Rust

Overview

Rust is a systems programming language designed for performance, memory safety, and reliability. In the newsletter context, it appears as the implementation target for ambitious, AI-assisted engineering efforts, including a rewrite of Bun and a custom terminal rendering library built for highly responsive UI performance. For AI Product Managers, Rust matters because it increasingly shows up in products where speed, low-level control, and operational stability are core differentiators.

Rust is especially relevant when AI products need infrastructure that feels instantaneous to users or must handle complex runtime behavior without sacrificing safety. While many AI PMs work primarily with model APIs and application-layer tooling, Rust often sits underneath the experience: powering fast developer tools, terminal interfaces, inference-adjacent systems, and performance-critical product surfaces. Understanding where Rust fits helps AI PMs make better tradeoffs around latency, reliability, and implementation strategy.

Key Developments

• 2026-04-28 — Cognition built a custom terminal rendering library in Rust for fast, snappy UI performance, including support for an original 1970s VT-100 terminal, and made it available to try at devin.ai/terminal.
• 2026-05-11 — Thariq highlighted Jarred Sumner’s AI-assisted rewrite of Bun in Rust, noting that it passed 99.8% of the existing test suite and framing it as evidence that teams may be underestimating how ambitious AI-supported software rewrites can be.

Relevance to AI PMs

• Evaluate performance-sensitive product bets: Rust is a strong signal when a team is optimizing for latency, throughput, and responsiveness. If your AI product includes terminals, agents, developer tooling, desktop experiences, or runtime infrastructure, Rust may be part of the stack that enables a noticeably better user experience.
• Assess feasibility of AI-assisted code migration: The Bun rewrite example suggests AI can support substantial reimplementation efforts in Rust while preserving compatibility against existing tests. AI PMs can use this pattern to evaluate modernization projects, legacy rewrites, or performance-driven platform upgrades.
• Translate technical choices into product outcomes: Rust is not just a language preference; it often maps to concrete product goals like fewer crashes, safer concurrency, better resource usage, and faster interfaces. PMs who understand that linkage can better prioritize roadmap items and communicate tradeoffs across engineering and leadership.

Related

• Cognition — Mentioned for building a custom terminal rendering library in Rust to improve UI speed and responsiveness.
• Bun — A JavaScript runtime whose AI-assisted rewrite in Rust was highlighted as a high-ambition engineering effort.
• Jarred Sumner — Credited with the AI-assisted Bun rewrite in Rust that achieved 99.8% test-suite compatibility.
• Thariq — Amplified the Bun rewrite example and argued it shows teams should be aiming higher with AI-assisted software development.