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6/10/2026 · Alec Dibble

AI has changed the way software gets written.

A lot of teams are now using AI to generate first drafts of features, explore unfamiliar frameworks, scaffold systems, write tests, refactor code, and move faster through the boring parts of implementation. Some people call this “vibe coding.” The phrase is a little unserious, but the underlying shift is real: developers are increasingly steering, reviewing, correcting, and integrating code instead of typing every line by hand.

That creates a new question for engineering teams:

What languages and platforms work best when AI is part of the development process?

The answer is not just about which language an AI model can write most easily. The better question is which language helps generated code stay understandable after it lands. Agentic development works best when the system itself encourages clear boundaries, explicit contracts, narrow interfaces, and code that can be reviewed without reconstructing a pile of hidden assumptions.

For us, Rust has been one of the strongest answers so far.

Promising Results

At Dioko, we have been using Rust across native app development, cross-platform app development, and backend development. We did not come into this as long-time Rust experts. We had strong engineering fundamentals, but not a deep production Rust background. We have been learning it by building with it, reading, testing, refactoring, and using AI as a development accelerator.

So far, the results have been extremely encouraging.

Rust has given us a rare combination: high performance, strong correctness guarantees, great portability, a modern package ecosystem, and a type system that makes AI-generated code much easier to validate, constrain, and improve.

More importantly, Rust pushes both humans and AI agents toward stronger architectural boundaries. That may be the single most important thing for keeping agentic code manageable, readable, and clean.

Rust gives AI a better surface area to work with

One of the biggest problems with AI-generated code is not whether it can produce something plausible. It usually can.

The harder problem is knowing whether the generated code is actually right.

In dynamic languages, a lot of mistakes hide until runtime. Wrong shapes, missing fields, bad assumptions, nil errors, invalid states, forgotten edge cases, and weak boundaries can all survive the first generation pass. You may not find them until a test hits the exact case, or worse, until production does.

Rust changes that dynamic.

Rust’s type system, ownership model, exhaustive pattern matching, and compiler checks create a much tighter feedback loop. AI can generate code, but the compiler immediately acts as a rigorous reviewer. It does not care that the code looks reasonable. It cares whether lifetimes, ownership, borrowing, types, traits, match arms, and error handling are actually coherent.

That matters enormously when working with AI.

The compiler becomes part of the development workflow in a stronger way. You can ask AI to write or refactor something, run the compiler, feed the errors back into the loop, and converge toward code that is not merely syntactically valid, but structurally aligned with the program.

This does not replace human understanding. It raises the quality floor.

Rust makes boundaries harder to ignore

The most important thing Rust gives an AI-assisted workflow may not be speed, memory safety, or even compiler feedback in isolation.

It may be boundaries.

AI-generated code has a tendency to sprawl when the surrounding system lets it. Responsibilities blur. Data gets passed around too loosely. Optional states leak into places that should not know about them. Shared mutable state appears because it was the easiest path to a plausible answer. Interfaces grow wider than they need to be.

Rust fights that drift at the language level.

Ownership forces you to decide where data lives. Borrowing forces you to decide how access works. Traits force you to name capabilities. Enums force you to model states explicitly. Modules, visibility rules, and type boundaries make it harder to smear responsibility across the codebase without being noticed.

That does not mean Rust automatically produces good architecture. It means the path of least resistance is often closer to good architecture than it would be in a looser environment. The developer still has to make design decisions, but Rust keeps pulling those decisions into the open.

That is extremely valuable when agents are writing or modifying code. The system gives the agent less room to quietly improvise around unclear ownership, unclear state, and unclear module...