If you've been in technology long enough, you've seen programming languages come and go like fashion trends. Remember when everyone was betting on Ruby? When Node.js was going to eat the world? When we were all supposed to be writing everything in Go by now?
Well, here's a plot twist: Python, the language that's been around since 1991 (yes, it's older than the original Jurassic Park movie), just had its best year ever. And not by a little—we're talking a 7 percentage point surge that catapulted it to 25.35% market share on the TIOBE index. That's the highest rating any programming language has achieved since 2001, when Java was the undisputed king.
But here's what should really make you sit up and pay attention: Python now has a 15-point lead over the next most popular language. That's unprecedented. In the history of the TIOBE index, no language has ever had such a commanding lead.
The "Old and Boring" Strategy Wins Again
There's a certain irony in watching a language that predates the World Wide Web dominate in the age of AI. While everyone was chasing the shiny new frameworks and the "next big thing," Python just kept quietly solving real problems. It turns out that "old and boring" might be the most valuable trait in technology infrastructure.
Think about it this way: Python isn't winning because it's fast (it's not). It's not winning because it has the most elegant syntax (that's debatable). It's winning because it has something far more valuable—decades of accumulated wisdom, libraries, and most importantly, solutions to problems that your team is probably facing right now.
The AI Connection You Can't Ignore
Let's address the elephant in the room—or should I say, the AI in the datacenter. Python's surge isn't just about Python. It's about the AI revolution, and Python happened to be in exactly the right place at exactly the right time.
According to JetBrains' State of Python 2025 report, 41% of Python developers are using it specifically for machine learning. When TensorFlow, PyTorch, and scikit-learn all chose Python as their primary language, they didn't just choose a syntax—they chose an ecosystem. And that ecosystem has become the de facto standard for AI development.
But here's where it gets really interesting for leadership teams: AI isn't just running on Python. AI is making Python developers more productive than ever before.
The Great Equalizer: How AI Amplifies Python Skills
Here's something that should reshape how you think about hiring and team building: AI coding assistants have fundamentally changed what's possible with Python development. And no, I'm not talking about AI replacing developers (we can save that debate for another post). I'm talking about AI augmenting developers in ways that are actually measurable and meaningful.
Python has a unique advantage in the age of AI-assisted coding: it has the largest collection of publicly available code in history. GitHub shows a 22.5% year-over-year increase in Python contributions, and the language recently overtook JavaScript as the most-used language on the platform. That means when your developer is using GitHub Copilot, Cursor, or Claude Code, they're drawing from an absolutely massive corpus of Python examples, patterns, and solutions.
Think about what this means practically: That senior developer who's been writing Python for 15 years? They're now even more productive because AI can suggest boilerplate, catch edge cases they might have missed, and reference patterns from millions of repositories. That mid-level developer who's still learning? They're suddenly working at a level that would have taken them years to reach because they have instant access to best practices and can learn from millions of lines of production code.
And here's the kicker for hiring managers: that bootcamp graduate you were hesitant about? With AI assistance drawing from Python's vast codebase, they can now contribute meaningfully much faster than was previously possible. The AI tools don't just generate code—they teach, they suggest improvements, and they help developers understand why certain patterns work.
The Math That Should Influence Your 2025 Budget
Let's talk numbers, because that's what gets things approved in budget meetings:
- 1.19 million job listings on LinkedIn require Python skills right now. That's not a typo.
- 9.3% growth in 2024, way ahead of Java's 2.3%, JavaScript's 1.4%, and Go's 1.2%. Python didn't just grow—it lapped the competition.
- 41% of Python developers are working on machine learning projects, which means nearly half of Python development is directly tied to AI initiatives.
- 26.14% peak rating in August 2025, driven specifically by AI adoption and tooling improvements.
Now, here's where this gets strategic: if you're planning AI initiatives (and let's be honest, every company is), you're going to need Python developers. The supply-demand dynamics are increasingly favorable for companies that move now versus those who wait.
The Technology Stack Decision
I can hear the objections from here: "But Python is slow! What about performance? What about type safety?"
Fair points. Here's the reality check: for 90% of your use cases, Python is fast enough. And for the 10% where it isn't? The increasingly popular pattern is Python + Rust for performance-critical components. You get the development velocity and ecosystem of Python where it matters, and you get the performance of Rust where you need it.
As for type safety, Python 3.14 continues to improve type hints and static analysis tools. Tools like mypy and Pydantic have largely solved the type safety concerns for teams that care about them. Plus, with AI coding assistants, type-related bugs get caught earlier and more consistently than ever before.
The Real Performance Metric: Time to Value
Here's what I've learned after years in technology leadership: the performance that matters most isn't runtime speed—it's time to value. How fast can you go from idea to working prototype? How quickly can you iterate based on user feedback? How rapidly can you pivot when market conditions change?
Python excels at all of these. And with AI assistance, it's gotten even better.
The Age Advantage
There's another angle that doesn't get discussed enough: Python's maturity is a feature, not a bug. At 33 years old, Python has been around long enough to:
- Have mature solutions for virtually every problem domain
- Have established best practices that actually work in production
- Have comprehensive documentation (official and community-generated)
- Have been stress-tested by companies at every scale, from startups to Google
When your AI coding assistant suggests a Python pattern, it's drawing from decades of collective experience. That mid-level developer isn't just getting autocomplete—they're getting the accumulated wisdom of millions of developers who came before them.
This is particularly important as development teams become more distributed and less senior-heavy. The combination of Python's extensive codebase and AI assistance means you don't need as many gray-haired architects in every room (though they're still valuable—don't fire me).
What This Means for Your 2025 Strategy
If you're in leadership and you're planning your technology strategy for 2025 and beyond, here are the takeaways that should influence your decisions:
1. Python is the safe bet for AI/ML initiatives. This isn't about following trends—this is about choosing the platform with the most mature ecosystem, the most available talent, and the most AI tooling support.
2. AI-assisted development makes Python teams more productive. The combination of Python's massive codebase and AI coding assistants creates a multiplier effect. You're not just hiring a Python developer—you're hiring a Python developer with instant access to millions of examples and patterns.
3. The hiring market is competitive but manageable. Yes, there are 1.19 million job listings for Python skills. But Python is also one of the most-taught programming languages, meaning the talent pipeline is strong. And with AI assistance lowering the barrier to productivity, you can hire less experienced developers and get them contributing faster.
4. The moat is getting wider. Python's 15-point lead over the next language isn't just a statistic—it's a moat. The network effects are real. More developers → more libraries → more examples → better AI assistance → more productive developers → more companies choose Python → more developers learn Python. You get the idea.
5. Technical debt in other languages might be worth reconsidering. I'm not saying rewrite everything in Python (that's almost never the answer). But if you're making new investments, or if you're at a natural re-architecture point, Python's ecosystem advantage has never been stronger.
The Contrarian Take: What Could Go Wrong?
Because no technology analysis is complete without acknowledging the risks, here's what could derail Python's momentum:
- Performance requirements could shift dramatically. If edge computing or real-time processing becomes the dominant paradigm, Python's performance characteristics could become a bigger liability.
- A new AI-native language could emerge. It's possible (though I'd bet against it) that someone could create a language specifically optimized for AI workloads that's compelling enough to fragment the ecosystem.
- The governance could stumble. Python's governance has been solid, but languages can be derailed by poor stewardship (cough cough Perl cough).
That said, these all feel like relatively low-probability events, at least in the 3-5 year time horizon that most of us are planning for.
The Bottom Line
Python's 2025 surge isn't a fluke, and it's not just about AI hype. It's the result of decades of steady improvement, a massive and mature ecosystem, and being in exactly the right place when AI exploded onto the scene.
But the real story isn't just Python's growth—it's how AI coding assistants are amplifying Python's already strong advantages. The combination of Python's extensive codebase and AI-assisted development is creating a productivity multiplier that's hard to match with other languages.
For technology leaders, the question isn't whether Python will remain relevant—it's whether you're moving fast enough to take advantage of it. The companies that figure out how to leverage Python's ecosystem, combined with AI-assisted development, are going to have a significant competitive advantage in shipping AI-powered products.
And if you're still on the fence about Python? Well, you might want to make a decision soon. That 15-point lead isn't getting any smaller.
Now if you'll excuse me, I need to go update my "most boring technology choices that turned out to be brilliant" list. Python just moved to number one.