What we heard about Rust's challenges | Rust Blog
Mar. 20, 2026 · Jack Huey<br>on behalf of Vision Doc group
Author's note
The original version of this article has been retracted. I used an LLM to write the first draft, though this had come after many hours of planning and going through the data and analyses to identify the points to be made, as well as me going through the post line by line, editing into my voice and verifying the wording and scope of the text was accurate. However, many people still felt like the LLM-speak bled through in ways that felt uncomfortable. Given this, I and other members of the Rust Project have decided to retract the post in its entirety.
I stand by the content of the post. As I said, the LLM did not decide the points to be made - those were done well in advance of even beginning to write the blog post. And, admittedly, I did need to make edits to dampen the scope of them (in large part because I couldn't find specific quotes to substantiate them, even though I often "felt" that they were true given what I know as a Rust Project member), but in general I (and the Vision Doc team) defined the content, not an LLM.
Many people thought that the blog post felt "empty", with no "real substance". While I see the point here, this is unfortunately just how the data played out and goal of this effort. The Vision Doc team conducted ~70 interviews (mostly 1:1), which were the basis for the conclusions in this blog post. This is a lot of data, it's hard to fully capture the essence of them in a single blog post. And yet, it is also not enough data to fully capture the nuance of differences across groups of different types. On top of this, it shouldn't be that unexpected the problems we heard about in these interviews are the same problems that we (and many others) mostly already knew existed. The insight these interviews give us is that they allow us to begin to capture for whom which issues are most prominent.
The insights we identify and the conclusions we make are supported by the data we have gathered. When making these posts, the Vision Doc team has tried to stay as neutral as possible, doing our best to not exert bias by making any claims that cannot be supported as stated by the data itself. With drastically more time, I would have loved to pull in data from the ~5500 survey responses we got, which ultimately could help us make stronger claims or conclusions, but unfortunately that is time that I haven't had. That shouldn't diminish the substance of the insights and conclusions we have been able to make though.
Wording matters though. And it's clear that to many people, the blog post as-written didn't meet the mark that they want. LLMs are a tool that many people use (including me, obviously) to varying degrees to help do things that they couldn't do before (either for lack of skill, lack of time, or lack of motivation). In this case, I used an LLM to compensate for the lack of time for me to dedicate to sifting through transcripts for the ~70 interviews we did, and the many analyses that followed, to find specific quotes and write an early draft. It certainly did not help that writing and editing of this post happened over the span of about 3 months - meaning that things that "worked" in early edits did not necessarily work in later edits.
This all being said, I think that we as a Vision Doc team owe it to the Rust Project and the community to share (at least to some extent) what we have learned here. So, I have taken the original challenges identified by the team (without the recommendations or conclusions) and will provide a brief personal commentary on them. I've chosen to exclude any specific quotes - rather, just focus on the "high level" ideas. So, as a disclaimer, this will mean that the statements here will be much more biased than what we typically want to publish as part of the Vision Doc work.
Across the ~70 interviews the Vision Doc team conducted, we heard a lot of complaints. Of course, we tried to keep these interviews pretty high-level, not focusing on any particular technical details. Rather, we wanted to get a general sense of what the difficulties were that people encountered, among the other topics discussed during these interviews. Here, we've identified a few common challenges to most people, and then a few challenges that are more domain-specific.
Challenges that are universal
We heard a number of things that basically everyone said was an issue for them, in some capacity. Doing things to address these issues could have a universal impact, but that is not to say that these issues necessarily block people from using Rust.
The universal challenges, you've definitely heard before. If you write Rust, you've probably encountered them. That's what makes them universal. However, the point is that we share the data that we gather, and the fact that we have learned that these challenges do affect everyone is data in itself: we have sampled different...