AI Brings the Lab to the Desk: The Era of ‘One Scientist, One Research Institute’

AI Supports Literature Search, Hypothesis Generation, and Experimental Design...
A Major Paradigm Shift in Scientific Research

2026 Science Journalists Conference: "AI Semiconductors and Research Platforms Will Determine National Competitiveness"

Artificial intelligence (AI) is transforming the face of research labs. There is an emerging outlook that, moving forward, scientists all across the country will be able to operate AI research laboratories directly from their own desks.

As AI supports every stage of research—from literature searches and hypothesis generation to experimental design and data analysis—a single researcher can now accomplish what previously required an entire laboratory. Experts also note that securing AI semiconductors and next-generation computing technologies will be a deciding factor in determining a country's scientific and technological competitiveness.

Yoo Yonggyun, Head of the National Science AI Research Center at the National Research Council of Science and Technology (NST), is giving a presentation on "The Development of AI and the Changing Role of Human Scientists." Provided by the Korea Science Journalists Association

On July 28, at the Korea Science and Technology Center in Seoul, the first session of the "2026 Science Journalists Conference" was held under the theme "AI Transformation, Technology Hegemony and the Future of Society." Speakers and panelists discussed the ways in which AI is fundamentally changing scientific research and industrial ecosystems, as well as possible strategies for Korea to secure a lead in the technology hegemony race.

The Changing Lab: 'A Research Institute on Every Scientist's Desk'

Yoo Yonggyun, Director of the National Science AI Research Center at the National Research Council of Science & Technology (NST), stated, "AI is no longer just a tool for calculation or data analysis. It is evolving into an 'AI Scientist' that participates in the entire research process." He explained that the era has already begun in which AI can search academic papers, generate new hypotheses, design experiments, and analyze research results.

He particularly highlighted that the development of AI agents is expected to dramatically enhance productivity in research environments. Tasks such as repetitive literature review, data organization, coding, and experimental design will be handled by AI, while researchers will focus on formulating research questions and interpreting the results. This, he said, will fundamentally transform research culture.

Director Yoo described this transformation as the "democratization of research." He emphasized, "In the future, every scientist in the country will have a research institute on their own desk. By leveraging AI platforms, even individual researchers will be able to carry out research at a world-class level."

He went on to note that both the US and China are making massive national investments in building AI-based research platforms. He explained that Korea, too, should focus on shaping research data into AI-friendly formats, centered around the National Science AI Research Center, and creating a research ecosystem where any researcher can fully utilize AI. This marks an era where national competitiveness will depend not just on the ability to develop AI itself, but on how quickly new scientific discoveries can be made by harnessing AI.

The Key Battleground for AI: Semiconductors and Infrastructure

The crucial role of computing infrastructure in driving AI research innovation was also underscored by the presenters.

Professor Choi Rino of the Department of Materials Science and Engineering at Inha University is giving a presentation on "Changes in Semiconductors in the AI Era." Provided by the Korean Science Journalists Association.

Choi Rino, Professor in the Department of Materials Science and Engineering at Inha University, explained, "With the growth of generative AI, the core of the semiconductor industry is quickly shifting from CPU to GPU and high-bandwidth memory (HBM)." He stressed that large language models (LLMs) must process hundreds of billions of parameters simultaneously, which greatly exceeds the limits of conventional computing architectures, making newly optimized semiconductor structures essential for AI.

He predicted, "AI competitiveness going forward will not be determined solely by calculation speed, but by how efficiently data can be moved and how little power is consumed." He cited critical technological advances such as processing-in-memory (PIM), which allows computation within memory itself; three-dimensional stacking of semiconductors; and next-generation packaging technologies, all of which are becoming central pillars for the AI era.

He further diagnosed that, as the expansion of AI services rapidly increases the electricity consumption of data centers, factors such as power efficiency, cooling technologies, and the overall competitiveness of data center infrastructure—alongside improved semiconductor performance—will determine a nation's AI leadership going forward.

The subsequent panel discussion also raised the importance of preparing society for the AI era. One panelist commented, "Rather than taking away people's jobs, AI is evolving in a way that increases the amount of work one person can do. As AI spreads rapidly throughout research, industry, and daily life, it is essential to establish ethics and social consensus that match technological progress."

This content was produced with the assistance of AI translation services.