- This seems pretty huge. Not sure by what metric it wouldn't be civilizationally gigantic for everyone to save that much time per day.
- your google-fu isnt failing. there's simply only a couple large studies on this, and of those, zero that have a useful methodology.
- I have seen this study cited enough to have a copy paste for it. And no, there are not a bunch of other studies that have any sort of conclusive evidence to support this claim either. I have looked and would welcome any with good analysis.
"""
1. The sample is extremely narrow (16 elite open-source maintainers doing ~2-hour issues on large repos they know intimately), so any measured slowdown applies only to that sliver of work, not “developers” or “software engineering” in general.
2. The treatment is really “Cursor + Claude, often in a different IDE than participants normally use, after light onboarding,” so the result could reflect tool/UX friction or unfamiliar workflows rather than an inherent slowdown from AI assistance itself.
3. The only primary outcome is self-reported time-to-completion; there is no direct measurement of code quality, scope of work, or long-term value, so a longer duration could just mean “more or better work done,” not lower productivity.
4. With 246 issues from 16 people and substantial modeling choices (e.g., regression adjustment using forecasted times, clustering decisions), the reported ~19% slowdown is statistically fragile and heavily model-dependent, making it weak evidence for a robust, general slowdown effect.
"""
Any developer (who was a developer before March 2023) that is actively using these tools and understands the nuances of how to search the vector space (prompt) is being sped up substantially.
- claim that i claimed you claimed: "for any coder to claim AI tools slow them down"
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claim you made: "One thing I’ve noticed though that actually coding (without the use of AI; maybe a bit of tab auto-complete) is that I’m actually way faster when working in my domain than I am when using AI tools."
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You did make that claim but I'm aware my approach would bring the defensiveness out of anyone :P
- I hope I am never this slow to adapt to new technologies.
- it is absolutely poor skill, or disengenuous at best, for any coder to claim AI tools slow them down lol.
- not exactly nothing to do with it, they still use generative AI to assist search
and saying 'it is no more'... sigh. such a weird take. the world's coming for you
- This is just wrong though. They absolutely learn in-context in a single conversation within context limits. And they absolutely can explain their thinking; companies just block them from doing it.
- to fix having to approve commands over and over - use windows WSL. codex does not play nice with permissions/approvals on windows. WSL solves that completely
- Absolutely agreed. Thinking anything else is nothing but cope, and these comments are FULL of it. it would be laughable if they weren't so gate keepy and disengenuous about it.
- The amount that can be prototyped is astronomically higher with LLM's, which lowers the barrier to do these things and troubleshoot libraries/architectures. Naturally the amount of hobby projects has exploded multiple OOM's beyond what was done before, regardless of any gatekeeping you wish you could do :P
- Chatgpt has this baked in, as you can revert branches after editing, they just dont make it easy to traverse.
This chrome extension used to work to allow you to traverse the tree: https://chromewebstore.google.com/detail/chatgpt-conversatio...
I copied it a while ago and maintain my own version but it isnt on the store, just for personal use.
I assume they dont implement it because it is such a niche user that wants this and so isnt worth the UI distraction
- This sounds like a romanticization of creativity.
Fundamentally discovery could be described as looking for gaps in our observation and then attempting to fill in those gaps with more observation and analysis.
The age of low hanging fruit shower thought inventions draws to a close when every field requires 10-20+ years of study to approach a reasonable knowledge of it.
"Sparks" of creativity, as you say, are just based upon memories and experience. This isn't something special, its an emergent property of retaining knowledge and having thought. There is no reason to think AI is incapable of hypothesizing and then following up on those.
Every AI can be immediately imparted with all expert human knowledge across all fields. Their threshold for creativity is far beyond ours, once tamed.
- Coding and algorithmic advance does not require real world experimentation.
- It's a logical presumption. Researchers discover things. AGI is a researcher that can be scaled, research faster, and requires no downtime. Full stop if you dont find that obvious you should probably figure out where your bias is coming from. Coding and algorithmic advance does not require real world experimentation.
- Accelerating. Below is a list of the SOTA's over time (with some slight wiggle room between similar era models)
gpt4 | 3/2023
gpt4-turbo - 11/2023
opus3 | 3/2024
gpt4o | 5/2024
sonnet3.5 | 6/2024
o1-preview | 9/2024
o1 | 12/2024
o3-minihigh | 1/2025
gemini2pro | 2/2025
o3 | 4/2025
gemini2.5pro | 4/2025
opus4 | 5/2025
??? | 8/2025
This is also not to mention the miniaturization and democratization of intelligence that is the smaller models which has also been impressive.
Id say this shows that improvements are becoming more frequent.
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Each wave of models was a significant step above what came previously. One needs only to step back a generation to be reminded of the intelligence differential.
Some notable differences have been with o3mh and gemini2.5's ability to spit out 1-3k loc(lines of code) with accurate alterations (most of the time). Though better prompting should be used to not do this in general, the ability is impressive.
Context length with gemini 2.5 pro's intelligence is incredible. To load 20k+ loc of a project and recieve a targeted code change that implements a perfect update is ridiculous.
The amount of dropped imports and improper syntax has dramatically reduced.
I'd say this shows improvements are becoming more impressive.
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Also note the timespan.
We are only 25 months into the explosion kicked off by GPT-4.
We are only 12 months into the reasoning paradigm.
We have barely scratched the surface of agentic tooling and scaffolding.
There are countless architectural improvements and alternatives in development and research.
Infrastructure buildouts and compute scaling are also chugging along, allowing faster training, faster inference, faster testing, etc. etc.
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This all paints a picture of an acceleration in time and depth of capability
- The current trend of continual improvement of LLM coding ability to solve previously unseen problems, handle larger codebases, operate for longer periods of time, and improved agentic scaffolding.
The reward-verifier compatability of programming and RL.
Do you have a stronger precedent for that not being the case?
We are headed towards (or already in) corporate feudalism and I don't think anything can realistically be done about it. Not sure if this is nihilism or realism but the only real solution I see is on the individual level: make enough money that you don't have to really care about the downsides of the system (upper middle class).
So while I agree with you, I think I just disagree with the little bit you said about "cant expect anything to change without-" and would just say: cant expect anything to change except through the inertia of what already is in place.