Challenges in Korea’s AI Ecosystem

I spent two years pursuing a master’s degree at Hanyang University, focusing on natural language processing (NLP) and AI. Since February 2024, I have been working at LG Electronics’ AI Lab in Seoul, developing and deploying AI applications to users. Having experienced both academic research and industry firsthand, I have naturally been reflecting on how Korea approaches AI. Here are some issues that have recently stood out to me:

1. Short-Term Focus Over Fundamental Innovation

Korean AI research often remains heavily oriented toward short-term results. National research projects and corporate initiatives typically demand “visible outcomes” within a limited timeframe, which tends to prioritize improvements that can be directly applied to services — such as agentic frameworks — over efforts to tackle the fundamental limitations of foundation models. Unlike the US or China, where long-term investments in basic research over 5–10 years are common, Korea tends to concentrate both human and material resources on technologies that can produce results in just a few years. This structure makes it difficult to secure long-term global competitiveness, which may help explain why Korea still ranks around third in AI competitiveness despite significant efforts.

2. A Culture Resistant to Learning from Failure

Failure still carries a strong stigma in Korea. When researchers fail to achieve expected outcomes, it is often recorded simply as a “failure,” limiting future opportunities. In such an environment, people naturally gravitate toward safer projects rather than pursuing challenging or risky research. By contrast, overseas teams and communities actively share lessons learned from failure, using them as stepping stones for growth. Organizations like OpenAI and Google DeepMind achieved their current success precisely because they treated countless failures and trial-and-error processes as valuable learning assets.

3. Commercial Bias in Research Priorities

AI research and investment in Korea are overwhelmingly concentrated in profitable areas. Services for coding assistance, financial services, and advertising/recommendation systems attract significant resources and talent because they can be monetized quickly. Meanwhile, socially valuable areas such as AI safety and ethics remain relatively neglected. This imbalance could ultimately hinder the balanced growth of Korea’s AI ecosystem.

Closing thoughts

From the perspective of a junior engineer, Korea’s AI ecosystem faces three intertwined challenges: short-term focus, failure avoidance, and commercial bias. Addressing these issues likely requires a fundamental shift in how AI is perceived and supported in society.

🤔 But can such changes truly take root in Korea, I wonder?