Beyond Domains of Competence | Generated by AI
Question: Is it correct to understand people as having specific “domains of strength” (like books in their brain), where each person is strong in some areas and weak in others—for example, Newton in physics, Lei Jun in business, Yin Wang in programming, and Paul Graham in startups?
Answer:
Your intuition is directionally right—but it needs sharpening to avoid oversimplification.
1. The useful part of your model (keep this)
Thinking of people as having “domains of competence” is powerful.
Examples:
- Isaac Newton → deep strength in classical physics and mathematics
- Lei Jun → strong in execution, product strategy, and trend timing
- Yin Wang → strong in compilers, systems thinking, and critical reasoning
- Paul Graham → strong in startups, writing, and Lisp
This aligns with a real concept in cognitive science: 👉 Expertise is highly domain-specific Being excellent in one field does not transfer automatically to another.
So yes—treating people like “collections of knowledge modules” is a good starting abstraction.
2. Where your model becomes inaccurate
The problem is labeling too rigidly or reductively, like:
- “X is poor at English”
- “Y is not technical”
Reality is more nuanced:
(1) Skills are multi-dimensional, not binary
Take Jack Ma:
- Not a strong engineer → true
- But deep understanding of business systems, communication, and global markets → also true
Calling him “poor at technical stuff” misses the fact that: 👉 He built one of the most technically complex companies by leveraging others’ expertise
(2) Time matters (people evolve)
Example:
- Early Lei Jun → strong engineer background
- Later → more focused on strategy, branding, capital, ecosystem
So a person is not a static “book”—more like a continuously updated library.
(3) Hidden skills matter more than visible ones
For example:
- Paul Graham is not just “good at Lisp”
-
His real strength is:
- identifying talent
- simplifying complex ideas
- shaping founder thinking
These are meta-skills, not just “topics”
3. A more precise model (upgrade yours)
Instead of “people = books”, use this:
A person = (knowledge domains) + (thinking style) + (meta-skills) + (time evolution)
Break it down:
(A) Knowledge domains (your “books” idea)
- Physics, business, programming, etc.
(B) Thinking style
- First-principles (Newton)
- Pragmatic execution (Lei Jun)
- Critical/deconstructive (Yin Wang)
- Pattern recognition (Paul Graham)
(C) Meta-skills (often more important)
- Learning ability
- Communication
- Judgment
- Taste
(D) Time dimension
- Skills shift over decades
4. A more accurate comparison of your examples
-
Isaac Newton → Not just physics, but invented new ways of thinking (calculus, scientific method refinement)
-
Lei Jun → Not just “catching trends,” but systematic execution + supply chain mastery + timing
-
Yin Wang → Not just programming, but deep abstraction + independent thinking
-
Paul Graham → Not just startups, but idea compression + insight generation
5. Final refined principle
Your idea becomes solid if you state it like this:
“Each person has unevenly distributed strengths across domains, shaped by their thinking style and experience. To understand them, identify their strongest domains, their way of thinking, and how these evolved over time.”
6. Practical takeaway (this is the real value)
If you apply this correctly:
- Don’t copy people blindly
-
Extract specific modules, like:
- From Newton → first-principles thinking
- From Lei Jun → execution discipline
- From Yin Wang → deep understanding
- From Paul Graham → clear thinking + writing
👉 Treat people not as “labels” but as systems you can decompose and learn from
If you want, I can turn this into a framework you can use to analyze any high performer systematically (like a checklist you can apply to people you meet or study).