Microsoft is exploring a bold long-term project to fundamentally transform the way it builds software: the company aims to replace its legacy C and C++ code with the Rust programming language across its largest codebases by the end of the decade. According to a recent job posting and commentary from a senior Microsoft engineer, this effort would pair artificial intelligence with sophisticated tooling to automate large-scale code translation potentially reshaping the future of Microsoft’s software stack, engineering practices, and overall product security.
This initiative is part of a broader industry shift toward memory-safe languages that reduce common software vulnerabilities. Rust’s design, which enforces strict memory ownership and safety rules at compile time, has attracted attention as a logical successor to older systems languages like C and C++ historically responsible for many security bugs that plague complex software.
Vision: From C/C++ to Rust by 2030
The linchpin of this proposal stems from a LinkedIn post by Galen Hunt, a distinguished engineer at Microsoft, who articulated a seemingly audacious objective: “My goal is to eliminate every line of C and C++ from Microsoft by 2030.” He described a methodology that blends AI and algorithmic tooling to automatically convert C and C++ code at scale.
Hunt’s post highlights a symbolic metric the team has adopted: “1 engineer, 1 month, 1 million lines of code.” This phrase acts as a “North Star” for the project’s tooling development, emphasizing automation over manual rewrites, and signaling that AI-assisted transformation must shoulder the bulk of the work.
Every Microsoft product from operating systems like Windows to cloud services, SDKs, and internal tooling contains vast swathes of C and C++ code written over decades. Retiring those languages entirely would be among the largest migrations in software history if fully realized, dwarfing most previous language transitions undertaken by tech companies.
Microsoft has reportedly begun building the infrastructure needed to automate this massive transition. The approach relies on creating a code-processing pipeline that models source code as a scalable graph, analyzes structural dependencies, and then applies AI agents guided by algorithms to modify code at scale.
This infrastructure combines graph-based program analysis with generative AI models trained on source code to interpret idiomatic patterns and generate Rust equivalents. By breaking large codebases into structured representations, the system could theoretically identify opportunities for safe translation and help guide AI in producing functionally equivalent, Rust-based implementations.
From a technical standpoint, this kind of automation tackles a classic problem in software engineering: semantic preservation during translation. Tools must understand not just syntax (how code looks) but semantics (what code means), which is markedly harder especially in systems programming languages where memory and performance characteristics matter deeply.
Why Rust: Safety, Performance, and Security
Rust’s growing appeal stems from its memory-safe paradigm, which eliminates whole classes of bugs common in C and C++ such as buffer overflows, use-after-free errors, and other undefined behavior. These bugs have historically been exploited by attackers to compromise systems, and Rust’s strict compile-time checks help prevent them from ever occurring in safe code.
Many governments and industry groups have actively encouraged the adoption of memory-safe languages like Rust to improve software security across critical infrastructure. That policy momentum aligns with Microsoft’s interest in minimizing security risks and reducing technical debt tied to decades of legacy systems.
Rust also supports high performance without garbage collection, making it suitable in theory for systems-level development that was traditionally the domain of C and C++. Given Microsoft’s massive footprint in OS kernels, device drivers, and low-level infrastructure, the promise of Rust lies in combining performance with safety.
If successful, this Rust migration could transform how engineers across the industry view legacy codebases and language evolution. Companies often delay moving away from entrenched languages because of the risk, cost, and sheer scale of rewriting systems that have been built over decades. Microsoft’s effort especially if aided by AI may chart a new path for how large organizations modernize foundational software.
From a workforce perspective, demand for Rust expertise could skyrocket as development teams seek engineers familiar with memory-safe programming, compiler internals, and systems architecture. At the same time, integrating AI into large-scale refactoring workflows could redefine what it means to be a developer in the future.
Microsoft’s initiative to map and translate its C and C++ legacy into Rust represents a visionary stride in software engineering, leveraging AI to tackle one of the most persistent challenges in modern computing. Whether this vision materializes fully by 2030 or evolves into a more nuanced and incremental strategy remains to be seen.
What’s clear is that the company’s focus on memory safety, automated tooling, and language evolution reflects a broader industry trend where security and maintainability are paramount, and where AI is becoming a central enabler of ambitious technological transformations.
