Artificial intelligence (AI) is everywhere—from virtual assistants to systems that predict economic trends or assist in healthcare. Yet in today’s landscape, a critical question arises: should we massify AI to make “everything accessible to everyone,” or focus on specialization to truly enhance human capability?
One of the main challenges of current AI models is the massification of information. Having access to massive amounts of data does not necessarily lead to efficiency or better outcomes. Information without proper sectorization or specialization can even dilute the development of concrete ideas and projects.
Consider a real-world example: Albert Einstein, widely regarded as one of the greatest physicists in history, the mind behind the theories that underpin modern science. Paradoxically, Einstein admitted that he struggled with mathematics during his early studies and performed poorly in undergraduate courses. This raises a profound question: do we really need to be experts in everything, or can we focus on mastering a specific area?
Applied to AI, the reflection is similar. AI does not need to be an expert in every field. Its true value may lie in enhancing human knowledge in specialized areas, allowing experts to reach their full potential, optimizing processes, and making smarter decisions.
Sam Altman, CEO of OpenAI, has suggested that we may soon see companies run by a single employee. A deeper interpretation indicates that the future might instead lie in trillionaires leading teams of experts supported by top-tier AI, where each individual, empowered by specialized artificial intelligence, achieves efficiency, perfection, and scalability in their field of expertise.
In conclusion, the challenge is not just to create more AI or provide massive access to raw information. The real task is to develop specialized AI that amplifies human talent, promotes mastery in specific domains, and transforms the way people and businesses create value.