I think I’m the type of person who gets into things after everyone. To that regard AI is no different, and for a long time I considered LLMs a toy - this was truer of older models, such as the original chatGPT models that came out in 2022-2023.

The discourse has understandably evolved over time and it’s clear that AI is not going anywhere. It’s like quadcopters in warfare, or so many other new techs before. As much as we’d like them not to be used or exist, they will still be. To refuse to adopt new advancements means to be left behind and giving oneself a disadvantage on purpose.

Ultimately the problems around AI stem from capitalism. Yes, there are excesses. But this is true of humans too.

AI - especially LLMs, which I have more experience with - are great at some tasks and absolutely abysmal at others. Just like some people are good at their job and others don’t know the first thing about it. I used to get an ad on Twitter about some guy’s weird messianic book, and in it he showed two pages. It was the most meaningless AI bullshit, just faffing on and on while saying nothing, written in the most eye-rolling way.

That’s because LLMs currently aren’t great at writing prose for you. Maybe if you prompt them just right they might, but that’s also a skill in itself. So we see that there is bottom-of-the-barrel quality, and better quality, and that exists with or without AI. I think the over-reliance on AI to do everything for them regardless of output will eventually be pushed out, and people who do it will stop finding success (if they even found it in the first place, don’t readily believe people when they boast about their own success).

I use AI to code, for example. It’s mostly simpler stuff, but:

1- I would have to learn entire coding languages to do it myself, which takes years. AI can do it in 30 minutes and better than I could in years, because it knows things I don’t. We can talk about security for example, but would a hobbyist programmer know to write secure web code? I don’t think so.

2- You don’t always have a coder friend available. In fact, the reason I started using AI to code my solutions is because try as we might to find coders to help, we just never could. So it was either don’t implement cool features that people will like, or do it with AI.

And it works great! I’m not saying it’s the top-tier quality I mentioned, but it’s a task that AI is very good at. Recently I even gave deepseek all the JS code it previously wrote for me (or even handwritten code) and asked it to refactor the entire file, and it did. We went from a 40kb file to 20 after refactoring, and 10kb after minifying. It’s not a huge file of course, but it’s something AI can do for you.

There is of course the environmental cost. To that I want to say that everything has an environmental cost. I don’t necessarily deny AI is a water-hog, just that the way we go about it in capitalism, everything is contributing to climate change and droughts. Moreover to be honest I’ve never seen actual numbers and studies, everyone just says “generating this image emptied a whole bottle of water”. It’s just things people repeat idly like so many other things; and without facts, we cannot find truth.

Therefore the problem is not so much with AI but with the mode of production, as expected.

Nowadays it’s possible to run models on consumer hardware that doesn’t need to cost 10,000 dollars (though you might have seen that post of the 2000$ rig that can run the full deepseek model). Deepseek itself is very efficient, and there are even more efficient models being made to the point that soon it will be more costly (and resource-intensive) to meter API usage than give it out for free.

I think the place you have as a user is finding where AI can help you individually. People also like to say AI fries your brain, that it incentivizes you to shut your brain off and just accept the output. I think that’s a mistake, and it’s up to you not to do that. I’ve learned a lot about how linux works, how to manage a VPS, and how to work on mediawiki with AI help. Just like you should eat your vegetables and not so many sweets, you should be able to say “this is wrong for me” and stop yourself from doing it.

If you’re a professional coder and work better with handwritten code, then continue with that! When it comes to students relying on AI for everything, then schools need to find other methods. Right now they’re going backwards to doing pen and paper tests. Maybe we should rethink the entire testing method? When I was in school, years before AI, my schoolmates and I already could tell that rote memorization was torture and a 19th century way of teaching. I think AI is just the nail in the coffin for a very, very outdated method of teaching. Why do kids use AI to do their homework for them? That is a much more important question than how are they using AI.

As a designer I’ve used AI to help get me started on some projects, because this is my weakness. Once I get the ball rolling it becomes very easy for me, but getting it moving in the first place is the hard part. If you’re able to prompt it right (which is definitely something I lament, it feels like you have to say the right magic words and they don’t work), it can help with that, and then I can do my thing.

Personally part of my unwillingness to get into AI initially was from the evangelists who like to say literally every new tech thing is the future. Segways were the future, crypto was the future, VR was the future, NFTs were the future, google glasses were the future… They make money on saying these things so of course they have an incentive to say it. It still bothers me that they exist, if you were wondering (if they bother you too lol), but ultimately you have to ignore them and focus on your own thing.

Another part of it I think is how much mysticism there is around it, with companies and let’s say AI power users who are so unwilling to share their methods or how LLMs actually work. They retain information for themselves, or lead people to think this is magic and does everything.

Is AI coming for your job? Yes, probably. But burying our heads in the sand won’t help. I see a lot of translators talking about the soul of their art - everything has a soul and is art now (even saw a programmer call it that to explain why they don’t use AI in their work), we’ve gone full circle back to base idealism to “explain” how human work is different from AI work. AI already handles some translation work very well, and professionals are already losing work to it. Saying “refuse to use AI” is not materially sound, it is not going to save their client base. In socialism getting your job automated is desirable, but not in capitalism of course. But this is not new either, machines have replaced human workers for centuries now, as far back as the printing press to name just one. Yet nobody today is saying “return to scribing monks”.

I think it would be very useful to have an AI guide written for communists by communists. Something that everyone can understand, written from a proletarian perspective - not the philosophy of it but more like how the tech works, how to use it, etc. I can put it up on the ProleWiki essays space if someone wants to write it, we’ve put up guides before, e.g. if you want to see a nutrition and fitness guide written from a communist perspective.

  • ☆ Yσɠƚԋσʂ ☆@lemmygrad.ml
    61·
    2 days ago

    the mit article was written this may, and as it notes, ai datacenters still use much more electricity than other datacenters, and that electricity is generated through less environmentally-friendly methods. openai, if it is solvent long enough to count, will

    Nobody is advocating for the service model companies like openai use here. I think this tech should be done using open source models that can be run locally. These companies also lack any clear business model. This is a great write up on the whole thing https://www.wheresyoured.at/the-haters-gui/

    even the most efficient models take several orders of magnitude more energy to create than to use:

    Creating models is a one time effort. The usage is what really counts. Also, most new models aren’t trained from scratch either. They use foundational models as the base then tweak the weights. There are also techniques like LoRA that let you adjust a trained model.

    However, even this is improving rapidly. Here’s one example:

    With only 27 million parameters, HRM achieves exceptional performance on complex reasoning tasks using only 1000 training samples. The model operates without pre-training or CoT data, yet achieves nearly perfect performance on challenging tasks including complex Sudoku puzzles and optimal path finding in large mazes. Furthermore, HRM outperforms much larger models with significantly longer context windows on the Abstraction and Reasoning Corpus (ARC), a key benchmark for measuring artificial general intelligence capabilities. These results underscore HRM’s potential as a transformative advancement toward universal computation and general-purpose reasoning systems.

    and overall, ai datacenters use

    Now compare that with DeepSeek.

    DeepSeek has claimed it took just two months and cost under $6 million to build an AI model using Nvidia’s less-advanced H800 chips.

    i’m not gonna make the argument that aggregate demand is growing, because i believe that the uses of llms are rather narrow, and if ai is being used more, it’s because it is being forced on the consumer in order for tech companies to post the growth numbers necessary to keep the line growing up.

    These aren’t the really interesting uses of AI. The reason there’s so much focus on chatbots in the west is cause there’s no industry to speak of. Compare this with China:

    In the future, he said, 98% of AI applications in the market will serve industrial and agricultural needs while only 2% will serve consumers directly.

    Very similar sentiment from the founder of Alibaba cloud as well https://www.youtube.com/watch?v=X0PaVrpFD14

    but if you’re super concerned about code quality you’re not using an llm anyway. at least unless they’ve made large strides since i last used one.

    I really don’t see what code quality has to do with LLMs to be honest. You have the final say on what the code looks like, and my experience is that you can sketch out the high level structure of code, and have LLM fill it in. Generally it’ll produce code that’s perfectly fine, especially for common scenarios like building a UI, an API endpoint, etc. This is precisely the kind of tedious code I have little interest writing, and I can focus on the actual interesting parts of the project.

    If you haven’t used them for even a couple of months, then yes you’re missing out on very large strides. The quality of output is improving on practically monthly basis right now, and how you use the models matters as well. If you’re just typing stuff in a chat you’ll have a very different experience from using something like plandex or roocode where the model has access to the whole project, it can run tests, and iterate on a solution.

    It’s easy to dismiss this stuff when you already have a bias against it and don’t want it to work, but the reality is that it’s already a useful tool once you learn where and when to use it.

    • into_highest_invite@lemmygrad.ml
      1·
      2 days ago

      Nobody is advocating for the service model companies like openai use here. I think this tech should be done using open source models that can be run locally.

      this is definitely fair. i think my big issue with it is the inordinate amount of capital (land, carbon emissions, water) that go into it. maybe i’ve unfairly associated all ai with openai and gemini and meta.

      Now compare that with DeepSeek.

      my understanding of deepseek is that most of their models are trained by engaging in dialogue with existing models. the cost of training and running those models should be taken into account in that case. if it is from scratch that might change things, if the carbon and water numbers are good.

      In the future, he said, 98% of AI applications in the market will serve industrial and agricultural needs while only 2% will serve consumers directly.

      i think that’s a problem with the definition of ai. it’s not clear to me what tim huawei defines ai as. i’m not arguing against the concept of machine learning, to be clear. i thought we were talking specifically about language models and photo and video generation and whatnot

      It’s easy to dismiss this stuff when you already have a bias against it and don’t want it to work, but the reality is that it’s already a useful tool once you learn where and when to use it.

      yeah that’s fair enough. i didn’t mean to get into a huge discussion over llms because there’s definitely an element of that in my head. idk, i guess my point in saying that was that you can shit out a more-or-less working piece of code in any language pretty quickly, if you don’t need it to be idiomatic or maintainable. my understanding was ai was kind of the same in that regard.

      i guess if training large language models can be done with negligible emissions and cooled with gray or black water, i can’t be against it. programming is definitely the main field where there’s no arguing that llms aren’t useful at all. i’m still unconvinced that’s what’s happening, even with deepseek, but if they’re putting their datacenters on 3-mile island and using sewage to cool their processors, i guess that would assuage my concerns.

      • ☆ Yσɠƚԋσʂ ☆@lemmygrad.ml
        2·
        1 day ago

        this is definitely fair. i think my big issue with it is the inordinate amount of capital (land, carbon emissions, water) that go into it. maybe i’ve unfairly associated all ai with openai and gemini and meta.

        I very much expect the whole bubble to pop because these companies still haven’t found a viable business model. I agree the way these big companies approach things is incredibly problematic. At the same time, the best thing to do is to promote development of this tech outside corporate control. We already saw the panic over DeepSeek being open sourced, and the more development happens in the open the less leverage these companies will have. There’s also far more incentive to make open solutions efficient because people want to run them locally on commodity hardware.

        my understanding of deepseek is that most of their models are trained by engaging in dialogue with existing models. the cost of training and running those models should be taken into account in that case. if it is from scratch that might change things, if the carbon and water numbers are good.

        Sure, but that also shows that you don’t need to train models from scratch going forward. The work has already been done and now it can be leveraged to make better models on top of it.

        i thought we were talking specifically about language models and photo and video generation and whatnot

        Doing text, image, and video generation is just one application for these models. Another application of multimodal AI is that it can integrate information from different sensors like vision, sound, and tactile feedback, and this makes it useful for building world models robots can leverage to interact with the environment. https://www.globaltimes.cn/page/202507/1339392.shtml

        • into_highest_invite@lemmygrad.ml
          1·
          21 hours ago

          Sure, but that also shows that you don’t need to train models from scratch going forward. The work has already been done and now it can be leveraged to make better models on top of it.

          yeah but you gotta count the emissions by the datacenters running the old models. i don’t think that accounting is being done by openai, and i don’t think it’s possible for deepseek. actually, i don’t think openai is doing any accounting.

          https://www.globaltimes.cn/page/202507/1339392.shtml

          is this the same kind of ai as above? idk, the unqualified term “ai” is kind of ambiguous to me.

          • ☆ Yσɠƚԋσʂ ☆@lemmygrad.ml
            1·
            17 hours ago

            We already agree that companies like openai are a problem. That said though, even these companies have an incentive to use newer models that perform better to reduce their own costs and stay competitive. If openai needs a data centre to do what you can do on consumer grade hardware with a model like qwen or deepseek, they’re not gonna stay in business for very long.

            And yeah Global Times article is specifically talking about multimodal LLMs which is the same type of AI.