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.

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    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

    this is an overstatement. once you learn the basics of one programming language (which does not take a full year), you can apply the knowledge to other programming languages, many of which are almost identical to one another.

    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.

    according to a commonly-cited 2023 study:

    training the GPT-3 language model in Microsoft’s state-of-the-art U.S. data centers can directly evaporate 700,000 liters of clean freshwater, but such information has been kept a secret

    the global AI demand is projected to account for 4.2 – 6.6 billion cubic meters of water withdrawal in 2027, which is more than the total annual water withdrawal of 4 – 6 Denmark or half of the United Kingdom.

    GPT-3 needs to “drink” (i.e., consume) a 500ml bottle of water for roughly 10 – 50 medium-length responses, depending on when and where it is deployed.

    there’s also the energy costs:

    according to google’s 2024 environmental report:

    In 2023, our total GHG emissions were 14.3 million tCO2e, representing a 13% year-over-year increase and a 48% increase compared to our 2019 target base year. This result was primarily due to increases in data center energy consumption and supply chain emissions. As we further integrate AI into our products, reducing emissions may be challenging due to increasing energy demands from the greater intensity of AI compute, and the emissions associated with the expected increases in our technical infrastructure investment.

    according to the mit technology review:

    The carbon intensity of electricity used by data centers was 48% higher than the US average.

    and

    [by 2028] AI alone could consume as much electricity annually as 22% of all US households.

    there’s also this article by the UN, but this comment is getting kinda long and the whole thing is relevant imo so it is left as an exercise to the reader

    i have my own biases against ai, so i’m not gonna try to write a full response, but this is what stood out to me

    • CriticalResist8@lemmygrad.mlOP
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      this is an overstatement. once you learn the basics of one programming language (which does not take a full year), you can apply the knowledge to other programming languages, many of which are almost identical to one another.

      I’ve tried getting into javascript at different points. My brain doesn’t like OOP for some reason. Then after that you have to learn jquery, then apparently React or Vue.js… That’s when I stopped looking lol because as much as in my job knowing web dev is useful I’m not a frontend dev either.

      I could maybe get something working after 6-9 months on it, if I don’t give up. But it would be inefficient, amateurish and might not even work the way I want it to.

      I’m not even talking about full apps with GUIs yet, just simple-ish scripts that do specific things.

      Or I can send the process to AI and it does it in five minutes. By passing it documentation and the code base it can also stay within its bounds, and I can have it refactor the code afterwards. People say it has a junior dev level and I agree, but it may not stay that way for much longer and it’s better than my amateur level.

      To say “you must learn programming it’d the only way” was true only before 2022. I would still say it’s good/necessary to know how code and computers work so you know how to scope the AI but aside from that like I said we don’t always have a programmer friend around to teach us or make our scripts for us (as much as I love them)

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        To add to the last part, my preferred way is to get acquainted with the library/framework if I’m gonna be using it a lot, and then complete everything else with AI. That way I still learn and know how it works under the hood so I can also guide the AI if it starts getting off topic.

        It’s a teaching by example tool. I don’t necessarily read or review the code but I ask it, why do it this way? Wait, I didn’t think you would do it like that, explain?

        A lot of the time documentation is severely lacking or meant for other devs. I remember getting in bootstrap years ago took me weeks. With AI I could probably get around to it in an hour.

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        yeah, i guess it’s fair to do vibe coding or whatever. idk, when it comes to existing codebases i hate the thought of having ai contributions mixed in with real contributions. but i guess realistically, if there are no developers anyway, and if the model is running locally, none of my hangups apply

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          I totally get you. I think AI has the potential to get more people interested in FOSS, and contributing too. I now take the view that they will only get better at handling codebases, not worse or plateau, and it will allow everyone to start making apps or even modifying them for themselves. There’s even a lot of open-source models that can run locally on machines we already have, so their power consumption is nothing different from running a modern triple A game or rendering software (and no water wastage in the machine itself). It might not be at that level yet for collaborative projects, but imo it’s important for FOSS devs to look into it and answer the question regarding their projects and needs. I have ideas for some projects (incl. Lemmy) and would love to contribute working code to it instead of just being an ideas guy.

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            That’s actually a really interesting point you bring up that I haven’t thought of. LLMs make open source even more useful because they pave the way for users to modify the source themselves even if they’re not technical. If somebody grabs an open source app, and they want a specific feature, they can have a coding agent add it for them and describe what they want using natural language. Maybe it won’t be as good as what an actual human dev would do, but if it solves their problem then it’s a net win. This makes the whole open source model of development incredibly powerful because anybody can adapt software to their needs and create their own customized computing environment.

            I also think that it would make sense to move away from the way applications are structured currently where the UI is tightly coupled to the backend. It would make far more sense to structure apps using a client/server model with a well defined API between them. This approach makes it possible to make scripts that combine functionality from different apps, the same way we do scripts in the shell. And this is where LLMs could be really useful. You could just have a UI that’s a canvas with a prompt, and the LLM connect to the APIs all your apps provide. Then the LLM could leverage functionality from different apps and render a UI for the specific request you make. You could also have it create custom UIs for whatever workflow you happen to have.

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              I definitely need to get in on agents, I’ve been wanting to bring custom changes to my apps for a while now, or even make simple scripts for myself and my workflow, and having to feed the AI the code manually has been pretty limiting.

              One could also imagine bringing these custom additions to the codebase too to keep foss projects thriving, but probably in a less “this is my very specific solution” and more “you can adapt my specific solution to be a little bit more specific to you” (e.g. in keyboard shortcuts, which I love having and would love to add to some apps I use).

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                Agent workflow is a huge quality of life improvement. I feel like you could write a whole book on the subject of how to structure code in LLM friendly way. I really think that we might see more interest in functional languages since they favor writing everything by composing small building blocks that can be reasoned about in isolation.

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      I think it is helpful to put some things in perspective, like for electricity usage, data centers only take up 1-1.5% of global electricity usage. Like stated here https://www.iea.org/energy-system/buildings/data-centres-and-data-transmission-networks

      What is the role of data centres and data transmission networks in clean energy transitions?

      Rapid improvements in energy efficiency have helped limit energy demand growth from data centres and data transmission networks, which each account for about 1-1.5% of global electricity use. Nevertheless, strong government and industry efforts on energy efficiency, renewables procurement and RD&D will be essential to curb energy demand and emissions growth over the next decade.

      To also cite form that article, there also this mention to.

      Data centres and data transmission networks are responsible for 1% of energy-related GHG emissions

      So even for overall GHG, data center’s general account very little. Of course with this technology being used more, electricity usage will rise a bit more but it still likely will be small in the grand scheme of things. Another question how much of that is specifically AI in regards to data centers in general? One cited figure is 10-20% of data centers is designated to AI usage. Like here https://time.com/6987773/ai-data-centers-energy-usage-climate-change/

      Porter says that while 10-20% of data center energy in the U.S. is currently consumed by AI, that percentage will likely “increase significantly” going forward.

      So, a lot of data centers are just being used for lots of other things like cloud stuff for example, but the share by AI is growing a bit more however.

      Besides that, to go to the water usage, that is a problem, especially when data centers, in general, are built in areas that can’t really sustain such things. However this is just data centers in general, and this was happening before AI in the last two years. I think it is also worth mentioning to that like, google and the rest are able to buy water rights to which also completely fucks over First Nations to which don’t get a say in these things.

      To quote Kaffe, who I think is also on here to??

      Instead of weaponizing climate anxiety to attack AI merely to defend property law and labor aristocracy, let’s cut to specific issues like Meta’s and Google’s ability to purchase water in violation of treaties.

      https://xcancel.com/probablykaffe/status/1905480887594361070#m

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        the IEA report was made in mid-2023, and i would imagine ai electricity usage has skyrocketed since then. as mentioned in the mit source, dating to may 2025, electricity usage by ai is 48% dirtier than the us average. my problem with ai isn’t that it violates intellectual property rights, it’s that llms are a net-negative to society because of their climate effects. if ai datacenters were built using clean energy and cooled using dirty water, it would likely be little more than a mild annoyance for me. as it stands, we are putting the global south underwater so that people who are surrounded by yes-men can have yes-robots too.

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          Skyrocketed by how much? To also use that mit source,

          In 2017, AI began to change everything. Data centers started getting built with energy-intensive hardware designed for AI, which led them to double their electricity consumption by 2023.The latest reports show that 4.4% of all the energy in the US now goes toward data centers.

          They link to a report from last year, but based on that, that still rather small no? That just only in the U.S alone and not globally. I do agree the share by datacenters, and by extension LLMs, will use more electricity in the future, but like in the grand scheme of things, like it is still relatively small.

          To add, I mainly quote Kaffe, because I mainly see lots of people elsewhere say how data centers are taking so much water, but then it fails to ignore things like Kaffe mentioned in that quote. Another thing I wanted to add, that I forgot to in my early reply, but why is it that LLM is getting the brunt of this, but wasteful water practices in farming don’t get mention like the growing of alfalfa in the southwest in the united states that leads to a lot of water waste as well?

          as it stands, we are putting the global south underwater so that people who are surrounded by yes-men can have yes-robots too.

          I’m not exactly sure how data centers, or rather LLM’s are doing that or being the sole contributor to that? Besides guessing that you mean the use of mines, along with introducing more mines, and the transportation needed to continue and make said data centers. Along with the green house emissions that result from said activities, and with generating electricity to power said data centers usually from dirty sources like in the united states. Yet like again, data centers shares in green house emissions, is little compared to more direct things like the u.s military being the largest polluter, or from other things like transportation contributing a large share of green house emissions.

          I just feel like it just goes again back to the issues of capitalism that AI exists in that context of, like CriticalResist said, and AI not really unique in these regards to contributing to climate change?

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            The other thing that people are forgetting is that AI is also helping make data centers more efficient. How much energy is wasted on inefficient code? I’d bet it is more than 1%.

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              it seems dubious to make the claim that large language models write more efficient code. the popularity of node.js alone makes me doubt there’s all that much efficient code out there for it to train on, at least percentage-wise. i mean, the most popular app for hardcore gamers to run in the background packages and runs on its own copy of google chrome. add to that hallucinations and code quality and whatever else and i doubt its code is achieving the efficiency of a high school coding class, at least in the general case

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            LLMs get the brunt of it because alfalfa has more uses than chatgpt. maybe it’s the result of my own bias but i would consider golf courses more useful than chatgpt. LLMs aren’t even close to the sole contributor to climate change, but they are emblematic of venture capitalists more than i think anything else. but it’s hard for me to justify the creation and use of these things when they have very narrow use cases, often create as much work as they save, and suck down clean drinking water like i suck down whiskey sours

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      I’ve been doing programming for a long time, and I can tell you that learning to use a language effectively takes a long time in practice. The reality is that it’s not just syntax you have to learn, but the tooling around the language, the ecosystem, its libraries, best practices, and so on. Then, there are families of languages. If you know one imperative language then core concepts transfer well to another, however they’re not going to be nearly as useful if you’re working with a functional language. The effort in learning languages should not be trivialized. This is precisely the problem LLMs solve because you can focus on what you want to do conceptually, which is a transferable skill, and the LLM knows language and ecosystem details which is the part that you’d be spending time learning.

      Meanwhile, studies about GPT3 are completely meaningless today. The efficiency has already improved dramatically and models that outperform those requiring a data centre even a year ago, can now be run on your laptop. You can make the argument that the aggregate demand for using LLM tools is growing, but that just means these tools are genuinely useful and people reach for them more than other tools they used to use. It’s worth noting that people are still discovering new techniques for optimizing models, and there’s no indication that we’re hitting any sort of a plateau here.

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        The efficiency has already improved dramatically

        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

        build as many as 10 data centers (each of which could require five gigawatts, more than the total power demand from the state of New Hampshire)

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

        it’s estimated that training OpenAI’s GPT-4 took over $100 million and consumed 50 gigawatt-hours of energy

        and overall, ai datacenters use

        millions of gallons of water (often fresh, potable water) per day

        i’m doubtful that the uses of llms justify the energy cost for training, especially when you consider that the speed at which they are attempting to create these “tools” requires that they use fossil fuels to do it. 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. i know that i don’t want gemini giving me some inane answer every time i google something. maybe you do.

        if you use a pretrained model running locally, you know the energy costs of your queries better than me. if you use an online model running in a large datacenter, i’m sorry but doubting the environmental costs of making queries seems to be treatler cope more than anything else. even if you do use a pretrained model, the cost of creation likely eclipses the benefit to society of its existence.

        EDIT: to your first point, it takes a bit to learn how to write idiomatic code in a new paradigm. 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.

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          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.

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            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
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              19 hours 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
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                15 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
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                  11 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.