Rust Dominates AI Engineering: Top Labs' New Language Choice

The same pattern shows up inside major AI labs. Andrej Karpathy provided one of the clearest signals late last year. While documenting experiments with AI-assisted coding, he mentioned building a tokeniser in Rust instead of relying on existing libraries.

Tokenisers sit directly on the performance path of every model. Choosing Rust there is a quiet admission that Python's convenience ends where correctness and speed begin. Even Elon Musk has publicly agreed that Rust is the language for AGI.

At OpenAI, the shift is explicit rather than implied. The Codex command line interface was rewritten entirely in Rust and released publicly. OpenAI said the Rust version is faster, more stable and easier to reason about as it scales.

Engineers pointed directly to Rust's guarantees as a reason. For autonomous agents that read and write files, manage tools and run unattended, that property matters far more than syntactic comfort. A similar learning curve is visible at Anthropic.

Engineers there have discussed learning Rust while building tooling around Claude, often with AI systems help them write the code itself. Rust is no longer treated as too difficult for fast-moving teams. AI assistance has flattened the learning curve enough that teams can get Rust's benefits without years of accumulated muscle memory.

Popular Meta apps, including Instagram and Facebook Messenger, utilised a decade-old C codebase for their messaging library, it led to problems with memory management and a subpar developer experience. In July last year, the company finally switched to Rust. On an episode of Meta Tech Podcast, three of the company's engineers, Eliane W, Buping W and Pascal Hartig, shared their experience with using C, discussing the challenges faced, the migration process and their success with Rust.

"I think one of the biggest things about Rust is the compile-time memory safety enforcement.