AI Research
Inside the relentless race for AI capacity
These spikes threaten cascading power outages, affecting homes and businesses that feed off the same grid network. Last summer, utility providers in Virginia had to grapple with a sudden surge in power after a cluster of facilities switched to back-up generators as a safety precaution, leading to an excess supply that risked grid infrastructure.
With abundant power the priority, operators have also ended up in areas with significant water constraints. Hyperscale and colocation sites in the US consumed 55bn litres of water in 2023, according to researchers at the LBNL. Indirect consumption tied to energy use is markedly higher at 800bn litres a year, the equivalent annual water usage of almost 2mn US homes.
In 2023, Microsoft said that 42 per cent of its water came from “areas with water stress”, while Meta said roughly 16 per cent of its water usage was derived from similar areas during the same time period. Google said last year almost 30 per cent of its water came from watersheds with a medium or high risk of “water depletion or scarcity”. Amazon does not disclose its figure.
Data centres in drought-prone states such as Arizona and Texas have led to concern among locals, while residents in Georgia have complained that Meta’s development in the state has damaged water wells, pushed up the cost of municipal water and led to shortages that could see water rationed.
Some believe that this endless race for ever-greater computing power is misplaced.
Sasha Luccioni, AI and climate lead at open-source start-up Hugging Face, said alternative techniques to train AI models, such as distillation or the use of smaller models, were gaining popularity and could allow developers to build powerful models at a fraction of the cost.
“It’s almost like a mass hallucination where everyone is on the same wavelength that we need more data centers without actually questioning why,” she said.
Increased chip density has another unwanted effect: heat. About two-fifths of the energy used by an AI data centre stems from cooling chips and equipment, according to consultants McKinsey.
Early data centres running cloud workloads deployed industrial-grade air conditioning units similar to those used in offices to cool servers. But as chips started to draw more power, it has become harder to keep them within their safe operating range between 30 and 40C, with data centres requiring more advanced cooling methods to avoid malfunctions. The challenge has led to significant investment in cutting-edge innovations.
Operators have turned to installing pipes filled with cold water in the server room to transfer heat away from equipment. This water is then directed to large cooling towers that use evaporation to reduce the facility’s temperature to a safe range. But the approach leads to water loss, with a single tower churning through about 19,000 litres per minute.
Microsoft and others have adopted a closed-loop system that depends on chillers — in effect, a refrigerator —to cool the water. This process is less wasteful and more efficient than evaporative options.
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Changes in Learning: Cheating and Artificial Intelligence
When reading the news, one gets the impression that all students use artificial intelligence to cheat in their studies. Headlines in newspapers such as The Wall Street Journal or the New York Times often mention ‘cheating’ and ‘AI’. Many stories, similar to a publication in New York Magazine, describe students who openly testify about using generative AI to complete assignments.
With the rise of such headlines, it seems that education is under threat: traditional exams, readings, and essays are filled with cheating through AI. In the worst cases, students use tools like ChatGPT to write complete works.
This seems frustrating, but such a thought is only part of the story.
Cheating has always existed. As an educational researcher studying cheating with AI, I can assert that preliminary data indicate that AI has changed the methods of cheating, but not its volumes.
Our early data suggest that AI has changed the method, but not necessarily the scale of cheating that was already taking place.
This does not mean that cheating using AI is not a serious problem. Important questions are raised: Will cheating increase in the future due to AI? Is the use of AI in education cheating? How should parents and schools respond to prepare children for a life that is significantly different from our experience?
The Pervasiveness of Cheating
Cheating has existed for a very long Time — probably since the creation of educational institutions. In the 1990s and 2000s, Don McCabe, a business school professor at Rutgers University, recorded high levels of cheating among students. One of his studies showed that up to 96% of business students admitted to engaging in ‘cheating behavior’.
McCabe used anonymous surveys where students had to indicate how often they engaged in cheating. This allowed for high cheating rates, which varied from 61.3% to 82.7% before the pandemic.
Cheating in the AI Era
Has cheating using AI increased? Analyzing data from over 1900 students from three schools before and after the introduction of ChatGPT, we found no significant changes in cheating behavior. In particular, 11% of students used AI to write their papers.
Our diligent work showed that AI is becoming a popular tool for cheating, but many questions remain to be explored. For example, in 2024 and 2025, we studied the behavior of another 28000-39000 students, where 15% admitted to using AI to create their work.
Challenges of Using AI
Students are accustomed to using AI but understand that there are boundaries between acceptable and unacceptable use. Reports indicate that many use AI to avoid doing homework or to gain ideas for creative work.
Students feel that their teachers use AI, and many consider it unfair when they are punished for using AI in education.
What Will AI Use Mean for Schools?
The modern education system was not designed with generative AI in mind. Traditionally, educational tasks are seen as the result of intensive work, but now this work is increasingly blurred.
It is important to understand what the main reasons for cheating are, how it relates to stress, time management, and the curriculum. Protecting students from cheating is important, but ways of teaching and the use of AI in classrooms also need to be rethought.
Four Future Questions
AI has not caused cheating in educational institutions but has only opened new possibilities. Here are questions worth considering:
- Why do students resort to cheating? The stress of studying may lead them to seek easier solutions.
- Do teachers adhere to their rules? Hypocrisy in demands on students can shape false perceptions of AI use in education.
- Are the rules concerning AI clearly stated? Determining the acceptability of AI use in education may be vague.
- What is important for students to know in a future rich in AI? Educational methods must be timely adapted to the new reality.
The future of education in the age of AI requires an open dialogue between teachers and students. This will allow for the development of new skills and knowledge necessary for successful learning.
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