The artificial intelligence field is actively seeking the next major breakthrough that will change the way we develop these systems and move us closer to achieving General AI (AGI) — an intelligence that can understand, learn, and apply knowledge across many tasks with human-like skill.

Last year, Elon Musk — one of the most influential voices in technology, as well as a co-founder of OpenAI and founder of xAI — suggested on his social platform that Rust could be the programming language to help us reach this milestone.

Challenging Python’s Dominance: Why Rust Is on the Rise

Some now argue that Python’s reign in the AI world is coming to an end, and that Rust’s moment has finally arrived.

To understand Elon Musk’s perspective and why Rust is gaining traction in the AI community, it helps to look at the core reasons behind this shift.

For nearly twenty years, Python has been the go-to language for AI and machine learning, thanks to its user-friendly nature and extensive library support. However, as AI models become larger and more complex, and as real-time, high-performance applications multiply, some of Python’s drawbacks are becoming more pronounced.

Python’s slower runtime speed, reliance on interpreters, and memory inefficiencies have led many developers to question its suitability for the next generation of AI. As Chris Lattner, the creator of LLVM, Clang, and Swift, has noted, Python can seem “too slow, too bloated, and, paradoxically, too inconvenient” when building cutting-edge systems that demand greater efficiency, security, and reliability.

This is where Rust enters the picture. According to the Stack Overflow Developer Survey, Rust has been the “most loved” programming language among developers for multiple years in a row, a strong signal that it’s more than just a niche technology. Large companies like Google, Meta, Amazon, and Microsoft have already embraced Rust for performance-critical components. This shift hints that the balance of power in AI development tools may be moving toward languages better suited for handling complexity at scale.

What Does Rust Bring to the Table?

Rust is a relatively new programming language that may not be as famous as Python, but it holds great promise for the future of AI. Why does Rust stand out for this field? There are several important reasons:

1. High Performance:

Rust compiles to efficient machine code without the extra layers of interpreted languages like Python. In other words, it combines ease of use with performance levels close to C or C++.

2. Memory Safety Without Garbage Collection:

Unlike languages like Java or C#, Rust guarantees memory safety without relying on a garbage collector. Its “ownership” and “borrowing” system ensures that memory is managed smoothly behind the scenes, eliminating unpredictable pauses that can disrupt real-time operations.

3. Optimized Compiler:

Rust’s compiler uses LLVM, a powerful backend that optimizes the code for many different processor types. This versatility makes Rust suitable for a wide range of platforms and devices.

4. Protection Against Data Races:

Data races — when multiple parts of a program access the same data at the same time — are a common headache in concurrent programming. Rust’s rules for ownership and borrowing prevent these issues, helping developers avoid tricky, hard-to-fix bugs.

5. Built-In Package Manager:

Just like Python, Rust comes with a built-in package manager. This makes it simpler to manage external libraries and streamline the development process, a big advantage over languages like C and C++ that lack a standard tool for this task.

6. Direct Hardware Access:

Similar to C and C++, Rust lets developers interact directly with hardware and memory. This low-level control is crucial for creating embedded systems and specialized AI applications that need to run efficiently on various devices.

Rust + Wasm: A Powerful Duo for AI Infrastructure

One of the most intriguing ways Rust extends its reach in AI is through its compatibility with WebAssembly (Wasm), a binary format that can run at near-native speed inside web browsers and lightweight runtimes. By compiling Rust code to Wasm, developers can deliver AI inference and processing directly in browsers or in constrained environments without sacrificing performance.

Projects like WasmEdge, a cloud-optimized Wasm runtime, enable Rust code to perform efficiently in microservices and serverless functions — traditionally a space dominated by Python and Docker-based workflows. Because Rust-Wasm builds tend to be small and load quickly, developers can deliver fast, resource-friendly AI apps that scale well and run consistently across different platforms. This results in a far more efficient setup compared to traditional Python-based solutions.

Rust’s AI Future

While Python will remain an important language in AI for years to come — its ecosystem, community support, and research momentum are not disappearing overnight — Rust is positioning itself as a prime candidate for the next generation of AI development. High-profile leaders like Elon Musk foresee a future where AGI demands languages that can handle massive complexity, real-time decisions, and flawless reliability.

Given Rust’s track record of winning developer mindshare, its ability to bridge low-level efficiency with high-level safety, and its expanding presence in AI-related projects, it’s clear why many believe Rust will guide the future of intelligent systems. As we inch closer to AGI, the tools we use to build it must evolve. Rust seems poised to help lead the way, offering the performance, stability, and scalability required for tomorrow’s artificial intelligence breakthroughs.