Most people who work with artificial intelligence write in Python. Underneath, however, the heavy computation is usually carried out by code written in faster, compiled languages. This article describes three languages that matter at that level: Mojo, a newcomer designed with AI in mind; C++, the long-established standard for high-performance software; and Rust, which is built around memory safety.
Mojo: Python-like syntax, compiled speed
Mojo is a young language developed by the company Modular. Its aim is to join the ease of Python with the speed of a compiled language. Its syntax is deliberately close to Python, so a programmer who knows Python can read Mojo code with little difficulty.
Several features set it apart from Python.
- Static typing. In Python, the type of a variable is checked while the program runs. Mojo allows types to be declared and checked when the program is compiled. This helps the compiler produce faster code and catches some errors earlier.
- Structs. Alongside Python-style code, Mojo offers struct types whose layout in memory is fixed at compile time, as in C++ and Rust. This makes memory use predictable and efficient.
- MLIR. Mojo is built on MLIR (Multi-Level Intermediate Representation), a compiler technology designed to target different kinds of hardware, including CPUs and GPUs.
A note of caution is in order. Mojo is still under development. Its tools and libraries are far less mature than those of the other two languages, and its features continue to change.
C++: control and performance
C++ has been used for demanding software for decades, and the core of several widely used machine learning frameworks is written in it. Its strength is control: the programmer decides precisely how memory and hardware are used.
- Several styles of programming. C++ supports object-oriented programming with classes and objects, as well as procedural and generic styles. This helps to organise very large projects.
- Manual memory management. The programmer controls when memory is allocated and released. Modern C++ provides tools that make this safer, but mistakes remain possible and can cause memory leaks, crashes and security flaws.
- Templates. Templates allow code to be written once and generated by the compiler for many types. Template metaprogramming goes further and performs computation at compile time. It is powerful and has a reputation for being hard to learn.
Rust: safety first
Rust was designed to offer performance close to that of C++ while preventing a whole class of memory errors. It does this without a garbage collector, through rules that the compiler enforces.
- Ownership. Every value has a single owner, and the value is released when its owner goes out of scope. This removes the need to free memory by hand.
- The borrow checker. Code may borrow a reference to a value without owning it. The compiler checks that such references never outlive the data they point to, and that data is not changed from two places at once. In safe Rust this rules out dangling references and data races.
- Zero-cost abstractions. Rust’s high-level features are designed to compile into code as efficient as a hand-written low-level version.
The price is a steeper learning curve. The compiler rejects programs that break its rules, and newcomers often need time to get used to it. Rust’s collection of AI libraries is growing but is smaller than that of Python or C++.
How they compare
- Coming from Python: Mojo is the easiest of the three to read and to start with, with the caveat that it is new and still changing.
- Maximum control and an established ecosystem: C++ remains the standard choice, and it demands experience.
- Reliability: Rust suits software in which memory errors would be costly.
None of the three replaces Python for everyday work in AI. They serve the layer beneath it, where speed and efficiency are decided.
Why the choice matters
The choice of a programming language can look like a matter for engineers alone. It has wider effects. Efficient code uses less computing time and less energy. Memory errors are a common source of security weaknesses, so safer languages mean more dependable software. And a language that is easier to learn widens the circle of people who can take part in building these systems. For those reasons the tools behind AI are worth understanding, even for readers who will never write a line of code.
