Remember that old adage that says “a jack of all trades is a master of none?” It seems the connotation is no longer as negative as we thought. I recently read an article in which a Middle East AI official did not just stop at the beginning of the saying. He also added a new end, which goes like this, “But most times better than a master of one.” He says that in a world dominated by AI, people who focus their skills across multiple areas would be able to survive in any situation.

I have written many articles about AI. There is no question that it is here, reshaping industries, redefining productivity, and unsettling the very foundations of how we think about work. However, amid the excitement lies a quieter, more unsettling emotion: The fear of being useless in an AI-driven workplace. It is the sinking feeling that gets to you when you watch an AI tool complete a task in seconds that took you a decade to master. This fear is not irrational—it reflects a profound shift in how human skills are valued, and it raises urgent questions about how education and workforce development must evolve.

The rise of AI is pushing our national economy toward a productivity paradox. It promises extraordinary gains in national productivity. However, we will be confronted with an uncomfortable macroeconomic truth: National productivity is poised to skyrocket because of AI, and it may well do so at the expense of human jobs.

We are entering an era where we can produce exponentially more goods, services, and code with significantly fewer human hours. It is entirely possible for a nation’s GDP to soar while its labor participation rate plummets. This decoupling of productivity from employment is the engine of our current anxiety. A richer society with a sidelined workforce is a recipe for profound social instability.

We have heard the phrase many times, “AI destroys jobs but creates new ones.” It captures part of the truth but not the whole picture. While historically true, this mantra may have reached its limit. We must stop using it to dismiss genuine fears. The difference today is the pace and nature of displacement. Yes, new roles will emerge—AI prompt engineers, ethicists, and AI managers —but these jobs are fewer in number and often demand specialized skills. The replacement rate is not one-to-one. A factory that once employed 1,000 workers may now need only 100 engineers to oversee automated systems. The net effect is displacement, not balance.

How then can we prepare our workforce to be more adaptable and maintain its relevance? The traditional education model—designed for an industrial economy—emphasizes specialization. Students are trained to become “I-shaped” professionals: Deep expertise in one area, with limited breadth. Master’s degree in this and a PhD in that. Such education might lead to expert physicists or educators, but also might render their students poorly equipped for dealing with an environment in which all sorts of simple, routine, or technological operations are performed by machines. If your value is knowing a specific set of rules or data, an AI model has already surpassed you.

In an AI workplace, this model is increasingly fragile. A narrow skill set can be easily automated or rendered obsolete. It will soon become harder to answer the question “What do you do?”

We need an educational redesign that fosters Comb-shaped individuals—people with broad foundational knowledge anchored by multiple deep specializations across disparate fields. A Comb-shaped professional might possess deep expertise in nursing, a secondary depth in data analytics, and a third in behavioral psychology.

Why does the “Comb” win? Because AI struggles with synthesis across highly distinct domains. An AI can diagnose an illness (medicine) and it can crunch numbers (data), but it struggles to intuitively understand how a patient’s fear (psychology) will affect their adherence to a data-driven treatment plan. The Comb-shaped human connects these dots, using AI as a tool for each individual vertical, but providing the uniquely human connective tissue between them.

For the existing workforce, waiting for educational reform is not an option. To increase employability, professionals must aggressively build new “legs” on their comb. If you are a deep specialist in marketing, you must stop viewing AI as a competitor and start viewing it as an exoskeleton. Do not just learn to use generative AI; develop a deeper understanding of how these models work. Lean into the skills that remain stubbornly human—complex negotiation, ethical stewardship, and empathetic leadership—while simultaneously becoming highly literate in the tools threatening to replace you.

The fear of uselessness is a signal that the old ways of working are dying. Our value in the AI workplace will not just come from what we know, but also from how we synthesize it across multiple domains in ways a machine cannot yet replicate.

(The author is an executive member of the National Innovation Council, lead convener of the Alliance for Technology Innovators for the Nation (ATIN), vice president of the Analytics and AI Association of the Philippines, and vice president of UP System Information Technology Foundation. Email: [email protected])