"We did it, Patrick! We made a technological breakthrough!"
A place for all those who loathe AI to discuss things, post articles, and ridicule the AI hype. Proud supporter of working people. And proud booer of SXSW 2024.
AI, in this case, refers to LLMs, GPT technology, and anything listed as "AI" meant to increase market valuations.
link to archived Reddit thread; original post removed/deleted
I've said it time and time again: AIs aren't trained to produce correct answers, but seemingly correct answers. That's an important distinction and exactly what makes AIs so dangerous to use. You will typically ask the AI about something you yourself are not an expert on, so you can't easily verify the answer. But it seems plausible so you assume it to be correct.
Thankfully, AI is bad at maths for exactly this reason. You don't have to be an expert on a very specific topic to be able to verify a proof and - spoiler alert - most of the proofs ChatGPT 5 has given me are plain incorrect, despite OpenSlop's claims that it is vastly superior to previous models.
I've been through the cycle of the AI companies repeatedly saying "now it's perfect" only admitting it's complete trash when they release the next iteration and claim "yeah it was broken, we admit, but now it's perfect" so many times now...
Problem being there's a massive marketing effort to gaslight everyone and so if I point it out in any vaguely significant context, I'm just not keeping up and most only have dealt with the shitty ChatGPT 5.1, not the more perfect 5.2. Of course in my company they are about the Anthropic models so it is instead Opus 4.5 versus 4.6 now. Even proving the limitations in trying to work with 4.6 gives anthropic money, and at best I earn a "oh, those are probably going to be fixed in 4.7 or 5 or whatever".
Outsiders are used to traditional software that has mistakes, but those are straightforward to address so a close but imperfect software can hit the mark in updates. LLMs not working that way doesn't make sense. They use the same version number scheme after all, so expectations should be similar.
My own advise for people starting to use AI is to use it for things you know very well. Using it for things you do not know well, will always be problematic.
The problem is that we've had a culture of people who don't know things very well control the purse strings relevant to those things.
So we have executives who don't know their work or customers at all and just try to bullshit while their people frantically try to repair the damage the executive does to preserve their jobs. Then they see bullshit generating platforms and see a kindred spirit, and set a goal of replacing those dumb employees with a more "executive" like entity that also can generate reports and code directly. No talking back, no explaining that the request needs clarification, that the data doesn't support their decision, just a "yes, and..." result agreeing with whatever dumbass request they thought would be correct and simple.
Finally, no one talking back to them and making their life difficult and casting doubt on their competency. With the biggest billionaires telling them this is the right way to go, as long as they keep sending money their way.
I suspect this will happen all over with in a few years, AI was good enough at first, but over time reality and the AI started drifting apart
AI is literally trained to get the right answer but not actually perform the steps to get to the answer. It's like those people that trained dogs to carry explosives and run under tanks, they thought they were doing great until the first battle they used them in they realized that the dogs would run under their own tanks instead of the enemy ones, because that's what they were trained with.
Holy shit, that's what they get for being so evil that they trained dogs as suicide bombers.
And then, the very same CEOs that demanded the use of AI in decision making will be the ones that blame it for bad decisions.
while also blaming employees
Of course, it is the employees who used it. /s
They haven't drifted apart, they were never close in the first place. People have been increasingly confident in the models because they've increasingly sounded more convincing, but the tenuous connection to reality has been consistently off.
I somehow hope this is made up, because doing this without checking and finding the obvious errors is insane.
This is probably real, as it isn't the first time it happened: https://www.theguardian.com/technology/2025/jun/06/high-court-tells-uk-lawyers-to-urgently-stop-misuse-of-ai-in-legal-work
As someone who has to deal with LLMs/AI daily in my work in order to fix the messes they create, this tracks.
AI's sole purpose is to provide you a positive solution. That's it. Now that positive solution doesn't even need to be accurate or even exist. It's built to provide a positive "right" solution without taking the steps to get to that "right" solution thus the majority of the time that solution is going to be a hallucination.
you see it all the time. you can ask it something tech related and in order to get to that positive right solution it'll hallucinate libraries that don't exist, or programs that don't even do what it claims they do. Because logically to the LLM this is the positive right solution WITHOUT utilizing any steps to confirm that this solution even exists.
So in the case of OPs post I can see it happening. They told the LLM they wanted analytics for 3 months and rather than take the steps to get to an accurate solution it ignored said steps and decided to provide positive solution.
Don't use AI/LLMs for your day to day problem solving. you're wasting your time. OpenAI, Anthropic, Google, etc have all programmed these things to provide you with "positive" solutions so you'll keep using them. they just hope you're not savvy enough to call out their LLM's when they're clearly and frequently wrong.
Probably the skepticism is around someone actually trusting the LLM this hard rather than the LLM doing it this badly. To that I will add that based on my experience with LLM enthusiasts, I believe that too.
I have talked to multiple people who recognize the hallucination problem, but think they have solved it because they are good "prompt engineers". They always include a sentence like "Do not hallucinate" and thinks that works.
The gaslighting from the LLM companies is really bad.
Use of AI in companies would not save any time if you were checking each result.
It has happened in the open, so I don't see why it wouldn't happen even more behind closed doors:
Deloitte will provide a partial refund to the federal government over a $440,000 report that contained several errors, after admitting it used generative artificial intelligence to help produce it..
Joke's on you, we make our decisions without asking AI for analytics. Because we don't ask for analytics at all
I feel like no analytics is probably better than decisions based on made-up analytics.
I don't need AI to fabricate data. I can be stupid on my own, thank you.
As an unemployed data analyst / econometrician:
lol, rofl, perhaps even... lmao.
Nah though, its really fine, my quality of life is enormously superior barely surviving off of SSDI and not having to explain data analytics to thumb sucking morons (VPs, 90% of other team leads), and either fix or cover all their mistakes.
Yeah, sure, just have the AI do it, go nuts.
I am enjoying my unexpected early retirement.
When you delegate, to a person, a tool or a process, you check the result. You make sure that the delegated tasks get done and correctly and that the results are what is expected.
Finding that it is not the case after months by luck shows incompetence. Look for the incompetent.
Before anything else: whether the specific story in the linked post is literally true doesn’t actually matter. The following observation about AI holds either way. If this example were wrong, ten others just like it would still make the same point.
What keeps jumping out at me in these AI threads is how consistently the conversation skips over the real constraint.
We keep hearing that AI will “increase productivity” or “accelerate thinking.” But in most large organizations, thinking is not the scarce resource. Permission to think is. Demand for thought is. The bottleneck was never how fast someone could draft an email or summarize a document. It was whether anyone actually wanted a careful answer in the first place.
A lot of companies mistook faster output for more value. They ran a pilot, saw emails go out quicker, reports get longer, slide decks look more polished, and assumed that meant something important had been solved. But scaling speed only helps if the organization needs more thinking. Most don’t. They already operate at the minimum level of reflection they’re willing to tolerate.
So what AI mostly does in practice is amplify performative cognition. It makes things look smarter without requiring anyone to be smarter. You get confident prose, plausible explanations, and lots of words where a short “yes,” “no,” or “we don’t know yet” would have been more honest and cheaper.
That’s why so many deployments feel disappointing once the novelty wears off. The technology didn’t fail. The assumption did. If an institution doesn’t value judgment, uncertainty, or dissent, no amount of machine assistance will conjure those qualities into existence. You can’t automate curiosity into a system that actively suppresses it.
Which leaves us with a technology in search of a problem that isn’t already constrained elsewhere. It’s very good at accelerating surfaces. It’s much less effective at deepening decisions, because depth was never in demand.
If you’re interested, I write more about this here: https://tover153.substack.com/
Not selling anything. Just thinking out loud, slowly, while that’s still allowed.
I'm a data analyst and primary authority on the data model of a particular source system. Most questions for figures from that system that can't be answered directly and easily in the frontend end up with me.
I had a manager show me how some new LLM they were developing (which I had contributed some information about the model to) could quickly answer some questions that usually I have to answer manually, as part of a pitch to make me switch to his department so I can apply my expertise for improving this fancy AI instead of answering questions manually.
He entered a prompt, got a figure that I knew wasn't correct and I queried my data model for the same info, with a significantly different answer. Given how much said manager leaned on my expertise in the first place, he couldn't very well challenge my results and got all sheepish about how the AI still in development and all.
I don't know how that model arrived at that figure. I don't know if it generated and ran a query against the data I'd provided. I don't know if it just invented the number. I don't know how the devs would figure out the error and how to fix it. But I do know how to explain my own queries, how to investigate errors and (usually) how to find a solution.
Anyone who relies on a random text generator - no matter how complex that generation method to make it sound human - to generate facts is dangerously inept.
This would suggest the leadership positions aren't required for the function of the business.
This has always been the case, in every industry.
To everyone I've talked to about AI, I've suggested a test. Take a subject that they know they are an expert at. Then ask AI questions that they already know the answers to. See what percentage AI gets right, if any. Often they find that plausible sounding answers are produced however, if you know the subject, you know that it isn't quite fact that is produced. A recovery from an injury might be listed as 3 weeks when it is average 6-8 or similar. Someone who did not already know the correct information, could be damaged by the "guessed" response of AI. AI can have uses but it needs to be heavily scrutinized before passing on anything it generates. If you are good at something, that usually means you have to waste time in order to use AI.
Jesus Christ, you have to have a human validate the data.
Exactly, this is like letting excel auto-fill finish the spreadsheet and going "looks about right"
And that's a good analogy, as people have posted screenshots of Copilot getting basic addition wrong in Excel.
Whoever implemented this agent without proper oversight needs to be fired.
Except the ceo and executives ultimately responsible will blame their underlings that will be fired, even though it was an executive level decision. They didn't get to the pinnacle of corporate governance by admitting mistakes. That's not what they were taught at their ivy league schools, they were taught to lie and cheat to steal, and further slander their victims to excuse it.
It was bad before the current president set his outstanding example for the rest of the country. See what being a lying cheating piece of shit gets you? Everything. Nothing matters. We have the wrong people in charge across the board, from business to government to institutions.
It doesn't matter. Management wants this and will not stop until they run against a wall at full speed. 🤷
Dumbasses. Mmm, that's good schadenfreude.
My broseph in Christ, what did you think a LLM was?
Bro, just give us a few trillion dollars, bro. I swear bro. It'll be AGI this time next year, bro. We're so close, bro. I just need need some money, bro. Some money and some god-damned faith, bro.
But don't worry, when it comes to life or death issues, AI is the best way to help
Haha, "chat, how do I stop the patients nose from bleeding"
"Cut his leg off."
"Well, you're the medicAI. Nurse, fetch the bonesaw"
My workplace, the senior management, is going all in on Copilot. So much so that at the end of last year to told us to use Copilot for year end reviews! Even provided a prompt to use, told us to link it to Outlook (not sure why, since our email retention isn't very long)... but whatever.
I tried it, out of curiosity because I had no faith. It started printing out stats for things that never happened. It provided a 35% increase here, a 20% decress there, blah blah blah. It didn't actually highlight anything I do or did. And I'm banking that a human will partially read my review, not just use AI.
If someone read it, I'm good. If AI reads it, I do wonder if I screwed myself. Since senior mgmt is just offloading to AI...
Ah yes, what a surprise. The random word generator gave you random numbers that aren't actually real.
Tbf at this point corporate economy is made up anyway so as long as investors are gambling their endless generational wealth does it matter?
Apparently that reddit post itself was generated with AI. Using AI to bash AI is an interesting flex.
Our AI that monitors customer interactions sometimes makes up shit that didn't happen during the call. Any agent smart enough could probably fool it into giving the wrong summary with the right key words. I only caught on when I started reading the logs carefully, but I don't know if management cares so long as the business client is happy.
The output from tools infected with LLMs can intrinsically only ever be imprecise, and should never be trusted.
I guarantee you this is how several, if not most, fortune 500 companies currently operate. The 50k DOW is not just propped up by the circlejerk spending on imaginary RAM. There are bullshit reports being generated and presented every day.
I patiently wait. There is a diligent bureaucrat sitting somewhere going through fiscal reports line by line. It won't add up.. receipts will be requested.. bubble goes pop
I hope they sue whoever sold it to them. it's not artificial intelligence, it's a machine learning chat bot. they may as well be running their company with a magic eight ball.