What I Found Running Bun's Rust Rewrite in Production After 341 AI Assistance Hours

Mohit Gulati

27 Aug 2026 • 5 min read

Three months ago, I started running Bun's Rust rewrite in production while tracking every AI interaction that helped me through the migration. 341 hours later, I have data that contradicts what most people think about AI-assisted development—and about Bun itself. The real story isn't the performance gains everyone's discussing. It's how AI helped me understand that my original bottlenecks had almost nothing to do with Zig versus Rust.

The Setup: Why I Tracked Everything

My production environment processes roughly 2 million API requests daily across a distributed system that handles real-time data ingestion. When Bun's Rust rewrite landed, I was already hitting scaling issues that felt language-adjacent—slow builds, memory spikes during peak traffic, and deployment pipeline headaches that made rapid iteration painful.

I decided to track AI assistance not from some productivity optimization mindset, but because I was genuinely curious about where the help would actually prove useful. I logged every conversation: debugging sessions, architecture discussions, code reviews, even the times AI led me down unproductive paths. I wanted real data on where 341 hours of AI pair programming would take me.

My initial hypothesis was straightforward. The Rust rewrite would deliver measurable performance improvements, AI would help me navigate syntax differences and catch migration bugs, and after three months I'd have cleaner benchmarks and a more maintainable codebase.

Month One: The Performance Story Everyone Expected

The early numbers looked promising. Build times dropped from 47 seconds to 12 seconds. Memory usage during peak loads decreased by roughly 20%. Response latency improved, particularly for the heaviest computational endpoints—though the gains were more incremental than transformative.

Where the Rust rewrite truly delivered was in deployment stability. The original Zig implementation had this frustrating habit of occasional memory leaks during traffic spikes that I could never fully pin down. Those disappeared almost immediately with the Rust version.

But by week three, I noticed something unexpected. The performance improvements had plateaued, yet my AI usage was increasing. I was spending more time in architectural discussions with Claude and GPT-4 than in syntax translation mode. The conversations were getting deeper, more probing. AI kept asking me why I had structured certain components the way I had.

The breakthrough moment came during a debugging session in week four. I was trying to understand why one particular service was still showing latency spikes despite the Rust migration. I described the issue to Claude, expecting help with profiling or optimization techniques.

Instead, it started questioning my data flow architecture. Why was I processing certain operations synchronously when they could be batched? Why had I designed the caching layer to invalidate so aggressively? Why was I making three separate database calls when the data relationships suggested I could restructure around a single query?

These weren't Rust-specific questions. They weren't even Zig-specific questions. AI was surfacing assumptions I'd made months earlier when building the original system—design decisions that had calcified into "that's just how it works" without me ever revisiting them.

341 Hours of AI Conversations: What Actually Happened

Breaking down those 341 hours reveals patterns I didn't expect:

Architecture discussions: 127 hours — Far more than I anticipated
Debugging and profiling: 89 hours — Expected this to be higher
Code review and refactoring: 73 hours — Valuable but diminishing returns
Syntax and migration help: 52 hours — Much lower than expected

The most valuable AI assistance came when I treated it as an architectural sounding board rather than a coding assistant. The conversations that led to genuine insights were the ones where I explained my system design and AI pushed back on assumptions I hadn't examined.

But I also hit diminishing returns around hour 200. AI started repeating suggestions I'd already explored, and I found myself seeking validation for decisions I was perfectly capable of making independently. The tool was most useful when I brought specific, well-formed questions rather than general uncertainty.

The Zig vs Rust Revelation

Here's what surprised me: the deeper I went with AI's help, the more I realized that Bun's original Zig implementation wasn't hitting walls because of language limitations. The bottlenecks I'd attributed to Zig were actually architectural choices that would have created problems in any language.

My database connection pooling strategy was naive. My error handling patterns were creating unnecessary overhead. I was processing data in ways that fought against the natural grain of the system rather than working with it.

The Rust rewrite forced me to reconsider these patterns because the migration itself required thinking through every component relationship. But it wasn't Rust's superior performance or safety guarantees that solved my problems—it was the enforced architectural review that came with any major rewrite.

This shifted my entire mental model. The question isn't whether to rewrite in Rust or refactor in Zig. The question is whether you understand why your current architecture creates the problems you're experiencing.

What Changed About My Development Process

341 hours of AI pair programming changed how I approach system design problems. I now start architectural discussions by describing my current approach out loud (or in writing to an AI), not to get solutions but to hear my own assumptions reflected back.

When I'm considering any major system change, I've learned to ask: Am I solving a language problem, a tooling problem, or an architecture problem? Most of the time, it's architecture masquerading as something else.

AI also helped me recognize patterns in my own thinking. I tend to optimize prematurely for problems I might face rather than problems I actually have. I gravitate toward complex solutions when simple ones would work better. Having these tendencies surfaced by an external observer—even an artificial one—proved more valuable than I expected.

But I've also learned where AI assistance becomes counterproductive. For fundamental architectural decisions, I need to think independently first, then use AI to stress-test my reasoning. The reverse order leads to dependency on AI's architectural preferences rather than developing my own.

Three Months Later: The Real Numbers

Production metrics after three months show solid improvements: roughly 25% better average response times, 30% reduction in memory usage, and zero of the mysterious memory leaks that plagued the Zig version. Build and deployment processes are notably smoother.

But development velocity tells a more complex story. While individual coding tasks became faster with AI assistance, architectural decision-making became slower—not because AI slowed me down, but because it encouraged more thorough consideration of design trade-offs.

The hidden cost I didn't anticipate: cognitive dependency. After 200+ hours of AI assistance, I found myself reaching for AI input on decisions I should have been making independently. I had to consciously rebuild confidence in my own architectural judgment.

What I Still Don't Understand

I'm still uncertain about the long-term maintainability implications of AI-assisted code. The Rust rewrite is cleaner and more performant, but I can't yet tell if that's because Rust encouraged better practices, because the rewrite forced architectural improvements, or because AI guidance led to more thoughtful implementation.

I also wonder whether the architectural insights I gained are transferable to other systems or specific to this particular migration context. The problems AI helped me identify might have been obvious to a human colleague with fresh eyes on the codebase.

Most fundamentally, I'm still working out the right balance between AI assistance and independent thinking. The tool is powerful for surfacing blind spots and stress-testing ideas, but the real value seems to come from using it to enhance rather than replace architectural judgment.