AI coders think they’re 20% faster — but they’re actually 19% slower
AI coders think they’re 20% faster — but they’re actually 19% slower
pivot-to-ai.com
AI coders think they’re 20% faster — but they’re actually 19% slower
108 comments
-
Devs are famously bad at estimating how long a software project will take.
No, highly complex creative work is inherently extremely difficult to estimate.
Anyway, not shocked at all by the results. This is a great start that begs for larger and more rigorous studies.
-
You're absolutely correct that the angle approach that statement is bullshit. There is also that they want to think making software is not highly complex creative work but somehow is just working an assembly line and the software devs are gatekeepers that don't deserve respect.
-
"Devs are famously bad at estimating how long a software project will take."
No, highly complex creative work is inherently extremely difficult to estimate.
Akshually... I'm on a dev team where about 60% of us are diagnosed with ADHD. So, at least in our case, it's both.
-
-
Megacorp LLM death spiral:
- Megacorp managers at all levels introduce new LLM usage policies.
- Productivity goes down (see study linked in post)
- Managers make the excuse that this is due to a transitional period in LLM policies.
- Policies become mandates. Beatings begin and/or intensify.
- Repeat from 1.
-
I've been through the hellscape where managers used missed metrics as evidence for why we didn't need increased headcount on an internal IT helpdesk.
That sort of fuckery is common when management gets the idea in their head that they can save money on people somehow without sacrificing output/quality.
I'm pretty certain they were trying to find an excuse to outsource us, as this was long before the LLM bubble we're in now.
-
oh, absolutely. I mean you could sub out "LLM" with any bullshit that management can easily spring on their understaff. Agile, standups, return to office, the list goes on. Management can get fucked
-
I wish I could make more people both know about, and understand, Goodhart’s law
-
-
As someone that has had to double check peoples code before, especially those that don't comment appropriately, I'd rather just write it all again myself than try and decipher what the fuck they were even doing.
-
@dgerard I normally consider myself a 10x developer. With the 10x speedup of AI I now consider myself a 100x developer. I can replace an entire small business worth of developers with just myself and my LLM bot assistance. Just pay me $100 million up front no strings and I'll prove it to you! /s
-
I have the deal of a lifetime for you.
I represent a group of investors in possession of a truly unique NFT that has been recently valued at over $100M. We will invest this NFT in your 100x business - in return you transfer us the difference between the $100M investment and the excess value of the NFT. Standard rich people stuff, don’t worry about it.
Let me know when you’re ready to unlock your 100x potential and I’ll make our investment available via a suitable escrow service.
-
@Silic0nAlph4 Sold! LOL.
-
-
Mark Zuckerberg would like to know your location
-
Don't be silly. Mark Zuckerberg already knows our location.
-
-
Something something grindset mindset
-
-
ahahaha holy shit. I knew METR smelled a bit like AI doomsday cultists and took money from OpenPhil, but those "open source" projects and engineers? One of them was LessWrong.
Here's a LW site dev whining about the study, he was in it and i think he thinks it was unfair to AI
I think if people are citing in another 3 months time, they'll be making a mistake
dude $NEXT_VERSION will be so cool
so anyway, this study has gone mainstream! It was on CNBC! I urge you not to watch that unless you have a yearning need to know what the normies are hearing about this shit. In summary, they are hearing that AI coding isn't all that actually and may not do what the captains of industry want.
around 2:30 the two talking heads ran out of information and just started incorrecting each other on the fabulous AI future, like the worst work lunchroom debate ever but it's about AI becoming superhuman
the key takeaway for the non techie businessmen and investors who take CNBC seriously ever: the bubble starts not going so great
-
Yeah, METR was the group that made the infamous AI IS DOUBLING EVERY 4-7 MONTHS GRAPH where the measurement was 50% success at SWE tasks based on the time it took a human to complete it. Extremely arbitrary success rate, very suspicious imo. They are fanatics trying to pinpoint when the robo god recursive self improvement loop starts.
-
I think if people are citing in another 3 months time, they’ll be making a mistake
In 3 months they'll think they're 40% faster while being 38% slower. And sometime in 2026 they will be exactly 100% slower - the moment referred to as "technological singularity".
-
Here’s a LW site dev whining about the study, he was in it and i think he thinks it was unfair to AI
There a complete lack of introspection. It seems like the obvious conclusion to draw from a study showing people's subjective estimates of their productivity with LLMs were the exact opposite of right would inspire him to question his subjectively felt intuitions and experience but instead he doubles down and insists the study must be wrong and surely with the latest model and best use of it it would be a big improvement.
-
-
5% "coding"
95% cleanup
-
I have an LLM usage mandate in my performance review now. I can’t trust it to do anything important, so I’ll get it to do incredibly noddy things like deleting a clause (that I literally always have highlighted) or generate documentation that’s more long-winded than just reading the code and then go to the bathroom while it happens.
-
I just want to point out that every single heavily downvoted, idiotic pro-AI reply on this post is from a .ml user (with one programming.dev thrown in).
I wonder which way the causation flows.
-
Machine learning is essentially AI with a paper-thin disguise, so that makes sense
-
It's kind of the opposite, GenAI is downstream of machine learning which is how artificial neural networks rebranded after the previous AI winter ended.
Also after taking a look there I don't think lemmy.ml has anything in particular to do with machine learning, it looks more like a straight attempt at a /r/all clone.
-
-
-
Anyone who has had to unfuck someone else’s work knows it would have been faster to do the work correctly from scratch the first time.
-
Software and computers are a joke at this point.
Computers no longer solve real problems and are now just used to solve the problems that overly complex software running on monstrous cheap hardware create.
"Hey I'd like to run a simple electronics schematic program like we had in the DOS days, it ran in 640K and responded instantly!"
"OK sure first you'll need the latest Windows 11 with 64G of RAM and 2TB of storage, running on at least 24 cores, then you need to install a container for the Docker for the VM for the flatpak for the library for the framework because the programmer liked the blue icon, then make sure you are always connected to the internet for updates or it won't run, and somehow the program will still just look like a 16 bit VB app from 1995."
"Well that sounds complicated, where's the support webpage for installing the program in Windows 7?"
"Do you have the latest AI agents installed in your web browser?"
"It's asking me to click OK but I didn't install the 1GB mouse driver that sends my porn browsing habits to Amazon..."
"Just click OK on all the EULAs so you lose the right to the work you'll create with this software, then install a few more dependencies, languages, entire VMs written in byte code compiled to HTML to run on JAVA, then make sure you have a PON from your ISP otherwise how can you expect to have a few kilobytes of data be processed on your computer? This is all in the cloud, baby!"
-
-
@dgerard What fascinates me is why coders who use LLMs think they're more productive. Is the complexity of their prompt interaction misleading them as to how effective the outputs it results in are? Or something else?
-
Here's a random guess. They are thinking less, so time seems to go by quicker. Think about how long 2 hours of calculus homework seems vs 2 hours sitting on the beach.
-
This is such a wild example to me because sitting at beach is extremely boring and takes forever whereas doing calculus is at least engaging so time flies reasonably quick.
Like when I think what takes the longest in my life I don't think "those times when I'm actively solving problems", I think "those times I sit in a waiting room at the doctors with nothing to do" or "commuting, ditto".
-
-
What fascinates me is why coders who use LLMs think they’re more productive.
As @dgerard@awful.systems wrote, LLM usage has been compared to gambling addiction: https://pivot-to-ai.com/2025/06/05/generative-ai-runs-on-gambling-addiction-just-one-more-prompt-bro/
I wonder to what extent this might explain this phenomenon. Many gambling addicts aren't fully aware of their losses, either, I guess.
-
The reward mechanism in the brain is triggered when you bet. I think it also triggers a second time when you do win, but I'm not sure. So, yeah, sometimes the LLM spits out something good, and your brain rewards you already when you ask it. Hence, you probably do feel better, because you constantly get hits dopamine.
-
-
Most people want to do the least possible work with the least possible effort and AI is the vehicle for that. They say whatever words make AI sound good. There's no reason to take their words at face value.
-
-
From the blog post referenced:
We do not provide evidence that:
AI systems do not currently speed up many or most software developers
Seems the article should be titled “16 AI coders think they’re 20% faster — but they’re actually 19% slower” - though I guess making us think it was intended to be a statistically relevant finding was the point.
That all said, this was genuinely interesting and is in-line with my understanding of the human psychology that’s at play. It would be nice to see this at a wider scale, broken down across different methodologies / toolsets and models.
-
@dgerard@awful.systems who is your illustrator? These are consistently great.
-
these are stock images! Which are surprisingly cheap. By Valeriy Kachaev, who puts stuff up as Studiostoks on a pile of stock image sites. His pics are bizarre and keep being the perfect thing.
-
I'm not sure how much this observation can be generalized, but I've also wondered how much the people who overestimate the usefulness of AI image generators underestimate the chances of licensing decent artwork from real creatives with just a few clicks and at low cost. For example, if I'm looking for an illustration for a PowerPoint presentation, I'll usually find something suitable fairly quickly in Canva's library. That's why I don't understand why so many people believe they absolutely need AI-generated slop for this. Of course, however, Canva is participating in the AI hype now as well. I guess they have to keep their investors happy.
-
-
-
And generate shit code
-
For each time saved, you're having that one kink that will slow you down by a fuck ton, something that AI just can't get right, something that takes ai 5 hours to fix but would've taken you 10-20 to write from scratch
-
-
I dabble in conversational AI for work
yeah this may be the wrong sub for you
-
-
The point isn’t to increase employee productivity (or employee happiness, obviously).
The point is to replace most employees.
In order for that to happen, LLM-assisted entry-level developers merely need to be half as good as expert human unassisted developers, at scale, at a lower aggregate cost.
-
Are these entry-level developers that are merely half as good as expert human unassisted developers in the room with us right now?
-
LLM-assisted entry-level developers merely need to be half as good as expert human unassisted developers
- This isn't even close to existing.
- The theoretical cyborg-developer at that skill level would surely be introducing horrible security bugs or brittle features that don't stand up to change
- Sadly i think this is exactly what many CEOs are thinking is going to happen because they've been sold on openai and anthropic lies that it's just around the corner
-
That's a fool's errand. Only those highly skilled experienced devs are going to spot certain subtle issues or architectural issues that will bite everyone the moment the product grows larger.
-
as one of the people representing the “hero group” (for lack of a better term) your comment references: eh. I didn’t start out with all this knowledge and experience. it built up over time.
it’s more about the mode of thinking and how to engage with a problem, than it is about specific “highly skilled” stuff. the skill and experience help/contribute, they refine, they assist in filtering
the reason I make this comment is because I think it’s valuable that anyone who can do the job well gets to do the thing, and that it’s never good to gatekeep people out. let’s not unnecessarily contribute to imposter syndrome
-
That’s a fool’s errand
Of course it is.
They’re still gonna do it.
-
-
This is a very "nine women can make a baby in one month".
The idea that there can even be two half as good developers is a misunderstanding of how anything works. If it worked like that, the study would be a dud because people could just run two AIs for 160% productivity.
-
Entry-level devs ain't replacing anyone. One senior dev is going to be doing the work of a whole team
-
-
The N=16 keeps getting buried. Deliberate?
-
You're acting like this is a gotcha when it's actually probably the most rigorous study of AI tool productivity change to date.
-
Paragraph 2:
METR funded 16 experienced open-source developers with “moderate AI experience” to do what they do.
-
... and just a few paragraphs further down:
The number of people tested in the study was n=16. That’s a small number. But it’s a lot better than the usual AI coding promotion, where n=1 ’cos it’s just one guy saying “I’m so much faster now, trust me bro. No, I didn’t measure it.”
I wouldn't call that "burying information".
-
<vapid statement>
. Debate me bro? (jk)
-
-
-
You have to know what an AI can and can't do to effectively use AI.
Finding bugs is on of the worst things to "vibe code": LLM can't debug programs (at least as far as I know) and if the repository is bigger than the context window they can't even get a overview of the whole project. LLMs only can run the program and guess what the error is based on the error messages and user input. They can't even control most programs.
I'm not surprised by the results, but it's hardly a fair assessment of the usefulness of AI.
Also I would prefer to wait for the LLM and see if it can fix the bug than hunt for bugs myself - hell, I could solve other problems while waiting for the LLM to finish. If it's successful great, if not I can do it myself.
-
To be fair, you have to have a very high IQ to effectively use AI. The methodology is extremely subtle, and without a solid grasp of theoretical computer science, most of an LLM’s capabilities will go over a typical user’s head. There’s also the model’s nihilistic outlook, which is deftly woven into its training data - its internal architecture draws heavily from statistical mechanics, for instance. The true users understand this stuff; they have the intellectual capacity to truly appreciate the depths of these limitations, to realize that they’re not just bugs—they say something deep about an AI's operational boundaries. As a consequence, people who dislike using AI for coding truly ARE idiots- of course they wouldn’t appreciate, for instance, the nuance in an LLM’s inability to debug a program, which itself is a cryptic reference to the halting problem. I’m smirking right now just imagining one of those addlepated simpletons scratching their heads in confusion as the LLM fails to get an overview of a repository larger than its context window. What fools.. how I pity them. 😂 And yes, by the way, I DO have a favorite transformer architecture. And no, you cannot see it. It’s for the ladies’ eyes only- and even they have to demonstrate that they’re within 5 IQ points of my own (preferably lower) beforehand. Nothing personnel kid 😎
-
Babe wake up, new copypasta variant just dropped
-
Thank you for doubling down on irony at the end, you had me going!
-
-
"This study that I didn't read that has a real methodology for evaluating LLM usefulness instead of just trusting what AI bros say about LLM usefulness is wrong, they should just trust us, bros", that's you
-
Hey tech bro! how much money did you loose on NFTs? 😂
-
It may be hard to believe but I am not a 'tech bro'. Never traded crypto or NFTs. My workplace doesn't even allow me to use any LLMs. As a software developer that's a bit limiting but I don't mind.
But in my own time I have dabbled with AI and 'vibe coding' to see what the fuss is all about. Is it the co-programmer AI bros promise to the masses? No, or at least not currently. But useful non the less if you know what you do.
-
-
I’m not surprised by the results, but it’s hardly a fair assessment of the usefulness of AI.
It's a more than fair assessment of the claims of usefulness of AI which are more or less "fire all your devs this machine is better than them already"
-
And the other "nuanced" take, common on my linkedin feed, is that people who learn how to use (useless) AI are gonna replace everyone with their much increased productive output.
Even if AI becomes not so useless, the only people whose productivity will actually improve are the people who aren't using it now (because they correctly notice that its a waste of time).
-
-
108 comments