This is a fair point. Although it’d be fun in the absolutely extreme scenario people are presenting here where all the coders would lose their coding job but get coding jobs generating random AI training data instead.
Some of the ways both critics and shills present the future of this technology are kinda nuts.
We’ve polluted the water. One active area that LLMs are being deployed is in reading scanned text, so my best guess is that the next few models are going to be trained on a new corpus of previously unscanned written text.
I’m talking legal documents from the 80s, company documents that were never digitized, and anythibg else google books hasnt fully OCR’d.
If I recall correctly the American standard for digitizing written doctor notes for medical trials in other countries is typing them out by hand by two different staff members. Then any discrepancies are resolved before they’re counted. Tablets and laptops helped this a ton since they start digitized.
We’re also looking for more efficiency and cutting out low skilled processes. Technology has facilitated this over decades and we produce much higher quality products and content than ever before.
Unfortunately capitalism has caused the dragons to hoard their profits and keep salaries low. Ideally the increase in efficiency would allow people to work less, like 32 hr work weeks, and still earn enough to not worry about basic needs. Own your own home, have kids, feed and clothe them, time/money to have kids do hobbies/sports, time/money to have your own hobbies and money/insurance to handle any health expenses.
All of that would increase the quality of life of the working class.
I mean… the same data they use now? And presumably other LLM output based on that, which is something that may or may not affect things a lot, nobody really knows.
Even if AIs consumed data when they trained on it so it couldn’t be used for training again, which they do not, it’s not like code stops being created, stored and datamined by the people who own the creation and storage.
Bit of a tautology, that. Presumably for AI to “replace programmers wholesale” it would need to produce human-quality code. Presumably human-quality code would not degrade anything because it’d be… you know, human-quality.
From what I can tell the degradation you’re talking about relates to natural language data. Stuff like physics simulations seems to be working fine to train models for other tasks, and presumably functional code is functional code. I don’t know if there is any specific analysis about code, though, I’ve only seen a couple of studies and then only as amplified by press.
I haven’t looked into it specifically because it really seems like a bit of a pointless hypothetical. Either AI can get better from the training data available or it can’t and then it is as good as it’s going to get unless the training methods themselves improve. At the moment it sure seems that there is a ton of research claiming both paths for growth and growth stalling that are both getting disproven by implementation almost faster than the analysis can be produced.
This argument mostly matters to investors itching to get ahead of a trend where they can fully automate a bunch of jobs and services and I’m more than happy to see them miss that mark and learn what the tech can do the hard way.
To be absolutely clear, AI is not “going to put everything else out of business”. Certainly LLMs won’t. Not even in programming.
It’s not that AI is going to successfully replace programmers - it is that large corporations want it to replace as much labor as possible, and they will use it to replace programmers because programmers will be the only people that can explain to them exactly why it won’t work. Everyone else will keep sayinng “go”, and, well, the nature of short-sighted profit seeking means they will go.
Outsourcing has often been a bad idea and the implementation deeply flawed - that didn’t stop anyone from outsourcing.
You should definitely look into the input/output thing. It’s absolutely real and applies to all generative AI and LLM operation, as far as I’m aware. The fact that you’re not familiar with it completely erodes your credibility on this topic.
Machine learning algorithms break when you turn them into an ouroborous and feed them their own outputs. Something about the statistically non-deterministic calculations and relatively insignificant artifacts they generate propagates and amplifies with each pass through the algorithm until the output is incomprehensible.
My “credibility on this topic” is of zero interest to me. I am not here to appeal to authority. I know you didn’t mean it like that, but man, it’s such a social media argument point to make it jumped right at me. For the record, it’s not that I haven’t heard about problems with training on AI-generated content (and on filtering out that content). It’s that I don’t need to flaunt my e-dick and will openly admit when I haven’t gone deep into an issue. I have not read the papers I’ve heard of and I have not specifically searched for more of them, so I’ll get back to you on that one if and when I do.
Anyway, that aside, you are presenting a bizarre scenario. You’re arguing that corporations will be demonstrably worse off by moving all coding to be machine-generated but they will do it anyway. Ad infinitum. Until there are no human coders left. At which point they will somehow keep doing it despite the fact that AI training would have entirely unraveled as a process by then.
Think you may have extrapolated a bit too far on that one? I think you may have extrapolated a bit too far on that one. Corpos can do a lot of dumb shit, but they tend to be very sensitive about stuff that costs them money. And even if that wasn’t the case, the insane volume of cheap skilled labor that would generate pretty much guarantees some competing upstart would replace them with the, in your sci-fi scenario, massively superior alternative.
FWIW, no, that’s not the same as outsourcing. Outsourcing hasn’t “often been a bad idea”. Having been on both sides of that conversation, it’s “a bad idea” when you have a home base with no incentive to help their outsourced peers and a superiority complex. There’s nothing inherently worse about an outsourced worker/developer. The thing that closes the gap on outsourcing cost/performance is, if anything, that over time outsourced workers get good and expect to get paid to match. I am pretty much okay with every part of that loop. Different pet peeve, though, we may want to avoid that rabbit hole.
It’s such a widespread and significant issue that it’s not really appropriate to make broad claims about the future of AI when you don’t understand one of the key limitations about current implementations of AI. This is the type of information that is critical for decision making about the usage of this technology.
To put it in your terms, it’s an extremely social media mindset to speak up on a wildly important topic that will impact everyone’s lives without learning about the core concepts of that topic.
I’m not making some infinite doomsday slippery slope scenario. It’s just a pattern. More corporations will try to replace more programmers with more half-assed implementations of AI, and the quality of all programming will suffer as a result. It’s not “all or nothing”, it’s just a piece of a greater whole.
Your point about the home base and superiority complex is exactly the type of issue I’m talking about - most corporations won’t implement AI well.
They will come up with ideas that sound great but can’t be accomplished with the current generation of tools, and a whole lot of people will lose their jobs for no good reason, and a whole lot of people will stop seeking those jobs for very good reason, and the vicious cycle will turn and turn.
You are saying a lot of things that sound good to you without much grounding. You claiming this is a “widespread and significant issue” is going to need some backing up, because I may be cautious about not claiming more knowledge than I have, but I know enough to tell you it’s not particularly well understood, nobody is in a position to predict the workarounds and it’s by no means the only major issue. The social media answer would be to go look it up, but it’s the weekend and I refuse to let you give me homework. I have better things to do today.
That’s the problem with being cautious about things. Not everybody has to. Not everybody knows they should or when. I don’t know if you’re dunning kruger incarnate or an expert talking down to me (pretty sure it’s not the second, though).
And I’m pretty sure of that because yeah, it is an infinite doomsday slippery slope scenario. That I happen to know well enough to not have to be cautious about not having done all the reading.
I mean, your original scenario is that. You’re sort of walking it back here where it’s just some effect, not the endgame. And because now you’re not saying “if AI actually replaces programmers wholesale” anymore the entire calculation is different. It goes back to my original point: What data will AI use to train? The same data they have now. Because it will NOT in fact replace programmers wholesale and the data is not fungible, so there still will be human-generated code to train on (and whatever the equivalent high enough quality hybrid or machine-generated code is that clears the bar).
AI has a problem with running out of (good) data to train on, but that only tells you there is a hard limit to the current processes, which we already knew. Whether current AI is as good as it’s going to get or there is a new major breaktrough in training or model design left to be discovered is anybody’s guess.
If there is one, then the counter gets reset and we will see how far that can take the technology, I suppose. If there is not, then we know how far we’ve taken it and we can see how far it’s growing and how quickly it’s plateauing. There is no reason to believe it will get worse, though.
Will companies leap into it too quickly? They already have. We’re talking about a thing that’s in the past. But the current iteration of the tech is incapable of removing programmers from the equation. At most it’s a more practical reference tool and a way to blast past trivial tasks. There is no doomsday loop to be had unless the landscape shifts signfiicantly, despite what AI shills have been trying to sell people. This is what pisses me off the most about this conversation, the critics are buying into the narrative of the shills aggressively in ways that don’t really hold up to scrutiny for either camp.
I mean, I’ll be honest, beyond the allegations of Dunning Kruger, I think this is actually just a grammatical mixup. I didn’t mean to write it the way you read it, and that may be my fault.
“If” AI replaces human programmers wholesale, new human code will stop being created
It starts with “if”. As in, it’s not a prediction of the future, it’s a response to the hypothetical future of AI being advocated for by techbros and corporations.
And “wholesale” doesn’t mean universally, it just means a lot.
And “new human code will stop being created” is true - I wasn’t saying all human code will stop being created. But AI replacing humans will stop humans from creating code. Many human projects will end, be reduced in scope, or won’t start, as AI is forced into projects that it isn’t yet ready for.
New human code will stop being created is a true - if ambiguous - statement. I do apologize for the ambiguity.
But given that AI does not perform well with retraining on AI output - and I’m sorry but I’d be happy to hear from anyone who can tell me that’s not a given - the ouroborous eats its own tail in more ways than one.
Less human code means less AI training. More AI creating code with less human input therefore leads to less developments and advancements in programming in general.
I just wonder what the LLMs will be trained on once they put everything else out of business.
LLM companies hire people to create information. They already hire math majors to work on high quality math data, for example.
This is a fair point. Although it’d be fun in the absolutely extreme scenario people are presenting here where all the coders would lose their coding job but get coding jobs generating random AI training data instead.
Some of the ways both critics and shills present the future of this technology are kinda nuts.
We’ve polluted the water. One active area that LLMs are being deployed is in reading scanned text, so my best guess is that the next few models are going to be trained on a new corpus of previously unscanned written text.
I’m talking legal documents from the 80s, company documents that were never digitized, and anythibg else google books hasnt fully OCR’d.
If I recall correctly the American standard for digitizing written doctor notes for medical trials in other countries is typing them out by hand by two different staff members. Then any discrepancies are resolved before they’re counted. Tablets and laptops helped this a ton since they start digitized.
We’re also looking for more efficiency and cutting out low skilled processes. Technology has facilitated this over decades and we produce much higher quality products and content than ever before.
Unfortunately capitalism has caused the dragons to hoard their profits and keep salaries low. Ideally the increase in efficiency would allow people to work less, like 32 hr work weeks, and still earn enough to not worry about basic needs. Own your own home, have kids, feed and clothe them, time/money to have kids do hobbies/sports, time/money to have your own hobbies and money/insurance to handle any health expenses.
All of that would increase the quality of life of the working class.
I mean… the same data they use now? And presumably other LLM output based on that, which is something that may or may not affect things a lot, nobody really knows.
Even if AIs consumed data when they trained on it so it couldn’t be used for training again, which they do not, it’s not like code stops being created, stored and datamined by the people who own the creation and storage.
AI gets real weird when you keep feeding it inputs that were once AI outputs. It’s a well known fact that the outputs severely degrade.
The whole point is if AI actually replaced programmers wholesale, new code would stop being created by humans.
Bit of a tautology, that. Presumably for AI to “replace programmers wholesale” it would need to produce human-quality code. Presumably human-quality code would not degrade anything because it’d be… you know, human-quality.
From what I can tell the degradation you’re talking about relates to natural language data. Stuff like physics simulations seems to be working fine to train models for other tasks, and presumably functional code is functional code. I don’t know if there is any specific analysis about code, though, I’ve only seen a couple of studies and then only as amplified by press.
I haven’t looked into it specifically because it really seems like a bit of a pointless hypothetical. Either AI can get better from the training data available or it can’t and then it is as good as it’s going to get unless the training methods themselves improve. At the moment it sure seems that there is a ton of research claiming both paths for growth and growth stalling that are both getting disproven by implementation almost faster than the analysis can be produced.
This argument mostly matters to investors itching to get ahead of a trend where they can fully automate a bunch of jobs and services and I’m more than happy to see them miss that mark and learn what the tech can do the hard way.
To be absolutely clear, AI is not “going to put everything else out of business”. Certainly LLMs won’t. Not even in programming.
It’s not that AI is going to successfully replace programmers - it is that large corporations want it to replace as much labor as possible, and they will use it to replace programmers because programmers will be the only people that can explain to them exactly why it won’t work. Everyone else will keep sayinng “go”, and, well, the nature of short-sighted profit seeking means they will go.
Outsourcing has often been a bad idea and the implementation deeply flawed - that didn’t stop anyone from outsourcing.
You should definitely look into the input/output thing. It’s absolutely real and applies to all generative AI and LLM operation, as far as I’m aware. The fact that you’re not familiar with it completely erodes your credibility on this topic.
Machine learning algorithms break when you turn them into an ouroborous and feed them their own outputs. Something about the statistically non-deterministic calculations and relatively insignificant artifacts they generate propagates and amplifies with each pass through the algorithm until the output is incomprehensible.
My “credibility on this topic” is of zero interest to me. I am not here to appeal to authority. I know you didn’t mean it like that, but man, it’s such a social media argument point to make it jumped right at me. For the record, it’s not that I haven’t heard about problems with training on AI-generated content (and on filtering out that content). It’s that I don’t need to flaunt my e-dick and will openly admit when I haven’t gone deep into an issue. I have not read the papers I’ve heard of and I have not specifically searched for more of them, so I’ll get back to you on that one if and when I do.
Anyway, that aside, you are presenting a bizarre scenario. You’re arguing that corporations will be demonstrably worse off by moving all coding to be machine-generated but they will do it anyway. Ad infinitum. Until there are no human coders left. At which point they will somehow keep doing it despite the fact that AI training would have entirely unraveled as a process by then.
Think you may have extrapolated a bit too far on that one? I think you may have extrapolated a bit too far on that one. Corpos can do a lot of dumb shit, but they tend to be very sensitive about stuff that costs them money. And even if that wasn’t the case, the insane volume of cheap skilled labor that would generate pretty much guarantees some competing upstart would replace them with the, in your sci-fi scenario, massively superior alternative.
FWIW, no, that’s not the same as outsourcing. Outsourcing hasn’t “often been a bad idea”. Having been on both sides of that conversation, it’s “a bad idea” when you have a home base with no incentive to help their outsourced peers and a superiority complex. There’s nothing inherently worse about an outsourced worker/developer. The thing that closes the gap on outsourcing cost/performance is, if anything, that over time outsourced workers get good and expect to get paid to match. I am pretty much okay with every part of that loop. Different pet peeve, though, we may want to avoid that rabbit hole.
It’s such a widespread and significant issue that it’s not really appropriate to make broad claims about the future of AI when you don’t understand one of the key limitations about current implementations of AI. This is the type of information that is critical for decision making about the usage of this technology.
To put it in your terms, it’s an extremely social media mindset to speak up on a wildly important topic that will impact everyone’s lives without learning about the core concepts of that topic.
I’m not making some infinite doomsday slippery slope scenario. It’s just a pattern. More corporations will try to replace more programmers with more half-assed implementations of AI, and the quality of all programming will suffer as a result. It’s not “all or nothing”, it’s just a piece of a greater whole.
Your point about the home base and superiority complex is exactly the type of issue I’m talking about - most corporations won’t implement AI well.
They will come up with ideas that sound great but can’t be accomplished with the current generation of tools, and a whole lot of people will lose their jobs for no good reason, and a whole lot of people will stop seeking those jobs for very good reason, and the vicious cycle will turn and turn.
You are saying a lot of things that sound good to you without much grounding. You claiming this is a “widespread and significant issue” is going to need some backing up, because I may be cautious about not claiming more knowledge than I have, but I know enough to tell you it’s not particularly well understood, nobody is in a position to predict the workarounds and it’s by no means the only major issue. The social media answer would be to go look it up, but it’s the weekend and I refuse to let you give me homework. I have better things to do today.
That’s the problem with being cautious about things. Not everybody has to. Not everybody knows they should or when. I don’t know if you’re dunning kruger incarnate or an expert talking down to me (pretty sure it’s not the second, though).
And I’m pretty sure of that because yeah, it is an infinite doomsday slippery slope scenario. That I happen to know well enough to not have to be cautious about not having done all the reading.
I mean, your original scenario is that. You’re sort of walking it back here where it’s just some effect, not the endgame. And because now you’re not saying “if AI actually replaces programmers wholesale” anymore the entire calculation is different. It goes back to my original point: What data will AI use to train? The same data they have now. Because it will NOT in fact replace programmers wholesale and the data is not fungible, so there still will be human-generated code to train on (and whatever the equivalent high enough quality hybrid or machine-generated code is that clears the bar).
AI has a problem with running out of (good) data to train on, but that only tells you there is a hard limit to the current processes, which we already knew. Whether current AI is as good as it’s going to get or there is a new major breaktrough in training or model design left to be discovered is anybody’s guess.
If there is one, then the counter gets reset and we will see how far that can take the technology, I suppose. If there is not, then we know how far we’ve taken it and we can see how far it’s growing and how quickly it’s plateauing. There is no reason to believe it will get worse, though.
Will companies leap into it too quickly? They already have. We’re talking about a thing that’s in the past. But the current iteration of the tech is incapable of removing programmers from the equation. At most it’s a more practical reference tool and a way to blast past trivial tasks. There is no doomsday loop to be had unless the landscape shifts signfiicantly, despite what AI shills have been trying to sell people. This is what pisses me off the most about this conversation, the critics are buying into the narrative of the shills aggressively in ways that don’t really hold up to scrutiny for either camp.
I mean, I’ll be honest, beyond the allegations of Dunning Kruger, I think this is actually just a grammatical mixup. I didn’t mean to write it the way you read it, and that may be my fault.
“If” AI replaces human programmers wholesale, new human code will stop being created
It starts with “if”. As in, it’s not a prediction of the future, it’s a response to the hypothetical future of AI being advocated for by techbros and corporations.
And “wholesale” doesn’t mean universally, it just means a lot.
And “new human code will stop being created” is true - I wasn’t saying all human code will stop being created. But AI replacing humans will stop humans from creating code. Many human projects will end, be reduced in scope, or won’t start, as AI is forced into projects that it isn’t yet ready for.
New human code will stop being created is a true - if ambiguous - statement. I do apologize for the ambiguity.
But given that AI does not perform well with retraining on AI output - and I’m sorry but I’d be happy to hear from anyone who can tell me that’s not a given - the ouroborous eats its own tail in more ways than one.
Less human code means less AI training. More AI creating code with less human input therefore leads to less developments and advancements in programming in general.
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