“I want to dedicate myself to artificial intelligence.” This phrase is increasingly being heard among those who have just completed their university entrance exams, perhaps with the same conviction previously associated with fields like medicine or engineering. However, this statement holds a trap : dedicating oneself to AI can mean many things, and not all paths require the same qualifications. Four specialized voices—Pilar Manchón, Antonio Ortiz, Andrés Torrubia, and Jon Hernández—help us dissect this declaration. Their insights clarify what to study, what skills to develop, and what pitfalls to avoid so that one doesn’t lose their way.
Where to Start: Understanding What it Means to Dedicate Yourself to AI
We invite you to dive into a nuanced report. There is no single correct answer here: each expert offers a distinct perspective, and in this contrast lies what is truly interesting. Not everyone envisions the future of this discipline the same way, nor do they agree on what to study, how to train, or where to begin. Nevertheless, they highlight signs, warnings, and evolving certainties that can guide those looking to carve their own path in a world that is still being defined and is likely to demand a prominent role in the future.
The first step before choosing a career is to understand that “AI” is not a single profession. Within this field, there are many different profiles, from those who specialize in maximizing tools like ChatGPT or Gemini without needing to know the underlying mathematics, to those aiming to delve deep into the system’s core. Pilar Manchón, Senior Director of Engineering at Google, summarizes it well: “There are options for everyone, depending on whether you feel more comfortable being a user of these tools […] versus those who want to get into the system’s inner workings, wanting to know exactly how it works and inventing a way that makes it more efficient.”
A similar distinction is made by Antonio Ortiz, an AI communicator and co-founder of Weblogs SL, who currently hosts the podcast ‘Monos Estocásticos‘. Ortiz emphasizes that it should not be assumed that everyone wanting to work in this field must become an architect of models or a specialist in machine learning. “If someone says, ‘I need to dedicate myself to artificial intelligence,’ does that mean I have to become an expert in machine learning, learn to train an LLM, work with NVIDIA graphs, and focus on designing a transformer model?” he queries.
This complexity is also highlighted by Jon Hernández, an AI communicator with a YouTube channel followed by over 380,000 people. His self-taught experience allows him to draw a clear line between those who create AI and those who harness it as a tool. “I think we need to differentiate between two paths, right? One is dedicating oneself to artificial intelligence itself […] The other is how to leverage artificial intelligence in this huge opportunity that has opened up for everyone.”
It’s clear that “dedicating oneself to AI” does not imply a singular path or a single professional profile. For some, the journey will involve understanding how models are built from the ground up. For others, it may be about exploring the limits of existing tools. But before diving in, there’s an unavoidable question: what should one study?
Choosing what to study is no easy task. However, there seems to be an undeniable principle for those who wish to delve into AI: a solid foundation is essential. This is the belief of Andrés Torrubia, co-founder of the Institute of Artificial Intelligence. He summarizes it emphatically: “To this day, I would recommend to those taking university entrance exams to study the fundamental subjects that will never change: mathematics, physics, and programming paradigms, knowing that increasingly, a lot of programming will be handled by an artificial intelligence system.”
A view echoed by Antonio Ortiz: “Artificial intelligence is software,” he reminds us. “So whatever discipline, whether vocational training or university, should help you build better software.” Ortiz advocates for broad-spectrum studies that do not close doors and allow for a solid foundation in software development: “I would recommend investing in foundational knowledge and building a strong intellectual base, rather than seeking shortcuts by learning just the techniques that the market demands now.”
Jon Hernández adds another dimension to the conversation: the importance of personal motivation. “I believe we should advise young people to study what they like and are passionate about,” he asserts. His argument is that beyond trends or current job prospects, the decisive factor will be knowing how to apply artificial intelligence in one’s area of interest: “If they have the necessary passion to do so and, importantly, apply artificial intelligence to what they are studying, I think they will become the best professionals in that field.” He even suggests a curious idea: “The most demanded profession in five years will be philosophers.”
What University Provides (and What It Doesn’t)
University remains a powerful avenue for entering the world of artificial intelligence, but it is not the only option. Pilar Manchón defends formal education as a valuable tool, especially for what it offers beyond academic content: “You gain not only flexible curricula where you can study practically anything but also, through this experience, you interact with the world […], socializing with others.” However, she notes that while this route is “necessary, it is not sufficient.”
Andrés Torrubia offers a similar yet more structured approach: “University provides three things that start with the letter C,” he explains:
- Knowledge, which lays the necessary technical foundations.
- Peer connections, fostering community growth and project sharing.
- Cachet, the prestige of the institution and the network of contacts one can build.
“Try to choose the [university] with the most cachet and the best contacts you can make,” he advises. Still, he insists that learning cannot depend solely on the academic realm: “This is a discipline where often, you learn more by doing than by studying.” Therefore, he recommends starting using the tools as soon as possible: “I would tell them to start using the tools before they begin studying.” His key advice: complement academic studies with self-directed learning and practical experience, because “companies seek individuals who have done, experimented, and utilized the tools.”
Andrés Torrubia: “Try to choose the university with the most cachet and the best contacts you can make”
Manchón concurs with the need to broaden horizons. From Google, she notes, courses are offered ranging from a basic introduction to AI to advanced fine-tuning of large language models. Other universities like Stanford or platforms from companies like AWS and Microsoft also provide open resources that allow learning at various paces and levels. “You can become an expert much more easily,” she concludes. “Not only through formal education but also by using these tools that help you advance at your own speed.”
Not all experts agree that training as a developer is the best move today. Jon Hernández raises a provocative warning: “Yes, don’t do it. I would say that at this point, we might already be too late.” His argument is that by the time one completes a university degree, AI could have evolved to the extent that this education may no longer be in demand.
“We’re seeing how models are increasingly powered by synthetic data, and how models have impressive programming capabilities,” he notes. According to Hernández, the challenge isn’t just the advancement of AI but its impact on the productivity of the developers themselves: “Programmers assisted by AI are dramatically more productive.” While some human oversight will still be necessary, the expectation—citing NVIDIA’s CEO, Jensen Huang—is that fewer developers will be needed for tasks that once required hundreds.
His conclusion is stark: “For 20 years, we have been telling young people to focus on studying programming. And by the time they finish, there will likely no longer be jobs available.”
Whatever You Study, Artificial Intelligence Will Reach You
Thinking that dedicating oneself to artificial intelligence necessarily means studying engineering is an understatement. Antonio Ortiz presents a powerful idea: AI will filter into almost every profession, from medicine to law to communication. “Even if I do not plan to specialize in AI but in any other discipline—such as audiovisual communication, business administration, or medicine—since artificial intelligence is already present in those professions, I believe it is important for everyone in those fields to approach it.”
The key, according to Ortiz, is to become a professional in your field while understanding how AI integrates into that discipline. “In the end, you’ll be a professional in your field, but you also need to comprehend the technical development and integration of artificial intelligence into it,” he explains. In this scenario, digital knowledge transforms: it will no longer suffice to command the basic tools; one must now adopt a new language. “It will be like digital literacy, but on steroids.”
Antonio Ortiz: “It will no longer suffice to command the basic tools; one must now adopt a new language.”
Andrés Torrubia also warns that AI’s impact will be cross-disciplinary. “I believe that most careers will be impacted by artificial intelligence. Not just technical careers but, in one way or another, professionals in health and possibly psychologists as well.”
A view shared by Pilar Manchón from her experience at Google: “We will see continuous disruptions in almost all fields of knowledge and science with the use of artificial intelligence. Therefore, the real challenge lies not only in mastering the technology but in imagining how we can use it.”
What Does It Take to Thrive in a Continuously Redefined Field?
Technical knowledge matters, of course, but it is not the only factor. Personal qualities, mindsets, and attitudes that, according to the experts, will become increasingly vital. Pilar Manchón is clear about it: everything begins with curiosity. “Even at 17 or 18, I think there are many people whose curiosity is piqued. It’s not just about discovering what you don’t know, but also what the world doesn’t yet know. It’s a different level.”
Pilar Manchón on curiosity: “It’s not just about discovering what you don’t know, but what the world doesn’t know yet.”
Jon Hernández, on the other hand, emphasizes the importance of soft skills over technical knowledge. “For me, the most crucial skill today, by far, is critical thinking. AIs tend to display an incredible bias of authority in both programming and other fields.” He goes further. He highlights the value of entrepreneurial skills—“I believe that entrepreneurship will be the greatest job opportunity from 2026 onward”—and other less obvious yet equally relevant skills: emotional management, human leadership, and teamwork.
For Andrés Torrubia, the key is to develop mental flexibility and the ability to identify problems that AI can solve, rather than obsessing over the tools themselves. He also values initiative: “Having created your own projects, even if they are trivial, while you are studying.”
Dedicating oneself to artificial intelligence is not a singular endeavor, nor is there a singular pathway to achieve it. It can involve developing models, applying them in other disciplines, or merely understanding them to integrate them into daily work. What’s crucial, the experts agree, is to build a strong foundation, foster curiosity, stay adaptable, and not lose sight of the most important question: how do we want to engage in a world that, with artificial intelligence as an ally, is changing faster than we ever imagined.
Images | Javier Miranda | World Economic Forum | Jon Hernández | Instituto de Inteligencia Artificial | Xataka | Wes Hicks
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