181–190 of 501 posts
AI by design can only repeat and recombine past material. Therefore actual invention is out.
Is that actually proven?
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This is really disingenuous when you also say that OpenAI and Anthropic have provided you with access and compute credits (on https://metr.org/about ). Not all payment is cash. Compute credits is still by all means compensation.
Is it "really" disingenuous, or is it just a misinterpretation of what it means to be "compensated for"? Seems more like quibbling to me.
Wow 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…
The "economic experts" and "ml experts" are in many cases effectively the same group-- companies pushing AI coding tools have a vested interest in people believing they're more useful than they are. Executives take this at face value and broadly promise major wins. Economic experts take this at face value and use this for their forecasts.
This propagates further, and now novices and casual individuals begin to believe in the hype. Eventually, as an experienced engineer it moves the "baseline" expectation much higher.
Unfortunately this is very difficult to capture empirically.
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> 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?
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> 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?
Even then though, “technology gets better over time” shouldn’t be surprising, as it’s pretty common.
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This is really disingenuous when you also say that OpenAI and Anthropic have provided you with access and compute credits (on https://metr.org/about ). Not all payment is cash. Compute credits is still by all means compensation.
Those are compute credits that are directly spent on the experiment itself. It's no more "compensation" than a chemistry researcher being "compensated" with test tubes.
You're extrapolating, it's not saying this anywhere.
> It's no more "compensation" than a chemistry researcher being "compensated" with test tubes.
Yes, that's compensation too. Thanks for contributing another example. Here's another one: it's no more compensation than a software engineer being compensated with a new computer.
Actually the situation here is way worse than your example. Unless the chemistry researcher is commissioned by Big Test Tube Corp. to conduct research on the outcome of using their test tubes, there's no conflict of interest here. But there is an obvious conflict of interest on AI research being financed by credits given by AI companies to use their own AI tools.
As an open source maintainer on the brink of tech debt bankruptcy, I feel like AI is a savior, helping me keep up with rapid changes to dependencies, build systems, release methodology, and idioms.
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Figure 6 which breaks-down the time spent doing different tasks is very informative -- it suggest: 15% less active coding 5% less testing, 8% less research and reading 4% more idle time 20% more AI interaction time The 28% less coding/testing/research is why developers reported 20% less work. You might be spending 20% more time overall "working" while you are really idle 5% more time and feel like you've worked less…
i just realized the figure is showing the time breakdown as a percentage of total time, it would be more useful to show absolute time (hours) for those side-by-side comparisons since the implied hours would boost the AI bars height by 18%
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 Curso…
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well we used to have a sort of inverse pareto where 80% of the work took 80% of the effort and the remaining 20% of the work also took 80% of the effort. I do think you're onto something with getting pebbles out of the road inasmuch as once I know what I need to do AI coding makes the doing much faster. Just yesterday I was playing around with removing things from a List object using the Java streams API and I kept r…
Code reuse at scale: 80 + 80 = 160% ~ phi...coincidence? I think this may become a long horizon harvest for the rigorous OOP strategy, may Bill Joy be disproved. Gray goo may not [taste] like steel-cut oatmeal.