Earlier quoted context omitted.
Yep, sorry, meant to post this somewhere but forgot in final-paper-polishing-sprint yesterday! We'll be releasing anonymized data and some basic analysis code to replicate core results within the next few weeks (probably next, depending). Our GitHub is here ( http://github.com/METR/ ) -- or you can follow us ( https://x.com/metr_evals ) and we'll probably tweet about it.
Cool, thanks a lot. Btw, I have a very tiny tiny (50 to 100 audience ) podcast where we try to give context to what we call the "muck" of AI discourse (trying to ground claims into both what we would call objectively observable facts/évidence, and then _separately_ giving out own biased takes), if you would be interested to come on it and chat => contact email in my profile.
Measuring the impact of AI on experienced open-source developer productivity
291–300 of 501 posts
Re: Measuring the impact of AI on experienced open-source developer productivity
#292Earlier quoted context omitted.
The issue is that it is slow and verbose, at least in its default configuration. The amount of reading is non trivial. There’s a reason most references are dense.
Those issues you can partly solve by changing the prompt to tell it to be concise and don't explain its code. But nothing will make them stick to the one API version I use.
Re: Measuring the impact of AI on experienced open-source developer productivity
#293Earlier quoted context omitted.
Sorry, that’s not my take. I didn’t think these tools were useful until the latest set of models, that is, they crossed the threshold of usefulness to me. Even then though, “technology gets better over time” shouldn’t be surprising, as it’s pretty common.
Do you really see a massive jump? For context, I've been using AI, a mix of OpenAi + Claude, mainly for bashing out quick React stuff. For over a year now. Anything else it's generally rubbish and slower than working without. Though I still use it to rubber duck, so I'm still seeing the level of quality for backend. I'd say they're only marginally better today than they were even 2 years ago. Every time a new model c…
The jump has been massive.
Re: Measuring the impact of AI on experienced open-source developer productivity
#294Earlier quoted context omitted.
> 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 is what I heard about strong type systems (especially Haskell's) about 20-15 years ago. "History does not repeat, but it rhymes." If we rhyme "strong types will change the world" with "agentic LLMs will change the world," what do we get? My personal the…
I don't think that's a fair comparison. Type systems don't produce probabilistic output. Their entire purpose is to reduce the scope of possible errors you can write. They kind of did change the world, didn't they? I mean, not everyone is writing Haskell but Rust exists and it's doing pretty well. There was also not really a case to be made where type systems made software in general _worse_. But you could definitely…
Re: Measuring the impact of AI on experienced open-source developer productivity
#295Earlier quoted context omitted.
That's not the issue. Their complaint is that proponents keep revising what ought to be fixed goalposts... Well, fixed unless you believe unassisted human developers are also getting dramatically better at their jobs every year. Like the boy who cried wolf, it'll eventually be true with enough time... But we should stop giving them the benefit of the doubt. _____ Jan 2025: "Ignore last month's models, they aren't goo…
Fair enough. For what it's worth, I've always thought that the more reasonable claim is that AI tools make poor-average developers more productive, not necessarily expert developers.
Re: Measuring the impact of AI on experienced open-source developer productivity
#296Wow these are extremely interesting results, specially this part: > 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%. I wonder what could explain such large difference between estimation/experience vs reality, any ideas? Maybe our brains are measuring mental effort and distortin…
I also wonder how many of the numerous AI proponents in HN comments are subject to the same effect. Unless they are truly measuring their own performance, is AI really making them more productive?
Re: Measuring the impact of AI on experienced open-source developer productivity
#297Earlier quoted context omitted.
> Current LLMs One thing that happened here is that they aren't using current LLMs: > Most issues were completed in February and March 2025, before models like Claude 4 Opus or Gemini 2.5 Pro were released. That doesn't mean this study is bad! In fact, I'd be very curious to see it done again, but with newer models, to see if that has an impact.
> One thing that happened here is that they aren't using current LLMs I've been hearing this for 2 years now the previous model retroactively becomes total dogshit the moment a new one is released convenient, isn't it?
It's been a very noticeable uptick in power, and although there have been some nice increases with past model releases, this has been both the largest and the one that has unlocked the most real value since I've been following the tech.
Re: Measuring the impact of AI on experienced open-source developer productivity
#298Earlier quoted context omitted.
Here's a scary thought, which I'm admittedly basing on absolutely nothing scientific: What if agentic coding sessions are triggering a similar dopamine feedback loop as social media apps? Obviously not to the same degree as social media apps, I mean coding for work is still "work"... but there's maybe some similarity in getting iterative solutions from the agent, triggering something in your brain each time, yes? If…
> The LLMentalist Effect: how chat-based Large Language Models replicate the mechanisms of a psychic’s con https://softwarecrisis.dev/letters/llmentalist/ Plus there's a gambling mechanic: Push the button, sometimes get things for free.
Re: Measuring the impact of AI on experienced open-source developer productivity
#299Earlier quoted context omitted.
> One thing that happened here is that they aren't using current LLMs I've been hearing this for 2 years now the previous model retroactively becomes total dogshit the moment a new one is released convenient, isn't it?
I've been a proponent for a long time, so I certainly fit this at least partially. However, the combination of Claude Code and the Claude 4 models has pushed the response to my demos of AI coding at my org from "hey, that's kind of cool" to "Wow, can you get me an API key please?" It's been a very noticeable uptick in power, and although there have been some nice increases with past model releases, this has been both…
Re: Measuring the impact of AI on experienced open-source developer productivity
#300Wow these are extremely interesting results, specially this part: > 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%. I wonder what could explain such large difference between estimation/experience vs reality, any ideas? Maybe our brains are measuring mental effort and distortin…
Here's a scary thought, which I'm admittedly basing on absolutely nothing scientific: What if agentic coding sessions are triggering a similar dopamine feedback loop as social media apps? Obviously not to the same degree as social media apps, I mean coding for work is still "work"... but there's maybe some similarity in getting iterative solutions from the agent, triggering something in your brain each time, yes? If…
I wish there was a simple way to measure energy spent instead of time. Maybe nature is just optimizing for something else.