Lots of Money

Big tech company Nvidia recently made a deal with the company behind ChatGPT, OpenAI, for $100B of funding to build new data centers. To put that into perspective, according to the Forbes 2025 World Billionaires list, only 15 people in the world have this much money to spend (Durot, 2025).

Why are we spending this much money on AI? Many people ask the same question every day, and yet, we continue to waste time and funds on the ability to generate a funny picture or an essay for a homework assignment.

A Firm NO

To put it simply, AI should not be an endeavor humanity pools all their resources into. It is using up all our resources including water and energy, both of which have been on the low recently due to droughts and an increase in global electricity usage.

On top of that, the usage of AI for tasks like writing essays has been shown to decrease cognitive abilities, especially with heavy usage.

Lastly, with the addition of accurate image and video generation tools, creative people such as artists and actors are starting to feel threatened by new models that imagine ‘new’ content by transforming things that have already been made, often without original creator approval. With all the regrettable effects AI has been introducing into society, the direction we’re moving should not be forwards, but back.

A Little Background

AI as we know it is still a fairly new concept. On the image generation front, DALL-E was generally considered to be the first good implementation of a GPT (generative pre-trained transformer) to generate pictures.

DALL-E was developed by OpenAI and released to the public on January 5th, 2021, and gained traction due to its unrivaled ability to generate (albeit sometimes haunting) images with never before seen detail.

Later, in November of 2022, OpenAI released ChatGPT, bringing LLMs (large language models) to the average person, which marked the beginning of the AI fad of marketing and tech products.

Since then, many other companies have released their own take on conversational AI, such as Google’s Gemini and Anthropic’s Claude, which were both also widely successful. However, the success of these products is very dependent on one thing: data centers.

Using Up the Energy

Data centers are the driving force behind Large Language Models like ChatGPT, but they need an excessive amount of energy to do so. According to The U.S. Department of Energy:

“…data centers consumed about 4.4% of total U.S. electricity in 2023 and are expected to consume approximately 6.7 to 12% of total U.S. electricity by 2028.”
(U.S. Department of Energy, 2024).

In addition, they also stated that:

“…total data center electricity usage climbed from 58 TWh in 2014 to 176 TWh in 2023…”

To contextualize this, 176 TWh is about the same power generated by 126 million lightning strikes, which is downright absurd.

There are third-world countries that want power and connection, but we’re spending it all on Will Smith eating spaghetti.

Drinking All the Water

Another resource we’re running low on that AI is sucking up: water. Data centers need a lot of water to cool all the electronics, because they spit out a ton of heat doing all the math required to make up haikus.

As reported by Miguel Yañez-Barnuevo (2025) in a paper by the Environmental and Energy Study Institute:

“Large data centers can consume up to 5 million gallons per day, equivalent to the water use of a town populated by 10,000 to 50,000 people.”

What’s worse is that this water the data centers are using isn’t salt water, of which we have several ocean’s worth. They use freshwater, which only makes up about 3% of the world’s water supply, and could be used for drinking, cooking, cleaning, all sorts of things other than cooling hot chunks of sand that can think.

Yañez-Barneuvo continues, later stating that:

“Approximately 80% of the water (typically freshwater) withdrawn by data centers evaporates, with the remaining water discharged to municipal wastewater facilities.”

These municipal wastewater facilities can very easily be overwhelmed, as they usually are not designed to take in and process that much extra water, leading to backups and an excess of unfiltered waste.

Destruction to Art

Speaking of unfiltered waste, image and video generation have been the topic of huge debate ever since it got good enough to catch eyes. Is it real art? Is it just stealing and reforming existing art without retribution?

Well, Guillermo Del Toro, an accomplished screenwriter, director, and producer, who worked on masterpieces such as Pan’s Labyrinth, The Shape of Water, and The Hobbit, has spoken out against it.

In an interview at the 2017 BFI Film Festival in London, he deliberated:

“The value of art is not how much it costs and how little effort it requires, it’s how much would you risk to be in its presence? How much would people pay for those screensavers? Are they gonna make them cry because they lost a son? A mother? Because they misspent their youth? Fuck no.”
(Del Toro, 2017)

AI generated art is in many cases being treated the same as real art, which is hurting these people who spend their lives perfecting the practice and creating pieces with feeling and emotion. However, Del Toro isn’t the only one who thinks this way about AI.

Michael Bay, legendary director of many successful movie series such as Transformers, A Quiet Place, and The Purge, had this to say about it in an Instagram post:

“Speaking about A.I. - it doesn’t CREATE it just IMITATES. And will create a whole bunch of lazy people. So to all the Original Creators out there, have No Fear!”
(Bay, 2023)

As he said, AI is trained on data such as pictures and videos, which are usually scraped from the world’s biggest dataset, The Internet, and it reforms them into something based on the data it was trained on. It does not create something new, and it certainly should not be considered true art on the same level as Del Toro’s and Bay’s works.

Cognitive Loss

Pivoting away from art towards science, some interesting discoveries are being made about how AI impacts the operations in our brain after extended use.

A research team at MIT recently conducted a study where three groups of participants were given SAT essay prompts to write for in 4 separate sessions. One group was given ChatGPT to help for the first three sessions, one group was given search engines, and the last group could only use their brains.

Then, for session four, the ChatGPT group had to write their essays using only their brain, referred to as the LLM-to-Brain group, and the brain group was given ChatGPT to help write their essays, referred to as the Brain-to-LLM group.

They described the essays written by the LLM-to-Brain group in session 3 as:

“Low effort. Mostly copy-paste. Not significant distance to the default ChatGPT answer to the SAT prompt. Minimal editing. Impaired perceived ownership…”
(Kosmyna et al., 2025)

By the end of the LLM-only part of the study for this group, they had become lazy, resorting to simply copying and pasting ChatGPT responses without review. They had lost most of their self-thinking that was shown in the Brain-to-LLM group in the first three sessions, but still scored relatively high with the help of human teachers, partly because the use of AI was not being accounted for.

Not All Bad

But what about all the good stuff AI is doing? In the field of biology and medicine alone, AI has helped with identifying cancerous cells, predicting protein structures, and mapping out the brain, which would have taken years longer if not aided by AI.

There are some genuinely good uses for AI in the workplace, but these uses are not the way AI resources are currently being used by the majority of the population of the world. Right now, people are using AI to devalue the art of others, and destroy the environment, without even knowing it themselves.

Conclusion

In conclusion, the cons of the continued growth and funding of AI egregiously outweigh the pros. There is no question as to whether or not this trend is good for our species as a whole and if we should keep going in this direction.

Too many people have had the shades pulled over their eyes, praising AI for all the work it has saved us and all the advancements it’s making, while secretly disabling our ability to think for ourselves.
 
 

Stop with the AI,

References

Bay, M. [@michaelbay]. (2023, November 7). Speaking about AI - it doesn’t create it just imitates [Photograph]. Instagram. https://www.instagram.com/p/CzWU-2tMjuo/

Del Toro, G. (2017, December 6). BFI screen talk: Guillermo del Toro | BFI London film festival 2017 [Video]. YouTube. https://www.youtube.com/watch?v=rfbD3OBir64

Durot, M. (2025, April 1). The $100B dollar club: These 15 people have 12-figure fortunes. Forbes. https://www.forbes.com/sites/mattdurot/2025/04/01/the-100-billion-club-these-15-people-have-12-figure-fortunes/

Kosmyna, N., Hauptmann, E., Yuan, Y., Situ, J., Liao, X., Beresnitzky, A., Braunstein, I., & Maes, P. (2025, June). Your brain on ChatGPT: Accumulation of cognitive debt when using an AI assistant for essay writing task. arXiv. https://arxiv.org/pdf/2506.08872v1

U.S. Department of Energy. (2024, December 20). DOE releases new report evaluating increase in electricity demand from data centers. https://www.energy.gov/articles/doe-releases-new-report-evaluating-increase-electricity-demand-data-centers

Yañez-Barnuevo, M. (2025, June 25). Data centers and water consumption. Environmental and Energy Study Institute. https://www.eesi.org/articles/view/data-centers-and-water-consumption