What is Python? Why is it widely used in AI?

What you need to know

High-level and interpreted

High-level means you describe what you want, not how the machine does it. You never allocate memory or free it; a garbage collector does that. You never declare a variable's type; the value carries it.

Interpreted needs one correction. The standard implementation, CPython, first compiles your .py file to bytecode (the .pyc files in __pycache__), then a virtual machine runs that bytecode one instruction at a time. There is no separate compile step for you, which is why you can try an idea in a notebook in seconds.

Why it is slow, and why that rarely hurts AI work

Every Python operation does extra work: look up the type, find the right method, create a new object for the result. A pure-Python loop over millions of numbers is therefore slow. Libraries fix this by doing the loop in compiled code.

1prices = [float(i) for i in range(1_000_000)]23# Pure Python: the interpreter runs one million loop steps4total = 0.05for p in prices:6    total += p * 1.18          # add 18% GST7print(round(total))            # 58999941000089# With NumPy the same maths is one call, and the loop runs in C:10#   import numpy as np11#   arr = np.array(prices)12#   total = (arr * 1.18).sum()

The NumPy version is commonly tens of times faster, because Python only makes one call and the C code does the million steps. This pattern — thin Python on top, fast native code underneath — is how PyTorch trains models on GPUs too.

The ecosystem is the real reason

Research code is released in Python, tutorials are in Python, and the team next to you writes Python. Switching languages would mean rebuilding all of that.

A real-life example

A fintech team in Bengaluru wants a prototype that flags suspicious UPI payments. In one week, one engineer uses pandas to load 2 million transactions from a CSV, trains a scikit-learn model, wraps it in a FastAPI endpoint, and calls an LLM through the provider's Python SDK to write a one-line explanation for each flag. Every piece is a pip install away.

The first version has one hot spot: a Python for loop that computes a risk score row by row takes 40 seconds. The engineer rewrites it as one vectorised pandas expression and it drops to under a second. That is the typical story: Python is fast enough, as long as the heavy loops live inside the libraries.

Follow-up questions to expect

• "Is Python compiled or interpreted?" — Both, in a sense. CPython compiles source to bytecode automatically, then interprets that bytecode on a virtual machine.
• "What is the GIL, and does it matter for AI?" — It stops two threads running Python bytecode at once. It matters little for AI, because NumPy and PyTorch release it inside their C code, and API calls are I/O-bound.
• "When would you not use Python?" — For mobile apps, code running in a browser, or tight latency budgets such as high-frequency trading. Even LLM inference servers keep Python at the edges and put the hot path in C++, CUDA or Rust.