was Python-based—FastAPI on the backend, pandas and PyTorch on the ML side. They didn’t care about React or Rust; they wanted someone who knew the Python ecosystem inside out.
But here’s the catch.
Python’s popularity means brutal competition, especially at the junior level.
Job listings have grown by 20% over the past two years, but most of them ask for real-world experience with frameworks like Django, FastAPI, or libraries like scikit-learn and LangChain.
If you’re still stuck in the basics, you’ll get buried under thousands of resumes.
Also, in fields like game dev, embedded systems, and ultra-fast trading, Python is fading.
Speed bottlenecks and GIL limitations push those companies toward C++, Rust, or Go.
So yeah, Python isn’t a universal weapon anymore.
But in enterprise? Python is thriving.
Netflix, Spotify, Airbnb, and Instagram all have major components written in Python.
Not because it’s perfect, but because it saves them engineering time—and in business, time is profit.
In fact, many companies treat Python as a “business language”, like a smarter, programmable Excel.
It bridges the gap between data teams, analysts, and engineers.
I even met a business analyst last year who could manipulate massive reports and automate reports using just pandas and openpyxl. No need to touch Java.
Still, there’s a subtle shift happening.
Companies don’t want “just Python developers.”
They want polyglot devs—people who can combine Python with Rust for performance, or use Python for orchestration while deploying models through C++-backed servers.
So if you want to future-proof your job prospects, don’t just learn Python syntax.
Learn how Python fits into AI agent workflows, vector databases, data pipelines, and cloud-native systems.
Also, pick up tools like MyPy for type-checking, Pydantic for data validation, or experiment with Mojo to bridge speed gaps. ⚡
Bottom line?
Python jobs are still booming.
But the winners are those who go deeper, not broader.
Become the person who knows when to use Python and how to pair it with the right tools.
That’s how you’ll stand out in 2025 and beyond.
Final Verdict: Should You Still Invest in Python?
Yes. Python is still worth learning in 2025 — but only if you know where it fits.
It’s no longer the “do-everything” golden boy.
It’s becoming a niche king.
If you’re in AI, data science, automation, or building quick backends — Python isn’t going anywhere.
In fact, 90% of all ML-related libraries on GitHub are still written in Python (GitHub Octoverse 2024).
Python Alone Won’t Get You Hired
Here’s where most people go wrong.
They blindly learn Python thinking it’ll land them a job on its own.
It won’t.
Python is now like Excel for developers — powerful only when combined with real-world context.
I remember building a small chatbot that connected to an API and pulled answers from a vector DB.
Guess what? I spent 70% of my time designing workflows and only 30% writing Python code.
Python made it easy, yes — but knowing what to build mattered more.
The Job Market Still Loves Python
Despite all the hype around Rust, Mojo, and Go, Python remains one of the top 3 most in-demand languages globally (StackOverflow Survey 2024).
Why?
Because companies don’t hire Python devs for fancy algorithms — they hire them to glue things together fast.
Startups love it.
AI firms rely on it.
Even boring legacy fintech companies use it in their data workflows.
But if you’re aiming for mobile apps, game development, or real-time systems — honestly, Python sucks.
I once tried building a 2D game engine in Python just for fun — laggy, no proper threading, clunky asset handling 🫠.
Will Python Be Replaced?
Languages like Mojo are promising insane speeds and Python compatibility.
But let’s be real — no company is rewriting their production system just because something is 10x faster in theory.
Performance matters, yes. But ecosystem and trust matter more.
Python has the community, libraries, and industry momentum.
That’s why even OpenAI tools, LangChain, and LlamaIndex are still built in Python.
When You Should Learn Python — and When You Shouldn’t
Learn Python if you’re going into:
- AI or machine learning
- Automation or scripting
- Web backends (FastAPI is 🔥)
- API integration or data engineering
Avoid Python if your focus is:
- Native mobile apps
- High-performance gaming
- Real-time streaming systems
- Embedded devices
The Real Advantage: Speed of Execution (Not Runtime)
What Python lacks in runtime speed, it makes up for in speed of development.
I’ve shipped MVPs in a weekend using Flask + SQLite + a few APIs — while others were still deciding on the stack.
That’s Python’s real magic: build fast, iterate faster ⚡💻.
Final Word?
Python won’t win every race. But in the right lanes, it’s still miles ahead.