Python has long been the engine behind the AI boom. From research labs to startups to Big Tech, it’s been the language of choice for building and deploying machine learning systems. But as AI goes mainstream. With products shipping at scale and latency becoming a competitive metric, Python is starting to show its age.
Today, companies aren’t ditching Python. But they’re no longer relying on it alone. The future of AI, it turns out, might not be written entirely in Python, and that’s not a bad thing.
“We still prototype everything in Python,” says Elina Gilmanova, a senior software engineer at Amazon. “But in production, we’re moving more and more critical components to Rust or Go.”
Elina works on large-scale AI infrastructure, where milliseconds matter and scale is non-negotiable. Her experience mirrors a broader trend: Python remains the language of experimentation, but production systems are increasingly polyglot.
Why Python won and where it still shines
Python’s dominance didn’t happen by accident. When deep learning took off in the early 2010s, Python offered the perfect mix: readable syntax, strong scientific libraries, and backing from giants like Google (TensorFlow) and Meta (PyTorch). A massive ecosystem followed — NumPy, pandas, Hugging Face, Jupyter — and with it, a generation of AI talent trained to think in Python.
That momentum remains strong. For research, prototyping, data analysis, and early-stage model development, Python is unmatched.
“Python still gives you the shortest path from idea to code,” says Elina. “That matters a lot, especially when you’re moving fast or exploring.”
Its dynamic nature and Global Interpreter Lock (GIL) make it less suitable for high-performance systems. Yes, libraries like NumPy and PyTorch push compute-heavy tasks to C or CUDA. But everything else (data orchestration, preprocessing, serving logic) runs at Python speed.
That’s fine in a demo. Not so much when you’re serving millions of requests per day.
Concurrency is another pain point. Async I/O has improved, but threading remains clunky. Scaling with multiprocessing introduces complexity. Compare that to Go, where concurrent programming is a first-class experience, or Rust, where performance and safety come together.
Then there’s the headache of dependency management — still a sore spot for many teams despite better tooling. Environment drift, version conflicts, and broken requirements.txt files are all too familiar.
Production Reality: Performance, Concurrency… and a Polyglot Turn
The pain points are familiar to anyone running AI in production: CPU-bound performance, the GIL’s constraints on true multithreading, awkward concurrency trade-offs, and dependency drift that containers often paper over rather than solve. As traffic and SLAs tighten, the Python wrapper — preprocessing, routing, serving logic — can become the bottleneck even when the model itself is fast. That’s why more teams pair Python with Rust for inference paths and Go for reliable, concurrent infrastructure; TypeScript/JS increasingly shows up at the edge and in the browser. The result isn’t a Python retreat — it’s a stack that assigns each language to what it does best.
“We still prototype in Python — it lets teams iterate quickly,” Elina told. “But for latency-sensitive components we move to Rust, and we rely on Go for scalable orchestration. It’s not about abandoning Python; it’s about choosing the right tool at the right layer.”
What founders and PMs need to know
What founders and PMs need to knowIf you’re building AI-powered products in 2025, here’s the takeaway: Python still matters — a lot. But treating it as your only engineering tool is risky. You’ll hit limits in performance, scalability, and team specialization.
Instead, think in layers:
- Prototype in Python
- Optimize in Rust
- Deploy with Go or containerized infra
- Integrate across platforms with ONNX, Docker, or browser-based runtimes
AI is becoming more like traditional software: built in layers, shipped in parts, optimized per use case.
Bottom line
Python isn’t going anywhere. It’s still the language of choice for AI exploration, research, and early development. But as the industry scales up, relying solely on Python is like racing with a prototype engine.
The AI world is growing up. And Python, while still essential, is growing up with it, not as a one-size-fits-all solution, but as part of a broader, smarter stack.
