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.
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…
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.
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.
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.
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.
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…
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.
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.
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?
They dressed up a parrot and called it the golden goose and now they’re chasing a wild goose.
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.
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.
That is good news, assuming numbers being reported by a US government agency are accurate, which is no longer a certainty.
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.
Some decent news at least
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?
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.
I’m using Deepseek R1 (8B) and Gemma 3 (12B), installed using LM Studio (which pulls directly from Hugging Face).
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
brace for the pop, this one gonna be loud.
I mean the automatic speech recognition and transcription capabilities are quite useful. But that’s about it, for me for now.
It could be interesting for frame interpolation in movies at some point maybe, I guess.
I dream of using it for the reliable classification of things. But I haven’t seen it working reliably, yet.
For the creation of abstracts and as a dialog system for information retrieval it doesn’t feel exact/correct / congruent enough to me.
Also: A working business plan to make money with actual AI services has yet to be found. Right now it is playing with a shiny new toy and the expectations and money of their investors. Right now they fail to deliver and the investors might get restless. Selling the business while it is still massively overrated, seems like the only way forward. But that’s just my opinion.
I mean the automatic speech recognition and transcription capabilities are quite useful.
That’s what LLM are made for; text stuff, not knowledge stuff.
That’s what LLM are made for;
Hence the Name? :)
IMO, AI is a really good demo for a lot of people, but once you start using it, the gains you can get from it end up being somewhat minimal without doing some serious work.
Reminds me of 10 other technologies that if you didn’t get in the world was going to end but ended up more niche than you’d expect.
As someone who is excited about AI and thinks it’s pretty neat, I agree we’ve needed a level-set around the expectations. Vibe coding isn’t a thing. Replacing skilled humans isn’t a thing. It’s a niche technology that never should’ve been sold as making everything you do with it better.
We’ve got far too many companies who think adoption of AI is a key differentiator. It’s not. The key differentiator is almost always the people, though that’s not as sexy as cutting edge technology.
The key differentiator is almost always the people, though that’s not as sexy as cutting edge technology.
Evidently you haven’t worked with me. I’m actually quite sexy.
The technology is fascinating and useful - for specific use cases and with an understanding of what it’s doing and what you can get out of it.
From LLMs to diffusion models to GANs there are really, really interesting use cases, but the technology simply isn’t at the point where it makes any fucking sense to have it plugged into fucking everything.
Leaving the questionable ethics many paid models’ creators have used to make their models aside, the backlash against so is understandable because it’s being shoehorned into places it just doesn’t belong.
I think eventually we may “get there” with models that don’t make so many obvious errors in their output - in fact I think it’s inevitable it will happen eventually - but we are far from that.
I do think that the “fuck ai” stance is shortsighted though, because of this. This is happening, it’s advancing quickly, and while gains on LLMs are diminishing we as a society really need to be having serious conversations about what things will look like when (and/or if, though I’m more inclined to believe it’s when) we have functional models that can are accurate in their output.
When it actually makes sense to replace virtually every profession with ai (it doesn’t right now, not by a long shot) then how are we going to deal with this as a society?
I’ve got a friend who has to lead a team of apparently terrible developers in a foreign country, he loves AI, because “if I have to deal with shitty code, send back PRs three times then do it myself, I might as well use LLMs”
And he’s like one of the nicest people I know, so if he’s this frustrated, it must be BAD.
I had to do this myself at one point and it can be very frustrating.
It’s basically the “tech makes lots of money” effect, which attracts lots of people who don’t really have any skill at programming and would never have gone into it if it weren’t for the money.
We saw this back in earlier tech booms and see it now in poorer countries to were lots of IT work has been outsourced - they still have the same fraction of natural techies as the rest but the demand is so large that masses of people with no real tech skill join the profession and get given actual work to do and they suck at it.
Also beware of cultural expectations and quirks - the team I had to manage were based in India and during group meetings on the phone would never admit if they did not understood something of a task they were given or if there was something missing (I believe that it was so as not to lose face in front of others), so ended up often just going with wrong assumptions and doing the wrong things. I solved this by, after any such group meeting, talking to each member of that outsourced team, individually and in a very non-judgemental way (pretty much had to pass it as “me, being unsure if I explained things correctly”) to tease from them any questions or doubts, which helped avoid tons of implementation errors from just not understanding the Requirements or the Requirements themselves lacking certain details and devs just making assumptions on their own about what should go there.
That said, even their shit code (compared to what us on the other side, who were all senior developers or above, produced) actually had a consistent underlying logic throughout the whole thing, with even the bugs being consistent (humans tend to be consistent in the kind of mistakes they make), all of which helps with figuring out what is wrong. LLMs aren’t as consistent as even incompetent humans.
Cyberspace, hypertext, multimedia, dot com, Web 2.0, cloud computing, SAAS, mobile, big data, blockchain, IoT, VR and so many more. Sure, they can be used for some things, but doing that takes time, effort and money. On top of that, you need to know exactly when to use these things and when to choose something completely different.
oh the horror
