Every minute, millions of people use artificial intelligence to generate text, images, and videos without truly understanding the greater consequences of the technology they use. For instance, generating a single image using AI uses as much electricity as charging a cell phone to fifty percent (Luccioni et al. 6). While this use of electricity might not seem like much, it is crucial to consider just how many people use AI technology now. If its use continues to grow in popularity, the potential effects will be staggering. This consequence, however, is just a fraction of the implications that generative artificial intelligence has in all facets of life. The general population, companies, and governments fail to see the outcomes of AI’s overuse on account of the convenience it can provide in everyday life, even without knowing how it works or where it is stored. Additionally, a very large portion of the population never even considers the space that artificial intelligence models can take up. In reality, they are computer programs, occasionally called neural networks, that are given data and trained to create a specified output, such as a piece of writing, an image, a video, or even a song. Although many claim AI can improve work efficiency, education, and research, the functions that AI technologies perform have already had and will continue to have major effects on the larger world. Generative artificial intelligence, as a new and developing technology, must be thoroughly regulated by researchers, government agencies, and corporations due to its ravenous consumption of natural resources, drastic effects on the economy, destruction of trust in school systems, and severe damage to thinking itself.
Today, many people utilize AI for many purposes, including the creation of advertisements, writing, and as a replacement for conventional search engines. Although the boom in popularity of AI has been recent, modern AI models trace their roots back to the early twentieth century and inventions such as the Markov chain. Developed in 1906 by
mathematician Andrey Markov, the Markov chain is a process that allows random behavior to be statistically analyzed to find and create patterns. Most AI programs today rely on methods similar to the Markov chain, where data is collected to make predictions about what word or pixel should come next. Even simpler forms of text generation, such as the predictive text function on a phone, use the same methods as text-generating AI programs, also known as large language models. The most major difference between these devices is that modern AI models, like ChatGPT, use much larger data sets, allowing them to generate more accurate predictions (Zewe). While the concept of artificial intelligence has existed for over a century, the history of AI has moved at a much faster pace in recent years.
Although artificial intelligence can create a product when given a specified prompt, it is not capable of complex thought, introspection, or drawing inferences. An artificial intelligence model is able to understand the data it is given by converting each word or image it is given into a digital token, or a representation of that sample in the form of data which a computer can understand. These tokens are placed on a map that displays that token’s relation among all others. For instance, the word “apple” would be closer to “fruit” on the map than “vegetable.” In this way, any AI model only understands a sample’s relationship with all other samples. It cannot understand the denotation or connotation of a word unless they are specifically mentioned in its
dataset. It cannot analyze what makes the Mona Lisa beautiful without first consuming data from millions of other paintings. Without data, AI is capable of nothing, and even with data, it simply mimics the knowledge it is fed. When a large language model or image generator is given a prompt, it recognizes the relationship between each word or pixel and makes a prediction about what should come next. This process is repeated hundreds to thousands of times until the text or picture is completed (Zewe). For this reason, generating just ten seconds of video requires immense amounts of processing power regardless of the device used.
Due to the vast quantity of actions that computers must make in a short amount of time to make these programs function, AI technologies consume extremely large amounts of electricity. Furthermore, they require enormous amounts of data to ensure that their predictions are accurate. As a result, almost all artificial intelligence that exists today is stored within massive buildings known as data centers. These warehouses contain thousands of servers capable of storing the data needed to fuel and contain an AI model. Other recently developed technological concepts, such as digital trade and cloud storage, are also stored within these colossal storage facilities. Terms like “the cloud” distract from the reality that these data farms are in constant need of electricity, water, and land. Generative AI specifically relies on hyperscale data centers that are at least 10,000 square feet in area and have at least 5,000 servers each (Jonker and Gomstyn). As more data centers are constructed, they will continue to occupy space that could otherwise be used for farming, housing, or other buildings, such as schools. While data centers alone require massive amounts of resources, they fuel a beast just as hungry and ferocious in the form of AI. Generative artificial intelligence’s insatiable appetite for resources warrants it to be considered a major threat to the environment due to its high carbon footprint. All of the electricity that powers AI must come from some source, and like most other electronic devices, AI relies primarily on fossil fuels for power. A direct consequence of this fossil fuel reliance is massive carbon dioxide emission, which threatens to swallow Earth’s atmosphere whole. Specifically, generating one thousand images with an AI model produces as much carbon dioxide as 0.0006 to 4.1 miles in an average gasoline-powered car (Luccioni et al. 7). At face value, this impact is small, but it will very quickly rise considering that more users generate content with AI every day. Just like how AI can quickly devour as much electricity as cell phones, these technologies can contribute to the looming threats of carbon dioxide pollution and climate change. \
One possible solution to AI’s climate impacts would be to further develop carbon-neutral energy sources across the world, such as solar panels, wind turbines, and hydroelectric dams. However, building such a vast amount of new technology for AI models would be costly in terms of land, money, and natural resources, but until the infrastructure is developed, AI will continue to damage the environment. Several researchers at the University of Massachusetts Amherst, including Emma Strubell, an assistant professor of computer science at Carnegie Mellon University, concisely explained the dilemma by stating, “…the high energy demands of these models are still a concern since…energy is not currently derived from carbon-neu[t]ral sources in many locations…and energy spent training a neural network might better be allocated to heating a family’s home” (1). One aspect of resource consumption that many fail to consider is how it directly affects the world’s population. By dedicating so many resources to AI, companies are sacrificing materials that could help people live more comfortable lives. Many tech researchers have found that there are opportunities for AI to reduce pollution by analyzing energy usage to increase efficiency, finding the most optimal times to use energy, and scheduling traffic lights to reduce greenhouse gas emissions by vehicles. One specific example of AI being used to help the environment is Gemimus AI, a company that has been using neural networks to stop methane flaring at oil and gas operations. This technique could significantly improve the environment, as methane is responsible for about 30% of global warming (St. John and McDermott). While these methods of environmental protection are certainly impactful, they are being counteracted by the costs of AI’s expansion. Corporations and government authorities must find a way to balance AI’s environmental costs and benefits, lest it continue to damage the planet. Unless the proper actions are immediately taken by the people and those in power, the natural world will sink into the gaping maw of artificial intelligence.
Inefficient cooling methods and the lack of carbon-neutral solutions may make data centers more environmentally dangerous than the gluttonous AI models they house. Because data centers are filled with thousands of computers, cooling methods must be implemented to prevent servers from overheating. The two primary forms of data center cooling involve water pumps and electric fans. If the former option is implemented, a staggering amount of water is consumed. On average, data centers use hundreds of millions of gallons per year, which is often a greater water consumption than entire cities where data centers are built. Some data centers can use up to 980 million gallons annually. To put it another way, individual data centers can consume more water in a year than four million homes (Nguyen and Green 5). As a consequence of data centers’ absurdly high demand for water, communities are robbed of a resource that could support citizens for years to come. Better yet, the water used to cool data centers could provide relief to starving communities in third-world countries. Data centers have a multitude of technologies to cool the machines housed within them, but these methods can only optimize either water or electricity efficiency. As a result, data centers have a widespread effect on the environment regardless of the efficiency method used due to the necessary waste of resources in large quantities (Nguyen and Green 5). In order to reduce the environmental impact of data centers, technology researchers must find a way to synergize water and electricity efficiency. Constructing more renewable energy sources would assist in solving this issue, but there are currently a multitude of problems associated with such a herculean task.
Even if more sustainable energy sources were to be developed to make data centers less harmful to the environment, they would be impossible to implement due to a combination of necessary uptime and poor management. Data centers must remain active for between 99.971% and 99.995% of a given year in order to uphold the high demand for digital trade, AI data training, and many other internet activities. Currently existing renewable energy sources have no feasible way to keep up with the high uptime of data centers. Additionally, promises by companies to eventually utilize more powerful energy sources, such as nuclear energy, have yet to come to fruition (Nguyen and Green 6). As a result of these issues, the solution to data centers’ environmental detriment is stuck between a rock and a hard place. Using current alternative energy sources would require shutting down many online resources for long periods of time, gravely harming the economy. However, waiting for a better energy source means that the environment will continue to be polluted. Until researchers find an easily implementable and renewable energy source, or until businesses choose to fully utilize nuclear energy, environmental damage is a necessary evil to uphold the digital trade and data that much of the world now relies on.
Artificial intelligence’s environmental effects have now led to the immediately noticeable impacts of AI’s economic harm, where people are now forced to pay the price of the technology’s expansion. Because AI models consume so many resources, the price of those resources has greatly increased since generative AI’s boom in popularity. Data centers are responsible for this demand as well, resulting in massive price increases on electricity bills. Their large demand for electricity raises prices in communities that are in close proximity to data centers (Nguyen and Green 3). This rise in prices directly affects consumers, making life just a bit more difficult due to the demand to fuel AI. Building a new data center near a community does not only significantly raise electricity prices, as residents are frequently made to cover much of the cost. Since the rise in the use of data centers for cloud and AI services, many states have offered tax breaks for companies planning to construct a data center in their area. These tax breaks often include sales tax exemptions on equipment, lower electricity rates, and property tax abatements. Unfortunately, these tax breaks usually fail to follow up on their promises of high-paying jobs for citizens. Often, tax breaks for companies only mean that communities are forced to carry the burden (Nguyen and Green 3). When a data center company receives a tax exemption, local tax revenue decreases because they are not paying their full share. On a large scale, an increase in data centers means an improvement in AI’s effectiveness, but in local terms, communities suffer due to failed promises of higher revenues. To the average person who is not tech savvy, AI’s greatest impact is raising electricity bills and tax costs without any real benefit to their city.
On top of financially damaging communities, data centers rarely provide jobs to residents, especially ones that are secure, safe, and sustainable. While one may assume that the creation of a data center would allow for dozens of new jobs, there are very few opportunities that open for locals. Even if someone does acquire a job at a data center, they often suffer from a lack of important benefits. Terry Nguyen and Ben Green, researchers at the University of Michigan who study the relationship between artificial intelligence and public policy, describe the conditions of data center jobs by stating, “[t]he jobs that data centers do create locally are typically low-wage, term-limited, non-technical positions such as security, maintenance, and janitorial work…they lack union protections, benefits, and job security. As a result, these positions…do not contribute to sustained economic growth or long-term career opportunities for local residents” (7). Alongside the economic hardships a job at a data center provides, the lack of union protections means a person can do little to protect themselves against mistreatment from a data center company. These issues can greatly impact the wellbeing of workers, causing further despair throughout a community. Once again, the expansion of AI proves to only hinder the development of cities and the people who live in them. If municipal leaders wish to see economic improvement from the creation of a new data center, corporations must provide workers with livable wages, the ability to unionize, proper benefits, and job security.
Since working at a data center proves to be an insecure and unsustainable occupation, many would consider taking a job as a programmer, historian, or translator, but as it turns out, AI is highly capable of replacing these jobs and many others. Large language models have the capacity to assist with or even replace a large amount of the workforce, especially jobs that rely on information processing and communication. The vulnerability of information jobs is directly caused by how AI consumes information from its data set, which often contains much of the knowledge contained on the internet. Jobs that could be affected by generative AI include translators, historians, programmers, management analysts, and many others (Tomlinson et al. 1). Currently, most AI models are not capable of completely replacing the human workforce, but the potential still fully exists. If the expansion of artificial intelligence continues at its current pace, many businesses will consider laying off human workers in favor of soulless machines for the sake of profit. This drastic move could lead to a drastic increase in poverty unless governments choose to intervene. In a field such as art, choosing to generate, share, or engage with AI images convinces companies that people do not care whether a human or a machine creates the art they use. This complacency does nothing but enable corporations to lay off their workers, potentially resulting in poverty for millions. While not as at risk for replacement as other jobs listed above, teachers could eventually be substituted for large language models due to their similar roles as providers of information, and this replacement would result in consequences that reach as deep as the human brain itself.
Large language models, despite now being commonly relied on as a source of information, make for dangerously ineffective teachers due to their ability to quickly spread misinformation. While many people now use AI programs, especially ChatGPT and Google’s AI overview feature, to quickly find information on the internet, generative AI is very prone to making mistakes when generating an answer to one’s prompt. Because artificial intelligence simply generates the best answer based on patterns in a given data set, it may find irrelevant or nonexistent patterns. When this mistake happens, the AI generates false information that it believes to be correct, which is commonly referred to as a hallucination (MIT Management). As a result, AI cannot entirely be trusted upon as a reliable source of information. Considering how quickly information can spread on the internet, a single mistake from an AI model can spread like wildfire and become fact. Unless people diligently fact check every piece of knowledge provided by AI, they can be quickly misled by AI hallucinations. Regardless of the damage to real information that hallucinations can cause, people often refuse or simply forget to double-check the search results they find.
The use of artificial intelligence in schools, while seemingly useful in the short-term, will strain school relationships, decrease attendance, and impair the wellbeing of both students and educators in the long-term. Supporters of artificial intelligence in education argue that it could be useful for helping young students develop fundamental literacy skills. With artificial intelligence’s capability of quickly summarizing information, many claim it could assist in correcting grammar and developing critical thinking (Kasneci et al.). Overrelying on large language models at such a young age, however, could damage the fundamental literacy skills every person needs. Despite this consequence, students nationwide now use AI to complete school assignments for them. This growing dependence on AI by students has caused the line between genuine work and plagiarism via artificial intelligence to blur (Mao et al. 7). Teachers have now had to use AI detection software when grading essays, which can lead to students with writing styles similar to an AI’s being accused of academic dishonesty. A major consequence of this constant blaming would be a rise in distrust in the school environment. If schools begin to develop a negative atmosphere, student engagement and attendance will decrease, causing further academic turmoil (Mao et al. 4). Additionally, double-checking every student’s work for academic dishonesty means even more hours of work for teachers. By allowing students to take the easy path, AI forces teachers to work even harder. If a teacher wishes to utilize AI in their curriculum, they should do so with caution and moderation to prevent the rise of any academic issues or further stress when grading.
The effects of AI in a school curriculum are not even as drastic as the ones it can have on the brain. On top of damaging a student’s academic integrity, using artificial intelligence can damage brain development if used. Doctor Matthias Stadler, a professor of medical education at the Ludwig Maximilian University of Munich who studies the integration of AI into teaching found that subjects conducting research using a large language model had lowered cognitive effort when researching compared to a test group that used regular search methods. However, the students using AI had weaker reasoning and arguments when compared to traditional researchers (Stadler et al.). Though students may find artificial intelligence appealing due to its ease of use and reduced mental load, those using it may suffer weaker reasoning if they choose to rely entirely on it. Similar research supports the theory that students cognitively suffer despite perceived short-term benefits of using large language models. A study performed by researchers at the Massachusetts Institute of Technology found that subjects using ChatGPT to write an essay showed lower cognitive activity. Additionally, they had a lesser ability to quote from their own essays compared to control groups using regular search engines or their own minds (Kosmyna et al. 1). These results have severe implications for students and their ability to critically think if they choose to use AI rather than their own head. Unless proper restrictions are placed on artificial intelligence throughout school districts, students using generative AI will have a noticeable academic disadvantage, even in areas outside of essay writing. When people choose to blindly use a new technology, they will suffer consequences that they never took the time to understand. Like the internet, for example, generative AI must be examined from all angles before it should be used in school curriculums.
Ultimately, generative artificial intelligence, if left to expand at its current rate, will affect many more aspects of life than just the environment, the economy, and the education system. Despite the potential future benefits that AI models can provide to the world, it is currently too early to implement these technologies at such a large scale. It is likely that businesses will do little to control the damage artificial intelligence has caused, such as implementing nuclear energy to prevent carbon emissions, treating workers with more care for the sake of communities and the global market, and restricting AI use in schools to the benefit of both students and teachers. These changes would most likely be too costly for artificial intelligence companies or school districts to even consider. Businesses have carved a wound into the world using AI, and it will most likely not get smaller, but we as a society can make it a smaller part of ourselves. We have the choice to not use artificial intelligence, to make decisions that are less environmentally deadly, and to support students to critically think. When governments eventually realize the negative consequences of generative AI, they will be able to fully moderate these models and the companies that own them, but the first step towards that goal begins with us: humanity.
Author’s Notes:
“AI is Not Invisible” began during a conversation I had with Mr. Lochhead in October of this school year. Though I do not remember the context surrounding the conversation, its topic eventually centered on the use of artificial intelligence. I attempted to explain AI’s damage from an environmental perspective, a point of view that I believed to be novel for many.
Mr. Lochhead countered our stance with a question that went something like this: “If the costs of AI are really that great, then what about the impact of video games? Would you consider those a negative to society?”
I had no evidence to answer his question. Frustrated, I spent the rest of the day looking for data, and I decided I would prove him wrong in the pettiest way possible: write an entire research paper about the negative consequences of AI.
When the second semester began, English III students were assigned a research paper based on a topic of their choice. I declared my topic to Mrs. Ray, and she was stunned. Her response still makes me chuckle, even as I am writing this.
“I thought AI was invisible.”
No. No, it is not… but at least I now had my title.
Photo copyright: حمزة مستقيم / https://commons.wikimedia.org/wiki/File:Aerial-utah-data-center.jpg
References
حمزة مستقيم, CC BY-SA 4.0
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