Again with the strawman. Having reasons to dislike one technology is not tantamount to hating all technology. Autocomplete is fine as assistive technology for people who can’t type faster than 15 WPM. Likewise, a spellchecker is a project that has a very clear end, a very clear benefit, it can easily be transparent in how it works, and does not require you to put everything through a brute-force recursive association. There are aspects to technologies that we can appraise and discern whether they’re beneficial to us or not.
I don’t celebrate cars either, even if they are electric and Chinese. They flatten and alienate the physical environment in a remarkably analogous way to how LLMs flatten and alienate the digital environment.
Vibe code has a lot of externalities, it is slower and less resource efficient in ways that accumulate as further vibe coding goes on, and it doesn’t build or hone people’s skills. Use of LLMs has directly been shown to diminish attentional and critical thinking capacity. Hell, I remember you personally arguing not too long ago that “something that systematically disincludes the human input is a fascist aesthetic”.
We have brought on the age of the mass deepfakes, while cultivating a process that materially/logistically cannot be sustained by the Earth’s resources for another 100 years, by looking at tech without broad long-term considerations. Diving faster headfirst into it will not solve our problems.
Data centers (which you can’t have LLMs without) were largely bad for humanity before 2022, but in a way that could have possibly been amended by socialist governance. At this point they’re just bad for everything except short-term market speculation. The world has not gotten any better in the past 5 years despite spending more on compute and on AI research by several orders of magnitude. It’s not going to fix our problems, it’s just going to be a crutch that also creates new ones.
If you keep arguing that personal LLMs/genAI are a useful and adaptive thing for society, I might just as well outsource to one and have you talk to it instead. What a great forum we would have then.
There is no straw man, I pointed out that you draw an arbitrary line at technology you’re familiar with. Nothing you’ve said here contradicts my point.
Also, you love making these sweeping statements like vibe code is slower and less resource efficient. Evidently you haven’t actually seen most code that humans write, because boy would you be shocked then. What LLMs do however is allow people who have no coding skills to solve problems and automate things in a way they couldn’t before unless they paid thousands of dollars to a developer. Meanwhile, these studies you reference use laughably small samples, but since they fit your narrative you choose to cite them uncritically as facts. And I’m not even going to touch your Reductio ad Hitlerum there.
The whole energy use argument is likewise hilarious given the context of this technology having become dramatically more efficient in just a single year. Not to mention that we were already building data centers for shit like bitcoing mining for decades. The actual problem here is capitalism but you’re just fixating on how a particular technology is being abused under it instead of doing any sort of systemic analysis.
Honestly, if you used one, it might’ve come up with a more coherent argument.
How is life qualitatively or quantitatively better now that we have access to these things? What were we all going without, for our entire lives, that is a noticeable net improvement, and that isn’t swallowed up by increased demand and noise?
It’s one thing to make up rubrics and say that the models are scoring ever higher on them, while “the graph goes up and to the right”. But on a practical level, what was so wrong or unbearable with the world before that introducing an LLM would revolutionize for the better? What software was so broken, or what process was so inaccessible, that the raw computational approach became the best solution? I’m actually open to hearing about this.
There are tons of ways LLMs have already improved daily life. Before LLMs, trying to translate a document required either hiring a translator or wrestling with clunky rule-based systems that often produced gibberish. Today, a model delivers usable translations in seconds. These might not be perfect, but they’re far ahead of anything that was possible before. And these things are also helping people with disabilities, somebody with dyslexia or a blind professional had to wait days for accessible documents. Now, screen readers paired with LLMs can summarize, rephrase, and read aloud any PDF in real time. Researchers spent countless hours skimming through hundreds of pages of regulatory filings or legal opinions to find a single relevant clause. Now, they ask a question and have a machine find the answer with citations. These are just a few examples of genuine and cumulative improvements that free people from drudgery and lower barriers for those who were previously locked out.
And here’s a concrete illustration of how this tech can be used on the left to level the playing field allowing regular people to do what used to take a whole team of analysts before. https://www.youtube.com/watch?v=KQog2g_zoJ8
Examples of this tech being useful are only hard to find for people who choose to be aggressively ignorant on the subject.
Again with the strawman. Having reasons to dislike one technology is not tantamount to hating all technology. Autocomplete is fine as assistive technology for people who can’t type faster than 15 WPM. Likewise, a spellchecker is a project that has a very clear end, a very clear benefit, it can easily be transparent in how it works, and does not require you to put everything through a brute-force recursive association. There are aspects to technologies that we can appraise and discern whether they’re beneficial to us or not.
I don’t celebrate cars either, even if they are electric and Chinese. They flatten and alienate the physical environment in a remarkably analogous way to how LLMs flatten and alienate the digital environment.
Vibe code has a lot of externalities, it is slower and less resource efficient in ways that accumulate as further vibe coding goes on, and it doesn’t build or hone people’s skills. Use of LLMs has directly been shown to diminish attentional and critical thinking capacity. Hell, I remember you personally arguing not too long ago that “something that systematically disincludes the human input is a fascist aesthetic”.
We have brought on the age of the mass deepfakes, while cultivating a process that materially/logistically cannot be sustained by the Earth’s resources for another 100 years, by looking at tech without broad long-term considerations. Diving faster headfirst into it will not solve our problems.
Data centers (which you can’t have LLMs without) were largely bad for humanity before 2022, but in a way that could have possibly been amended by socialist governance. At this point they’re just bad for everything except short-term market speculation. The world has not gotten any better in the past 5 years despite spending more on compute and on AI research by several orders of magnitude. It’s not going to fix our problems, it’s just going to be a crutch that also creates new ones.
If you keep arguing that personal LLMs/genAI are a useful and adaptive thing for society, I might just as well outsource to one and have you talk to it instead. What a great forum we would have then.
There is no straw man, I pointed out that you draw an arbitrary line at technology you’re familiar with. Nothing you’ve said here contradicts my point.
Also, you love making these sweeping statements like vibe code is slower and less resource efficient. Evidently you haven’t actually seen most code that humans write, because boy would you be shocked then. What LLMs do however is allow people who have no coding skills to solve problems and automate things in a way they couldn’t before unless they paid thousands of dollars to a developer. Meanwhile, these studies you reference use laughably small samples, but since they fit your narrative you choose to cite them uncritically as facts. And I’m not even going to touch your Reductio ad Hitlerum there.
The whole energy use argument is likewise hilarious given the context of this technology having become dramatically more efficient in just a single year. Not to mention that we were already building data centers for shit like bitcoing mining for decades. The actual problem here is capitalism but you’re just fixating on how a particular technology is being abused under it instead of doing any sort of systemic analysis.
Honestly, if you used one, it might’ve come up with a more coherent argument.
How is life qualitatively or quantitatively better now that we have access to these things? What were we all going without, for our entire lives, that is a noticeable net improvement, and that isn’t swallowed up by increased demand and noise?
It’s one thing to make up rubrics and say that the models are scoring ever higher on them, while “the graph goes up and to the right”. But on a practical level, what was so wrong or unbearable with the world before that introducing an LLM would revolutionize for the better? What software was so broken, or what process was so inaccessible, that the raw computational approach became the best solution? I’m actually open to hearing about this.
There are tons of ways LLMs have already improved daily life. Before LLMs, trying to translate a document required either hiring a translator or wrestling with clunky rule-based systems that often produced gibberish. Today, a model delivers usable translations in seconds. These might not be perfect, but they’re far ahead of anything that was possible before. And these things are also helping people with disabilities, somebody with dyslexia or a blind professional had to wait days for accessible documents. Now, screen readers paired with LLMs can summarize, rephrase, and read aloud any PDF in real time. Researchers spent countless hours skimming through hundreds of pages of regulatory filings or legal opinions to find a single relevant clause. Now, they ask a question and have a machine find the answer with citations. These are just a few examples of genuine and cumulative improvements that free people from drudgery and lower barriers for those who were previously locked out.
And here’s a concrete illustration of how this tech can be used on the left to level the playing field allowing regular people to do what used to take a whole team of analysts before. https://www.youtube.com/watch?v=KQog2g_zoJ8
Examples of this tech being useful are only hard to find for people who choose to be aggressively ignorant on the subject.
I found a YouTube link in your comment. Here are links to the same video on alternative frontends that protect your privacy: