Every argument to try and make AI seem different from any other kind of tool just is entirely incoherent, I mean what even is this.
The technology in itself was never a problem, the infrastructure that relies on the technology to profit off the collective work of all creative workers is a problem.
You’re doing literally what the image criticizes: conflating exploitation of the worker with the technology itself. You provide no logical reason as to why AI inherently requires worker exploitation. Huawei is behind a lot of AI infrastructure in China and is a worker co-operative in a socialist country. Your argument makes no sense.
Of course, the infrastructure around the technology can be quite bad, like with Anthropic and OpenAI, but this is true of literally any technology under capitalism at all, as the infrastructure around all technology in capitalist society is, well, capitalism, so of course it will be exploitative. But nothing about that is unique to AI. The issue is capitalism, not AI.
It’s not anti-tech to not want wars, we’re not wishing guns were never invented. It’s the same with mass-deep-learning, we want to stop doing it not wish we never knew how to do it
What on earth even is this attempt at a comparison? AI recently just made progress in mathematics, disproving a nearly century old math conjecture. How is stopping this analogous to being anti-war?
And especially, “we want to stop doing it not wish we never knew how to do it,” I’m not sure how you think that makes you not anti-tech, when you explicitly want to stop building the technology. Nobody has ever brought up wanting to destroy knowledge of how to build the technology in the first place, that’s entirely unrelated. Luddites want to “stop doing it,” as you say, not, destroy the knowledge of how to do it.
People need to remind themselves the foundation that LLMs use have been assisting doctors make correct diagnosis since the 90s - the technology isnt new nor is the dynamics making the exploitation of the tool bad, or even the vectors into which the upper classes manipulate ludditie tendencies to dismantle class based movements.
The most recent developments are ‘new’ in the sense they have a hollywood style advertisement budget that dwarves the actual development costs and that it is now anchored to cover up the failing US stock market and economy. You can see it in how China’s LLM models cost a fraction to develop, its mostly just marketing hype to coverup the fact they are inflating your wage to be worth nothing.
It’s not just marketing. China has expensive AIs as well and has been experimenting with scaling them up to large scales also. There’s a lot of corruption in the US and that leads to a lot of marketing overhype, but it’s not just that.
ANN technology in general has existed for decades, yes. The main difference recently is that most computer scientists were convinced scaling them up was pointless, because the main issue was models overfitting. Overfitting is when the model scores highly on your tests by just memorizing all the data rather than actually learning to abstract the patterns you’re trying to teach it from the data. If you give it a larger neural network, it will have more long-term memory to memorize more data, and so it would seem at first glance that scaling up a model would just make them more prone to overfitting and thus less intelligent.
However, as GPUs got better, people just started to experiment with scaling them up, and it turned out that this wasn’t true. Scaling models up does actually tend to improve their ability to learn the abstract patterns. Nobody actually knows why. The thing about ANNs is that they are kind of a black box, it’s not even fully understood how they work. But the discovery of these “scaling laws,” just as an empirical fact, is what kicked off the international race for AI.
It is kind of like when Einstein and co wrote that letter to FDR about the potential for the atomic bomb. They could see it in the data that such a thing was possible, even though no one had built it, and so they predicted it, and this made the government race towards building one before anyone else could. The discovery of the “scaling laws” implied that if you kept scaling ANNs up, they would get smarter, and so who could scale them up the largest would have the most advanced AI.
Although, in practice, it’s more complicated than this, because two ANNs can be of the same scale yet vastly different in intelligence. The work to combat overfitting is still useful, because better training methods allow you to squeeze out more intelligence out of the same number of parameters without scaling it up. Chinese researchers and companies like at Alibaba and DeepSeek have been much more focused on trying to improve the quality of the model by improving its training methodology to minimize overfitting rather than simply scaling them up infinitely, and this has allowed them to drastically improve the capabilities of even fairly small models you can run locally.
But they still do try to scale as well. Alibaba’s Qwen3.7-Max has over a trillion parameters, and it costs similarly to Anthropic’s models to use. Scaling is, in practice, empirically proven to at least be one tool in improving the capability of ANNs. Modern ANNs tend to have a development loop where they will take the latest and most up-to-date training methodologies and use that to train a model at as large of a scale as possible, but then someone will discover new training methodologies that let you improve the quality of the model without scaling it up, and so then they will go back to the beginning of the loop, training as large of a model as possible using this new training methodology.
Every time there is an improvement in the training methodology, this trickles down to models of all scales which all improve, but the largest scale models still always remain the most intelligent, and those models require an enormous amount of compute to train, and thus are intrinsically costly. While Chinese models tend to be cheaper, they are still not that cheap for the largest models. You could easily wrack up several hundred dollar bill over a month using Qwen-3.7-Max if you are building projects with it continuously.
Every argument to try and make AI seem different from any other kind of tool just is entirely incoherent, I mean what even is this.
You’re doing literally what the image criticizes: conflating exploitation of the worker with the technology itself. You provide no logical reason as to why AI inherently requires worker exploitation. Huawei is behind a lot of AI infrastructure in China and is a worker co-operative in a socialist country. Your argument makes no sense.
Of course, the infrastructure around the technology can be quite bad, like with Anthropic and OpenAI, but this is true of literally any technology under capitalism at all, as the infrastructure around all technology in capitalist society is, well, capitalism, so of course it will be exploitative. But nothing about that is unique to AI. The issue is capitalism, not AI.
What on earth even is this attempt at a comparison? AI recently just made progress in mathematics, disproving a nearly century old math conjecture. How is stopping this analogous to being anti-war?
And especially, “we want to stop doing it not wish we never knew how to do it,” I’m not sure how you think that makes you not anti-tech, when you explicitly want to stop building the technology. Nobody has ever brought up wanting to destroy knowledge of how to build the technology in the first place, that’s entirely unrelated. Luddites want to “stop doing it,” as you say, not, destroy the knowledge of how to do it.
People need to remind themselves the foundation that LLMs use have been assisting doctors make correct diagnosis since the 90s - the technology isnt new nor is the dynamics making the exploitation of the tool bad, or even the vectors into which the upper classes manipulate ludditie tendencies to dismantle class based movements.
The most recent developments are ‘new’ in the sense they have a hollywood style advertisement budget that dwarves the actual development costs and that it is now anchored to cover up the failing US stock market and economy. You can see it in how China’s LLM models cost a fraction to develop, its mostly just marketing hype to coverup the fact they are inflating your wage to be worth nothing.
It’s not just marketing. China has expensive AIs as well and has been experimenting with scaling them up to large scales also. There’s a lot of corruption in the US and that leads to a lot of marketing overhype, but it’s not just that.
ANN technology in general has existed for decades, yes. The main difference recently is that most computer scientists were convinced scaling them up was pointless, because the main issue was models overfitting. Overfitting is when the model scores highly on your tests by just memorizing all the data rather than actually learning to abstract the patterns you’re trying to teach it from the data. If you give it a larger neural network, it will have more long-term memory to memorize more data, and so it would seem at first glance that scaling up a model would just make them more prone to overfitting and thus less intelligent.
However, as GPUs got better, people just started to experiment with scaling them up, and it turned out that this wasn’t true. Scaling models up does actually tend to improve their ability to learn the abstract patterns. Nobody actually knows why. The thing about ANNs is that they are kind of a black box, it’s not even fully understood how they work. But the discovery of these “scaling laws,” just as an empirical fact, is what kicked off the international race for AI.
It is kind of like when Einstein and co wrote that letter to FDR about the potential for the atomic bomb. They could see it in the data that such a thing was possible, even though no one had built it, and so they predicted it, and this made the government race towards building one before anyone else could. The discovery of the “scaling laws” implied that if you kept scaling ANNs up, they would get smarter, and so who could scale them up the largest would have the most advanced AI.
Although, in practice, it’s more complicated than this, because two ANNs can be of the same scale yet vastly different in intelligence. The work to combat overfitting is still useful, because better training methods allow you to squeeze out more intelligence out of the same number of parameters without scaling it up. Chinese researchers and companies like at Alibaba and DeepSeek have been much more focused on trying to improve the quality of the model by improving its training methodology to minimize overfitting rather than simply scaling them up infinitely, and this has allowed them to drastically improve the capabilities of even fairly small models you can run locally.
But they still do try to scale as well. Alibaba’s Qwen3.7-Max has over a trillion parameters, and it costs similarly to Anthropic’s models to use. Scaling is, in practice, empirically proven to at least be one tool in improving the capability of ANNs. Modern ANNs tend to have a development loop where they will take the latest and most up-to-date training methodologies and use that to train a model at as large of a scale as possible, but then someone will discover new training methodologies that let you improve the quality of the model without scaling it up, and so then they will go back to the beginning of the loop, training as large of a model as possible using this new training methodology.
Every time there is an improvement in the training methodology, this trickles down to models of all scales which all improve, but the largest scale models still always remain the most intelligent, and those models require an enormous amount of compute to train, and thus are intrinsically costly. While Chinese models tend to be cheaper, they are still not that cheap for the largest models. You could easily wrack up several hundred dollar bill over a month using Qwen-3.7-Max if you are building projects with it continuously.
Yeah I guess because you’ve managed to twist them in a wierd way
Yes, good thing we agree about this, why do you mention it?
Because it’s not about exploitation it’s about image and sound generation being useless.
Sorry for not stating the obvious fact that scientific uses of deep learning techniques were always out of the discussion.
The analogy is that some industries can be ruled as useless/harmful so workers just stop operating them
Now one tip for the future : stop assuming the worst from people and treating them like they’re stupid, that’s not how you’re gonna build communism