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
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Mark Zuckerberg would like to know your location
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.
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.
Gotta justify all that money that they have just spent without any trials, testing or end user input.
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!”
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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 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?
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.
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@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.
all the stock sites are. use case: an image that’s almost perfect but you wanna tweak it
LEARN PAINT YOU GHOULS
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
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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
“I’m not scared a LLM is going to be able to replace me. I’m scared that CEO are going to think that”
AI->cocaine filter: Cocaine isn’t going to replace you. Someone using cocaine is going to replace you.
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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.
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)
this user has been removed for commenting without reading the article
being from programming dot dev is just the turd on top
programming.dev: statistical sampling excellency (worst edition)
programmers learned what N means in statistics and immediately realized that “this N is too small” is a cool shortcut to sounding smart without reading the study, its goals, or its conclusions. and you can use it every time N is smaller than the human population on earth!
This N is too small: N
Skill issue - this N is even smaller:
spoiler
Bit late to the party, but this should prolly be tagged “Paper” on pivot.
I want to put together a little pitch for the data-brained that AI is Not Good Actually®, and this is the most smoking gun I can think of.
ty!
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
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).
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this is not debate club, per the sidebar
apparently it isn’t, as per the deleted posts for no reason whatsoever…
holy fuck please learn when to shut the fuck up
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I dabble in conversational AI for work
yeah this may be the wrong sub for you
