Why Korea cannot buy its way into AI productivity

Korea can purchase compute, models, and infrastructure faster than it can build the institutions that make them productive.
AI productivity depends on complementarity among human skill, intellectual capital, physical capital, and organizational capability.
Industrial policy should identify the binding institutional input instead of treating expenditure as evidence of transformation.

The Korean Policy Question

Korean technology policy has long used concentrated investment to accelerate strategic industries. That approach works best when capital is the scarce input and the surrounding production system already knows how to absorb it. AI complicates the model because data, expertise, organizational redesign, and governance must improve together. Republic of Korea: Selected Issues places Korea’s wider productivity challenge in this structural context.

The contrast between access and implementation in The AI Premium Is About Implementation, Not Access is therefore a national policy problem. Public procurement and infrastructure can expand access rapidly, but they cannot by themselves determine which decisions change, who validates outputs, or how productivity gains are distributed across firms.

Why Inputs Must Be Studied Together

Many weak strategies begin with a true observation and end with an incomplete prescription.

An organization lacks AI capability, so it hires more specialists. A university appears underproductive, so it receives more funding. A country wants an AI industry, so it buys computing infrastructure. Each action increases an input. None guarantees a productive system.

The missing idea is complementarity. Skilled people need access to usable knowledge and suitable capital. Knowledge becomes productive when people can interpret and adapt it. Capital generates returns when an organization knows what to build and how to operate it.

No input is independently sovereign.

A Three-Capital Production Framework

• human capital: the capability embodied in people;
• intellectual capital: theories, methods, data, documentation, routines, and accessible knowledge;
• physical and financial capital: compute, laboratories, software, facilities, and funding; and
• organizational effectiveness: the rules and coordination that allow the inputs to work together.

This is a multiplicative production framework. It is not a complete empirical description of an economy. It is a disciplined way to ask how several necessary inputs jointly produce output.

Quantity and Productivity Are Different

The model separates the amount of an input from the effectiveness with which it is used.

A firm may have a large stock of physical and financial capital because it owns expensive computing resources. If the firm cannot choose suitable models, clean its data, or integrate results into decisions, organizational effectiveness is low. The investment exists, but its productive effect is weak.

Likewise, an organization may hire many educated people, increasing the apparent quantity of human capital. If roles are poorly matched and initiative is suppressed, effective human capital remains limited.

Table 1. Complementary inputs in an AI-intensive production system

Input	High quantity, low effectiveness	High quantity, high effectiveness
Human capital	Many credentials, weak task ownership	Independent specialists assigned to suitable problems
Intellectual capital	Large document archive, poor retrieval or adaptation	Shared models, validated data, and cumulative learning
Physical and financial capital	Expensive infrastructure without a use case	Resources selected for a defined computational workload
Organizational effectiveness	Fragmented authority and incentives	Clear interfaces, review, and decision rights

Counting inputs without examining their productivity can make a weak system appear strong.

How Complementarity Works

This is the practical meaning of complementarity. Increasing intellectual capital raises the marginal product of human capital, and increasing human capital raises the marginal product of intellectual capital.

A researcher with access to better data, literature, and methods can produce more. A knowledge repository becomes more valuable when capable people can interrogate it. The value is created in the interaction.

Bottlenecks and Balanced Investment

The last unit of budget should produce the same marginal gain across uses. If one ratio is much larger, that input is underprovided relative to its price.

This condition does not mean equal spending. It means balanced marginal returns.

An AI team with strong models and excellent staff but unreliable data infrastructure may obtain its highest return from data engineering. A team with abundant compute and clean data but weak statistical reasoning may need education or different hiring. A university with capable faculty but no time for research may need workload reform rather than another building.

Why Capital Alone Often Disappoints

Physical investment is visible. It can be announced, photographed, and recorded in a budget. Human and intellectual capital accumulate more slowly and are harder to verify.

This creates a political and managerial bias toward physical and financial capital.

Read as a diagnostic rather than a second scorecard, the framework becomes clear: Large-model training lab — human capital: Medium; intellectual capital: Low; physical and financial capital: Very high; Likely constraint: Model and data knowledge. Applied forecasting unit — human capital: High; intellectual capital: High; physical and financial capital: Moderate; Likely constraint: Organizational adoption.

The first project may attract more attention because its capital expenditure is larger. The second may create more value because its inputs are better aligned with a decision.

More hardware cannot indefinitely compensate for missing knowledge.

Intellectual Capital Is More Than Information

Information becomes intellectual capital only when it can be used in a productive process.

• models that organize observations;
• documented assumptions;
• reusable code and validated data;
• records of failed approaches;
• vocabulary shared across teams;
• access to external scientific communities; and
• procedures for revising inherited knowledge.

This distinction is especially important in AI. General-purpose models can retrieve and recombine information, but they do not automatically supply the local definitions, causal structure, or responsibility required for a decision.

An organization's intellectual capital improves when knowledge is cumulative and contestable.

Organizational Capital as the Integration Term

Organizational effectiveness is often treated as a residual. For management, it is a design variable.

The framework distinguishes coordination quality, governance clarity, the quality of feedback, and organizational friction.

The framework is illustrative, but it changes the managerial question. Low productivity need not mean that workers are lazy or technology is obsolete. The problem may be that the system prevents the inputs from combining.

• analysts cannot access operational data;
• technical teams cannot speak directly to decision makers;
• errors are punished, so weak assumptions remain hidden;
• procurement selects technology before the task is defined; and
• project knowledge disappears when a contractor leaves.

These are failures of organizational effectiveness.

A Diagnostic Matrix

Before expanding an AI initiative, an institution can identify its likely bottleneck.

This matrix discourages a universal remedy. It also makes responsibility more precise.

Implications for Education

An educational institution participates in all four terms.

It develops human capital through instruction and assessment. It curates intellectual capital through curriculum, readings, cases, and research. It supplies physical and financial capital through data, software, and computational resources. It shapes organizational effectiveness through sequencing, feedback, faculty coordination, and standards.

This is why a school cannot define quality only by admission selectivity or faculty credentials. Its output depends on whether the educational system combines the inputs.

A demanding student with weak guidance can remain directionless. Excellent lecture notes without assessment may become passive information. Large technology budgets without a coherent curriculum may produce demonstrations rather than learning.

The institution's educational production framework is also complementary.

Implications for AI Strategy

AI strategy should begin with the production system, not the shopping list.

1. Define the output and decision.
2. Identify the capability required.
3. Inspect the existing knowledge and data base.
4. Determine the necessary computational and financial resources.
5. Design the organizational interfaces.
6. Invest in the input with the highest marginal return.

This approach may lead to more compute, more hiring, or more education. It may also reveal that the main problem is documentation, incentives, or governance.

Complementarity also explains why balanced investment can outperform a larger but one-sided budget. A firm that buys more compute without improving data governance may raise capacity while leaving usable output unchanged. A university that hires technical specialists without coordinating a curriculum may increase expertise while weakening the student’s path through it. The relevant marginal return is conditional: the value of one more unit of capital depends on the current levels of skill, knowledge, and organization. Strategy should therefore identify the weakest complement first, then test whether improving it raises the productivity of assets already in place. The test is empirical: after the constraint is relaxed, the returns to the other inputs should rise as the complementarity model predicts. If they do not, the diagnosis or the assumed production relationship must be reconsidered. Balanced systems create compounding returns; isolated inputs often create expensive evidence of the missing complement. Coordination converts the bundle into output.

SIAI’s current research gives this production logic direct empirical and institutional relevance. From AI Access to Organizational Capability treats workflow integration and governance as fixed costs that determine whether models become productive. Beyond Robot Relations places human–AI relations inside management systems rather than isolated tasks, while Invisible Value explains why data, software, knowledge, and organizational assets are poorly captured by conventional investment measures. The return to any one input depends on the capabilities surrounding it.

The Limits of Capital-Led AI Policy

If inputs are complements, the return to additional compute is low where usable data, model judgment, or organizational authority are scarce. The same is true in reverse: advanced training produces limited national value when graduates enter roles that cannot deploy it. Balanced development is not rhetorical moderation; it follows from the production framework.

Korea should evaluate major AI programs by the complementary capabilities they activate. A compute facility requires a research-user base, access rules, reproducible workflows, and links to industrial problems. A model-development subsidy requires evaluation capacity and pathways into production. A training program requires employers capable of creating technically substantive roles.

This approach does not imply smaller investment. It implies investment sequenced around bottlenecks, with outcome measures that distinguish installed capacity from productive use. Korea’s AI advantage will depend less on the amount it can purchase than on the system it can coordinate.

Conclusion

Skills, knowledge, and capital generate value together. Their complementarity explains why apparently strong institutions can underperform: they may possess visible inputs without the less visible system that connects them.

The production framework is useful because it separates quantities, elasticities, and coordination. It asks not only how much an organization has, but how effectively each input is used and which input limits the return to the others.

In an AI-intensive economy, the winning strategy is rarely to maximize one input. It is to build a balanced system in which skilled people, cumulative knowledge, suitable technology, and organizational judgment reinforce one another.

References

Robert M. Solow, “Technical Change and the Aggregate Production Framework”, Review of Economics and Statistics, 1957.
Paul M. Romer, “Endogenous Technological Change”, Journal of Political Economy, 1990.
Nicholas Bloom and John Van Reenen, “Measuring and Explaining Management Practices Across Firms and Countries”, NBER Working Paper 12216, 2006.
Swiss Institute of Artificial Intelligence (2026) ‘From AI Access to Organizational Capability: Pricing the Corporate AI Transition’, SIAI Working Papers, 9 August.
Swiss Institute of Artificial Intelligence (2026) ‘Beyond Robot Relations: Managing, Measuring and Organizing the AI-Dependent Firm’, SIAI Working Papers, 9 August.
Swiss Institute of Artificial Intelligence (2026) ‘Invisible Value: Why AI Intangible Assets Make the Economy Look Smaller Than It Is’, SIAI AI Memo, 22 January.
International Monetary Fund (2025) “Republic of Korea: Selected Issues”.
Swiss Institute of Artificial Intelligence (2026) “The AI Premium Is About Implementation, Not Access”.