After years of climbing the ranks, Python has done it. The language now holds the #1 position across every major programming language index, from TIOBE to GitHub to the Stack Overflow Developer Survey. But this isn't just about bragging rights. Python's rise reflects a fundamental shift in how software gets built, and understanding that shift matters for anyone making technology decisions.
Whether you're evaluating languages for a new project, considering upskilling your team, or curious about where the industry is headed, the data tells a compelling story about why Python has become the default choice for so many developers and organizations.
The Numbers Behind Python's Dominance
Let's start with the hard data. Python's position at the top isn't just marketing hype. It's backed by multiple independent measures of programming language popularity.
TIOBE Index
Python holds the #1 spot with a 26.14% rating, the highest any programming language has ever achieved in the index's history. For context, C sits at 10.64% and Java at 9.6%. That's not a small gap. It's a generational shift in how developers choose their tools.
GitHub Activity
GitHub's 2024 Octoverse report confirmed what many suspected: Python has overtaken JavaScript as the most-used language on the platform. With a 22.5% year-over-year increase in contributions, Python's growth shows no signs of slowing. This is the first time in a decade that JavaScript hasn't held the top spot.
Stack Overflow Developer Survey
In the 2025 Stack Overflow survey, Python saw a 7 percentage point increase from the previous year. It's the most desired language among developers, with 41.9% saying they want to learn or work with it. That combination of current usage and future interest signals sustained momentum.
AI and Machine Learning: The Primary Growth Driver
You can't talk about Python's rise without talking about artificial intelligence. The explosion of interest in AI and machine learning has been powered almost entirely by Python's ecosystem.
According to JetBrains' State of Python 2025 report, 41% of Python developers use the language specifically for machine learning work. The libraries that define modern AI, including PyTorch, TensorFlow, scikit-learn, and Hugging Face Transformers, are all Python-first. When researchers develop new techniques, they publish Python implementations. When companies build AI products, they reach for Python.
This creates a self-reinforcing cycle. More AI work happens in Python, which attracts more AI talent, which produces more AI tools, which makes Python even more dominant in the space. For organizations investing in AI capabilities, Python isn't just a good choice. It's often the only practical choice.
Data Science and Analytics
Before AI captured everyone's attention, Python had already established itself as the go-to language for data work. That foundation remains strong.
51% of Python developers report involvement in data exploration and processing. Tools like pandas, NumPy, and Jupyter notebooks have become standard equipment for data scientists and analysts. A 2023 analysis found Python mentioned in 78% of data scientist job postings.
The data science use case also bridges nicely into machine learning. Teams that start with data analysis in Python can transition to building predictive models without switching languages or learning entirely new toolsets. That continuity matters when you're trying to move quickly.
Beyond Data: Python's Expanding Territory
While AI and data science drive the headlines, Python's versatility extends much further:
- Web Development: Frameworks like Django and FastAPI power backends for companies from Instagram to Spotify. FastAPI in particular has seen explosive growth, with a 5-point increase in the latest Stack Overflow survey, one of the most significant shifts in the web framework space.
- Automation and Scripting: Python remains the default choice for automating repetitive tasks, from DevOps scripts to data pipelines to internal tooling.
- Scientific Computing: Research institutions and laboratories rely on Python for everything from bioinformatics to climate modeling to physics simulations.
- Education: Python is the most popular first programming language in computer science curricula. Students learning to code today are learning Python, which shapes the talent pool for years to come.
This breadth matters. A language that can handle web APIs, process data, train machine learning models, and automate workflows with a single team's skillset provides real organizational efficiency.
Enterprise Adoption Is Accelerating
Python's popularity isn't limited to startups and research labs. Enterprise adoption is projected to grow 25% by the end of 2025.
20 out of 25 US unicorn companies use Python for development, including Instacart, DoorDash, Airbnb, and SpaceX. When GitHub Copilot reports 90% adoption among Fortune 100 companies, it's primarily generating Python and JavaScript code.
Industries that traditionally relied on other languages are increasingly adopting Python:
- Healthcare: Bioinformatics, patient data analysis, and medical imaging
- Finance: Predictive risk models, algorithmic trading, and fraud detection
- Retail: Recommendation systems and demand forecasting
- Logistics: Route optimization and supply chain analytics
The common thread? These use cases benefit from Python's data and AI capabilities while also needing production-grade web services, something Django and FastAPI deliver reliably.
Career Implications
For developers, Python skills translate directly to opportunity. LinkedIn currently shows over 1.19 million job listings requiring Python. The average Python developer salary in the United States ranges from $98,000 to $128,000 per year, and organizations are paying premium rates for AI/ML expertise. 44% of organizations report increasing pay for AI and machine learning skills.
For organizations, the Python talent pool is both deep and growing. New computer science graduates arrive with Python experience. Experienced developers from other ecosystems increasingly learn Python to access AI and data opportunities. That labor market dynamic makes Python a lower-risk technology choice from a staffing perspective.
What This Means for Your Technology Decisions
Python's dominance isn't just an interesting trend. It has practical implications for technology strategy:
- New AI/ML initiatives: Python is the clear default. Fighting against the ecosystem typically creates more friction than any alternative language's benefits justify.
- Web applications: Django and FastAPI are proven, production-ready options with strong security and scalability. They're particularly compelling when your application involves significant data processing or AI integration.
- Hiring: Building on Python means access to a larger, growing talent pool. For many organizations, this alone justifies choosing Python over alternatives.
- Long-term maintenance: Languages with strong community support and active development tend to age better. Python's trajectory suggests it will remain well-supported for the foreseeable future.
Of course, language choice always depends on context. Specific use cases may still call for other technologies. But the data makes clear that Python has earned its position as the first option to consider for a wide range of applications.
Building with Python
At Cuttlesoft, Python and Django have been core to our work for years. We've built everything from healthcare analytics platforms to fintech applications to AI-powered products using Python's ecosystem. If you're considering Python for your next project or looking to add AI capabilities to existing systems, we'd be happy to discuss how we can help.
The data is clear: Python isn't just popular. It's the foundation of modern software development. Understanding why and what it means for your organization is the first step toward making better technology decisions.
