In a historic shift that surprised few who’ve been paying attention, Python has overtaken JavaScript as the most-used programming language, commanding an unprecedented 25.87% share on the TIOBE index as of June 2025. This isn’t just another ranking shuffle—it represents the highest rating any programming language has ever achieved in the index’s 23-year history. Python hit a staggering 26.14% in 2025, and in January 2026, it maintained its dominance at 22.61%, while Java trails at 8.71%.
The driving force behind this surge? The explosive growth of artificial intelligence and machine learning. But here’s the paradox that has puzzled developers for years: Python is objectively slow. It’s an interpreted language with dynamic typing and significant memory overhead. So why did it win the ML/AI war when performance-obsessed fields like gaming and systems programming chose C++ and Rust? And more importantly for enterprise developers, how can Java teams integrate Python’s ML capabilities without abandoning their existing infrastructure?
1. The Numbers Don’t Lie: Python’s Meteoric Rise
The data tells a compelling story. Python saw a 7 percentage point increase from 2024 to 2025—the largest single-year jump for any major programming language. Meanwhile, Python overtook JavaScript on GitHub with a 22.5% year-over-year increase in contributions, reflecting real coding activity rather than just survey responses.
In the U.S. job market as of February 2025, Python leads with over 64,000 open positions, significantly ahead of Java (43,000+) and JavaScript (30,000+). The message is clear: every company wants AI, and AI runs on Python.
2. The Performance Paradox: Why Python Won Despite Being Slow
Let’s address the elephant in the room. Benchmarks don’t lie—a billion nested loop iterations might take 0.5 seconds in C or Rust, while Python might stretch that into many, many more seconds. So why does a “slow” language dominate the most computationally intensive field in computing?
1. Performance Isn’t Where You Think It Is
Here’s the secret that ML engineers discovered early: Python code doesn’t do the heavy lifting. When you call model.fit() in PyTorch or TensorFlow, that Python code is just a thin wrapper. The actual matrix multiplications, convolutions, and backpropagation happen in highly optimized C++ and CUDA code running on GPUs.
Python serves as the “glue language,” orchestrating calls to these high-performance native libraries. As one developer put it: “Deep learning relies on well-written and well-optimized frameworks that do the heavy lifting. Nobody wants to write convolutions from scratch in C every time.”
2. Developer Velocity Trumps Execution Speed
Performance isn’t only measured in CPU cycles—it’s also measured in time-to-solution. Your code might execute in 0.5 seconds, but if it takes three days to write and debug versus three hours in Python, you’ve lost more productivity than you gained from runtime speed.
Python’s design choices—dynamic typing, concise syntax, and batteries-included standard library—slash development time dramatically. For ML research where iteration speed matters more than execution speed, this is game-changing.
3. The Ecosystem Network Effect
Python won ML/AI because it got there first and accumulated an unstoppable ecosystem advantage. Consider these pillars:
| Category | Key Libraries | Why It Matters |
|---|---|---|
| Deep Learning | PyTorch, TensorFlow, Keras | Complete frameworks from research to production |
| Data Processing | NumPy, Pandas, SciPy | Efficient handling of massive datasets |
| Visualization | Matplotlib, Seaborn, Plotly | Understanding and presenting results |
| Classical ML | Scikit-learn, XGBoost, LightGBM | Pre-built algorithms for common tasks |
| NLP | Hugging Face Transformers, spaCy | State-of-the-art language models |
Once this ecosystem reached critical mass, it became self-reinforcing. Researchers publish papers with Python code. Students learn ML in Python. Companies hire Python ML engineers. New libraries target Python first. The cycle continues.
3. The Framework Showdown: PyTorch vs TensorFlow in 2026
The ML framework landscape has evolved into a mature duopoly, with PyTorch and TensorFlow serving different but often overlapping needs.
3.1 PyTorch: The Research Darling
PyTorch, developed by Meta (formerly Facebook), has become the overwhelming choice for ML research. According to recent data, PyTorch dominates 85% of deep learning research papers, while holding 25.69% market share with 17,196 companies using it globally.
Why researchers love PyTorch:
- Dynamic Computation Graphs: Build graphs on-the-fly, making debugging and experimentation natural
- Pythonic API: Feels like native Python, not a DSL (Domain Specific Language)
- Immediate Feedback: See results line-by-line during development
- torch.compile(): PyTorch 2.0+ delivers 20-25% speedups with a single line of code
PyTorch Typical Use Cases: Generative AI and LLMs (like GPT models), Computer Vision research, Rapid prototyping, Academic research where flexibility matters more than deployment infrastructure.
3.2 TensorFlow: The Production Workhorse
TensorFlow, developed by Google, commands a larger market share at 37.51% with over 25,000 companies using it. While PyTorch wins research, TensorFlow excels where it counts for enterprises: production deployment.
TensorFlow’s enterprise advantages:
- TensorFlow Serving: Battle-tested API deployment with automatic batching and version management
- TensorFlow Extended (TFX): Complete ML pipelines from data validation to model analysis
- TensorFlow Lite: Optimized for mobile and embedded devices
- TPU Integration: First-class support for Google’s Tensor Processing Units
TensorFlow Typical Use Cases: Large-scale recommendation systems, Production ML pipelines with strict SLAs, Speech recognition and NLP at scale, Enterprise applications requiring long-term stability.
3.3 The Convergence
The gap between PyTorch and TensorFlow has narrowed significantly. TensorFlow 2.x adopted eager execution (like PyTorch), while PyTorch introduced TorchScript and compilation for production. As one ML engineer noted: “The most valuable AI engineers in 2026 are ‘bilingual’ in frameworks.”
Interestingly, Keras 3.0 has emerged as a potential unifier, offering a single high-level API that runs on PyTorch, TensorFlow, or JAX with minimal code changes—a development that could end framework wars for many developers.
4. Java Meets Python: Bridging the ML Divide
Here’s the enterprise reality: most production applications still run on Java. Banking systems, e-commerce platforms, and enterprise software are built on decades of JVM investment. But in 2026, every Java application increasingly needs ML capabilities. The question isn’t whether to integrate ML—it’s how to do it without rewriting your entire stack in Python.
Two primary approaches have emerged for Java developers: Deep Java Library (DJL) and ONNX Runtime.
Approach 1: Deep Java Library (DJL)
DJL, developed by Amazon and open-sourced in 2019, is a framework that brings ML inference directly into Java applications. It’s engine-agnostic, meaning you can run PyTorch, TensorFlow, or MXNet models from the same Java API.
How DJL Works:
DJL acts as a bridge between Java and native ML engines. When you load a PyTorch model in DJL, it uses Java’s ServiceLoader to detect the DJL PyTorch engine implementation, which then loads the PyTorch native binaries. Critically, DJL runs models without any decrease in performance or loss in accuracy because it uses the same native engines that power Python.
Typical DJL Workflow for Java Developers:
- Train in Python: Data scientists develop models using PyTorch or TensorFlow
- Export Model: Save the trained model in the framework’s native format
- Load in Java: Use DJL to load and run inference in your Java application
Here’s a simple example of loading a pre-trained ResNet model for image classification:
DJL Image Classification Example:
The code defines criteria for a computer vision image classification task, loads a pre-trained ResNet-50 model from the model zoo, creates a predictor, and runs inference on an image. The entire process happens in pure Java without touching Python at runtime.
DJL Advantages:
- Native Java API—no context switching between languages
- Engine-agnostic—switch between PyTorch, TensorFlow, ONNX, or MXNet
- Rich Model Zoo with pre-trained models ready to use
- Production-ready with DJL Serving for model deployment
- Full JVM integration—use existing monitoring, logging, and security
DJL Limitations:
- Limited access to cutting-edge research models (Python gets them first)
- Engine-specific configurations can vary, requiring deeper understanding
- Smaller community compared to Python ML ecosystem
Approach 2: ONNX Runtime
ONNX Runtime takes a different approach. ONNX (Open Neural Network Exchange) is a platform-agnostic intermediate representation for neural networks, developed initially by PyTorch’s team at Meta with contributions from Microsoft, IBM, and others.
Think of ONNX like bytecode for neural networks—just as Java compiles to bytecode that runs on any JVM, ML models convert to ONNX that runs on any platform.
The ONNX Workflow:
- Train Anywhere: Use PyTorch, TensorFlow, scikit-learn, or any supported framework
- Export to ONNX: Convert your trained model to the ONNX format
- Run Anywhere: Load the ONNX model in Java, Python, C++, JavaScript, or C#
As InfoQ’s enterprise architecture guide explains: “Enterprise systems can now run transformer-class models directly within the JVM using ONNX, unlocking AI capabilities without disrupting Java-based pipelines or introducing Python dependencies.”
ONNX Runtime Advantages:
- True Platform Independence: One model file runs everywhere
- Hardware Acceleration: Supports CPU, GPU, NPU with execution providers
- Production Optimization: Graph optimizations specific to target hardware
- No Python Dependency: Eliminate Python runtimes from production
- Battle-Tested: Powers Microsoft products including Windows, Office, Bing
ONNX Gotchas:
- Not all operations convert cleanly—some models fail export
- Debugging ONNX conversion errors can be cryptic
- Tokenizers and preprocessing must be handled separately
- Performance can vary depending on conversion quality
Which Approach Should Java Developers Choose?
| Scenario | Recommendation | Why |
|---|---|---|
| Need flexibility to switch engines | DJL | Engine-agnostic design supports multiple frameworks |
| Maximum platform portability | ONNX Runtime | One model runs on JVM, .NET, mobile, web |
| Using transformer/LLM models | DJL or ONNX (with care) | Both work, but tokenizer handling is critical |
| Need pre-trained models | DJL | Rich model zoo with ready-to-use models |
| Microservice architecture | Either | Both support containerized deployment well |
| Eliminating Python from production | ONNX Runtime | Complete independence from Python runtime |
Real-World Integration Pattern
Many enterprises adopt a hybrid approach:
- Development: Data scientists train models in Python (PyTorch/TensorFlow)
- Export: Models are exported to ONNX format with validation tests
- Artifact Management: ONNX files are versioned and stored in artifact repositories
- Deployment: Java services load ONNX models via DJL or ONNX Runtime
- Monitoring: Existing Java observability stack monitors ML inference
This pattern, as documented by NashTech’s DJL guide, allows teams to “bring the model to where your business logic lives” rather than creating separate Python microservices.
4. The Future: Python’s Continued Dominance
Looking ahead, Python’s position in ML/AI appears unassailable. Several trends reinforce this:
- Free-Threading (PEP 703): Python is removing the Global Interpreter Lock, enabling true parallel processing
- Rust Integration: 25-33% of new Python extensions are now written in Rust instead of C, combining safety with performance
- Mobile Python: iOS and Android are becoming tier-3 supported platforms for CPython
- AutoML and No-Code Tools: Both PyTorch and TensorFlow are making ML more accessible to non-specialists
Meanwhile, the integration tools for Java developers continue improving. DJL’s roadmap includes better support for large language models and streaming inference, while ONNX Runtime continues expanding its hardware support and optimization capabilities.
The Bottom Line
Python won machine learning not despite being slow, but because speed was never the bottleneck. Developer productivity, ecosystem richness, and ease of experimentation mattered more than raw execution speed. The actual computation happens in optimized C++/CUDA anyway.
For Java developers, the message is clear: you don’t need to abandon your existing infrastructure to gain ML capabilities. Tools like DJL and ONNX Runtime provide production-ready paths to integrate Python-trained models into JVM applications, leveraging your existing observability, security, and deployment infrastructure.
The future isn’t Python vs. Java—it’s Python AND Java, each playing to their strengths in a comprehensive ML engineering stack.
5. What We’ve Learned
Python’s ascent to the #1 programming language with a historic 25.87% TIOBE index share is driven entirely by the AI/ML revolution. Despite being objectively slow as an interpreted language, Python won machine learning through superior developer experience, an unmatched ecosystem of libraries (PyTorch, TensorFlow, NumPy, Pandas), and the critical insight that ML performance happens in native C++/CUDA code beneath Python’s simple API.
The PyTorch vs TensorFlow landscape has matured into complementary choices: PyTorch dominates research (85% of papers) with its dynamic computation graphs, while TensorFlow commands production deployments (37.51% market share) with robust serving infrastructure. Both frameworks have converged on features, and the most valuable ML engineers are bilingual in both.
For Java developers facing the ML integration challenge, two production-ready solutions exist. Deep Java Library (DJL) provides an engine-agnostic Java API for running PyTorch, TensorFlow, or ONNX models directly in JVM applications without performance loss. ONNX Runtime offers true platform independence through an intermediate model representation, eliminating Python dependencies entirely from production systems. The optimal approach depends on your specific needs—DJL for engine flexibility and model zoos, ONNX Runtime for maximum portability.
The enterprise pattern is clear: train in Python where the tools are best, deploy in Java where your production infrastructure lives, and bridge the gap with DJL or ONNX Runtime to maintain a unified, observable, secure stack. Python’s dominance in ML/AI isn’t going anywhere, but Java developers can leverage that ecosystem without abandoning their platform.
