AI agents write code now. What they can't do is decide what's correct. That gap is where a compiler becomes your best friend. Rust's compiler is famously strict, and that's exactly why it's a powerful tool for working with AI tools. It offers a fast feedback loop, catching errors early and enforcing good practices. This gives you more confidence in the code AI produces.

I sat down with Jim Hodapp, lead coach in our Rust cohort, to talk about why Python developers should care about Rust right now, especially as AI coding agents become more widely used. Jim comes from 25 years of C and C++, discovered Rust in 2020, and has been teaching developers Rust since 2022.

Why Rust's Compiler Changes the AI Equation

AI coding agents are non-deterministic. That's important to keep in mind at all times. They'll produce code that compiles, passes tests, and still misses the idiomatic nuance of a language. Jim describes it clearly: TypeScript adds some strictness over JavaScript, but it can still meander. The same happens with Python. However, Rust doesn't give the agent that same amount of wiggle room.

The compiler is your harness (yes, similar to a test harness). When an agent writes Rust, a whole class of errors that would normally surface at runtime get caught at compile time. For example, exhaustive match expressions force every enum variant to be handled. Mutability is opt-in, not the default. The borrow checker prevents data from living longer than it should. These are not style preferences, they're language-level requirements and the agent can't skip them. It's not optional.

Jim points out that this means faster, more correct iteration loops when working with AI. You write the prompt, the agent generates code, the compiler rejects what's wrong. There's less guessing and ambiguity waiting for a test failure three layers deep. The feedback is immediate and structural.

Python is different. With Python, the discipline of writing safe, typed, well-structured code is on the developer. Good tooling helps, but when an AI agent is writing the Python, you're relying on the agent and your config to apply that discipline. You can bridge that gap, but there are fewer guarantees.

What Python Developers Actually Gain From Rust

The performance angle is real. In our cohort program, Python developers build a JSON parser in Rust over six weeks. In our last cohort, one student's Rust implementation ran 1.5 to 2.5x faster than Python's C-based JSON module (details). We were pleasantly surprised to see this student reach the type of performance numbers we thought would take months of additional experience after the program.

But performance isn't the main reason I recommend Rust to Python developers. The bigger gain is what happens to your thinking.

Python abstracts memory management away. The garbage collector handles it. That's useful and it's one of the reasons Python is so productive for fast prototyping. But it also means most Python developers have never had to think carefully about where their data lives, when things go out of scope, or what it means to move versus copy a value. Rust requires you to do so, otherwise the compiler won't accept your code and you can't run the latest version of your application.

As I wrote in Learning Rust Made Me a Better Python Developer, this changes how you write Python. You start asking questions about your Python code that you weren't asking before. When should this be immutable? Am I mutating data somewhere I shouldn't be? What's actually happening when I do x = y, do I copy or reference y? You start to see the hidden costs of certain patterns, and you can make more informed decisions about when to use them.

Jim frames this well: it's the same shift as going from a loosely typed language to a strictly typed one, except the whole language is enforcing it at every layer. Coding conventions and team standards only work as well as the humans enforcing them. The compiler never fails to hold you accountable.

The Tooling Argument

uv, Astral's Python packaging tool, was designed with Cargo as a model. If you've used uv and liked how it works, you've already been influenced by Rust's tooling philosophy. Cargo does dependency management, testing, formatting, and linting through one interface with zero configuration.

The cohort project reflects this: students build a JSON parser, add Python bindings with PyO3, and then benchmark it against other json libraries. By the end of six weeks they have working code, real performance data, and a project they understand end to end. The tools take up only a tiny bit of the curriculum, because they are intuitive and ergonomic, letting us focus on the code and the concepts.

The Bigger Picture

Towards the end, Jim and I discuss the divide that's opening up in the software industry right now: people who use AI to move fast (builders), and people who deeply care about what the code actually is (craftsmanship). Rust doesn't resolve that divide per se, but it does help enforce stricter boundaries and guardrails; you just can't vibe code your way past the borrow checker.

If you're building with AI agents and you care about correctness, adopting Rust is appealing. Not because it will replace Python, but because understanding what a strict compiler enforces makes you a better evaluator of what agents produce. Stronger fundamentals in programming and engineering matter even more now, and Rust is one of the best ways to build them. Even with AI, your ability to reason both at a high level and a lower level still matter.