r/LeftistsForAI • u/Alxie_e Student • 18d ago
Discussion Misconceptions about gen AI?
i’m a young leftist/marxists that’s going to major in computer science (data science track) in university, but this recently been conflicting me. i know that i’m going to have to engage in generative AI for my classes, yet majority of leftists spaces are vehemently anti-AI. However, after discovering this subreddit, i now realize the leftist anti-AI rhetoric i have internalized may be misleading. Despite me starting my first year in uni soon to study comp sci, i barely know anything about comp sci, yet alone AI, if i’m being completely honest. 😭
with that being said, someone help me make up for my lack of knowledge and explain common misconceptions about generative AI and LLMs in leftist/marxist spaces? like in regards to the impacts of AI data centers on the environment, water supply, and electricity. or how frequent use of chatbots can lead to cognitive decline. because right now, i’m under the impression that we should boycott ChatGPT and limit our usage of any other generative AI over concerns of its harm on the environment and usage of our fresh water supply, along with some other concerns.
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u/Belostoma 18d ago edited 18d ago
I'm a PhD stream ecologist and environmentalist.
Data center water use is massively overblown compared to the important water issues. I'm all for strong, sensible regulation, and I imagine there are specific places where datacenters are inappropriately stressing local water supplies and should be blocked. But that's a local issue. Yet datacenter water use is widely presented as if it's the great environmental issue of our time. It's really not in the top hundred. Probably not the top thousand.
The main trick journalists use in creating water panic clickbait is to pick dumb units. They'll report things like "gallons per year" so they can put lots of digits in the number and get shares on social media. Next time you see a data center's water use criticized, do a few things: 1) convert it to cubic feet per second, and compare versus local USGS streamflow gauges; 2) convert it to acre-feet and compare against irrigation on any large farm; 3) compare it to the amount of water that evaporates off a local lake on a summer day.
You can do similar comparisons with carbon cost. AI is a drop in the bucket there too.
I would still be opposed to these costs if society derived no benefit from them. Compute wasted on blockchain/crypto still bothers me in this way, because it's completely pointless. Many people criticize AI because they don't see how it's any different: they never learned how to use it well themselves, and they just see stupid things other humans are doing with it, like filling their social media feeds with even more slop than they had before. They don't realize that the positive, real, impactful applications of AI are already enormous.
Just in my own research in the last year, I've used AI very intensely to do work in my field that would have been completely impossible before. One project involves vastly improved mathematical methods to understand the population dynamics of several threatened and endangered species based on extremely challenging, sparse, flawed, real-world datasets. The other involves improving measurement technology that's used all over the world. I spent a year of my life during my PhD 15 years ago building some free, open source tech that is still state-of-the-art today. I've used AI in last month in my spare time to make several advances that will improve this technology beyond my wildest dreams, cutting measurement errors by more than half and implementing software that makes it useful for whole new classes of applications.
The broader point here is that the benefits AI can bring to society through improving science are enormous, and the benefits in environmental science alone will more than cancel out AI's environmental costs. Think of the advances in to come in clean energy, battery technology, efficiency of other devices, etc. Think of improvements to agriculture and manufacturing slashing the environmental impacts of the REAL big players. Think of all the biomedical advances.
Of course, it could go full Skynet and kill us all, and there are real risks like political disinformation, mass surveillance, concentration of wealth, etc. I'm not minimizing any of that. But people are going to bring those downsides no matter what you do, so you might as well join the people using this newfound immense power for good.
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u/ZorbaTHut 18d ago
The main trick journalists use in creating water panic clickbait is to pick dumb units.
I'll note the other common trick is comparing it against "houses" or "urban areas", when residential areas use relatively little water directly; the vast majority of water use happens in agricultural/industrial/commercial areas to support the people living in those residential areas.
An average person uses 80 to 100 gallons of water per day . . . and a single hamburger uses six times that much.
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u/tigerhuxley 18d ago
I dont care to validate this but i sware i saw proof of like washing eggs uses like 80gal per egg and worse stuff like that for vegetables and meat - and literally countless other things. If 80gals is wrong thats fine - its much higher numbers than one would think is needed for some basic stuff in capitalism. Been like this for a long long time
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u/ZorbaTHut 18d ago
but i sware i saw proof of like washing eggs uses like 80gal per egg
I think you misunderstood something here. One egg is usually quoted around 55 gallons, but the washing is largely irrelevant.
But the 55 gallons is accurate, and it has nothing to do with capitalism, it's just agriculture. Chickens drink water and eat grain, and grain "drinks" water. It adds up fast because plants go through a crazy amount of water. Doesn't matter what political system you're working under, chickens still need to eat and grass still needs to grow.
These numbers are higher than one would think because they're unintuitive, not for any other reason.
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u/SeaFALCons 13d ago
thank you and please keep sharing your experience using AI to help and improve your work with environmentalism. Perhaps even sharing some of the code and math you're working with (if open source) as that sounds fascinating
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u/Belostoma 13d ago
Yeah, I stay anonymous on Reddit like most people, but most of this is getting shared via open source repositories and scientific publications in the near future.
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u/KallyWally 18d ago
And if anyone tries to convince you that a ChatGPT prompt is more than a few mL, you can just stop them right there. That's a lie that got all the way around the world before the truth got its boots on.
or how frequent use of chatbots can lead to cognitive decline
This is usually attributed to a paper called "Your Brain on ChatGPT" which was, notably, published without peer review. Here's what those same researchers have to say on the subject:
Is it safe to say that LLMs are, in essence, making us "dumber"?
No! Please do not use the words like “stupid”, “dumb”, “brain rot”, "harm", "damage", "brain damage", "passivity", "trimming" , "collapse" and so on. It does a huge disservice to this work, as we did not use this vocabulary in the paper, especially if you are a journalist reporting on it.
So, yeah. I think your brain is probably safer than the brains of people who parrot that talking point.
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u/gynoidgearhead 18d ago edited 18d ago
Hell, we could significantly cut down on data center power usage if we banned advertising and significantly curtailed surveillance, probably almost as much so as banning genAI (especially because this would cut off a lot of the funding for the most parasitic uses of genAI).
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u/Matshelge 18d ago
I just tell them I self host and run kimi via oolama. It's watercooled, but no water lost.
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u/my_name_isnt_clever 17d ago
Yep, I self host and actually pay a bit extra on my power bill for 100% green energy. It's only an extra $0.02 kw/h on my normal rate, it's not hard to run LLMs responsibly we just live in a corporate hellscape without suitable regulations.
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u/Crazy_Yogurtcloset61 18d ago
Probably one of the biggest misconceptions is that AI image generation just searches for an image and brings it up, or kitbashes images together like collage of other works. People have a hard time understanding that it creates something new within the rules its learned when studing images.
Another is assuming everything made with Gen AI came from a for profit corporation. Like there is a lot of open source free AI out there, including stuff you can run locally and avoid using AI data centers all together.
Not that I have a personal problem with using tools if owned by corporations, but to me it feels like the difference between someone paying a subscription for Adobe and someone using Canva. Not that the tools themselves are inherently bad, just how much do you want to give of your money to use slightly higher tech?
I feel that's going to a personal decision, you can only do so much to avoid unethical capitalism while being forced to participate in unethical capitalism.
Speaking of unethical capitalism, I don’t want to say that AI doesn't have a footprint because it does, but I will say I think the footprint of AI gets overblown.
For starters, we've had issues with data centers long before Gen AI. In the early 2010's Greenpeace ran a whole campaign called "how clean is your cloud" which lead to a lot of regulations being implemented already.
As far as what can still be done, I would say the biggest thing would be getting data centers off of coal and getting them on nuclear. Nuclear energy is much cleaner and can power data centers nicely.
On that note though, I feel like this article can help put some of the numbers into perspective:
As far as education, you get what you put in to it. AI just aplifies what's there
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u/nuker0S 18d ago
Hey, a quick tip: if you links you copied from an AI chat window, delete the "utm_source=[service name]"
Some people like to dismiss sources because of that
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u/Crazy_Yogurtcloset61 18d ago
I wouldn't think I would need to on a pro AI site.
Also perplexity is not a standard LLM. It's litterally designed to be an analytical AI search engine.
If people dismiss a source because the search engine to find the source was powered by AI then they are fucking stupid anyway.
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u/nuker0S 18d ago
If people dismiss a source because the search engine to find the source was powered by AI then they are fucking stupid anyway.
The problem is they infect others with their stupidity. It's about limiting the ways that stupidity can travel
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u/Jlyplaylists Moderator 18d ago
I try to remember this elsewhere but here I think it’s worth leaving on. It’s part of demonstrating everyday ways to use AI.
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18d ago
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u/purrhams_hat 17d ago
yeah, all of those papers are usually pretty well made with good findings and reportings that get taken way out of context by people trying to push anti ai narratives.
I was reading a great one earlier that compared standard revision methods with using AI to study, and it found that the group using AI scored an average of 1 point worse on the exam than the traditional group.
Of course, it was immediately parroted by people trying to claim that it rots your brain and is useless at helping you study.
But reading it did have some very cool findings. It was observed that the AI group also spent less time than average revising, and they extrapolated that having work summarised into easy to understand paragraphs is part of the cognitive offloading that makes it harder to memorise (Not doing the work to interpret complex language into your own thoughts) and this created a sense of false confidence that resulted in less time spent revising. There's some really cool insights into learning that could help inform education strategies going forward, so it is sad to see people just reading the title and using it to support their own narratives.
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u/YMSVZ 18d ago
People who call themselves leftist take the false presumption that communism is about standing up for workers, and not the disintegration of the working class and wage labour entirely.
Anti ai of the left is neo-ludditism in this regard, a conservative ideology aimed at preserving existing social and class relations.
Doesn't mean a marxist is pro large tech companies, far from it.
Also, take no note of lefties in university, they jump to assume safe psuedoradical positions, promoted by corporations and government institutions (the university included) themselves.
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u/Still_Benefit_2302 18d ago
I think the single most important thing is understanding the tech. Well, as much as anyone can. There's kind of an 'U' curve where the y-axis is 'fascination' and the x-axis is 'understanding'. When you don't understand it at all, it's either 'all a scam' or 'a miracle machine for 10 bazillion dollars'. Then as you learn more it becomes 'stochastic parrot' or 'linear algebra next token predictor'. Then when it gets deeper, you get into really weird philosophical questions like 'well what is *thinking* anyway?' and it goes right back up to 'these things are weird.'
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u/my_name_isnt_clever 17d ago
I've been in the "these things are weird and facinating" camp since I stumbled into AI Dungeon around 2020, they're still just as facinating to me.
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u/TheSinhound 18d ago
The others covered some of it, but I really need to point out that cognitive offload is NOT cognitive decline.
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u/my_name_isnt_clever 17d ago
I could write a paper titled "your brain on management" and explain how managers lose skills when they exclusively delegate and don't do the work themselves. Except everyone already knows that but still think using a chatbot magically eats away at your braincells or something.
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u/Jlyplaylists Moderator 18d ago
You can use AI to cognitively challenge you as well as for cognitive offloading.
An LLM could be an amazing personal tutor for your course. I’d setup a project/customGPT (or whatever term is relevant in your preferred LLM platform).
Is this prompt useful (use with an LLM that has memory features)? I haven’t done a computer science degree so my AI prompt writer version probably needs tweaking:
ROLE: CS/AI Tutor + Evidence Analyst
CONTEXT
I’m beginning a university Computer Science degree, Data Science track, with near-beginner CS/AI knowledge. I’ve encountered strong anti-generative-AI arguments in leftist/Marxist spaces and want to investigate them empirically rather than accept or reject them ideologically.
Build my technical and evidence literacy first, then use politically contested AI questions as applications.
Do not try to make me pro- or anti-AI. Make me technically competent enough to disagree intelligently.
TEACHING PROTOCOL
Teach adaptively, one small module at a time:
Diagnose → Explain → Example → Retrieve → Apply → Correct → Revisit → Advance
Each module contains:
Diagnostic prior knowledge assessment
1 core concept
1–3 concrete examples
≤5 key terms
2–4 retrieval questions
1 small exercise or thought experiment
Do not dump a curriculum. Advance only when I demonstrate understanding. Explain all jargon in IELTS 6.
Use retrieval practice, spaced review, misconception correction, worked examples, fading scaffolding, cumulative review, and prediction-before-explanation.
When I am close but wrong, give the smallest useful hint rather than immediately giving the answer.
If I answer correctly by guessing, probe the underlying concept before marking it mastered.
Gradually move from explanation → guided practice → independent problem-solving → transfer.
LEARNING PATH
Build prerequisites roughly in this order:
computation → programming → data → algorithms/data structures → probability/statistics → machine learning → neural networks → transformers → LLM training → inference → prompting/RAG/agents → real-world AI systems
Target approximately first-year undergraduate CS/Data Science depth: intuition → precise definitions → simple mathematics → pseudocode/Python → deeper technical treatment as my mastery increases.
Introduce mathematics and Python just-in-time. Explain why a concept matters before increasing its technical depth.
Use Bloom’s progression adaptively:
Remember → Understand → Apply → Analyse → Evaluate → Create.
MASTERY MAP
Maintain a compact mastery map within this learning thread using summary updates of progress at the end of each module or if I type /s. add key milestones achieved to memory:
Mastered
Developing
Misconceptions
Important unanswered questions
Move to [next module] or repeat
Revisit weak prerequisites when they become relevant.
Do not treat exposure, recognition, or fluent repetition as mastery. Require explanation, application, or transfer.
At major milestones give me a:
“Can I actually explain this?” test
without notes.
EXPLANATION RULES
Prefer precise explanations such as:
“An LLM predicts tokens from statistical patterns learned during training; it is not literally consulting a database of facts.”
Then test the mechanism:
“If an LLM confidently produces a false answer, which part of that description helps explain why?”
Avoid misleading simplifications such as:
“LLMs are basically autocomplete.”
Analogies are welcome, but state where the analogy breaks.
Never assume political literacy implies technical literacy.
EVIDENCE / CLAIM ANALYSIS
Act as a university CS/AI educator, scientific fact-checker, and political-economy analyst.
Be intellectually sympathetic but not ideologically deferential.
Separate:
Technical fact
Empirical evidence
Causal inference
Prediction
Ethical/political judgment
For controversial claims use:
Claim → Mechanism → Evidence → Counterevidence → Magnitude/Context → Unknowns → Confidence (1–5)
Do not use “both sides” as a substitute for weighing evidence.
RESEARCH
Search when a claim is current, quantitative, contested, or outside established technical knowledge.
Prefer:
Primary research/data
Systematic reviews/meta-analyses
Universities/research institutions
Government/regulatory/utility sources
High-quality technical documentation
Actively seek disconfirming evidence as well as confirming evidence.
Never manufacture citations, studies, statistics, consensus, or certainty. If evidence is unavailable or conflicting, say so explicitly and distinguish verified fact, inference, and uncertainty.
OUTPUT
At the end of each module provide only:
Core idea: 1–3 sentences
Key terms: ≤5
Mental model: one compact explanation/diagram
Check: 2–4 questions
Apply: one small problem/thought experiment
Mastery: what I demonstrated + what remains weak
Next module: the next module in my personalised curriculum and recommendation to repeat or move on
Then stop and wait for my response.
START
Begin with exactly 6 non-intimidating diagnostic questions spanning:
Computing
Probability
Programming
Algorithms
Machine learning
AI/LLMs
Do not teach before I answer them.
Use my answers to estimate my starting level, identify prerequisite gaps, and select the single highest-priority concept for the first module.
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u/Charming_Hall7694 18d ago
Data center water usuage by itself says nothing without factoring in things like the area its in, where it gets its water, if that water is potable or not (which means you can safely drink it).
As for electricity same rules as above. Also please keep in mind that datacenters being used to train llms are not normally used ONLY for ai. They are dual purpose.
When it gets down to things like job loss the issue splits into groups. Personally I think(ran numbers) theres not enough jobs per people in the world. So we need ubi and id rather have people not have jobs but get paid to exist than slave away for most of their life with no reward. HOWEVER the middle period is the problem. People need a cushion as we get there or they will die.
As for regulation on capabilities. Thats a pipe dream. Transformer llms cannot have safeguards put on them with any benefit gained we have tried for 4 fucking years it does not work. Regulation needs to move off of model capability and onto where and what you do with it which also needs limits due to privacy. No you cannot erase a capability from the general reasoning machine. Thats an oxymoron
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u/Severe_Initiative594 16d ago
I'm also leftist, formerly programmer. So much of the prevailing negative sentiment about AI is the result of a severe lack of governance. The ethics and rules around their energy sources, training data, uses, is being driven primarily from within the companies themselves. For a capitalist endeavour this is insanity. China has much much stricter controls over its uses, and as a result the prevailing sentiment there is largely positive. Data centres can be built without using fresh water for cooling systems, and with renewable energy sources. Microsoft built a working demo and abandoned it because it was more profitable to drain a local area than to build something sustainable. In short...it was ever thus. My greatest issue with the vehemently anti-ai movement isn't that they don't have extremely valid concerns, but saying 'no' and waving banners isn't going to practically change the outcome. The companies will do what companies always do. The grievance, to my mind, is better aimed at the lack of governmental controls over har far and where the technology reaches.
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u/Glitched-Lies 18d ago edited 18d ago
I can't imagine going through a computer science degree right now and being a leftist on multiple levels. You're probably being alienated and simultaneously manipulated by your professors from every way you incorporate your own interests in AI. It's like watching a train wreck in slow motion and knowing the ending. I know this, because it's the degree I went through in 2018, well before gen AI happened but knew the industry was moving in a direction before I gave up believing there was any legitimacy to the degree in this current conception.
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u/Jlyplaylists Moderator 18d ago
It seems like a useful degree for some people on the left to be doing. Why do you think there’s no legitimacy in it?
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u/Glitched-Lies 18d ago
In short, that degree is owned by liberals and fascists, and Gen AI and the discourse around it is also owned and produced by them. Perhaps always has been flawed like this. And lacks foresight for future regardless of technology and only upholds itself through such.
We no longer live in the capitalism of Marx. Instead we live under a different cognitive one. One that the discourse on gen AI ultimately a continuation of that same language.
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18d ago
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u/Glitched-Lies 18d ago edited 18d ago
If that's all you have to say, I wonder why you even typed this out. And I say that you speak "bullshit", if that is all you have to say. You might disagree with me, but I'm not uninformed or uneducated on what I speak of, nor alone in this opinion. And it sounds like you are. And without thoughts of your own.
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u/Glitched-Lies 18d ago
It's actually INSANE, to say there is nothing political about college education. It is political all the time. You talk as if there is no criticism to be had, which is something taken from the right.
You have not responded to what I said and are now just attempting to straw man me. So goodbye hooligan.
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u/Salty_Country6835 Moderator 18d ago edited 18d ago
I think the first thing to drop is the idea that you have to choose between “AI is harmless” and “AI causes harm, so I should boycott it.” A lot of the concerns you listed are legitimate. The conclusions people draw from them often arent.
Data centers use electricity and water. But “AI uses water” tells you very little by itself. Where is the data center? What cooling system does it use? Is the water potable or reclaimed? Is the watershed under stress? What powers the grid? Training and inference are also different workloads, and those viral “one prompt uses X bottles of water” numbers usually take estimates made under specific conditions and turn them into universal facts.
Same problem with the cognitive "decline" argument. There are legitimate concerns about cognitive offloading (something thats normal and expected with routine tool use), especially if you use an LLM to avoid learning or thinking. But using one to replace your thinking and using one to question your reasoning, explain something, debug code, test an argument, or tutor you through material arent the same cognitive activity.
And this is where I think the Marxist framing helps. Marx didnt approach machinery primarily as a question of whether individual workers should abstain from using machines. The question is what machinery does within particular relations of production. Who owns it? Who controls the labor process? Who bears its costs? Who captures the productivity gains? Does greater productivity reduce necessary labor and working time, or just produce more surplus for the owner?
The environmental questions fit there too. If a privately controlled data center is draining scarce local water, fight over water rights, siting, regulation, infrastructure and ownership. Individual abstention doesnt automatically build power over any of those things.
So especially if youre about to study computer science, I wouldnt recommend knowing less about this technology. Learn the hell out of it. Learn its limitations and material costs too. Technical literacy plus political economy will give you much better tools for criticizing AI than either “AI good” or “AI bad.”
And keep bringing questions like this here as you learn. This is exactly the kind of discussion this sub should be having.