If by “AI” they mean “oligarch-owned and controlled AI”, we have common ground here. But then again, that is true of anything owned and monopolised by these people humanoids. Case in point: only few people will agree that…
oligarch-owned media empires have a positive impact on society
oligarch-owned social media networks have a positive impact on society
oligarch-owned space companies have a positive impact on society
…
The problem is not with the tech. The problem is that the tech is in the hands of a small clique of sociopaths.
The tech that the sociopaths built is an addictive control machine.
I don’t care whether the person selling the cigarettes to my kid has read Fanon and volunteers at the homeless center, they’re still addictive and poisonous.
The tech that the sociopaths built is an addictive control machine.
LLMs are not addictive control machines. Social media engagement farming and native advertising/propaganda machines are addictive control machines. We consistently get the marketing functions fudged up with the utility of the thing itself. Like blaming violence on video games or talking about addictive qualities of children’s cartoons.
I don’t care whether the person selling the cigarettes to my kid has read Fanon and volunteers at the homeless center
Cigarettes are actually a great example of engineered addiction. Historically, tobacco products had a mild addicting affect resulting from their short-term immediate stimulative benefits combined with the reliance on nicotine. But it wasn’t until the boom in marketing of the 1920s followed by the industrial scale experimentation of the formula in the 1940s that the addictive nature of the product surged.
The end result was an enormous increase in per-capita consumption, leaping from 54 cigarettes per person per year in 1900 to 4,345 cigarettes per person per year in 1963.
The quantitative surge in consumption radically changed the health consequences of cigarette use.
You could do similar analysis on everything from alcohol consumption to LSD. From TV to TikTok. At some point you have to draw a line between the utilitarian value of the thing versus the mass market distribution and profit-driven consumption behaviors. Otherwise, you just end up with 1920s Prohibition and a thriving black market that undercuts the policies you intended.
And then you get Joe Kennedy. Is that what you want for your country? More Kennedys?
Imo, LLMs do have a purpose (and their ethical sourcing problems, like you mentioned).
It’s just that right now, Silicon Valley sells it as the answer to every single problem out there when it clearly isn’t. A hammer is good for putting nails in the wall. Silicon Valley claims you can also use it to do your toenails, gullible managers mandate its use for that purpose, and now the waiting rooms are chock-full with people with broken toes…
We can figure out how to correctly use the hammer after we convince CEOs to stop bashing everyone’s feet with them. Until then, the foot bashers make it a moot point.
The computer science majors who are actually developing these technologies are not the CEOs who are exploiting them and creating all of the fallout in society. Addressing the CEOs and the rampant capitalist exploitation of AI is a completely different project than developing the technology.
Telling the scientists to stop until we’ve sorted out the CEOs doesn’t make any sense.
It’s more of a “Don’t confuse science with business” kind of argument.
Looking at neural network-based machine learning as being made up of only commercial LLM services and then condemning an entire field of research is short-sighed at best. The people profiting off of LLMs, stealing data, building data centers and buying up the world’s supply of computer hardware are not the scientists and researchers, it’s the rich capitalists who’re trying to buy monopoly powers using their excessive piles of wealth.
There’s more to machine learning than ChatGPT, but you couldn’t tell that by reading the social media hot takes.
Then go the hell on do your AI science. If it works then it works. Go build an AI that’s more than a speculation bubble and can actually help people instead of throwing us into nightmare hell world.
Probably because the scientists FauxLiving’s taking about are paid by the capitalists to make the problem-causing machines. The only way for them to continue working is to not address their employers.
And for the scientists trying to make the pure wholesome science AI, every advance (like cancer cell spotting) is overshadowed by a bigger issue like de-skilling the people who have to do the training, or worse (like an LLM rejecting people’s insurance and a chance to even see that deskilled doctor)
I don’t disagree. Most generative AI models are some variant on “plagiarism machine”, but categorizing and identifying data are extremely useful things that AI does.
LLMs are good at quickly generating code, but the issue in software is rarely how fast humans can write code. In fact, more speed with less understanding is a really bad combination (I am a developer working DevOps and anecdotally I see way more large scale bugs now than I did 5 years ago).
Agentic AI is, unfortunately, just an LLM pretending to be a person, and that’s a really bad thing. Like so incredibly bad. Did you know that humans are statistically more likely to make mistakes when under pressure? Cause the LLMs sure do. Create a narrative of pressure and the LLM cracks like a rotten egg. Cause that’s more statistically likely!
You’ve accurately described why having LLMs cobble together code is a terrible idea. With all the vibe-coded nonsense finding its way into production code because the amount of code generated in a short amount of time inevitably overwhelms human oversight, I wouldn’t want to work in cybersec these days.
That said, I do see applications for LLMs in areas where mistakes will not get people hurt or systems breached: they can
provide a first layer of customer and tech support to solve the really stupid stuff that needs no human attention (“Have you made sure that the device is plugged in? Have you tried turning it off and on again?”). This can be particularly useful when paired with a source of truth it can draw from.
do tedious tasks involving large amounts of text processing, e.g. automated translation, cross-referencing of legal texts.
provide pre-college learners with a tutor that is available 24/7, can explain simple academic subject matters and answer questions that naturally arise as part of every learning process. Again, pair with a source of truth for more reliable results.
The speed and ease at which LLMs allow you to generate code is a bug, not a feature in my opinion. In my org, a group of 3 very junior engineers wrote a 5k line shell script for building k8s clusters according to our business specs and it’s fucking awful. The actual time to get it out the door was short, but now it’s basically impossible to change it without fucking up like 20 different things. The fucking thing will randomly quit because the shit ass LLM thinks set -e is a good thing to use, and it’s full of unused variables everywhere. I had to add a feature to it (which is how I learned of its existence), and I spent a miserable week just reading the entire fucking thing so I could ensure that my change wouldn’t cause an oil refinery in the North Sea to explode due to a butterfly-effect series of bullshit.
The frustration and toil you feel as a software dev is a feature. If something is making you mad and is taking forever to write, that’s a sign you probably need to change your approach. If you’re using an LLM to write a bunch of boilerplate, why not just eliminate the boilerplate or like, make a factory to spit out a bunch of it or something? Your discomfort is a powerful tool and you are not best served by ignoring it. Those junior devs would have written something much better if they had been forced to experience the true toil and suffering of writing a 5k line shell script.
I fall in the junior category, but with more experience prior to becoming a certified full stack dev than most juniors. I was a sys admin for a decade where I taught myself how to code to simplify my job. Plus I had 1 year at university 15 years ago. I use my company provided license with AI very sparingly and never let it implement code. Mostly I use it like a glorified stack overflow when I run into a problem that I can’t work out by myself. Usually, it will suggest some code that’s not good, but it’s enough to highlight a concept I’m ignorant to and then I can do what I need. If there’s a block of code that has something that I don’t understand, I can highlight it and ask it to explain. It’s usually pretty good at listing out what something is doing or at least supposed to do.
I would love if AI disappeared immediately, but it’s not going to happen. If someone is using, it should be used as a tool and not a replacement. If you can’t do the thing that it’s doing, then you shouldn’t be use it to do that thing. I probably ask it questions less than once a week, and again, never put in code that I don’t understand what it does and why.
I have a good friend that’s a senior dev at a company using Claude code. He’s become an AI code reviewer, but much to my dismay likes it. He’s vibe coding his own fuck around app with it and it’s writing the backend in C#, a language he doesn’t know being a frontend dev. It’s so infuriating to me that someone that I know to be intelligent is so damn stupid.
I’ll say that given the way OpenAI and Anthropic have hideously overextended themselves (they have over a trillion dollars of financial commitments to companies like Oracle), it’s not impossible that the current crop of American LLM providers do just kinda… poof away. Traditional banks want nothing more to do with them, they’re getting majorly spooked. All that needs to happen is for private credit to lose confidence in them, which is already happening. When they’re out of cash, they’ll be on the hook for an absolute ridiculous amount of money and they’ll probably just get liquidated.
I’m sure there will be new companies that pop up, but they’re going to have to charge 10x what Anthropic is making enterprise customers pay, since inference likely still isn’t profitable at the price Anthropic is charging.
I don’t use LLMs as a knowledge base because if the problem is bad enough for me, I’m likely just grepping through kubernetes source code or something. That being said, I don’t necessarily have an issue with folks using an LLM that way as long as they fully understand exactly how bad it is at what it does. You’ll be fine if you lose access to LLMs, and that’s the number one thing in my book. Your friend? Not so much.
(As a fun bonus to all of this, Oracle is very likely to die if OpenAI can’t meet its commitments. Either way, Larry Ellison will probably stop being a billionaire, since almost all of his wealth is in Oracle stock).
The schadenfreude I will experience if I see these giants fall will be immeasurable. I’ll have to go visit my doctor have 4 hours, for reasons. I would love to see oracle, openAI, and anthropomorphic just dissolve, but it would be extra special if Microsoft was broken down to nothing. Google too, but while I don’t like Google, they’re definitely lower on the list than those first options, maybe ahead of Oracle, but mostly because I’m fortunate enough to not have to use any of their products.
Umm, excuse me. We’re delivering 400 points worth of story work with 40 points worth of dev time. Do you think that that’s somehow a bad thing? Our budget is stretching further than ever! (Once we figure out how to reduce token costs) /s
The issue is more that the triangle of code that works, code that scales well, and code that’s cheap will always, ALWAYS, prioritize works and is cheap, even if every action taken from then on costs more to make. I’ve been on a team that focused effort on keeping scalability a priority and every single thing we tried got kneecapped to “keep to the budget”.
Not a paradox. It plagiarizes because it isn’t capable of creating thoughts. It creates statistically likely combinations of tokens. Those “statistically likely” models were made by stealing a whole bunch of information.
The model hallucinates because it doesn’t actually know what any of the tokens mean, just that they exist in a likely probability space.
If you took two papers about a very similar subject, copied them both out in their entirety, then replaced similar phrases from one copy into the other, the resultant paper would both contain inaccuracies and would be plagiarism. That’s the same thing ai does, except the copies are the sum total of the digitized human written work. Increasing the number of sources you’re plagiarizing from doesn’t magically make it not plagiarism!
If by “AI” they mean “oligarch-owned and controlled AI”, we have common ground here. But then again, that is true of anything owned and monopolised by these
peoplehumanoids. Case in point: only few people will agree that…The problem is not with the tech. The problem is that the tech is in the hands of a small clique of sociopaths.
The tech that the sociopaths built is an addictive control machine.
I don’t care whether the person selling the cigarettes to my kid has read Fanon and volunteers at the homeless center, they’re still addictive and poisonous.
LLMs are not addictive control machines. Social media engagement farming and native advertising/propaganda machines are addictive control machines. We consistently get the marketing functions fudged up with the utility of the thing itself. Like blaming violence on video games or talking about addictive qualities of children’s cartoons.
Cigarettes are actually a great example of engineered addiction. Historically, tobacco products had a mild addicting affect resulting from their short-term immediate stimulative benefits combined with the reliance on nicotine. But it wasn’t until the boom in marketing of the 1920s followed by the industrial scale experimentation of the formula in the 1940s that the addictive nature of the product surged.
The end result was an enormous increase in per-capita consumption, leaping from 54 cigarettes per person per year in 1900 to 4,345 cigarettes per person per year in 1963.
The quantitative surge in consumption radically changed the health consequences of cigarette use.
You could do similar analysis on everything from alcohol consumption to LSD. From TV to TikTok. At some point you have to draw a line between the utilitarian value of the thing versus the mass market distribution and profit-driven consumption behaviors. Otherwise, you just end up with 1920s Prohibition and a thriving black market that undercuts the policies you intended.
And then you get Joe Kennedy. Is that what you want for your country? More Kennedys?
Hmmmm, not much actual use for the hallucinating plagiarism machine, but I do see your point.
Imo, LLMs do have a purpose (and their ethical sourcing problems, like you mentioned).
It’s just that right now, Silicon Valley sells it as the answer to every single problem out there when it clearly isn’t. A hammer is good for putting nails in the wall. Silicon Valley claims you can also use it to do your toenails, gullible managers mandate its use for that purpose, and now the waiting rooms are chock-full with people with broken toes…
Also, AI can be so much more than just LLMs.
We can figure out how to correctly use the hammer after we convince CEOs to stop bashing everyone’s feet with them. Until then, the foot bashers make it a moot point.
We can do both at the same time.
The computer science majors who are actually developing these technologies are not the CEOs who are exploiting them and creating all of the fallout in society. Addressing the CEOs and the rampant capitalist exploitation of AI is a completely different project than developing the technology.
Telling the scientists to stop until we’ve sorted out the CEOs doesn’t make any sense.
I sense a “we can’t let Chy-na get there first” type of argument, but I may be wrong.
It’s more of a “Don’t confuse science with business” kind of argument.
Looking at neural network-based machine learning as being made up of only commercial LLM services and then condemning an entire field of research is short-sighed at best. The people profiting off of LLMs, stealing data, building data centers and buying up the world’s supply of computer hardware are not the scientists and researchers, it’s the rich capitalists who’re trying to buy monopoly powers using their excessive piles of wealth.
There’s more to machine learning than ChatGPT, but you couldn’t tell that by reading the social media hot takes.
Then go the hell on do your AI science. If it works then it works. Go build an AI that’s more than a speculation bubble and can actually help people instead of throwing us into nightmare hell world.
Who is stopping you?
Probably because the scientists FauxLiving’s taking about are paid by the capitalists to make the problem-causing machines. The only way for them to continue working is to not address their employers.
And for the scientists trying to make the pure wholesome science AI, every advance (like cancer cell spotting) is overshadowed by a bigger issue like de-skilling the people who have to do the training, or worse (like an LLM rejecting people’s insurance and a chance to even see that deskilled doctor)
I don’t disagree. Most generative AI models are some variant on “plagiarism machine”, but categorizing and identifying data are extremely useful things that AI does.
LLMs are good at quickly generating code, but the issue in software is rarely how fast humans can write code. In fact, more speed with less understanding is a really bad combination (I am a developer working DevOps and anecdotally I see way more large scale bugs now than I did 5 years ago).
Agentic AI is, unfortunately, just an LLM pretending to be a person, and that’s a really bad thing. Like so incredibly bad. Did you know that humans are statistically more likely to make mistakes when under pressure? Cause the LLMs sure do. Create a narrative of pressure and the LLM cracks like a rotten egg. Cause that’s more statistically likely!
You’ve accurately described why having LLMs cobble together code is a terrible idea. With all the vibe-coded nonsense finding its way into production code because the amount of code generated in a short amount of time inevitably overwhelms human oversight, I wouldn’t want to work in cybersec these days.
That said, I do see applications for LLMs in areas where mistakes will not get people hurt or systems breached: they can
The speed and ease at which LLMs allow you to generate code is a bug, not a feature in my opinion. In my org, a group of 3 very junior engineers wrote a 5k line shell script for building k8s clusters according to our business specs and it’s fucking awful. The actual time to get it out the door was short, but now it’s basically impossible to change it without fucking up like 20 different things. The fucking thing will randomly quit because the shit ass LLM thinks
set -eis a good thing to use, and it’s full of unused variables everywhere. I had to add a feature to it (which is how I learned of its existence), and I spent a miserable week just reading the entire fucking thing so I could ensure that my change wouldn’t cause an oil refinery in the North Sea to explode due to a butterfly-effect series of bullshit.The frustration and toil you feel as a software dev is a feature. If something is making you mad and is taking forever to write, that’s a sign you probably need to change your approach. If you’re using an LLM to write a bunch of boilerplate, why not just eliminate the boilerplate or like, make a factory to spit out a bunch of it or something? Your discomfort is a powerful tool and you are not best served by ignoring it. Those junior devs would have written something much better if they had been forced to experience the true toil and suffering of writing a 5k line shell script.
I fall in the junior category, but with more experience prior to becoming a certified full stack dev than most juniors. I was a sys admin for a decade where I taught myself how to code to simplify my job. Plus I had 1 year at university 15 years ago. I use my company provided license with AI very sparingly and never let it implement code. Mostly I use it like a glorified stack overflow when I run into a problem that I can’t work out by myself. Usually, it will suggest some code that’s not good, but it’s enough to highlight a concept I’m ignorant to and then I can do what I need. If there’s a block of code that has something that I don’t understand, I can highlight it and ask it to explain. It’s usually pretty good at listing out what something is doing or at least supposed to do.
I would love if AI disappeared immediately, but it’s not going to happen. If someone is using, it should be used as a tool and not a replacement. If you can’t do the thing that it’s doing, then you shouldn’t be use it to do that thing. I probably ask it questions less than once a week, and again, never put in code that I don’t understand what it does and why.
I have a good friend that’s a senior dev at a company using Claude code. He’s become an AI code reviewer, but much to my dismay likes it. He’s vibe coding his own fuck around app with it and it’s writing the backend in C#, a language he doesn’t know being a frontend dev. It’s so infuriating to me that someone that I know to be intelligent is so damn stupid.
I’ll say that given the way OpenAI and Anthropic have hideously overextended themselves (they have over a trillion dollars of financial commitments to companies like Oracle), it’s not impossible that the current crop of American LLM providers do just kinda… poof away. Traditional banks want nothing more to do with them, they’re getting majorly spooked. All that needs to happen is for private credit to lose confidence in them, which is already happening. When they’re out of cash, they’ll be on the hook for an absolute ridiculous amount of money and they’ll probably just get liquidated.
I’m sure there will be new companies that pop up, but they’re going to have to charge 10x what Anthropic is making enterprise customers pay, since inference likely still isn’t profitable at the price Anthropic is charging.
I don’t use LLMs as a knowledge base because if the problem is bad enough for me, I’m likely just grepping through kubernetes source code or something. That being said, I don’t necessarily have an issue with folks using an LLM that way as long as they fully understand exactly how bad it is at what it does. You’ll be fine if you lose access to LLMs, and that’s the number one thing in my book. Your friend? Not so much.
(As a fun bonus to all of this, Oracle is very likely to die if OpenAI can’t meet its commitments. Either way, Larry Ellison will probably stop being a billionaire, since almost all of his wealth is in Oracle stock).
The schadenfreude I will experience if I see these giants fall will be immeasurable. I’ll have to go visit my doctor have 4 hours, for reasons. I would love to see oracle, openAI, and anthropomorphic just dissolve, but it would be extra special if Microsoft was broken down to nothing. Google too, but while I don’t like Google, they’re definitely lower on the list than those first options, maybe ahead of Oracle, but mostly because I’m fortunate enough to not have to use any of their products.
Umm, excuse me. We’re delivering 400 points worth of story work with 40 points worth of dev time. Do you think that that’s somehow a bad thing? Our budget is stretching further than ever! (Once we figure out how to reduce token costs) /s
The issue is more that the triangle of code that works, code that scales well, and code that’s cheap will always, ALWAYS, prioritize works and is cheap, even if every action taken from then on costs more to make. I’ve been on a team that focused effort on keeping scalability a priority and every single thing we tried got kneecapped to “keep to the budget”.
The AI paradox: It’s both original (hallucinating) and plagiarizing (copying things, word-for-word).
Not a paradox. It plagiarizes because it isn’t capable of creating thoughts. It creates statistically likely combinations of tokens. Those “statistically likely” models were made by stealing a whole bunch of information.
The model hallucinates because it doesn’t actually know what any of the tokens mean, just that they exist in a likely probability space.
If you took two papers about a very similar subject, copied them both out in their entirety, then replaced similar phrases from one copy into the other, the resultant paper would both contain inaccuracies and would be plagiarism. That’s the same thing ai does, except the copies are the sum total of the digitized human written work. Increasing the number of sources you’re plagiarizing from doesn’t magically make it not plagiarism!