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Measuring the impact of AI on experienced open-source developer productivity

(metr.org)
690 points dheerajvs | 1 comments | 10 Jul 25 16:29 UTC | HN request time: 0.226s | source
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simonw ◴[10 Jul 25 17:36 UTC] No.44523442[source]▶
Here's the full paper, which has a lot of details missing from the summary linked above: https://metr.org/Early_2025_AI_Experienced_OS_Devs_Study.pdf

My personal theory is that getting a significant productivity boost from LLM assistance and AI tools has a much steeper learning curve than most people expect.

This study had 16 participants, with a mix of previous exposure to AI tools - 56% of them had never used Cursor before, and the study was mainly about Cursor.

They then had those 16 participants work on issues (about 15 each), where each issue was randomly assigned a "you can use AI" v.s. "you can't use AI" rule.

So each developer worked on a mix of AI-tasks and no-AI-tasks during the study.

A quarter of the participants saw increased performance, 3/4 saw reduced performance.

One of the top performers for AI was also someone with the most previous Cursor experience. The paper acknowledges that here:

> However, we see positive speedup for the one developer who has more than 50 hours of Cursor experience, so it's plausible that there is a high skill ceiling for using Cursor, such that developers with significant experience see positive speedup.

My intuition here is that this study mainly demonstrated that the learning curve on AI-assisted development is high enough that asking developers to bake it into their existing workflows reduces their performance while they climb that learing curve.

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ivanovm ◴[10 Jul 25 23:53 UTC] No.44526996[source]▶
I find the very popular response of "you're just not using it right" to be big copout for LLMs, especially at the scale we see today. It's hard to think of any other major tech product where it's acceptable to shift so much blame on the user. Typically if a user doesn't find value in the product, we agree that the product is poorly designed/implemented, not that the user is bad. But AI seems somehow exempt from this sentiment
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1. Lerc ◴[11 Jul 25 00:51 UTC] No.44527365[source]▶
>It's hard to think of any other major tech product where it's acceptable to shift so much blame on the user.

Is that perhaps because of the nature of the category of 'tech peoduct'. In other domains, this certainly isn't the case. Especially if the goal is to get the best result instead of the optimum output/effort balance.

Musical instruments are a clear case where the best results are down to the user. Most crafts are similar. There is the proverb "A bad craftsman blames his tools" that highlights that there are entire fields where the skill of the user is considered to be the most important thing.

When a product is aimed at as many people as the marketers can find, that focus on individual ability is lost and the product targets the lowest common denominator.

They are easier to use, but less capable at their peak. I think of the state of LLMs analogous to home computing at a stage of development somewhere around Altair to TRS-80 level. These are the first ones on the scene, people are exploring what they are good for, how they work, and sometimes putting them to effective use in new and interesting ways. It's not unreasonable to expect a degree of expertise at this stage.

The LLM equivalent of a Mac will come, plenty of people will attempt to make one before it's ready. There will be a few Apple Newtons along the way that will lead people to say the entire notion was foolhardy. Then someone will make it work. That's when you can expect to use something without expertise. We're not there yet.