Only one developer in this study had more than 50h of Cursor experience, including time spent using Cursor during the study. That one developer saw a 25% speed improvement.
Everyone else was an absolute Cursor beginner with barely any Cursor experience. I don't find it surprising that using tools they're unfamiliar with slows software engineers down.
I don't think this study can be used to reach any sort of conclusion on use of AI and development speed.
Hey, thanks for digging into the details here! Copying a relevant comment (https://news.ycombinator.com/item?id=44523638) from the other thread on the paper, in case it's help on this point.
1. Some prior studies that find speedup do so with developers that have similar (or less!) experience with the tools they use. In other words, the "steep learning curve" theory doesn't differentially explain our results vs. other results.
2. Prior to the study, 90+% of developers had reasonable experience prompting LLMs. Before we found slowdown, this was the only concern that most external reviewers had about experience was about prompting -- as prompting was considered the primary skill. In general, the standard wisdom was/is Cursor is very easy to pick up if you're used to VSCode, which most developers used prior to the study.
3. Imagine all these developers had a TON of AI experience. One thing this might do is make them worse programmers when not using AI (relatable, at least for me), which in turn would raise the speedup we find (but not because AI was better, but just because with AI is much worse). In other words, we're sorta in between a rock and a hard place here -- it's just plain hard to figure out what the right baseline should be!
4. We shared information on developer prior experience with expert forecasters. Even with this information, forecasters were still dramatically over-optimistic about speedup.
5. As you say, it's totally possible that there is a long-tail of skills to using these tools -- things you only pick up and realize after hundreds of hours of usage. Our study doesn't really speak to this. I'd be excited for future literature to explore this more.
In general, these results being surprising makes it easy to read the paper, find one factor that resonates, and conclude "ah, this one factor probably just explains slowdown." My guess: there is no one factor -- there's a bunch of factors that contribute to this result -- at least 5 seem likely, and at least 9 we can't rule out (see the factors table on page 11).
I'll also note that one really important takeaway -- that developer self-reports after using AI are overoptimistic to the point of being on the wrong side of speedup/slowdown -- isn't a function of which tool they use. The need for robust, on-the-ground measurements to accurately judge productivity gains is a key takeaway here for me!
(You can see a lot more detail in section C.2.7 of the paper ("Below-average use of AI tools") -- where we explore the points here in more detail.)
1. That does not support these results in any way 2. Having experience prompting is quite a little part of being able to use agentic IDE tools. It's like relating cutting onion to being a good cook
I think we should all focus on how the effectivity is going to change in the long-term. We all know AI tooling is not going to disappear but to become better and better. I wouldn't be afraid to lose some productivity for months if I would acquire new skills for the future.
An interesting little detail. Any seasoned developer is likely going to take substantially longer if they have to use any IDE except their everyday one.
I've been using Vim/Neovim for over a decade. I'm sure if I wanted to use something like Cursor, it would take me at least a month before I can productive even a fraction of my usual.
I recently switched from vim (16 years) to vscode and perceived my productivity to be about the same after one week.
No objective measurements here; it might have even increased. But either way, "a month to regain a fraction of productivity" is extreme hyperbole, for me at least.
This is exactly my same take. Any tool an engineer is inexperienced with will slow them down. AI is no different.
This runs counter to the starry eyed promises of AI letting people with no experience accomplish things
That promise is true, though, and the two claims are not opposite. The devil is in details, specifically in what you mean by "people" and "accomplish things".
If by "people" you mean "general public", and by "accomplish things" you mean solving some immediate problems, that may or may not involve authoring a script or even a small app - then yes, this is already happening, and is a big reason behind the AI hype as it is.
If by "people" you mean "experienced software engineers", and by "accomplish things" you mean meaningful contributions to a large software product, measured by high internal code and process quality standards, then no - AI tools may not help with that directly, though chances are greater when you have enough experience with those tools to reliably give them right context and steer away from failure modes.
Still, solving one-off problems != incremental improvements to a large system.
> If by "people" you mean "experienced software engineers",
My post is a single sentence and I literally wrote "people with no experience"
He addressed your point in the paragraph before that. The paragraph from which you quoted was meant to show the difference between your point and the fact that the original research was indeed measuring software engineers.
My point is that I was very clear about what people I was referring to.
No need for all the "if by people you mean" rigamarole
Then your previous point is false, because "X helps Y" doesn't run counter to any promise that "X helps Z".
You said the second. You responded to the first.
Y = [experts]
Z = [noobs]
{Y, Z} ⊆ [all humans]
AI let's people with no experience accomplish things. People who have experience can create those things without AI. Those experienced folks will likely outperform novices, even when novices leverage AI.
None of these statements are controversial. What we have to establish is- Does the experienced AI builder outperform the experienced manual coder?