Use surplus tax revenue to innovate university basic research

South Korea stands at a potential inflection point in 2026, poised to become a leader in the artificial intelligence era, largely due to its advancements in the semiconductor industry. However, achieving sustained competitiveness in AI requires more than just technological accumulation; it depends critically on the capacity to generate novel and high-quality data. This ability, the article argues, originates from creative and challenging basic research, a consensus already formed within the research community.

The current support system for university research, however, falls short. Despite recent progress in increasing budgets and project numbers for basic research, the impact on the ground remains limited. Years of R&D budget cuts from the previous administration have created structural bottlenecks, affecting researchers who have been excluded from funding for three to four years. Even established mid-career researchers face uncertainty, struggling to continue their work amidst a precarious funding landscape. This environment not only diminishes the quality of research but also distorts its direction, favoring safe, predictable projects over potentially groundbreaking but riskier endeavors.

As discussions around the utilization of surplus tax revenue intensify, a strategic expansion of R&D investment, particularly in basic research crucial for nurturing future talent, is proposed as a key solution. This would involve not only alleviating funding bottlenecks but also ensuring the sustainability of research that may not yield immediate results. The article advocates for a complementary approach: expanding competitive basic research funding led by the Ministry of Science and ICT, alongside introducing a university-centered research support system managed by the Ministry of Education.

Providing universities with a stable, significant research budget would establish a foundation for researchers to continue their work irrespective of individual project selection outcomes. This system would act as a safety net, complementing the limitations of a purely competitive model and fostering long-term research in less mainstream areas. Such stability is essential for cultivating creative and challenging future talent and for attracting top international researchers. Ultimately, the competition in the AI era will be defined not by data possession, but by the creation of new knowledge, with university basic research serving as its crucial starting point.