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The Importance of Implementation in Policy-Making
The author is the president of Taejae University. In November 1970, a U.S. Economic Development Administration project aimed at revitalizing the Oakland port area failed. The initiative sought to convert a former naval base into a commercial hub to create jobs, but it ultimately did not succeed. Political scientists Aaron Wildavsky and Jeffrey Pressman analyzed this failure in their book “Implementation” (1984). They found that while stakeholders supported the plan during its development, they were passive during its execution. This highlights the importance of designing policies with implementation in mind from the beginning.
Today, the rise of AI presents a similarly complex policy challenge. Much like Christopher Columbus’s “discovery” of the New World, AI is reshaping the trajectory of human civilization. According to Google futurist Ray Kurzweil, AI will surpass human intelligence by 2029 and reach a point of “superintelligence” by 2045, pushing civilization toward a singularity.
National Strategies for AI Development
National strategies for AI typically focus on three pillars: computational infrastructure, talent, and data. The United States is building a megascale computing center through the Stargate Project, with OpenAI, Oracle, and SoftBank committing as much as 730 trillion won ($534 billion). The European Union is also investing 300 trillion won in AI infrastructure development plans. France has pledged 163 trillion won to its AI data centers. Singapore offers approximately 6.7 million won in monthly stipends to Ph.D. students in AI programs, regardless of nationality. China, relatively unburdened by privacy constraints, has developed its high-performing DeepSeek model by leveraging massive datasets.
Korea is also stepping up its efforts. The government recently announced a plan to invest 100 trillion won to build a sovereign AI computing center. During a recent visit to Shanghai and Hangzhou as part of the Korean Peninsula Peace Odyssey, the author met with AI firms and researchers at Zhejiang University, the birthplace of DeepSeek. One company emphasized a critical point: Even more important than computing power, data, and talent is AI governance. In other words, social systems — more than technical capability — will determine AI’s real-world impact.
The Changing Landscape of Work and Society
Much of today’s social structure stems from the mass production systems of the 20th century. Back then, large organizations thrived on standardized, repetitive work. Bureaucracy expanded in both manufacturing and office-based functions like human resources and finance. But AI is poised to replace much of this routine labor, while robots take over physical processes. This transformation will reach across sectors — from law and medicine to education, finance, and the arts.
A nation’s AI competitiveness will hinge less on raw technology and more on practical integration. For AI to enhance productivity and shift human labor toward more creative tasks, existing data and workflows must be opened up for machine learning. Resistance to this transition — especially the withholding of essential data — will render even the most advanced AI tools ineffective.
Challenges in Data Sharing and Integration
Take health care. Korea has comprehensive national medical data, yet if hospitals refuse to share it due to privacy concerns, medical AI development stalls. Legal precedents are digitally archived, but restricted access limits their usefulness for AI-based legal analysis. In manufacturing, fear of information leakage may prevent companies from providing data necessary for AI-driven productivity gains.
This is why governance must take precedence. Without a regulatory framework that enables AI to learn from existing data, investments in infrastructure and sovereign language models risk being wasted. The United States and China are advancing in AI not solely because of resources, but because they’ve built governance models that facilitate data access and usage.
The Need for Effective AI Governance
The Korean government, which has emphasized pragmatism and AI-driven national competitiveness, must prioritize the establishment of effective AI governance. Without policies that dismantle vested interests and enable AI to be deployed at scale, massive investments in technology may yield little public benefit. The success of AI initiatives depends not just on technological advancements but on the ability to integrate these innovations into society effectively.
In conclusion, the path forward for AI requires a balanced approach that combines technological innovation with robust governance. By focusing on implementation and addressing the challenges of data sharing and integration, nations can ensure that AI contributes meaningfully to societal progress.
