How to Read Future Talent in the AI Era: Samminam TV's Discovery of FDE Organization Theory from the Three Kingdoms

The first insight from Samminam TV is that simply becoming "a person who uses AI well" cannot make you a future talent. Even if you skillfully craft prompts and can handle multiple generative AIs, if you cannot judge what problems need to be solved, it is difficult to create meaningful results. This is where the concept of FDE (Field Deployed Expert—a role stationed in the field to discover problems and connect technology, data, and people to create actual performance) emerges.

The true competitiveness of the AI era comes from the ability to convert answers created by AI into field performance. This is not simply a matter of handling tools, but an integrated competency to truly define what the problem is, see field reality beyond documents and data, and translate the different languages of managers and practitioners, operations and technology. Samminam TV finds this competency in figures from the Three Kingdoms—particularly in the example shown by Zhuge Liang.

* Tool-using ability becomes commonplace quickly, but field problem-definition ability is scarce
* Without an organizational system to trust AI's answers, technology adoption fails
* "What to solve" is more competitive than "how to solve it"

Zhuge Liang embodied within one person all the roles of strategist, diplomat, administrator, logistics officer, and executor. He diagnosed the weaknesses of Liu Bei's forces, read the structure of the world, designed the possibility of alliance with Sun Quan, and conceived Jingzhou and Yizhou as strategic strongholds. With a single strategic document called the Longzhong Plan, he connected terrain, power, talent, justification, alliance, and time into one long-term strategy. This is exactly what a modern FDE-type talent looks like.

Yet Samminam TV's core question begins here: Why did Zhuge Liang fail to create an organization where integrated problem-solving talents like himself were repeatedly developed? Zhuge Liang did leave successors. There were figures like Zhang Wan and Fei Yi, and Shu Han was maintained for a period even after Zhuge Liang's death. However, it is difficult to say that Zhuge Liang's integrated problem-solving competency was systematically produced repeatedly within the organization.

* Excessive concentration: Judgment and execution concentrated in one person
* Lack of authority delegation: Successors lacked opportunities to experience real responsibility
* Dependence on tacit knowledge: Judgment standards and know-how were not structured

Ontology is the core concept emphasized by Samminam TV—a way of thinking that asks "what connects and why." It is a way of understanding people, events, resources, goals, rules, actions, and relationships not as simple individual elements but as interacting networks. This matters because when both AI and humans share the same structure, they can judge by the same standards.

The strategies in the Three Kingdoms already operated as relationship networks. Zhuge Liang's Longzhong Plan connected terrain, power, talent, justification, alliance, and time into one long-term strategy; Lu Su's Sun-Liu alliance redefined enemy and ally not as fixed attributes but as relationships of goals and circumstances; and Cao Cao's Tuntian (garrison farming) designed agriculture, households, logistics, administration, and talent as one operating system. All of these structured relationships and context so that the organization functioned beyond individual judgment.

* Ontology thinking converts individual tacit knowledge into organizational explicit assets
* When relationship structures are clear, the next generation can solve problems by the same standards
* When AI and humans share the same relationship network, decision-making speed and trust increase

The FDE-type talent as defined by Samminam TV is not simply a person with outstanding abilities, but someone who repeatedly executes five competencies as one cycle. First, problem discovery—finding hidden problems and constraints. Second, relationship translation—connecting the different languages of operations, technology, and management. Third, integrated execution—bundling people, data, and processes into one. Fourth, small experimentation—avoiding big risks and proving value through MVP (Minimum Viable Product). Fifth, institutionalization—leaving success and failure as standards and assets that the next generation can reuse.

Enter the field and discover constraints and opportunities beyond documents

Translate the language of operations so technical teams can understand it, and explain technology's potential so operations can trust it

Verify value through small experiments, then scale up execution

Document each experimental process as an organizational judgment standard

Systematize so new people can solve problems in the same way

The organization that can repeat this cycle is "an organization that operates without Zhuge Liang."

* In the discovery stage, the ability to observe informal constraints in the field is key
* If stakeholders share the same "problem" in the translation stage, execution speed accelerates significantly
* If the institutionalization stage is skipped, an ace's experience remains a personal asset, not an organizational asset

Cao Cao did not search for one type of talent but composed a portfolio of talents with different strengths. He looked at role fit rather than origin, and made different judgments about who was truly necessary depending on circumstances. Zhuge Liang, by contrast, unified and handled all roles himself. Both approaches succeeded in the short term, but there is a fundamental difference from the perspective of long-term organizational design.

Zhuge Liang's integrated concentration approach was fast in solving field problems but slow in spreading competency. Cao Cao's portfolio approach reduced dependency on individuals but increased coordination costs when multiple people collaborate. Samminam TV, beyond these two extremes, presents a third way: possessing explicit relationship networks and decision-making standards. It recognizes individual excellence while converting it into the organization's repeatable capability.

* Portfolio composition advantage: reduced individual dependency; disadvantage: increased coordination complexity
* Integrated concentration advantage: fast execution; disadvantage: difficult successor development
* Ontology-type organization goal: combining portfolio flexibility + clear standards

Many companies adopt AI, but AI's answers do not lead to actual workplace changes. Samminam TV diagnoses the cause of this failure not as lack of technology or data but as the organization lacking a "cycle of defining, structuring, verifying, and institutionalizing problems." If field workers do not trust AI's answers, no technology works. This is the same problem as when Zhuge Liang devised strategies but junior generals could not understand and execute them.

Without a middle translator (FDE) to convert even perfect data and algorithms into actual field behavior, an organization merely appears to have adopted technology but does not actually change. Conversely, when an FDE-type talent has clear problem definition and relationship structure, the same technology creates completely different results.

* Technology adoption failure is an organizational structure problem, not a technology problem
* For the field to trust AI's answers, they must understand "why that answer came about"
* When decision-making standards are explicitly structured, technology and field speak the same language

In the past, organizations could survive depending heavily on one outstanding person to a degree. Because that person could judge quickly and execute quickly. But as the pace of change accelerates, data increases, and problems become more complex, such methods become increasingly risky. If one genius holds all judgments in their head, the organization appears to move quickly, but when that person disappears, the judgment standards disappear with them.

In the AI era, organizations must move from "looking for Zhuge Liang" to "developing Zhuge Liang," and further to "operating without Zhuge Liang." The key is converting individual tacit knowledge into organizational explicit assets. What an ace observes and judges, what data they trust, what exceptional situations change their standards, and who they need to collaborate with for execution must be documented as relationship networks and decision-making rules.

This is the image of future organizations that Samminam TV presents, and to realize it, you must first diagnose at what stage your organization is, and what FDE-type talent is needed. For deeper understanding and organizational diagnosis, please seek consultation at 010-2397-5734 or jaiwshim@gmail.com.

Q1: Was Zhuge Liang really an FDE-type talent?

A: Yes, when you extend Palantir's FDSE (Forward Deployed Software Engineer) concept. Zhuge Liang understood Liu Bei's force's problems, analyzed data and field terrain to devise strategy, directly handled administration, logistics, and diplomacy while taking responsibility for results. By demonstrating all five stages of problem discovery, translation, integration, experimentation, and institutionalization, he reached the pinnacle of individual FDE. However, he failed to transform that competency into a repeatable organizational system.

Q2: Can ontology thinking actually be applied in real corporate settings?

A: Yes, it can. Ontology is a methodology for explicitly structuring relationships and context. For example, instead of simply saying "customers buy our product because quality is good," if you map the customer's specific situation, constraints, and decision-making standards, both sales teams and AI can move by the same standards. Three Kingdoms AI Talent Research Institute (CEO Kim Gap-yong, CEO Sim Jae-woo) conducts workshops designed for corporate organizations.

Q3: How do I determine if I am an FDE-type talent?

A: Ask yourself these questions: (1) Do you spend time defining what the problem is? (2) Can you translate the language of operations and technical teams? (3) Do you document your experience so others can solve it the same way? (4) Do you prove value through small experiments before scaling? If you answer "yes" to all four, you likely have FDE-type talent characteristics.

Q4: If an organization is too large, is ontology-type organizational design impossible?

A: Not at all. In fact, the larger the organization, the more important explicit relationship structure becomes. In large organizations, if each department makes decisions by different standards, conflicts arise and inefficiency increases. However, if you have a common ontology (for example, consistently defining customer, situation, constraint, and action), you can move in the same direction regardless of size.

| Item | Traditional Organization (Individual Dependence) | FDE-Type Organization (Concentrated Competency) | Ontology-Type Organization (Structured Design) |
|------|------|------|------|
| Problem-Solving Speed | Fast (ace judges) | Very fast (field execution) | Medium~Fast (clear standards) |
| Competency Diffusion Speed | Slow (individual know-how) | Medium (institutionalization effort) | Fast (structured assets) |
| Successor Development | Difficult (tacit knowledge) | Medium (experience-based) | Feasible (explicit standards) |
| Organizational Scale Expansion | Severe bottleneck | Difficulty in talent procurement | Scalable |
| When Core Leader Absent | Serious organizational gap | Difficult role reassignment | System maintainable |
| AI Adoption Efficiency | Low | Medium | High (same standards) |
| Considerations | Everything depends on individual competency | FDE talent shortage, difficult retention | Initial structuring cost, culture settling needed |

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The anxiety of turning on YouTube because you can't cook rice in your first week of living alone actually touches on the anxiety of office workers in the AI era. Some may think, "Wouldn't a few basic cooking videos suffice?" But the problem runs deeper. Even after learning to cook, you must continuously judge "How do I adjust to my taste?", "Can I make multiple dishes with the same ingredients?", "Is the way I first learned really best?" This is problem-definition ability.

The FDE and ontology thinking that Samminam TV presents goes beyond individual problem-solving ability to a methodology that makes organizational change sustainable. In the AI era, many people will become good at handling tools, and there will be many outstanding individuals. However, organizations capable of converting that individual competency into repeatable organizational assets will be rare. This is where the difference emerges.

Diagnose and design together what stage your organization is at, what FDE-type talent is needed, and how to introduce ontology thinking. Three Kingdoms AI Talent Research Institute dedicates itself to such organizational design and talent development in Ganghwa-gun, Incheon. If you need more specific consultation and organizational diagnosis, contact 010-2397-5734 or jaiwshim@gmail.com.

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