en.Wedoany.com Reported - During the TPC26 expert panel discussion, senior representatives from leading global research institutions explored how artificial intelligence is transforming the economics of scientific research, the importance of international collaboration, and the challenges of measuring return on investment.
Panelists included Dario Gil from the U.S. Department of Energy (DOE), Katie Antypas from the National Science Foundation (NSF), Rick Stevens from Argonne National Laboratory, Satoshi Matsuoka from RIKEN, and Per Oster from the IT Center for Science. Debra Goldfarb from Amazon Web Services (AWS) served as moderator. The experts also discussed how governments and research institutions should evaluate the impact of multi-billion-dollar investments in scientific infrastructure as AI becomes central to scientific discovery.
How to measure AI's impact on scientific research and discovery became a focal point of discussion. The panel agreed that while papers and scientific breakthroughs are key indicators, they no longer fully capture the total value generated by large-scale research projects.
As public investment grows, governments want to understand how these projects contribute to innovation and socioeconomic competitiveness. However, these outcomes often take years to materialize. Additionally, the proliferation of AI in education and industry further complicates efforts to quantify its impact using traditional methods.
The discussion then shifted to practical improvements. Speakers noted that AI's greatest contribution to science may not be a single breakthrough, but rather its ability to enhance research efficiency, helping researchers solve complex problems more quickly. This perspective was particularly prominent in discussions on national competitiveness. With aging populations and a scarcity of research talent, simply increasing the number of researchers is no longer sufficient to sustain innovation.
The panel suggested that AI's success in science should be measured by its ability to solve scientific challenges faster, at lower cost, with higher quality, or through other meaningful outcomes. Productivity gains become an important benchmark for assessing the long-term impact of the technology. The discussion then moved from productivity to collaboration. Experts argued that many of the most important scientific challenges still require international cooperation. Rising costs of AI infrastructure, increasing complexity of scientific research, and the need for interdisciplinary expertise make international collaboration essential.
Examples of international collaboration include the European High Performance Computing Joint Undertaking (EuroHPC), an initiative that coordinates investments across national projects while maintaining connections with local research communities. The discussion also focused on cooperation between the United States, Europe, and Japan.
Experts acknowledged that competition remains an important driver, but future success depends on sharing expertise and jointly building research capabilities. They emphasized that meaningful collaboration requires more open systems with shared infrastructure and interoperable systems, not just agreements at the national level.
Looking ahead to 2030, experts envisioned a future where AI is more deeply embedded in scientific research. Achieving this goal requires broader access to advanced computing resources and strong global partnerships. A key consensus was that AI has the potential to transform the way science is conducted, and as the technology scales, measuring and improving its impact on international collaboration will become increasingly important.
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