A new survey conducted by the U.S. Census Bureau and reported on by Apolloseems to show that large companies may be tapping the brakes on AI. Large companies (defined as having more than 250 employees) have reduced their AI usage, according to the data (click to expand the Tweet below). The slowdown started in June, when it was at roughly 13.5%, slipping to about 12% at the end of August. Most other lines, representing companies with fewer employees, are also at a decline, with some still increasing.
Because they are FUCKING TRASH.
Not for all use cases, but for most it is.
It’ll right itself when the CEOs stop investing in it and force it on their own companies.
When they’re not getting their returns, they’ll sell their stocks and stop paying for it.
It’ll eventually go back from slop generation to correction and light editing tools when venture stops paying for the hardware to run tokens and they have to pay to replace the cards. .
and they will drop it altogether.
That’s unfortunate because I want an excuse not to be a corporate slave
Tbh, better a corporate slave than a startup slave.
Personal Anecdote
Last week I used the AI coding assistant within JetBrains DataGrip to build a fairly complex PostgreSQL function.
It put together a very well organized, easily readable function, complete with explanatory comments, that failed to execute because it was absolutely littered with errors.
I don’t think it saved me any time but it did help remove my brain block by reorganizing my logic and forcing me to think through it from a different perspective. Then again, I could have accomplished the same thing by knocking off work for the day and going to the driving range.
Then again, I could have accomplished the same thing by knocking off work for the day and going to the driving range.
Hey, look at the bright side, as long as you were chained to your desk instead, that’s all that matters.
At one point I tried to use a local model to generate something for me. It was full of errors, but after some searching online to look for a library or existing examples I found a github repo that was almost an exact copy of what it generated. The comments were the same, and the code was mostly the same, except this version wasn’t fucked up.
It turns out text prediction isn’t that great at understanding the logic of code. It’s only good at copying existing code, but it doesn’t understand why it works, so the predictive model fucks things up when it takes the less likely result. Maybe if you turn the temperature to only give the highest prediction it wouldn’t be horrible, but you might as well just search online and copy the code that it’s going to generate anyway.
The bigger problem is that your skills are weakened a bit every time you use an assistant to write code.
The bigger problem is that your skills are weakened a bit every time you use an assistant to write code
Not when you factor in that you are now doing code review for it and fixing all its mistakes…
It depends how you’re using it. I use it for boilerplate code, for stubbing out classes and functions where I can tell it clearly what I want, for finding inconsistencies I might have missed, to advise me on possible tools and approaches for small things, and as a supplement to the documentation when I can’t find what I’m looking for. I don’t use it for architecting new things, writing complex and specialized code, or as a replacement for documentation. I feel like I have it fairly well contained to what it does well, so I don’t waste my time on what it does badly, and it isn’t really eating away at my coding brain because I still do the tricky bits myself.
This is exactly how it’s meant to be used. People who think it’s to be used for more than what you’ve described are not serious people.
There is no “meant to be used”. LLM were not created to solve a specific problem.
That is just dumb.
Your skills are weakened even more by copying code from someone else. Because you have the use even less of your brain to complete your task.
Yet you people don’t complain about that part at all and do it yourself all the time. For some it is even the preferred method of work.
“Using your skills less means they get weaker, who would have thought!”
With your logic, you shouldn’t use any form of help to code. Programmers should just lock themselves in a big black box until their project is finished, that will make sure their skills aren’t “weakened” by using outside help.
No that’s not the same thing. It’s the difference between looking up how to do something and having it done for you.
There have been multiple articles recently that show AI weakens skills.
Btw there’s no need to add strawman arguments with scenarios I didn’t mention.
Kind of a weird title. Of course adoption would slow? The people who want it have adopted it, the people who don’t haven’t.
We were initially excited by AI at my company, but after we used it a bit we didnt find any really meaningful use cases for it in our business model. And in most cases we spent a lot of time correcting its many errors which would actually slow down our processes…
Marx tapping the big sign marked “Tendency of the rate of profit is to fall”, but then looking at the already unprofitable AI spin-offs and just throwing his hands up in disgust.
I think there’s an argument to be made that the AI hype got a bunch of early adopters, but failed to entice more traditional mainstream clients. But the idea that we just ran out of new AI users in… barely two years? No. Nobody is really paying for this shit in a meaningful way. Not at the Enterprise Application scale of subscriptions. That’s why Microsoft is consistently losing money (on the scale of billions) on its OpenAI investment.
If people were adopting AI like they’d adopted the latest Windows OS, these firms would be seeing a steady growth in the pool of users that would signal profitability soon (if not already). But the estimates they’re throwing out - one billion AI adoptions in barely a year - are entirely predicated on how many people just kinda popped in, looked at the web interface, and lost interest.
It would also slow if companies were told insane lies about the capability of “AI” (“it’s living having a team of PHD level experts at your disposal!”) and then companies realized that many of these promises were total bullshit.
Why is the Census Bureau tracking LLM adoption?
They dressed up a parrot and called it the golden goose and now they’re chasing a wild goose.
Wild parrot surely
An undomesticated Psittaciformes.
For the things AI is good at, like reading documentation, one should just get a local model and be done.
I think pouring as much money as big companies in the us has been doing is unwise. But when you have deep pockets, i guess you can afford to gamble.
Could you point me to a model to do that and instructions on how get it up and running?
I’m using Deepseek R1 (8B) and Gemma 3 (12B), installed using LM Studio (which pulls directly from Hugging Face).
I dont have the hardware so I’m using “open web ui” to run queries on models accessible via huggingface API.
Works really well. I haven’t invested the time to understand how to use workspaces, which allow you to tune models, but aparently its doable.
As the other comment says, LM Studio is probably the easiest tool. Once you’ve got it installed it’s trivial to add new models. Try some out and see what works best for you. Your hardware will be a limit on what you can run though, so keep that in mind.
brace for the pop, this one gonna be loud.
Fucking finally. Maybe the hype wave has crested 🤞
finally. Maybe the hype wave has crested
Well one thing I can tell you is that art is gone, forever. They took that from us and our kids and all generations to come.
Naaa, AI “art” output is trash. You just need to train the eye to notize the patterns.
This is so melodramatic. Nobody is stopping you from drawing or painting or whatever.
It is absolutely a bubble, but the applications that AI can be used for still remain while the models continue to get better and cheaper. Here’s the actual graph:
This contradicts what I’m reading in that AI model costs grow with each generation, not shrink.
Also that is the cost to train them, not the cost to use them, which is different.
That was published a year ago, highly selective, doesn’t include something like Llama 4 Maverick.
13.5%, slipping to about 12%
I know that 1.5% could mean hundreds of businesses, but this still seems like such a nothing burger.
The ai companies haven’t even found a viable business model yet, are bleeding money while the user base is shrinking
The lack of business model is what’s freaking me out.
Around 2003 I was talking to a customer about Google going public and saying he should go all in.
“Meh, they’re a great search engine, but I can’t see how they’ll make any money.”
Still remember that conversation, standing in his attic, wiring his new satellite dish. Wonder if he remembers that conversation at well.
What gets me is that even the traditional business models for LLMs are not great. Like translation, grammar checking, etc. Those existed before the boom really started. DeepL has been around for almost a decade and their services are working reasonably well and they’re still not profitable.
Isn’t that the case with a lot of modern tech?
I vaguely recall Spotify and Uber being criticized relying on the “get big first and figure out how to monetize later” model.
(Not defending them, just wondering what’s different about AI.)
Spotify is a music streaming service with subscription fees generating recurring revenue, it would be fine in a world without an investor class obsessed with infinite growth. Uber is to taxis what crypto is to banks, essentially exploiting a gap in regulations to undercut an existing market.
“AI” is a solution desperately looking for a problem to justify all the money and resources being wasted on it.
What are you talking about? ChatGPT, Claude, Gemini, etc. all have “subscription fees generating recurring revenue” and are famously “exploiting a gap in regulations to undercut an existing market.”
Uber took 15 years to become profitable, and Spotify took 18 years.
Again, I’m not defending any of them (they all exploit the people who make their service work), but so far AI seems to be going down the same road.
Spotify provides a real, tangible service. I pay for access to music I get access to music.
What service does an LLM actually provide? They can’t be relied on for accurate information, they can’t reason, the only thing they seem to be able to do is psychologically manipulate their users. That makes money now, but in six months? A year? We’re already seeing usage fall despite some of the wealthiest companies on the planet burning unfathomable amounts of money.
“I pay for access to music I get access to music.” And with ChatGPT, you pay for access to an LLM, and you get access to an LLM.
Just because you personally don’t value that as a service doesn’t inherently invalidate it as a business model, now or in the future.
Netflix lost subscribers in 2011 and 2022, that didn’t kill the company. Uber stock tumbled during the pandemic and again in 2022. In 2023, Wired was writing about how “despite its popularity… [Spotify] has long struggled to turn consistent profits.”
This is a whole wave of companies where the survivors seem financially stable now, but had a long history of being propped up by venture capital and having an unclear path to profitability.
The only thing you’ve successfully shown is different so far is that you don’t think it’s a real service.
I generally agree, but I still don’t see anything that differentiates its trajectory from the Spotifys, Ubers, and Netflixes of the world.
That is more than a 10% loss of that customer base in 2 month.
For any industry that is huge.
But they’re already not making money, losing customers during the supposed growth phase is absolutely devastating. It’s occuring all while AI is being subsidized by massive investments from the likes of microsoft and google, and many more namelesss VCs through OpenAI, anthropic etc.
let’s not forget the us is pumping EVERYTHING into ai, 3-4% of the gdp are just the ai economy. here’s hoping it comes crashing down on them
Of course. Although ai, or more accurately llms do have use functions they are not the star trek computer.
I use chatgpt as a Grammer check all the time. It’s great for stuff like that. But it’s definitely not a end all be all solution to productivity.
I think corporations got excited llms could replace human labor… But it can’t.
Grammer
Grammar.
There’s nothing AI can do that an internet pedant can’t.
grammar
Mind your capitalization, fellow pedant.
No. Its grammer. No one says grammAR everyone says it with er. It’s spelled grammar due to tradition and nothing else. Same reason the ph is still prevelant in the English language.
Ehhhhhh the English language is terrible!
Sure, English is terrible. Don’t forget dollar, pillar, cougar, burglar, doctor, actor, or aviator. Yet, oddly enough, somehow most people deal with them, and life goes on.
Go read about The Great Vowel Shift; it’s pretty informative.
Large companies (defined as having more than 250 employees) have reduced their AI usage, according to the data (click to expand the Tweet below). The slowdown started in June, when it was at roughly 13.5%, slipping to about 12% at the end of August.
Someone explain to me how I am to see this “rate” as - is it adoption rate or usage rate? IF it is adoption rate 13.5% of all large firms are using it? and it’s declined to 12%? Or is it some sort of usage rate and if so, whatever the fuck is 12% usage?
It means 12% of those surveyed answered “yes” to the question “Are you using AI at your company?” (Note this is not the literal question from the survey, because I can’t be assed to dig up their methodology.)
It’s stupidly written like AI.
