AI slows down open source developers. Peter Naur can teach us why.
AI slows down open source developers. Peter Naur can teach us why.
Metr recently published a paper about the impact AI tools have on open-source developer productivity1. They show that when open source developers working in codebases that they are deeply familiar with use AI tools to complete a task, then they take longer to complete that task compared to other tasks where they are barred from using AI tools. Interestingly the developers predict that AI will make them faster, and continue to believe that it did make them faster, even after completing the task slower than they otherwise would!
When developers are allowed to use AI tools, they take 19% longer to complete issues—a significant slowdown that goes against developer beliefs and expert forecasts. This gap between perception and reality is striking: developers expected AI to speed them up by 24%, and even after experiencing the slowdown, they still believed AI had sped them up by 20%. Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity
We can't generalise these results to all software developers. The developers in this study are a very particular sort of developer, working on very particular projects. They are experienced open source developers, working on their own projects. This study tells us that the current suite of AI tools appear to slow such developers down - but it doesn't mean that we can assume the same applies to other developers. For example, we might expect that for corporate drones working on a next.js apps that were mostly built by other people who've long since left the company (me) see huge productivity improvements!
One thing we can also do, is theorise about why these particular open source developers were slowed down by tools that promise to speed them up.
I'm going to focus in particular on why they were slowed down, not the gap between perceived and real performance. The inability of developers to tell if a tool sped them up or slowed them down is fascinating in itself, probably applies to many other forms of human endeavour, and explains things as varied as why so many people think that AI has made them 10 times more productive, why I continue to use Vim, why people drive in London etc. I just don't have any particular thoughts about why this gap arises beyond. I do have an opinion about why they are slowed down.
So why are they slowed down
A while ago I wrote, somewhat tangentially, about an old paper by Peter Naur called programming as theory building. That paper states
programming properly should be regarded as an activity by which the programmers form or achieve a certain kind of insight, a theory, of the matters at hand
... continue reading