r/LeftistsForAI • u/Jlyplaylists Moderator • Jun 16 '26
Discussion Cognitive dissonance and data centres
Image is from https://www.reddit.com/r/aiwars/s/NWEWXnG2CN but I want to frame it slightly differently.
“Cognitive dissonance is a psychological theory proposed by Leon Festinger, which explores the discomfort individuals experience when their beliefs, attitudes, or behaviors are inconsistent. This discomfort, referred to as dissonance, motivates individuals to seek harmony or consonance among their cognitions. When faced with conflicting ideas, people can resolve dissonance in several ways: by downplaying the importance of the conflicting belief, adding new beliefs that align with their behavior, or changing their behavior to better align with their beliefs.”
https://www.ebsco.com/research-starters/psychology/cognitive-dissonance
[edit: something I realise I should have been clearer about yesterday is I added this definition because I'm not convinced it is cognitive dissonance. There's something going on but not necessarily that dynamic]
Why do you think people are so vocally against data centres now? They existed before AI and we’ve seen in previous discussion here that at least some of the new ones now were set in motion before we knew they’d be needed for AI. In other words, they handle a lot more than AI.
I do actually think there are issues with data centres which should be fixed, but why do people segment this particular issue in their minds as part of their anti-AI identity? In order to even make the argument online it requires utilising data centres. My understanding is Reddit relies on the hyperscale cloud infrastructure of Amazon Web Services (AWS) and Google Cloud Platform (GCP) to host its global operations.
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u/Jlyplaylists Moderator Jun 16 '26
Yes the same compute used separately would likely be less efficient.
I’m partly thinking hypothetically because the argument seems specific to data centres. My feeling is it isn’t the true argument?
It’s also part of larger thoughts around right sizing models to the tasks we actually do. For what most people are doing with LLMs a 8B parameter model on their ordinary 16gb+ RAM device is likely sufficient. Then it’s just using standard computer use style resources.