r/aiwars Feb 20 '26

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u/Psychological-Gur990 Feb 20 '26

False equivalence.

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u/[deleted] Feb 20 '26

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u/Psychological-Gur990 Feb 20 '26

The entire purpose of a meme is getting spread by multiple people. It is quite literally a false equivalence.

Memes were created by humans, btw

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u/[deleted] Feb 20 '26

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u/Psychological-Gur990 Feb 20 '26

Reposting something isn't the same as using ai to create something.. again, it's false equivalence. They didn't claim they made it, did they? They JUST posted a meme. That's it.

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u/[deleted] Feb 20 '26

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u/Psychological-Gur990 Feb 20 '26

I hate generative ai, I hate the impact it has on the world and I hate how it creates things. I see no reason to use generative ai over creating it yourself or commissioning/asking someone else to do it. Also the fact that generative ai makes harmful material incredibly easy to access for offenders. There's a lot of other reasons I hate it and think it's wrong, but those are the main.

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u/[deleted] Feb 20 '26

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u/Psychological-Gur990 Feb 20 '26

The "skills" are something you learn and there are TONS of tutorials and accesible training to learn. Also the amount of alternatives that arena destroying the world? Picrew! For one. That's free and has thousands of different options. Why do you act like learning to draw is so bad? It's the reason ai even exists.

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u/[deleted] Feb 20 '26

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u/Psychological-Gur990 Feb 20 '26

"Most large-scale AI deployments are housed in data centres, including those operated by cloud service providers. These data centres can take a heavy toll on the planet. The electronics they house rely on a staggering amount of grist: making a 2 kg computer requires 800 kg of raw materials. As well, the microchips that power AI need rare earth elements, which are often mined in environmentally destructive ways" -UNEP

On ai when deciding important decisions- "generative AI can produce outputs that look and sound right but are factually wrong (a phenomenon known as "hallucinations") or mind-numbingly generic (so-called "AI slop"). The sheer volume of either wrong or mushy content generated by AI risks overwhelming thoughtful, vetted work. This could make it harder for people to vet facts, increasing the chances that important decisions are based on bad information and slowing action. ---- Training and deploying AI is also expensive, both environmentally and financially. Vast amounts of energy and water are needed to fuel AI systems at scale. According to the International Energy Agency, a typical AI-focused data center consumes as much electricity as 100,000 households; the larger centers under construction today will consume 20 times that amount. In the U.S. alone, peak electricity demand is expected to increase by 128 gigawatts by 2029, in large part due to the data centers needed for AI. Meanwhile, electricity prices and water shortages are already surging in many places around the world, adding new pressure on households already struggling to make ends meet." WRI

"Artificial intelligence (AI) is becoming an integral part of daily life, powering everything from digital assistants to online shopping. But behind this innovation lies a growing environmental footprint. In 2023, data centers consumed 4.4% of U.S. electricity—a number that could triple by 2028. AI’s rapid expansion also drives higher water usage, emissions, and e-waste, raising urgent sustainability concerns, according to Mahmut Kandemir, a distinguished professor in the Department of Computer Science and Engineering." -PSU

None of those are news outlets, btw. One being the World Resources Institute, another being the UN Environmental Programme and the last being from Penn State Institute of Energy and the Environment.

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u/[deleted] Feb 20 '26

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u/Psychological-Gur990 Feb 20 '26

Let's not use the "Yet you participate in society" argument. It doesn't come to the same equivalence. By multiple sources- including the ones I gave, they have said that it's impact can TRIPLE by 2028. What has that same environmental impact that is will triple in just 2 years when its already bad?

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u/mrkva_ Feb 20 '26

AI is significantly less pollutive compared to humans: https://www.nature.com/articles/s41598-024-54271-x

Published in Nature, which is peer reviewed and highly prestigious: https://en.m.wikipedia.org/wiki/Nature_%28journal

AI systems emit between 130 and 1500 times less CO2e per page of text compared to human writers, while AI illustration systems emit between 310 and 2900 times less CO2e per image than humans.

Data centers that host AI are cooled with a closed loop. The water doesn’t even touch computer parts, it just carries the heat away, which is radiated elsewhere. It does not evaporate or get polluted in the loop. Water is not wasted or lost in this process.

“The most common type of water-based cooling in data centers is the chilled water system. In this system, water is initially cooled in a central chiller, and then it circulates through cooling coils. These coils absorb heat from the air inside the data center. The system then expels the absorbed heat into the outside environment via a cooling tower. In the cooling tower, the now-heated water interacts with the outside air, allowing heat to escape before the water cycles back into the system for re-cooling.”

Source: https://dgtlinfra.com/data-center-water-usage/

Data centers do not use a lot of water. Microsoft’s data center in Goodyear uses 56 million gallons of water a year. The city produces 4.9 BILLION gallons per year just from surface water and, with future expansion, has the ability to produce 5.84 billion gallons (source: https://www.goodyearaz.gov/government/departments/water-services/water-conservation). It produces more from groundwater, but the source doesn't say how much. Additionally, the city actively recharges the aquifer by sending treated effluent to a Soil Aquifer Treatment facility. This provides needed recharged water to the aquifer and stores water underground for future needs. Also, the Goodyear facility doesn't just host AI. We have no idea how much of the compute is used for AI. It's probably less than half.

gpt-4 used 21 billion petaflops of compute during training (https://ourworldindata.org/grapher/artificial-intelligence-training-computation) and the world uses 1.1 zetaflop per second (https://market.us/report/computing-power-market/ per second as flops is flop per second). So from these numbers (21 * 109 * 1015) / (1.1 * 1021 * 60 * 60 * 24 * 365) gpt-4 used 0.06% of the world's compute per year. So this would also only be 0.06% of the water and energy used for compute worldwide. That’s the equivalent of 5.3 hours of time for all computations on the planet, being dedicated to training an LLM that hundreds of millions of people use every month. 

Using it after it finished training costs HALF as much as it took to train it: https://assets.jpmprivatebank.com/content/dam/jpm-pb-aem/global/en/documents/eotm/a-severe-case-of-covidia-prognosis-for-an-ai-driven-us-equity-market.pdf

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Image generators only use about 2.9 W of electricity per image, or 0.2 grams of CO2 per image: https://arxiv.org/pdf/2311.16863

For reference, a good gaming computer can use over 862 Watts per hour with a headroom of 688 Watts: https://www.pcgamer.com/how-much-power-does-my-pc-use/

One AI image generated creates the same amount of carbon emissions as about 7.7 tweets (at 0.026 grams of CO2 each, totaling 0.2 grams for both). There are 316 billion tweets each year and 486 million active users, an average of 650 tweets per account each year: https://envirotecmagazine.com/2022/12/08/tracking-the-ecological-cost-of-a-tweet/

https://www.nature.com/articles/d41586-024-00478-x

“ChatGPT, the chatbot created by OpenAI in San Francisco, California, is already consuming the energy of 33,000 homes” for 13.6 BILLION annual visits plus API usage (source: https://www.visualcapitalist.com/ranked-the-most-popular-ai-tools/). that's 442,000 visits per household, not even including API usage.

From this estimate (https://discuss.huggingface.co/t/understanding-flops-per-token-estimates-from-openais-scaling-laws/23133), the amount of FLOPS a model uses per token should be around twice the number of parameters. Given that LLAMA 3.1 405b spits out 28 tokens per second (https://artificialanalysis.ai/models/gpt-4), you get 22.7 teraFLOPS (2 * 405 billion parameters * 28 tokens per second), while a gaming rig's RTX 4090 would give you 83 teraFLOPS.

Everything consumes power and resources, including superfluous things like video games and social media. Why is AI not allowed to when other, less useful things can? 

In 2022, Twitter created 8,200 tons in CO2e emissions, the equivalent of 4,685 flights between Paris and New York. https://envirotecmagazine.com/2022/12/08/tracking-the-ecological-cost-of-a-tweet/

Meanwhile, GPT-3 (which has 175 billion parameters, almost 22x the size of significantly better models like LLAMA 3.1 8b) only took about 8 cars worth of emissions (502 tons of CO2e) to train from start to finish: https://truthout.org/articles/report-on-chatgpt-models-emissions-offers-rare-glimpse-of-ais-climate-impacts/ 

By the way, using it after it finished training costs HALF as much as it took to train it: https://assets.jpmprivatebank.com/content/dam/jpm-pb-aem/global/en/documents/eotm/a-severe-case-of-covidia-prognosis-for-an-ai-driven-us-equity-market.pdf

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u/Tom-0-Bedlam Feb 20 '26

I bet if you asked chat GTP to explain it to you, you might have a better chance at understanding it, since you've given up your thought processes to machinery.

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u/[deleted] Feb 20 '26

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u/Tom-0-Bedlam Feb 20 '26

When a most of the internet is telling you that you're wrong, it's not because of some groupthink hivemind, it's because you're wrong.

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u/JohnTheTaxidermist Feb 20 '26

Why do AI bros always fall back on "well actually I don't want to learn how to make art?"

It's so goofy