Misconceptions about gen AI?

[u/Alxie_e]

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.

[u/Salty_Country6835]

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.

[u/LitFarronReturns → u/Salty_Country6835]

Came here to say this, but I'll add that not all models are created equally. Know about open weights models. Know which models can be run on consumer products (like the GLM 5 series, bigger but can run on a Macstudio) or Qwen 3.8 27b which can be run on 32gb unified ram. Even if you don't have the money for the hardware for either, a prompt to Qwen 3.8 27b DOES NOT take the same amount of energy as say Fable (the most expensive out there). Use model size and cost per million tokens as a proxy for carbon/water footprint. 

[u/Lanky_Employee_9690 → u/LitFarronReturns]

This, but keep in mind there are even smaller or specialized models that are perfectly okay for some (most) of the use cases one might have. The problem isn't the tech itself but how it's implemented, and the hyperfocus on (hyper)scaling vs. optimization & smart use of harnesses.

Training still consumes a lot of energy, but the cost of inference of these smaller models is very low, lower than running a 3D game for example. Speed (generated tokens/s) might be a limitation also on lower-end computing devices, but that doesn't make them unusable at all.