In this in-depth interview, celebrated Rust live coder and educator Jon Gjengset discusses the current state and future trajectory of the Rust programming language, touching upon its unique technical advantages, adoption challenges, industry proliferation, and the impact of AI on the future job market.
Gjengset attributes Rust's nine-year reign as the "most admired language" to its success in combining safety, performance, and ergonomics, making it highly desirable, especially in safety-critical sectors like defense, embedded systems, and high-performance cloud infrastructure (like AWS). He highlights that adoption remains slow due to high switching costs and corporate inertia.
A significant portion of the conversation focuses on the economic and ethical landscape of Rust development, including real-world salary comparisons (up to $400k TC in the US vs. lower figures in Europe) and Gjengset’s personal rejection of work in the speculative cryptocurrency/Web3 space. He expresses skepticism regarding current generative AI models, arguing they lack true reasoning capability, making them excellent tools for boilerplate replication but less effective for complex Rust code that requires deep understanding of concepts like the borrow checker. Ultimately, Gjengset believes the borrow checker is the mechanism that forces developers to become fundamentally better programmers, regardless of the language they use.
Rust 2025: Key Insights and Future Predictions
0:26 Rust's Core Value Proposition: Rust aims to deliver performance, safety, and ergonomics simultaneously, threading the needle between high-speed systems programming (like C/C++) and ergonomic languages (like Go or Java) that rely on garbage collection.
3:36 The Rust Paradox: Despite being the most admired language for nine consecutive years, adoption remains low (around 12–14% of developers) primarily due to high corporate switching costs and existing momentum in other languages.
6:16 AWS Adoption: Rust's growth at Amazon was grassroots, driven by teams seeking improved performance, specifically better tail latency, which is often difficult to achieve with garbage-collected languages like Java/Kotlin.
12:46 Key Industries: Rust is a clear winner in three major areas: embedded development (competing directly with C/C++), command-line tools (aided by easy-to-use crates like clap), and safety-critical applications (defense, automotive, space).
14:44 Pitching Rust: When pitching to Go teams, the primary argument is Rust’s ability to prevent runtime crashes. For C++ teams, the biggest draw is the concurrency safety enforced statically at compile time.
21:09 Salary Disparity: The US tech scene offers significantly higher compensation for senior Rust engineers (estimated total compensation up to $400,000 per year) compared to Europe (estimated senior pay around $150,000 in Norway).
22:41 Defense and Safety Criticality: Rust is perfect for defense tech (like Gjengset’s role at Helsing) because its expressive type system allows developers to encode and enforce critical safety invariants at compile time, reducing the risk of severe bugs.
27:32 Ethical Focus: Gjengset avoids job opportunities in the crypto, blockchain, and Web3 sectors because he feels the work lacks a positive societal impact, viewing the industry as primarily speculative "gambling."
31:16 AI Overhype: Gjengset is skeptical of current AI/LLMs replacing engineers, arguing they are excellent at pattern replication (writing boilerplate code) but lack the fundamental ability to truly reason or understand underlying system models.
39:40 AI Difficulty with Rust: It is inherently harder for generative AI to produce correct Rust code than, say, Python or Java, because the AI must successfully navigate and replicate the mental model required by the borrow checker and lifetimes.
41:05 Rust and AI Infrastructure: Python is favored for ML research and rapid iteration, but Rust is essential for deploying machine learning models into safe, high-performance production environments (e.g., in automotive or critical systems).
50:30 The Borrow Checker as a Teacher: Although the borrow checker is the most annoying feature, it serves a vital purpose: it forces developers to adopt an "internal borrow checker" mental model, which improves coding quality by providing a better sense of data flow, lifetimes, and allocation, even when writing in other languages like JavaScript or Go.
56:10 Best Compiler Feature: The Rust compiler’s high-quality, concise, and helpful error messages are its best feature, acting as an instructor that guides new programmers through the challenging learning curve.
59:04 Core Challenges: The biggest issues facing Rust developers remain slow compilation times, large resulting binaries/disk usage (linked to monomorphization), and the potential complexity/compile-time cost of heavily used procedural macros.
1:14:40 Community-Driven Future: Rust’s trajectory is on a healthy path, guided primarily by its dedicated community and individual team members. Big corporations contribute resources but do not exercise significant steering control over the language’s evolution.