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

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.

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

> It's hard to think of any other major tech product where it's acceptable to shift so much blame on the user.

It's completely normal in development. How many years of programming experience you need for almost any language? How many days/weeks you need to use debuggers effectively? How long from the first contact with version control until you get git?

I think it's the opposite actually - it's common that new classes of tools in tech need experience to use well. Much less if you're moving to something different within the same class.

> LLMs, especially at the scale we see today

The OP qualifies how the marketing cycle for this product is beyond extreme, and its own category.

Normal people are being told to worry about AI ending the world, or all jobs disappearing.

Simply saying “the problem is the user”, without acknowledging the degree of hype, and expectation setting, the is irresponsible.

AI marketing isn't extreme - not on the LLM vendor side, at least; the hype is generated downstream of it, for various reasons. And it's not the marketing that's saying "you're using it wrong" - it's other users. So, unless you believe everyone reporting good experience with LLMs is a paid shill, there might actually be some merit to it.

It is extreme, and on the vendor side. The OpenAI non profit vs profit saga, was about profit seeking vs the future of humanity. People are talking about programming 3.0.

I can appreciate that it’s other users who are saying it’s wrong, but that doesn’t escape the point on ignoring the context.

Moreover, it’s unhelpful communication. Its gives up acknowledging a mutually shared context, the natural confusion that would arise from the ambiguous, high level hype, and the actual down to earth reality.

Even if you have found a way to make it work, having someone understand your workflow can’t happen without connecting the dots between their frame of reference and yours.