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.
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…
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!
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.
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.
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.
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!
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.
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!