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688 points dheerajvs | 2 comments | | HN request time: 0.523s | source
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simonw ◴[] 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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1. onlyrealcuzzo ◴[] No.44523749[source]
How were "experienced engineers" defined?

I've found AI to be quite helpful in pointing me in the right direction when navigating an entirely new code-base.

When it's code I already know like the back of my hand, it's not super helpful, other than maybe doing a few automated tasks like refactoring, where there have already been some good tools for a while.

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2. smj-edison ◴[] No.44524529[source]
> To directly measure the real-world impact of AI tools on software development, we recruited 16 experienced developers from large open-source repositories (averaging 22k+ stars and 1M+ lines of code) that they’ve contributed to for multiple years.