1. Why are generalists so rare in human history?

When people think of polymaths, many first think of Leonardo da Vinci from the Renaissance: he was not only an outstanding artist, but also an engineer, anatomist, and nature observer, with his research spanning multiple fields such as art, science, and engineering.

But what truly amazes about Leonardo da Vinci is not his vast knowledge. In fact, simply possessing a large amount of knowledge does not define a person's ability—a person can read many books and memorize a great deal of information, but if this knowledge remains isolated, then he is closer to a knowledge collector than a generalist. The true ability of a generalist lies in their ability to traverse different fields, understand the underlying principles of different knowledge systems, and discover the hidden connections between them.

Leonardo da Vinci's value is precisely reflected here: his research on human anatomy was not merely a medical exploration, but also influenced his painting; his observations of natural laws went beyond scientific interest, further impacting his understanding of art and engineering problems. In his cognitive system, different fields were not independent collections of knowledge, but rather a whole capable of influencing and inspiring each other.

However, with the development of human society, this interdisciplinary ability has become increasingly difficult because the modern knowledge system has undergone tremendous changes compared to the past. With the continuous development of science and technology, the scale of knowledge within each field has grown rapidly, and the level of specialization has increased. Today, it may require a significant investment of time and energy for an individual to develop in-depth expertise in any field, such as computer science, medicine, materials science, or economics.

The growth in the scale of knowledge has gradually shifted human society from relying on individuals possessing knowledge in multiple fields to relying on specialized division of labor to solve complex problems. The enormous efficiency of modern civilization largely stems from this division of labor: some delve into artificial intelligence, some explore life sciences, some study the laws of physics, and some are responsible for designing complex engineering systems. Each person continuously deepens their expertise in their respective field, and through social collaboration, they collectively drive overall development.

However, while this model has greatly enhanced humanity's ability to solve complex problems, it has also created new challenges: as various fields continue to deepen, more and more complex problems can no longer be solved by relying solely on knowledge from a single field. Specialization has cultivated a large number of domain experts, and new breakthroughs often require someone who can understand the connections between different fields and promote the flow of knowledge between fields. Many important innovations arise precisely at the intersection of different knowledge systems.

For example, the development of artificial intelligence is itself the result of the long-term integration of multiple fields such as mathematics, statistics, and computer science; modern product design also increasingly requires the integration of knowledge from different fields such as engineering technology, psychology, and human-computer interaction. This means that as knowledge becomes more complex, the ability to transcend professional boundaries, understand the relationships between different fields, and reorganize them becomes even more important.

This is precisely the core value of generalist ability: it is not a replacement for a specialty, but a bridge between specializations. Because this ability transcends mere knowledge accumulation, generalists have always been a minority throughout history: they not only need to engage with multiple fields, but also need to integrate this seemingly scattered knowledge and form their own system of understanding.

2. Three obstacles to becoming a generalist

Of course, the degree of specialization within a knowledge system does indeed affect the difficulty of cross-disciplinary learning. For example, during the Renaissance, knowledge systems were not yet highly specialized, which meant that polymaths like Leonardo da Vinci faced relatively lower knowledge barriers when exploring different fields. However, this difference in era can only lower the threshold for entering different fields; it cannot reduce the complexity an individual faces when truly completing cross-disciplinary learning and integration.

Specifically, cross-domain capabilities are not simply about increasing the amount of knowledge, but rather about continuously completing three interconnected steps: how to acquire basic knowledge in unfamiliar fields, how to understand the knowledge systems behind different fields, and how to establish new connections between these knowledge systems.

First, cross-domain knowledge acquisition

The first challenge in becoming a generalist is to constantly explore new areas of knowledge.

In the past, knowledge was often scattered across different books, institutions, experts, and practical experiences. Entering a new field typically required finding suitable learning resources and even gradually building understanding through communication, visits, and long-term practice. Even in the internet age, while information access has been greatly improved, finding reliable sources and effective pathways amidst a massive amount of scattered information remains a challenge.

Therefore, acquiring cross-disciplinary knowledge is the first obstacle to becoming a generalist: how to enter an unfamiliar field and acquire the foundational knowledge needed to build a further understanding.

Second, cross-domain knowledge understanding

Acquiring knowledge is just the beginning. To truly enter the knowledge system of a field, one also needs to understand the hidden conceptual relationships and ways of thinking.

Different fields are not simply a matter of changing a set of technical terms. Behind every mature field lies its own conceptual system, research methods, and problem analysis approach.

For example, when discussing "systems," computer science, engineering, biology, and even social sciences may have different understandings and focuses on this concept. If a person merely memorizes related concepts from different fields without truly understanding their meaning and role within their respective knowledge systems, then no matter how much knowledge they acquire, it will only be an isolated accumulation.

Therefore, cross-disciplinary knowledge comprehension is the second obstacle to becoming a generalist: after acquiring basic knowledge of an unfamiliar field, how to further understand its knowledge system and master its core concepts, ways of thinking, and problem-solving methods.

Third, cross-domain knowledge connections

If we say "”Get“"It determines how many fields a person can access,"”understand“"What determines how deeply a person can delve into a field is..."”connect“"Ability determines whether a person truly possesses the value of a generalist."

This is because possessing knowledge from multiple fields does not mean that a person has developed a holistic understanding of that knowledge. The real difficulty lies in, after understanding the knowledge systems of different fields separately, further integrating them into one's own cognitive framework.

Therefore, the ability to connect knowledge across disciplines is the third obstacle to becoming a generalist: how to establish connections between knowledge systems in different fields and recombine existing knowledge to form new understandings and creations.

From knowledge acquisition to knowledge understanding and then to knowledge connection, the formation of a traditional generalist requires overcoming obstacles at these three levels. Therefore, becoming a generalist not only requires long-term accumulation but also means bearing an extremely high cognitive load.

So, what is cognition? Why do generalists place such high demands on cognitive abilities when engaging in cross-disciplinary learning and knowledge integration?

3. Cognition – the way humans understand the world

Cognition is the process by which humans filter, process, interpret, and associate external information, and form their understanding of the world accordingly. Knowledge, on the other hand, is the result of the continuous accumulation and sedimentation of these understandings.

Regarding how knowledge can be further formed into a structured cognitive system, this has been discussed in previous articles.Personal Knowledge Engineering (Part 2): From Cognitive Structure to Knowledge Engineering – Why Does Knowledge Need to Be Structured?This has been discussed in detail in [the article], and will not be repeated here.

From this perspective, the three obstacles discussed in the previous chapter actually involve different stages of the cognitive process: knowledge acquisition requires filtering effective content from external information, knowledge understanding requires processing and interpreting this content, and knowledge connection requires establishing connections between different understandings based on existing cognition.

In other words, the three obstacles that traditional generalists need to overcome are essentially the specific manifestations of different stages of the cognitive process in cross-disciplinary learning.

The difficulty of interdisciplinary learning lies in the fact that this process requires continuous filtering, processing, interpretation, and association of information. The human brain is not an infinite information processing system: attention determines how much content a person can focus on at the same time, working memory limits how much information a person can process simultaneously, and the establishment and maintenance of complex relationships further increases the brain's processing burden.

This limitation is amplified further when the problems to be addressed become more complex. A large number of concepts, complex relationships, and the mutual influence between multiple knowledge systems can significantly increase cognitive load—especially in cross-domain environments, where one not only needs to understand knowledge from different domains, but also needs to maintain the relationships between these knowledge and constantly compare, abstract, and reorganize them.

In the past, these processes of acquiring, understanding, and connecting knowledge almost all had to be completed independently by individuals. The number of people who can sustain this ability to learn, understand, and integrate is naturally extremely limited, which explains why generalists have always been a minority throughout history.

Even in modern times, despite the dramatic changes in knowledge acquisition conditions brought about by technologies such as the internet, knowledge comprehension and connection still heavily rely on individual cognitive abilities. People who can truly transcend multiple fields and establish deep connections between different knowledge systems remain rare.

4. The Redefinition of Generalist Ability in the AI Era

If the core difficulty of becoming a generalist in the past lay in the need for an individual to rely on their limited cognitive abilities to independently acquire, understand, and connect knowledge, then AI is changing that: unlike tools that primarily expanded information storage and retrieval capabilities in the past, AI is beginning to directly participate in human cognitive processes, assisting in tasks such as information organization, concept interpretation, relationship analysis, and knowledge organization, thus becoming a kind of "cognitive exoskeleton" that expands human cognitive abilities.

This means that the division of labor for cognitive tasks has changed:Humans are responsible for asking questions, setting goals, and making judgments, while AI undertakes a large amount of information processing and exploration work.The three obstacles that traditional generalists need to overcome will not disappear, but AI can significantly reduce the cognitive burden required to complete these tasks.

Cross-domain information processing capabilities

When faced with an unfamiliar field, the first step is to extract key concepts from a large amount of information, establish a basic framework, and determine which aspects are worth further exploration. Now, AI can participate in this process, organizing, summarizing, and reorganizing information to help people more quickly build a preliminary understanding framework for unfamiliar fields.

The significance of this change lies not in AI learning on behalf of humans, but in reducing the cognitive burden on humans when processing complex information.

Cross-domain understanding ability

In the process of knowledge understanding, the value of AI is not just in providing answers, but in providing a dynamic interpretive capability.

In the past, when faced with cross-disciplinary knowledge, humans could only gradually adjust their understanding by relying on learning materials and their existing cognitive frameworks. However, AI can interpret the same concept at different levels and from different angles according to different needs, helping people to continuously revise and improve their understanding.

This interactive understanding process enables people to grasp the conceptual framework, thinking methods, and core assumptions behind unfamiliar fields more quickly.

Cross-domain knowledge connection

As we mentioned before, the core of generalist ability is not possessing more isolated knowledge, but discovering new connections between different knowledge systems. AI can help people explore relationships that would otherwise be difficult to notice, and expand the scope of knowledge that individuals can analyze and compare simultaneously. For example, it can discover similar structures between concepts, propose cross-domain analogies, or provide new directions for combination.

The significance of this expanded capability lies not in AI replacing human creativity, but in helping people break through the boundaries of existing knowledge and discover more possible ways of connection.

In other words, once AI significantly lowers the barriers to knowledge acquisition, understanding, and connection, the real dividing line between ordinary people and generalists will shift from the past... “Who can handle more information?”Gradually turning “Who can use AI to ask more insightful meta-questions?”,as well as “"Who can make more sophisticated value judgments and aesthetic assessments in collaboration with AI?"”.

Therefore, while AI cannot automatically turn everyone into Leonardo da Vinci, it can decouple generalist abilities from an individual's cognitive capacity, making this ability no longer the exclusive domain of a very few geniuses, but allowing ordinary people to also reach this level.

Note: In the previous article, "..."AI and Cognition (Part 1): Reconstructing Creativity in the AI Era—From Information Processing to Cognitive CollaborationIn my previous article, I proposed four levels of AI usage, with the fourth level being "cognitive enhancement." However, at that time, I mainly presented this judgment at the conceptual level and did not further explain how AI expands human cognitive abilities. This article further supplements this content and attempts to map the path of ability changes behind "cognitive enhancement."