Where is the reality then?
A few years ago, my understanding of artificial intelligence came from the same places as most people’s: headlines, social media, YouTube videos, and the occasional viral breakthrough. AI seemed almost magical. Every week, there was a new claim about machines replacing jobs, achieving human-level intelligence, or transforming entire industries.
Then, I had the opportunity to spend time in a real research environment, for a matter of fact, one of the most important European scientific facilities: GSI/FAIR.
What I found out there was not disappointing; it was rather a turning point and an eye-opening experience.
Most of my time at high school, peers spoke about it when it came to homework and learning… but as the models advanced, the dependency on them became more and more noticeable.
The AI presented to the public is often a finished product: polished demos, impressive benchmarks, and bold predictions.
Research, however, looks very different. It is slower, more rigorous, and far less predictable. Progress is measured in months and years rather than days. Experiments fail. Models behave unexpectedly. Hardware limitations become impossible to ignore.
As a high school student, I expected to see groundbreaking algorithms everywhere. Instead, I learned that much of modern AI depends on fundamentals: mathematics, engineering, careful experimentation, and collaboration between researchers from different fields.
The biggest surprise was that AI research is not primarily about chasing intelligence. It is about solving problems. Researchers spend their time asking precise questions, collecting evidence, and testing assumptions. The process resembles science far more than the futuristic narratives often associated with AI.
So, what will Academia look like with the rise of LLM? It still seems to be the slowest to adapt to the various “hypes” and polished advertisements presented by the mainstream media, tech gurus, and big names.
The gap between AI hype and AI research is not a sign that the field is overestimated. Rather, it reflects the difference between public perception and the reality of scientific progress. The headlines show the destination; research reveals the journey.
Mathematics, programming, problem-solving, and critical thinking form the foundation of modern AI. Understanding how algorithms work, how data is processed, and why models behave the way they do is far more valuable than simply learning how to use the latest tool.
Research is full of experiments that do not produce the expected results. Progress often comes from understanding why something did not work rather than celebrating when it does. Curiosity and persistence are just as important as technical ability.
The future of AI will not be built by people who follow every trend. It will be built by those who understand the fundamentals deeply enough to create the next generation of ideas.
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