Are People Who Excel Across Diverse Fields Really Smarter?

There’s always that type of person—no matter what they do, they pick it up quickly and do it well.

From product management to investing, from technology to management, others often go through a long adjustment period when switching fields, sometimes even feeling overwhelmed. But these people only get better with each switch, as if every new domain builds on what they’ve already accumulated.

They may not be the smartest bunch, but they share one trait: for them, there’s almost no such thing as an “unfamiliar domain.” When they encounter something new, they grasp its “shape” at a glance—where the key points are, where the risks lie, and where the best approach is.

We often simplistically attribute this to talent or intelligence. But if it were just intelligence, they’d excel by memorizing more facts and finding more shortcuts, right?

Yet in reality, they often say they “know nothing,” yet manage to leave domain experts speechless with their questions. Behind this lies a seriously underestimated core ability—the ability to build abstract models! It doesn’t rely on rote memorization or pure experience accumulation. Instead, it lets you quickly grasp the essence of any field, achieving a sort of universal understanding.

Most people who have spent years deeply in field A and then jump to field B often experience a “disability period.” The knowledge, networks, and methodologies they accumulated suddenly become useless, and they have to start from the basics again. On the surface, it seems that A and B have nothing in common, but the deeper reason is: what they learned in A was mostly concrete content, not the “structure” of the field. Once the environment changes, those operational steps immediately fail, and they have to start over.

This aligns highly with most people’s learning style. When reading a business book, we remember cases and quotes; when learning a new course, we memorize processes and terminology.

We think we’re learning, but in fact we’re just moving information around—remembering “content” but not internalizing “structure.” That’s precisely why we panic when crossing fields and fail when switching domains. People who can freely navigate different fields don’t fill their minds with scattered knowledge points and operational steps; they carry “models” and “structures.”

When faced with a specific problem, they strip away the surface and keep only three things:

• Variables — What things are changing in this situation?
• Relationships — How do these variables affect each other?
• Constraints — What conditions define the boundaries of this system?

Once you break a domain down into these three components, it becomes a transferable “structure.”

People who master this structure, when encountering “two mutually restraining forces” in a new field, immediately react: Oh, this is a feedback system. The focus should be on where the equilibrium lies, how perturbations propagate, and whether lags cause oscillations. They don’t need to study long to grasp the key points. That’s the essence of abstract modeling:extracting structure, not memorizing specific steps.

Successful people always have a system. This system spans cognition, methods, practice, and resources—all connected. Even when facing an unfamiliar field, they can quickly get up to speed, not from zero but based on this overarching system: supplementing necessary information, applying thinking models, and quickly figuring out the new game’s rules, achieving a cross-domain understanding.

So how do you train your abstract modeling ability?

First, after learning something new, distill the “three-piece set.” Whenever you encounter a new theory, method, or case, don’t rush to apply it. Force yourself to answer:

• What are the key variables (no more than 5)?
• What are the relationships among them (who influences whom, and how)?
• What are the system’s constraints (under what conditions does it hold)?

Write it down! It’ll be hard initially—you think you understand, but you’ve only heard it. But stick with it, and your way of seeing the world will change, from “remembering it” to “deconstructing it.”

Second, every time you extract a new model, immediately look for isomorphisms. Ask yourself: where have I seen this structure before? The law of diminishing marginal utility in economics and training adaptation in fitness are essentially the same structure. Ecological niches in biology and differentiated competition in brand positioning are also highly isomorphic. You’ll increasingly find that the underlying models of most fields are universal. Over time, when you encounter something new, your first reaction will no longer be “what is it” but “what is it like.” That’s the core of multi-disciplinary thinking models.

Third, test your models using “defamiliarization.” Explain the model to someone completely outside the field, using the simplest language without jargon. If they can understand and use it to explain everyday phenomena, you’ve successfully extracted it; otherwise, go back and refactor. Real abstract modeling describes the world in the most straightforward language.

Finally, actively create cross-domain tasks. Staying in one field for too long will also shrink your modeling ability. Regularly read a non-specialist book, have deep conversations with people from different industries, or take on a small cross-disciplinary project. Every cross-domain experience forces your model library to recombine. Until new fields no longer feel “new” to you, you’ll realize: most so-called “new” things are just old structures with a new coat.

The difference between experts and ordinary people lies in resolution. We see isolated domains—technology is technology, business is business. But experts see recurring structures: where there’s feedback, leverage, path dependence, and marginal returns. So when they switch fields, they’re just swapping outer shells. Every piece of learning does not reset to zero when moving to a new domain.

Of course, no one can be proficient in everything. But it’s not hard to appear “knowledgeable” in the eyes of ordinary people and to see things they don’t. The key is to update your cognition and reshape your worldview. Because this is the underlying algorithm—if you don’t update it, your processing results will be wrong, and your actions will naturally lag behind.

The strong see the weak as if from a higher dimension, standing on a platform built by cognition, practice, and resources, viewing the world with a three-dimensional perspective. The weak, however, are often stuck in a narrow, low-level loop, going from mistake to mistake, never thinking of building their own system.

Abstract modeling ability is the underlying skill that almost all truly insightful people possess. It serves you for life, keeping you strong across all dimensions no matter how the times change.

Have you recently tried cross-domain work or model extraction in any field? Feel free to share your experiences and insights in the comments. Let’s discuss how to build a stronger cognitive system together. I’m really looking forward to your stories—maybe your sharing can inspire many!